Priority-Zone Mapping for Reforestation: Case Study in the Montane Dry Forests of Bolivia 3031203747, 9783031203749

Table of contents :
Acknowledgments
Contents
List of Figures
List of Tables
1 Introduction
1.1 Objective of the Research and Research Questions
1.2 Structure of This Work
References
2 Introduction to the Study Area
2.1 The Bolivian Montane Dry Forests
2.2 Introduction to the Case Study Site of Micani
References
3 Problems of Deforestation and Its Drivers
3.1 Problems and Challenges Perceived by the Local Population
3.2 Environmental Challenges
3.2.1 Habitat Loss and Biodiversity Decline
3.2.2 Soil Erosion
3.2.3 Climate Change
3.3 Drivers of Landscape Degradation
3.4 Summary of Regional Problems and Challenges in Relation to Land Degradation
References
4 FLR Potentials and Spatial Allocation Parameters
4.1 Protection of Existing Forest Habitats
4.2 Reduction of Erosion Potential
4.3 Sustainable Wood Supply
4.4 Protection of Village Infrastructure
4.5 Mitigation of Climate Change Impacts
4.6 Mitigation of Drivers of Landscape Degeneration
4.7 Summary of FLR Potentials
References
5 Creation of a Land Use/Land Cover Map
5.1 Remote Sensing
5.2 Random Forest Classification
5.3 Determination of Classes for the Classification
References
6 Methods
6.1 Land Use and Land Cover Classification
6.1.1 Data and Algorithm Source
6.1.2 Classes
6.2 Methods to Determine Thematic and Priority Zones for Reforestation
6.2.1 Mapping of Forest Buffer Zones
6.2.2 Mapping of Potential Green Corridors
6.2.3 Mapping of Highly Erosive Areas
6.2.4 Mapping of Village Proximity to Forests
6.2.5 Mapping of Endangered Village Infrastructure
6.3 Priority-Zone Mapping
References
7 Results
7.1 Results of LULC Classification
7.2 Results of Thematic Priority-Zone Analysis
7.2.1 Priority Zones for Protection of Remnant Forests
7.2.2 Priority Zones for Green Corridors
7.2.3 Priority Zones for Reforestation of Highly Erosive Areas
7.2.4 Priority Zones for Watershed Protection from Gully Erosion
7.2.5 Priority Zones of Reforestation for Wood Supply of Communities
7.2.6 Priority Zones for Protection of Village Infrastructure
7.3 Results of the Priority-Zone Mapping
References
8 Discussion
8.1 Discussion LULC Classification
8.2 Discussion of Thematic Priority-Zone Maps
8.2.1 Protecting Existing Forests
8.2.2 Green Corridors
8.2.3 Erosive Risk
8.2.4 Forest Proximity of Villages
8.2.5 Endangered Village Infrastructure
8.3 Discussion of the Priority-Zone Mapping
References
9 Conclusion and Outlook
References
Appendix Comparison Matrices as Classification Ground Truth
A.1 Agricultural Area
A.2 Forested Area
A.3 Barren Land
A.4 Shrub- and Grassland

Citation preview

SpringerBriefs in Geography Larissa Böhrkircher · Michael Leuchner · Fabio Bayro Kaiser · Christa Reicher

Priority-Zone Mapping for Reforestation Case Study in the Montane Dry Forests of Bolivia

SpringerBriefs in Geography

SpringerBriefs in Geography presents concise summaries of cutting-edge research and practical applications across the fields of physical, environmental and human geography. It publishes compact refereed monographs under the editorial supervision of an international advisory board with the aim to publish 8 to 12 weeks after acceptance. Volumes are compact, 50 to 125 pages, with a clear focus. The series covers a range of content from professional to academic such as: timely reports of state-of-the art analytical techniques, bridges between new research results, snapshots of hot and/or emerging topics, elaborated thesis, literature reviews, and in-depth case studies. The scope of the series spans the entire field of geography, with a view to significantly advance research. The character of the series is international and multidisciplinary and will include research areas such as: GIS/cartography, remote sensing, geographical education, geospatial analysis, techniques and modeling, landscape/regional and urban planning, economic geography, housing and the built environment, and quantitative geography. Volumes in this series may analyze past, present and/or future trends, as well as their determinants and consequences. Both solicited and unsolicited manuscripts are considered for publication in this series. SpringerBriefs in Geography will be of interest to a wide range of individuals with interests in physical, environmental and human geography as well as for researchers from allied disciplines.

Larissa Böhrkircher · Michael Leuchner · Fabio Bayro Kaiser · Christa Reicher

Priority-Zone Mapping for Reforestation Case Study in the Montane Dry Forests of Bolivia

Larissa Böhrkircher Department of Physical Geography and Climatology RWTH Aachen University Aachen, Germany

Michael Leuchner Department of Physical Geography and Climatology RWTH Aachen University Aachen, Germany

Fabio Bayro Kaiser Institute for Urban Design RWTH Aachen University Aachen, Germany

Christa Reicher Institute for Urban Design RWTH Aachen University Aachen, Germany

ISSN 2211-4165 ISSN 2211-4173 (electronic) SpringerBriefs in Geography ISBN 978-3-031-20374-9 ISBN 978-3-031-20375-6 (eBook) https://doi.org/10.1007/978-3-031-20375-6 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgments

We thank Aktion Sodis e. V. and Fundación Sodis for providing us access to data and information from the case study region that would otherwise not be available. Also, many thanks to the team at the Organization for Tropical Studies (OTS) for their scholarship and support in the context of Google Earth Engine usage. In addition, the lead author would like to thank her family and friends for their support and encouragement during the development of this work.

vii

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Objective of the Research and Research Questions . . . . . . . . . . . . . . . 1.2 Structure of This Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 4 4 5

2 Introduction to the Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1 The Bolivian Montane Dry Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Introduction to the Case Study Site of Micani . . . . . . . . . . . . . . . . . . . . 9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3 Problems of Deforestation and Its Drivers . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Problems and Challenges Perceived by the Local Population . . . . . . . 3.2 Environmental Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Habitat Loss and Biodiversity Decline . . . . . . . . . . . . . . . . . . . 3.2.2 Soil Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Drivers of Landscape Degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Summary of Regional Problems and Challenges in Relation to Land Degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 19 21 21 22 25 25

4 FLR Potentials and Spatial Allocation Parameters . . . . . . . . . . . . . . . . . 4.1 Protection of Existing Forest Habitats . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Reduction of Erosion Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Sustainable Wood Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Protection of Village Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Mitigation of Climate Change Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Mitigation of Drivers of Landscape Degeneration . . . . . . . . . . . . . . . . 4.7 Summary of FLR Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33 33 35 36 37 38 38 39 42

29 29

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Contents

5 Creation of a Land Use/Land Cover Map . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Random Forest Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Determination of Classes for the Classification . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 45 47 48

6 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Land Use and Land Cover Classification . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Data and Algorithm Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Methods to Determine Thematic and Priority Zones for Reforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Mapping of Forest Buffer Zones . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Mapping of Potential Green Corridors . . . . . . . . . . . . . . . . . . . 6.2.3 Mapping of Highly Erosive Areas . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Mapping of Village Proximity to Forests . . . . . . . . . . . . . . . . . 6.2.5 Mapping of Endangered Village Infrastructure . . . . . . . . . . . . 6.3 Priority-Zone Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 51 53 56 56 56 57 59 59 59 60

7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Results of LULC Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Results of Thematic Priority-Zone Analysis . . . . . . . . . . . . . . . . . . . . . 7.2.1 Priority Zones for Protection of Remnant Forests . . . . . . . . . . 7.2.2 Priority Zones for Green Corridors . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Priority Zones for Reforestation of Highly Erosive Areas . . . 7.2.4 Priority Zones for Watershed Protection from Gully Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.5 Priority Zones of Reforestation for Wood Supply of Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.6 Priority Zones for Protection of Village Infrastructure . . . . . . 7.3 Results of the Priority-Zone Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61 61 62 62 65 67

70 73 74 80

8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Discussion LULC Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Discussion of Thematic Priority-Zone Maps . . . . . . . . . . . . . . . . . . . . . 8.2.1 Protecting Existing Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Green Corridors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 Erosive Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Forest Proximity of Villages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.5 Endangered Village Infrastructure . . . . . . . . . . . . . . . . . . . . . . . 8.3 Discussion of the Priority-Zone Mapping . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81 81 82 82 83 84 86 87 88 90

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9 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Appendix: Comparison Matrices as Classification Ground Truth . . . . . . . 95

List of Figures

Fig. 1.1

Fig. 1.2

Fig. 2.1

Fig. 2.2

Fig. 2.3

Fig. 2.4 Fig. 2.5

Fig. 2.6

Land degradation is a multiscalar challenge. The effects of climate change in combination with local unsustainable land use lead to increasing environmental and social problems in rural Andean communities. The accelerating degradation in turn leads to an intensification of climate change . . . . . . . . . . . . Structural design of this work. Based on an analysis on the problems of the study area, thematical priority-zone maps are created. They are combined to generate a priority-zone map that could serve as discussion and planning framework for local stakeholders . . . . . . . . . . . . . . . Overview of the study region. This works case study site, Micani, is part of the Bolivian Montane Dry Forest ecoregion, which runs along the Central Cordillera of the Bolivian Andes (ecoregions based on data by Tryse 2017, landforms on data by Theobald et al. 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of the case study site. The villages are dispersed in the mountainous landscape (base image source: Esri World Imagery; Earthstar Geographics | Esri, HERE, Garmin, Foursquare, METI/NASA, USGS) . . . . . . . . . . . . . . . . . . . . . . . . . . Annual precipitation diagram for Micani. The red background marks the time of the dry season. The blue lines depict precipitation events. Data source ClimateEngine.org, CHIRPS precipitation dataset, Huntington et al. (2021) . . . . . . . . Overview of the superior and subordinate administrative units of Micani (based on INE 2012) . . . . . . . . . . . . . . . . . . . . . . . . Overview of the three subcentrals and the associated villages. The case study site consists of the three subcentral regions Micani, Ipote and Huaripampa (based on INE 2012) . . . . . . . . . . . Age demographics for Micani. The population is very young, with almost 50% being under 19 (based on INE 2012) . . . . . . . . .

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Fig. 2.7 Fig. 2.8

Fig. 2.9

Fig. 2.10

Fig. 3.1

Fig. 3.2

Fig. 3.3

Fig. 6.1

List of Figures

Energy source for cooking. Firewood is the most important resource for cooking in Micani (based on INE 2012) . . . . . . . . . . . Energy resource distribution in the three subcentrals of the study site—strong dependency on fire wood. In Micani, the second most important resource is gas and in Ipote solar energy. Huaripampa is relying to 99% on firewood for cooking, whilst 1% of the population does not have a kitchen (based on INE 2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Construction material usage in Micani. The main construction materials are adobe and rammed earth; wood plays only a minor role (based on INE 2012) . . . . . . . . . . . . . . . . . There are thirteen tree species mapped in the municipality of Micani. Kishara and thaci account together for almost 50% of the total tree cover. Eucalyptus, which is prominent in neighbouring districts, is only represented with 0.2% in the municipality of San Pedro de Buena Vista (based on GAM SPBV 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of a PRA analysis in the district of Micani. The problems that are most urgent to the participants were water shortage and contamination and deforestation. The bars highlighted in green correspond to problems that can arise directly or indirectly from land degradation and could be mitigated from forest and landscape restoration (Graphic based on IOG and Fundación Sodis 2017) . . . . . . . . . . . . . . . . . . . Drivers in the cycle of landscape degradation. The expansion of agricultural areas, overgrazing and wood extraction lead to an incremental reduction of vegetation cover. This causes higher rates of water run-off and increased wind speed, which both increase the risk of erosion. The progressing erosion leads to land degradation and reduces soil fertility and decreasing agricultural production. This leads to the demand for new fertile arable land and in turn to a reduction of vegetation cover. Grazing accelerates this process, as it disturbs natural regeneration and hinders the natural revegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme of incremental process of deforestation in the study region. The formerly forested hillsides are partly cleared for agricultural production. The introduction of grazing animals reduces the vegetation cover and disturbs natural regeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Map of the comparison area for this work’s classification. The area that is covered with Google Street View was used as digital site visit to compare satellite imagery with the local conditions. Image source Google, ©2021 Landsat/Copernicus, TerraMetrica [Accessed 05 Aug 2021] . . . . .

13

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20

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27

52

List of Figures

Fig. 6.2

Fig. 6.3 Fig. 7.1

Fig. 7.2

Fig. 7.3

Fig. 7.4

Fig. 7.5

Fig. 7.6

Fig. 7.7

Comparison matrix of barren land. The upper line of satellite imagery shows areas in the study site, which were anticipated to be bare. The lower lines show pictures with resemblance in the satellite imagery and the respective street-view pictures. Image source Google, ©2021 Maxar Technologies, CNES/Airbus [Accessed 05 Aug 2021] . . . . . . . . . . . . . . . . . . . . . . Random forest variable importance. The highest importance scored the NDVI, MNDWI and B5 bands . . . . . . . . . . . . . . . . . . . . Results of the land use and land cover classification. Large contiguous forest areas were located mainly in the north and north-east of the study site. Shrub- and grassland were dominantly found on slopes and in valleys of mountains and hills, especially on their southern flanks. Bare areas were vice versa often located on the northern slopes and on ridges. Agricultural areas were dispersed throughout the region in small clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fractions of the LULC classes at the study site. Following the classification, the majority of the area is covered by grassland and barren areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Protection of remaining forest habitats. The buffer zones around the forest areas have the potential to protect the remaining forest habitat, increase ecosystem services and thereby protect local biodiversity and contribute to landscape restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest cover of the subcentral areas and the total study site. Micani has the highest cover of remnant forests, whilst Huaripampa has the lowest. Also, when looking at the distribution of the total forest area, over half of the total forest area of the study site can be found in Micani and only 10% in Huaripampa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority-zone map for habitat reconnection. The dark green areas depict existing forests, whilst the light green areas represent potential green corridors. They are linking the respective forest patches to their nearest neighbour and can contribute to enhance species migration and natural regeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority zones for reforestation of bare lands. Bare lands cover about one third of the planning area. Huaripampa has the highest ratio of the three subcentrals with a coverage of 40% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification of slope inclinations in the study area. The shades of red indicate the steepness of the terrain. The darker the colour hue, the steeper. White areas indicate areas that are forested and therefore have a minor risk of erosion (FAO 2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

53 56

63

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64

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67

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Fig. 7.8

Fig. 7.9 Fig. 7.10

Fig. 7.11 Fig. 7.12

Fig. 7.13

Fig. 7.14

Fig. 7.15

Fig. 7.16

Fig. 7.17

Fig. 7.18

List of Figures

Fractions of the inclination classes of the total case study area. Over 80% of the area are classified as steep to very steep. Only a minor part of them is covered by forests . . . . . . . . . . Slope inclinations and their fraction of forest cover in the three subcentrals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dark blue areas indicate areas that are at risk of gully erosion. The funnel-shaped catchment areas of the individual gullies are clearly recognizable in some areas. The southern part of Huaripampa seems to be particularly affected . . . . . . . . . . . . . . Gully coverage per subcentral and the total study site . . . . . . . . . . Classification of villages regarding their proximity to the nearest forest area. The dark red and purple villages are located at a distance of more than 800 and 1,600 m form the nearest forest, respectively. It is striking that the majority can be found in Huaripampa. In Micani and Ipote they are in general located in closer proximity to the nearest forest area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority zones for reforestation to enable all villages the access to a forested area in under 400 m. The dark red areas show villages that lay outside a 400 m radius to the nearest forest. The green buffer zones mark potential zones for reforestation to provide the villages with fuelwood in close proximity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority zones for reforestation to protect village infrastructure. The dark red areas show agricultural areas that are at risk to be negatively influenced by erosion. The same applies to the purple-marked streets. Reforestation in these areas could mitigate the risk of erosion and protect the infrastructure from damage . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fractions of streets and agricultural areas which are at risk to be negatively affected by erosion. The graph shows the fractions of the total areas, respectively . . . . . . . . . . . . . . . . . . . Single thematic priority maps. Each map describes the potential spatial benefits of a FLR approach in the coloured areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Layering of thematical maps. The map gives insight where and which benefits can be achieved by reforestation in a respective area. Each colour represents one benefit of reforestation. The more colours are overlapping, the more benefits can be generated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority-zone map for Micani. The shades of green describe the priority-zone class—the darker the colour, the more benefit can be achieved by a reforestation of the area. The black circles mark high-priority zones, where dark colours are clustering—they indicate zones with high benefit of reforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

70 70

71 71

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76

76

77

79

List of Tables

Table 3.1 Table 4.1 Table 4.2 Table 4.3 Table 6.1 Table 7.1 Table 7.2 Table 7.3

Results of the PRA analysis regarding the supply of fuelwood . . . Slope gradient classes (based on FAO 2006) . . . . . . . . . . . . . . . . . Proximity classes for the villages to the nearest forest area . . . . . . Priority-zone mapping scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . Confusion matrix of gully cover classification . . . . . . . . . . . . . . . . Model accuracy indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coverage of the priority zones for each thematical map . . . . . . . . Areas with potential benefits of reforestation for each subcentral area and the total study site . . . . . . . . . . . . . . . . . . . . . .

21 36 37 40 58 62 78 80

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Chapter 1

Introduction

Across the world, ecosystem degradation and the growing impacts of climate change are leading to far-reaching consequences (UNEP 2021a). In South America, soil degradation has become a major threat to land and affects 68% of land resources (IPS 2016). Deforestation, caused by land-use change and overexploitation, has been identified as a main driver (UNCCD 2019) and is linked to the degradation of about 100 million ha (IPS 2016). This trend is also observable in the Montane Dry Forest zones in the Bolivian Andes (Zimmerer 1993; Brandt and Townsend 2006). Human activities have fundamentally altered the ecoregion, and the World Wildlife Fund (WWF) ranks its protection status as critical/endangered (Brooks 2018). This leads not only to a reduction of regional biodiversity, but also increasingly to a threat to the livelihood of the local population (FAO and UNCCD 2019) (Fig. 1.1). Many of the inhabitants of these regions are dependent on agricultural production and suffer from crop failure due to decreasing soil fertility and changing precipitation patterns (FAO and UNCCD 2019). To face these problems, which occur similarly in many parts of the world (UNEP 2021a), the United Nations (UN) declared this decade as ‘Decade of Sustainable Forest and Landscape Restoration’ (FAO et al. 2021). Forest and landscape restoration (FLR) are thereby defined as ‘the process of halting and reversing degradation, resulting in improved ecosystem services and recovered biodiversity’ (UNEP 2021a, p. 7). The proclamation aims to raise awareness to the importance of intact ecosystems and thrives to mobilize political, financial and private action to restore the world’s deforested and degraded ecosystems (GPFLR 2013; UNEP 2021a). This falls in line with the Sustainable Development Goals (SDGs) of the UN, which were developed as a guideline to restore the natural balance and health of the earth (UNEP 2021b). Integrated landscape restoration through reforestation with adapted species is therefore essential to protect the ecosystems and the local biodiversity, and to reestablish the important ecosystem services to improve the livelihood of the population (Griscom et al. 2017; UNEP 2021a, b).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_1

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Fig. 1.1 Land degradation is a multiscalar challenge. The effects of climate change in combination with local unsustainable land use lead to increasing environmental and social problems in rural Andean communities. The accelerating degradation in turn leads to an intensification of climate change

Yet, dry land forests have not attracted the same level of interest and investment like other ecosystems, for example tropical rainforests, although their need for restoration to mitigate the effects of drought, desertification and erosion has been indicated by research (Davies et al. 2012; FAO 2019). The implementation rate of reforestation measures is low and especially remote rural regions are not in the scope of the municipalities and might therefore lack basic support for their wellbeing (FAO and UNCCD 2019; UNEP 2021a). As the analysis and development of FLR interventions can be complex and demanding in terms of knowledge and technical requirements, the development of FLR projects is often confided to experts using their knowledge and experience to plan and execute the implementation (Mansourian et al. 2021). This approach might not be suitable for reforestation projects in rural regions of the dry forests of Bolivia. The remoteness of the communities could severely limit communication with planning officials or prevent it from taking place at all. In addition, the local municipalities are often not capable to cope with the transferred tasks, due to capacity and/or financial restrictions (Pacheco 2005). Community forestry, in which the local population decide for themselves what their main goals of the reforestation measures are, might therefore help in the initiation of reforestation projects and empower the local people (Nebel et al. 2003; Pacheco 2005; Charnley and Poe 2007). The development of an adaptable analysis method, that takes into consideration the local problems and challenges and building on that, describes an approach of how to define areas, where reforestation could have the most beneficial impact and could help the local authorities and communities in the planning and implementation of forest and landscape regeneration projects. There are a range of approaches focusing on the generation of data that aim at assisting in the planning of FLR projects, such as the Mediterranean Desertification

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and Land Use (MEDALUS) approach, a global assessment by the Global Partnership on Forest Landscape Restoration, and the Restoration Opportunities Assessment Methodology (ROAM). MEDALUS enables the analysis of the sensitivity of an area to land degradation and desertification and can be applied globally (Jafari and Bakhshandehmehr 2016; Ferrara et al. 2020; Song et al. 2021). This approach was designed to assess the direct causes and drivers as well as the sensitivity of land to degradation and desertification (Jafari and Bakhshandehmehr 2016; Ferrara et al. 2020). Whilst this assessment contributed important findings on areas where FLR could have a positive impact, it does not directly allow conclusions to be drawn of where reforestation would be most beneficial, e.g., for the local population. On this basis, no holistic statements can be made in assessing the greatest ecological, social and socioeconomic potentials of reforestation. Further, there are global FLR assessments that provide information on countries with potentially suitable areas for reforestation. For example, the Global Partnership on Forest Landscape Restoration commissioned a global assessment of restoration potential that helps in identifying countries, which have significant opportunities to restore degraded lands (IUCN and WRI 2014b). However, due to their low resolution and lacking incorporation of country level data, global assessments are often limited for adaptation on national level project development (IUCN and WRI 2014b). Thus, global assessments often have different foci than assessments on a smaller scale and can lead to different results (IUCN and WRI 2014b). Global scale assessment methods may therefore be unsuitable to be transferred to a regional level as a closer examination of the countries’ individual conditions is needed (IUCN and WRI 2014b). An approach that offers a higher resolution is ROAM, which was published by the International Union for Conservation of Nature (IUCN) in 2014. It was developed to facilitate the assessment of FLR opportunities and aims for multiple benefits in the restoration process (IUCN and WRI 2014a). The mapping approach for this spatial analysis is divided into several steps: First a list of restoration opportunities is created, defining which spatial datasets need to be acquired. These datasets are then reclassified into priority categories for reforestation. Following this, the datasets are combined to create a final map, which is based on the different layers of the classification. In a last step, suitable restoration measures are identified for the resulting priority areas (IUCN and WRI 2014a). ROAM is mainly used by national level institutions (IUCN and WRI 2014a; Clear Horizon 2016) and can therefore be considered as a large-scale approach, which assesses the general reforestation potential of a country or subnational area (Clear Horizon 2016). The results might therefore be too broad to serve as decision framework for community reforestation, as it could limit the incorporation of local, often highly individual, site conditions and thus to react to local problems (IUCN and WRI 2014a; Clear Horizon 2016). Thus, there seems to be an information gap in-between global and national assessment possibilities and the adaptability for local stakeholders. Nevertheless, the general approach of ROAM to spatially analyse regions based on the restoration potential and to create an accumulated priority-zone map has the potential to be transferred to small-scale analysis. The method, to not only focus on a country’s deforested or degraded lands, but to also integrate the potential socioeconomic and ecological benefits, seems beneficial

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to develop a locally appropriate restoration priority mapping. Thus, the process of the approach appears suitable also for the development of small-scale, regional FLR assessments. The factors on which the assessment is based need to be adapted and researched for the respective area.

1.1 Objective of the Research and Research Questions This work aims to develop a priority-zone map for reforestation measures in the Montane Dry Forest region of Bolivia, that depicts, where reforestation might have the greatest social and ecological benefits. The assessment of this study will be leaned on the approach of ROAM for the development of a detailed and site-specific priority mapping approach for reforestation in the case study region of Micani. The goal is to facilitate the implementation of reforestation for local communities and municipalities by serving as knowledge and decision framework for reforestation approaches. Therefore, this work seeks to answer the following research question: How can priority zones for reforestation of the Bolivian Montane Dry Forests be identified that maximize the potential to protect local ecosystems and improve the livelihood of the local population? The following subquestions need to be answered accordingly: (1) What are the regional problems and challenges caused by land degradation and what are their drivers? (2) Could reforestation mitigate these problems? (3) How can these areas be allocated to create a map that shows the priority zones for reforestation measures? The research is performed by a case study analysis. Literature research sets the theoretical background for the determination of characteristics of most beneficial reforestation sites. Using remote-sensing methods and GIS, these conditions are then mapped for the case study area and combined into a final priority map.

1.2 Structure of This Work This work is structured into nine chapters. Following the introduction, the objective of Chap. 2 is to provide an insight to the study area of this work, the Bolivian Montane Dry Forests, and the case study site of Micani. The regional problems and challenges of the region, which are related to increasing landscape degradation, are researched in Chap. 3. It further includes the investigation of the drivers for the progressive degradation. In Chap. 4, the benefits of reforestation and its potential to mitigate the studied problems and challenges are examined. It also describes the spatial characteristics in which reforestation can contribute to solving the problem and generate

References

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Fig. 1.2 Structural design of this work. Based on an analysis on the problems of the study area, thematical priority-zone maps are created. They are combined to generate a priority-zone map that could serve as discussion and planning framework for local stakeholders

added value, dependent on the topic. Chapter 5 gives an overview of the development of land use and land cover (LULC) maps. It forms the transition to Chap. 6, in which the methods are explained how this work’s LULC classification is conducted, how the potential areas for reforestation are mapped and how a summarizing priority map is created from them (Fig. 1.2). In Chap. 7, the results of the LULC classification are presented as well as the thematic maps for potential reforestation zones and the thereof resulting priority-zone map. Chapter 8 critically evaluates the informative value and reliability of the maps produced. Chapter 9 summarizes the findings of this work and places the possible contribution of the priority map in the context of a potentially following reforestation process.

References Brandt JS, Townsend PA (2006) Land use—land cover conversion, regeneration and degradation in the high elevation Bolivian Andes. Landsc Ecol 21(4):607–623. https://doi.org/10.1007/s10980005-4120-z Brooks D (2018) South America: in the mountain valleys of southern central Bolivia | Ecoregions | WWF. World Wildlife Fund (WWF), Washington, DC. https://www.worldwildlife.org/ecoreg ions/nt0206. Accessed 19 Aug 2022 Charnley S, Poe MR (2007) Community forestry in theory and practice: where are we now? Annu Rev Anthropol 36(1):301–336. https://doi.org/10.1146/annurev.anthro.35.081705.123143 Clear Horizon (2016) Rwandan forest landscape restoration opportunity assessment: tracing the influence. Prepared for the International Union for Conservation of Nature (IUCN). Clear Horizon Consulting, Cremorne

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Davies J, Poulsen L, Schulte-Herbrüggen B, MacKinnon K, Crawhall N, Henwood WD, Dudley N, Smith J, Gudka M (2012) Conserving dryland biodiversity. International Union for Conservation of Nature and Natural Resources, Nairobi FAO (2019) Trees, forests and land use in drylands: the first global assessment—full report. FAO forestry paper no. 184. Food and Agriculture Organization of the United Nations, Rome FAO and UNCCD (2019) Vulnerability to food insecurity in mountain regions: land degradation and other stressors. Food and Agriculture Organization of the United Nations, Bonn FAO, IUCN CEM and SER (2021) Principles for ecosystem restoration to guide the United Nations decade 2021–2030. Food and Agriculture Organization of the United Nations, Rome Ferrara A, Kosmas C, Salvati L, Padula A, Mancino G, Nolè A (2020) Updating the MEDALUSESA framework for worldwide land degradation and desertification assessment. Land Degrad Dev 31:1593–1607. https://doi.org/10.1002/ldr.3559 GPFLR (Global Partnership on Forest and Landscape Restoration) (2013) Ideas transform landscapes. Burlington Press, Switzerland Griscom BW, Adams J, Ellis PW, Houghton RA, Lomax G, Miteva DA, Schlesinger WH, Shoch D, Siikamäki JV, Smith P, Woodbury P, Zganjar C, Blackman A, Campari J, Conant RT, Delgado C, Elias P, Gopalakrishna T, Hamsik MR, Herrero M, Kiesecker J, Landis E, Laestadius L, Leavitt SM, Minnemeyer S, Polasky S, Potapov P, Putz FE, Sanderman J, Silvius M, Wollenberg E, Fargione J (2017) Natural climate solutions. Proceedings of the National Academy of Sciences 114(44):11645–11650. https://doi.org/10.1073/pnas.1710465114 IPS (Inter Press Service) (2016) Soil degradation threatens nutrition in Latin America. http:// www.ipsnews.net/2016/06/soil-degradation-threatens-nutrition-in-latin-america/. Accessed 22 Aug 2022 IUCN and WRI (International Union for Conservation of Nature and World Resource Institute) (2014a) Guide to the Restoration Opportunities Assessment Methodology (ROAM) assessing forest landscape restoration opportunities at the national or sub-national level. Working paper (road-test edition). International Union for Conservation of Nature and World Resource Institute, Gland IUCN and WRI (International Union for Conservation of Nature and World Resource Institute) (2014b) Forest landscape restoration: potential impacts. Assessing national FLR potential. Arborvitae 45. IUCN, Gland Jafari R, Bakhshandehmehr L (2016) Quantitative mapping and assessment of environmentally sensitive areas to desertification in Central Iran. Land Degrad Dev 27(2):108–119. https://doi. org/10.1002/ldr.2227 Mansourian S, Berrahmouni N, Blaser J, Dudley N, Maginnis S, Mumba M, Vallauri D (2021) Reflecting on twenty years of forest landscape restoration. Restor Ecol 29. https://doi.org/10. 1111/rec.13441 Nebel G, Bredahl Jacobsen J, Quevedo R, Helles F (2003) A strategic view of commercially based community forestry in indigenous territories in the lowlands of Bolivia. Paper presented at the international conference on rural livelihoods, forests and biodiversity, Bonn, 19–23 May 2003. Cifor, Bonn Pacheco P (2005) Decentralization of forest management in Bolivia: who benefits and why? In: Colfer CJP, Capistrano D (eds) The politics of decentralization: forests, power and people, 1st edn. Routledge, London, pp 166–183 Song C, Kim W, Kim J, Gebru BM, Adane GB, Choi YE, Lee WK (2021) Spatial assessment of land degradation using MEDALUS focusing on potential afforestation and reforestation areas in Ethiopia. Land Degrad Dev 33(1):79–93. https://doi.org/10.1002/ldr.4130 UNCCD (United Nations Convention to Combat Desertification) (2019) Forests and trees. At the heart of land degradation neutrality. UNCCD, Bonn UNEP (United Nations Environment Programme) (2021a) Becoming #GenerationRestoration: ecosystem restoration for people, nature and climate. UNEP, Nairobi UNEP (United Nations Environment Programme) (2021b) Measuring progress: environment and the SDGs. UNEP, Nairobi Zimmerer K (1993) Soil erosion and labor shortages in the Andes with special reference to Bolivia, 1953–91: implications for “conservation-with-development”. World Dev 21(10):1659–1675. https://doi.org/10.1016/0305-750X(93)90100-N

Chapter 2

Introduction to the Study Area

This chapter gives an overview of this work’s superordinate study area and the case study site. First the study’s framework is described by an introduction to the ecoregion of the Bolivian Montane Dry Forests. This is followed by a more in-depth analysis of the case study site of Micani, regarding the geographical and ecological characteristics as well as the social and political fabric of the region.

2.1 The Bolivian Montane Dry Forests The Bolivian Montane Dry Forests ecoregion has its main distributive area in the south-central area of Bolivia (Brooks 2018) (Fig. 2.1). It is located in the Cordillera Oriental along the eastern slopes of the Andes, in between the Central Andean Puna in the west and the South Andean Yungas in the east, and covers an area of approximately 80,000 km2 (Brooks 2018). At the ecoregion extends from 14° 11 7.764 S to 22° 2 34.195 S and 69° 3 24.017 W to 63° 31 43.545 W and forms a transition zone between the dry montane grass and shrub lands of the high Andes and the moist evergreen broadleaf forests of the lower Andes (Brooks 2018; One Earth 2019). The altitudes within the Bolivian Montane Dry Forests vary strongly due to high topographic variance (One Earth 2019) and range between 1,000 and 4,500 m above sea level. The region is characterized by a semi-arid climate with a distinct rainy and dry season (Brooks 2018; WWF 2021).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_2

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Water surface Bolivian Montane Dry Forest Ecoregion

Fig. 2.1 Overview of the study region. This works case study site, Micani, is part of the Bolivian Montane Dry Forest ecoregion, which runs along the Central Cordillera of the Bolivian Andes (ecoregions based on data by Tryse 2017, landforms on data by Theobald et al. 2015)

The natural vegetation of the ecoregion is dry broadleaf forest, which probably used to dominate the whole ecoregion’s extents (One Earth 2019). The forests are characterized by deciduous trees and are adapted to the dry conditions of the ecoregion (WWF 2021). During the dry seasons, which usually lasts several months, the trees shed their foliage to reduce their water requirements during that time (WWF 2021). With the beginning of the rainy season, the growth phase starts anew and new foliage is reformed (WWF 2021). The topographical variance of the ecoregion leads to highly diverse microclimates (WWF 2021). In adaptation to the varying site conditions, many rare and endemic plant and animal species can be found in this ecoregion, which may strongly alternate from one valley to the next (Brooks 2018). Of the natural vegetation, only remnant patches are still present (One Earth 2019). Human activities have fundamentally altered the ecoregion, and the WWF ranks its protection status as critical/endangered (Brooks 2018). It is estimated that only 6% of the original habitat is still intact (One Earth 2019).

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2.2 Introduction to the Case Study Site of Micani The case study site is part of the Bolivian Montane Dry Forest ecoregion and is located in the Central Cordillera of the Bolivian Andes (GAM SPBV 2017). It is characterized by a rugged topography, in which high mountains with steep slopes, lower hillsides and valleys form a differentiated landscape pattern (GAM SPBV 2017). The altitude ranges from 1,800 to 3,500 m asl (own assessment based on elevation mapping). The planning site is shaped by two rivers. The San Pedro River forms the western and northern border of the study site, and the Micani river, which rises in the peaks of Alpaca and flows into the San Pedro River (GAM SPBV 2017) (Fig. 2.2).

Fig. 2.2 Overview of the case study site. The villages are dispersed in the mountainous landscape (base image source: Esri World Imagery; Earthstar Geographics | Esri, HERE, Garmin, Foursquare, METI/NASA, USGS)

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Fig. 2.3 Annual precipitation diagram for Micani. The red background marks the time of the dry season. The blue lines depict precipitation events. Data source ClimateEngine.org, CHIRPS precipitation dataset, Huntington et al. (2021)

Climate The average temperatures show a low variation throughout the year and range from 14 °C in summer to 9 °C in winter (Weather Atlas 2021). Although the temperatures are rather stable, there are significant differences due to the altitudinal differences in the region. In the valleys, average temperatures range between 17 and 20 °C, whilst in the higher locations over 3,500 m asl, they vary from 6.7 to 8.5 °C (GAM SPBV 2017). The highest temperatures are recorded in October, November and December, the lowest in the months of May, June and July. The rainy season lasts from October to March and the dry season from April to September (GAM SPBV 2017). The months with the highest precipitation are January, February and March, with up to 250 mm in January. The least precipitation falls in May, June and July, with 8 mm (GAM SPBV 2017) (Fig. 2.3). Political Affiliation Together with the neighbouring municipality of Torotoro, San Pedro de Buena Vista forms the province of Charcas, which is located in the northern part of the department Potosí (INE 2012; GAM SPBV 2017). Micani is one of eight municipal districts of the municipality of San Pedro de Buena Vista (GAM SPBV 2017). The municipal district of Micani is divided into eight subcentres (Fig. 2.4): San Pedro, San Marcos, Moscari, Micani, Esquencachi, Quinamara, Toracari and Qhayana (GAM SPBV 2017). The rural subcentres consist of communities, which constitute the basic units of social organization. They are made up of groups of peasant families that share a common territory in which they develop their productive, economic, social and cultural activities (GAM SPBV 2017). This work’s study site is formed by the three subcentrals of Micani, Ipote and Huaripampa as shown in Fig. 2.5. They cover an area that covers 220 km2 . The 21 communities which this work is focusing on are all located in these three subcentrals

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Fig. 2.4 Overview of the superior and subordinate administrative units of Micani (based on INE 2012)

(GAM SPBV 2017): The subcentral of Micani includes the six communities: Micani (community), Vilapampa, Tanga Tanga, Pocoatillo, Millu Quirqui and Millo Caracha. Huaripampa consists of the nine communities: Huaripampa (community), Pallacachi, Suarani, Camata, Charoque, Allpaca, Sivingani, Alacruz and Chullpa. The third subcentral, Ipote, unites the six communities of Ipote (community), Ulupiquiri, Machacmarca, Llavini, Laca Laca and Cruz Pampa. The research area has a population of 2,400 inhabitants (calculations based on INE 2012). The study site was selected because it is representative for the challenges in the region and is part of the development collaboration with the two NGOs Aktion Sodis and Fundación Sodis, which are both active in the region. Population The total district of Micani has a population of 4,000 inhabitants and accounts for 14% of the municipality of San Pedro de Buena Vista (INE 2012). The people live in rural communities in small family groups, dispersed throughout the territory (GAM SPBV 2017). Regarding the age demographics for Micani, it is striking that the population is very young, with the fraction under the age of 19 accounting for almost half of the population (Fig. 2.6) (INE 2012). The level of education is to be considered low, and the percentage of children, who do not attend school is high (16%) (GAM SPBV 2017). Possible causes are low income, remoteness from educational facilities and low quality of education (GAM SPBV 2017). The majority (94%) of Micani’s

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Fig. 2.5 Overview of the three subcentrals and the associated villages. The case study site consists of the three subcentral regions Micani, Ipote and Huaripampa (based on INE 2012)

population identifies as Quechua, which is also the most spoken language in the region (GAM SPBV 2017). The people conduct subsistence agriculture, including farming and animal husbandry (INE 2012). The food supply is typical for the region as it is almost completely produced by the local farmers. The crops are mainly used for self-consumption. If surpluses are produced, they are sold at the regional markets (GAM SPBV 2017). This renders the economic income of the families strongly dependent on the environmental conditions. Sufficient food availability is already at risk today, as crop yields are decreasing due to changing climate, increasing land degradation, pests and diseases (GAM SPBV 2017). Ninety-three percent of the population lives in poverty (INE 2012). Therefore, an increase in migration can be observed, as many villagers or whole families temporarily or permanently move to other regions of the country to generate additional income (GAM SPBV 2017). Village Infrastructure Energy and Water Provision Most people in the subcentrals of Micani, Ipote and Huaripampa use firewood as main energy source for cooking and heating (92%) (INE 2012) (Fig. 2.7). There are however some differences in additional energy sources in the three subcentrals (Fig. 2.8): For meal preparation, the usage of gas ranks second in the subcentral of Micani (11%) and in Ipote the energy is additionally sourced from solar energy (17%) and gas (2%) (INE 2012). In Huaripampa, almost all communities use wood for cooking (99%), the remaining percent states to have no kitchen to cook in (INE 2012).

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Age demographics in the research area 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90-94 > 95 0

50

100

150

200

250

300

350

400

Fig. 2.6 Age demographics for Micani. The population is very young, with almost 50% being under 19 (based on INE 2012)

Besides the more urban area of Micani, there is no running water in the villages, which leads to generally low hygienic standards and health issues (IOG and Fundación Sodis 2017; GAM SPBV 2017).

Fig. 2.7 Energy source for cooking. Firewood is the most important resource for cooking in Micani (based on INE 2012)

Energy source for cooking 1%

3% 4%

92%

Gas cylinder

Solar energy/PV

Fire wood

No kitchen

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Micani

Ipote

Huaripampa

2%

11%

1% Gas cylinder

17%

Solar energy/PV Fire wood 89%

81%

99%

No kitchen

Fig. 2.8 Energy resource distribution in the three subcentrals of the study site—strong dependency on fire wood. In Micani, the second most important resource is gas and in Ipote solar energy. Huaripampa is relying to 99% on firewood for cooking, whilst 1% of the population does not have a kitchen (based on INE 2012)

Roads The access of the villages of Micani to the road network is very low. Only 28% of the total area is accounted to be covered by street access (GAM SPBV 2017). The finalization of a bridge construction between San Pedro de Buena Vista and Micani now facilitates the accessibility of some parts of the district, especially during the rainy season. Still, there is no primary road network in the municipal territory, only the municipal secondary network (GAM SPBV 2017). The mostly unasphalted roads present many difficulties during the rainy season, when they are affected by landslides and erosion (GAM SPBV 2017). Most connections between the communities are foot trails and are hardly accessible for vehicles. In some cases, the river beds serve as roads during the dry season, when less water runs through them and they can be used to transport products or to stock up on food (GAM SPBV 2017). Construction Material Figure 2.9 shows the most commonly used construction materials for housing in Micani. The most widely used material is adobe and rammed earth (57%), followed by straw, palm and reed (21%), sheet metal (12%), stone (5%) and brick, cement and concrete (4%) (INE 2012). Wood plays only a minor role as construction material (INE 2012). Although in Bolivia there is an observable trend of an increase of cement blocks and bricks for construction (Díez Lacunza 2016), this is not yet visible in the district of Micani. Local NGOs Two NGOs are active in the Micani region, Aktion Sodis and Fundación Sodis. They are involved in regional development, and their main goal is to contribute to the improvement of the living conditions of the local people (Aktion Sodis 2018). The projects include the improvement of health, food security and hygiene conditions. In addition, socio-economic projects have also been part of the development cooperation for some time, which are intended to strengthen the financial situation of village families and the role of women (Aktion Sodis 2018). The principle of ‘helping people to help themselves’ applies here, as the communities are actively involved in

2.2 Introduction to the Case Study Site of Micani

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Construction material used for housing in Micani 1%

4%

21%

12% 57% 0% 5%

Brick, cement, concrete Adobe, earth Stone Wood Sheet metal Straw, palm, reed Others

Fig. 2.9 Construction material usage in Micani. The main construction materials are adobe and rammed earth; wood plays only a minor role (based on INE 2012)

the development and dissemination of the projects through educational programmes and training of local indigenous experts, who are to anchor the concepts in the communities in the long term (Aktion Sodis 2018). Ecology Being part of the Montane Dry Forest ecoregion, the study site is dominated by creeping grasses, cacti, low and sparse bush vegetation and remnants of native dry forests (GAM SPBV 2017). The forests are thereby often confined to small, generally inaccessible sectors, which corresponds to the typical pattern of forests in the montane Andean regions (Brandt and Townsend 2006). The prominent species are polylepis, trichocereus, opuntias, stipas, cortaderas, chillcas and cebadilla. In Micani, a total of 13 tree species have been mapped (GAM SPBV 2017). As depicted in Fig. 2.10, the dominating tree species are kishara (30%) and thaci (20%), followed by jarka (8%), molle (8%), soto (6.6%) and aliso (6.4%) (GAM SPBV 2017). They can reach 10–15 m in height and are used by the communities for the construction of houses, fences and work tools (GAM SPBV 2017). Eucalyptus, which was mapped often in neighbouring districts of Micani, plays only a minor role (0.2%) (GAM SPBV 2017).

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Tree species in Micani (Quechua naming) ajranhuaya 1,8% alamo 0,3%

chiri molle 5,5%

chari 5,1% kishara 29,6%

jarka 8,2% quewuiña 0,2% eucylptus 0,2%

lloce 4,7% aliso 6,4% añahuaya 3,5%

molle 8,1% soto 6,6%

thaci 19,8%

Fig. 2.10 There are thirteen tree species mapped in the municipality of Micani. Kishara and thaci account together for almost 50% of the total tree cover. Eucalyptus, which is prominent in neighbouring districts, is only represented with 0.2% in the municipality of San Pedro de Buena Vista (based on GAM SPBV 2017)

References Aktion Sodis (2018) Unsere Vision—Aktion Sodis. https://www.aktion-sodis.org/?lang=en. Accessed 09 July 2022 Brandt JS, Townsend PA (2006) Land use—land cover conversion, regeneration and degradation in the high elevation Bolivian Andes. Landsc Ecol 21(4):607–623. https://doi.org/10.1007/s10980005-4120-z Brooks D (2018) South America: in the mountain valleys of southern central Bolivia | Ecoregions | WWF. World Wildlife Fund (WWF), Washington, DC. https://www.worldwildlife.org/ecoreg ions/nt0206. Accessed 19 Aug 2022 Díez Lacunza G (2016) Retrato de la vivienda en Bolivia: del techo de paja a la era de la calamina. In: Diario Pagina Siete 2016, 14 Mar 2016. https://www.paginasiete.bo/gente/retrato-de-la-viv ienda-en-bolivia-del-techo-de-paja-a-la-era-de-la-calamina-FHPS89733. Accessed 22 Aug 2022 GAM SPBV (Gobierno Autónomo Municipal San Pedro de Buena Vista) (2017) Plan territorial de desarrollo integral 2016–2020. San Pedro De Buena Vista Huntington J, Daudert B, Hegewisch L, Morton C, Abatzoglou J, McEvoy D (2021) Climate engine. https://app.climateengine.org/climateEngine. Accessed 30 Sept 2021 INE (Instituto Nacional de Estadística) (2012) Censo de Población y Vivienda 2012. Características de Viviendas. Ine-Bolivia: Redatam (CEPAL)—Diseminación de Información Estadística. http:// datos.ine.gob.bo. Accessed 26 Sept 2021

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IOG (Ingenieure Ohne Grenzen) and Fundación Sodis (2017) Bedarfserhebung in vier beispielhaften Gemeinden Micanis. Ingenieure Ohne Grenzen, Aachen One Earth (2019) Bolivian Montane Dry Forests. https://www.oneearth.org/ecoregions/bolivianmontane-dry-forests/. Accessed 22 Aug 2022 Theobald DM, Harrison-Atlas D, Monahan W, Albano CM (2015) Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS ONE 10(12). https://doi.org/10.1371/journal.pone.0143619 Tryse D (2017) Ecoregions 2017. https://ecoregions.appspot.com/. Accessed 27 Sept 2021 Weather Atlas (2021) Micani, Bolivia—detailed climate information and monthly weather forecast. Yu Media Group d.o.o. (ed). https://www.weather-atlas.com/en/bolivia/micani-climate. Accessed 22 Aug 2022 World Wildlife Fund (WWF) (2021) Tropical and subtropical dry broadleaf forests. https://www. worldwildlife.org/biomes/tropical-and-subtropical-dry-broadleaf-forests. Accessed 26 Sept 2021

Chapter 3

Problems of Deforestation and Its Drivers

The semi-arid ecosystems of the Bolivian Montane Dry Forest ecoregion are sensitive to land degradation and the effects of climate change and are therefore increasingly affected through water shortage, erosion and desertification (Brandt and Townsend 2006; Chakravarty et al. 2012; Rangecroft et al. 2013; FAO 2019), which may threaten agricultural potential (Zimmerer 1993; Brandt and Townsend 2006; FAO 2019; UNEP 2021). Most of the villagers in the remote rural regions of the Andes are subsistence farmers (GAM SPBV 2017), and their wellbeing is directly dependent on the environmental conditions. Assessing the environmental and socio-economic problems and challenges related to land degradation in the study region can provide insight to where reforestation can meet those. Socio-economic impacts include, e.g., local employment, livelihood and resource accessibility (UNEP 2021). Environmental impacts comprise erosion, impacts on watersheds and water quality, local climate and biodiversity impacts (Pearson et al. 2006; UNEP 2021). This chapter gives an overview of the conditions in the study site by first analysing the challenges that are perceived by the local population and secondly assesses the ecological conditions and their impact on land degradation and livelihood of the communities in the region.

3.1 Problems and Challenges Perceived by the Local Population In 2017, a Participatory Rural Appraisal (PRA) analysis was conducted by Engineers Without Borders that aimed to assess the people’s most urgent needs and challenges (IOG and Fundación Sodis 2017). In this analysis, people are guided to assess their own reality by collecting the most pressing problems and prioritizing them (Chambers 1994). The study was undertaken in four exemplary villages in the municipality of San Pedro de Buena Vista, of which two, Llavini and Ulupiquiri, are located © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_3

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within this case study region (IOG and Fundación Sodis 2017). As Fig. 3.1 shows, the participants classified the problems of water shortage, water contamination and deforestation as most urgent. The prioritization of deforestation could be linked to the supply of firewood, as most of the villages depend on wood as main energy source for daily cooking and heating (see Sect. 2.2) (INE 2012). The people of the four communities stated that they need to walk multiple hours (2–4 h) to collect enough fuelwood, mostly daily (Table 3.1). Due to the shortage of trees, some villages need to collect in neighbouring communities (IOG and Fundación Sodis 2017). The second highest number of mentioned challenges included erosion, missing street connections and electricity supply, indoor pollution, followed by malnutrition and weather damage to crops. Erosion was not as highly ranked as deforestation, but they were sometimes mentioned together. Although not explicitly defined, erosion, malnutrition and weather damage to crops could be linked to a decrease of productive yield, given the strong dependency on subsistence farming in the region (INE 2012). In conclusion, the communities that were part of the assessment are aware of the environmental problems like erosion and deforestation, and their wellbeing is negatively affected by them. The most direct benefit of a FLR would be the increase in the amount of wood available to secure energy resources of the villagers (FAO 2015). Further benefits could be the alleviation of erosion, a reduction of wind damage to crops and an overall increase in crop production (FAO 2015). This could result in an increased food supply for the communities and could help to solve the problem of malnutrition. Local problems how often they were mentioned in the PRA-analysis Water shortage Water contamination Street connections missing Lack of hygiene and sanitation Malnutrition Health and diseases Hail-and wind damage to crops Erosion Electricity supply Diseases of livestock Deforestation Indoor air pollution 0

1

2

3

4

5

Fig. 3.1 Results of a PRA analysis in the district of Micani. The problems that are most urgent to the participants were water shortage and contamination and deforestation. The bars highlighted in green correspond to problems that can arise directly or indirectly from land degradation and could be mitigated from forest and landscape restoration (Graphic based on IOG and Fundación Sodis 2017)

3.2 Environmental Challenges

21

Table 3.1 Results of the PRA analysis regarding the supply of fuelwood Community

Fuelwood supply

Llavini

No collection in own community possible due to lack of trees. Collection in neighbouring communities (3–4 h) every second day

Lupimarca

No collection possible in own community due to strong deforestation. Collection in neighbouring communities (3–4 h)

Mamania

n.a.

Ulupiquiri

Long journeys undertaken, due to big problems with deforestation. Often while grazing livestock (average 2 h)

In the three communities, of which data was available, the participants stated to walk multiple hours to collect fuelwood because not enough wood is available in proximity to the villages (based on IOG and Fundación Sodis 2017)

3.2 Environmental Challenges Building on the challenges that local communities face and are aware of, this chapter describes the environmental problems and challenges in the region that favour landscape degeneration and in effect influence the livelihood of the population. For this purpose, the topics of habitat loss and biodiversity decline, soil erosion and its drivers, and impacts of climate change were explored and placed in a regional context.

3.2.1 Habitat Loss and Biodiversity Decline One of the direct consequences of deforestation is the reduction of habitat for native flora and fauna (Davies et al. 2012; Ramirez-Villegas et al. 2012; UNCCD 2019). The loss and fragmentation of habitat obstructs the natural dispersal and exchange of species and leads to a decrease of biodiversity (Ramirez-Villegas et al. 2012; Brooks 2018; WWF 2021). The Andean Mountain Dry Forests ecoregion is heavily affected by deforestation, and it is estimated that only 6% of the original habitat is still intact (One Earth 2019). The WWF therefore considers the regional biodiversity as threatened (Brooks 2018). Human activities have fundamentally contributed to the alteration of the ecoregion, mainly through deforestation and unsustainable land use techniques (Brooks 2018; UNCCD 2019; WWF 2021). The protection of existing forests is considered a major importance in landscape restoration and reforestation projects, as not only can their loss hardly be compensated by any reforestation approach, but existing forests are also essential to increase the reforestation success (Ministry of Natural Resources—Rwanda 2014; Di Sacco et al. 2021). Remnant forest fragments provide habitat for many animals and plants and support regional biodiversity (PortilloQuintero and Sánchez-Azofeifa 2010). Furthermore, they provide planting material of genetically adapted species for natural regeneration and reforestation (FAO 2015;

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Di Sacco et al. 2021). The level of protection of remaining native forest areas in the case study site should be considered as low, with so far, no protected areas or policies in place to secure the remaining forests (GAM SPBV 2017).

3.2.2 Soil Erosion Erosion is one of the major challenges in many drylands (FAO 2015). It can lead to landslides, erosion gullies, a decrease in water quality, decreasing soil fertility and in turn lower productivity of arable land (Lal 2001; Desta and Adugna 2012). In consequence, erosion can pose a risk for local communities in terms of food security (Lal 2001) and potentially damage village infrastructure (Oyedepo 2013; Ngezahayo et al. 2019). There are many natural and anthropogenic factors that influence the erosion potential of a region. In general, surface erosion and its intensity are depending on three main factors (Lal 2001): firstly, the forces which may trigger erosive processes, like the impact of wind, precipitation or overland flow; secondly, the site-specific geophysical conditions, such as slope gradients and soil characteristics. And thirdly, the presence of a protective cover from rain impact, such as vegetation, litter or mulches. Natural Drivers of Erosive Processes The intensity of wind erosion depends on the velocity of the wind and the soil characteristics. The stronger the winds and the finer the soil texture, the higher is the potential of wind to carry away soil particles (Lal 2001). Hence, fine-grained soil can be especially affected by wind erosion. Precipitation and water run-off can lead to water erosion. During a rain event, the drops can detach soil particles as they hit the ground, also referred to as splash erosion (Geyik 1986; Lal 2001). In inclined surfaces, the detached particles then are carried away by the water in form of homogenous surface run-off (thus referred to as ‘sheet erosion’), leaving behind coarser and less fertile sands and gravel (Geyik 1986; Lal 2001). As the erosive process continues, the run-off leads to the development of erosion rills that increase the sediment carrying capacity because more water is collected in the depressions (Geyik 1986). Therefore, this state is also called rill erosion (Desta and Adugna 2012). With progressing rill erosion, the run-off water concentrates increasingly in small channels in the direction of slope (Geyik 1986). If no intervention stops the advance of erosive forces, the rills are growing in width and depth, as the concentrated water flows erode the sediments of the rills on their way (Geyik 1986). This can ultimately lead to the development of erosion gullies (Geyik 1986; Desta and Adugna 2012). Gully erosion is considered the most severe level of erosion (Desta and Adugna 2012; FAO 2015). It is characterized by large permanent rills that can no longer be undone by agricultural tillage and instead often require elaborate technical solutions (Geyik 1986). As the erosion proceeds and more water is drawn into the channel, the gully head is developing further upstream, which increases the carried rainwater amount (Desta and Adugna 2012). The gully is growing in size,

3.2 Environmental Challenges

23

velocity and strength and poses an increasing threat to neighbouring agricultural areas and village infrastructure, such as roads, foot paths, cattle trafficking lines and grazing spots (Desta and Adugna 2012). Furthermore, gullies can have a strong influence on the regional water quality, as the water transports sediments, fertilizers and other soil-bound materials into the river systems (Pabón-Caicedo et al. 2020). This may decrease the local as well as regional water quality; Pabón-Caicedo et al. (2020) indicated that 90% of the sediments in the Amazon River originated from the Andean cordillera. Hence, halting erosion gullies is an integral part of watershed protection. In the municipal territory of San Pedro de Buena Vista, water erosion can be observed in almost all its extension, ranging from less severe rill erosion to wide gully systems (GAM SPBV 2017). The intensity and frequency of precipitation events play an important factor in the potential intensity of water erosion (Ellenberg 1981; Geyik 1986). The stronger the precipitation event and the more overland flow occurs, the more soil can be washed away with it, and hence, the higher the erosive potential (Ellenberg 1981). Further, the soil moisture level influences erosion in two ways: when soils are dried out, they have a low absorption rate of rainwater at first, leading to more run-off (Ellenberg 1981). Therefore, dry soils are more easily eroded than moderately moist soils (Ellenberg 1981). But also, overly moist soils can be a cause for erosion. If the soil’s water storage capacity is saturated, e.g., after an intense rain event, the overland flows increase, wash-out the soils and subsequently increase the erosion rate (Ellenberg 1981). The natural precipitation pattern of the study region, thus, leads to a higher erosive risk. Through months with very low precipitation the soils dry out and when the heavy rains of the wet season start, the initial reduced water uptake capacity increases run-off and erosion (Geyik 1986; Oyedepo 2013). In the municipality of San Pedro de Buena Vista, it has been observed that, especially immediately after the first rains, both minor and major landslides occur (GAM SPBV 2017). During the rainy season, the high concentration of the annual rainfall in January, February and March can lead to a saturation of the soil’s water storage capacity and in turn increase erosion. The strong precipitation events during the rainy season (GAM SPBV 2017), thus, can have a strong erosive impact (Oyedepo 2013). Influence of Topographic Conditions Other factors, which strongly influence the erosive risk, are topography and slope inclination (Ellenberg 1981; Lal 2001). Generally, the steeper and longer the surface incline, the higher the water carrying potential and in effect the erosive potential (Ellenberg 1981; Geyik 1986; Desta and Adugna 2012). Thus, the rugged topography of the study site and the impact force of precipitation represent a high erosion potential. It has been observed that especially scarcely vegetated slopes of more than 45° were heavily affected by erosion and landslides (GAM SPBV 2017). Soil characteristics such as type and texture also influence the erosion rate. Finer soil components and sediments are more affected by erosion than coarser grains (Ellenberg 1981). Whether and to what extent very fine-pored soil types occur in the planning area could not be determined in this work and needs further research.

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Influence of Vegetation The presence of a protective cover like vegetation, litter and mulches can significantly mitigate erosion potential for both water and wind erosion (Geyik 1986; FAO 2015; Tatarko et al. 2019). The foliage decreases the amount and impact of water drops that otherwise directly reach the soil surface during rain events and hence can reduce the effects of splash erosion (FAO 2015). Further, their root network increases the water infiltration into the soils and thereby reduces run-off (Geyik 1986; FAO 2015). In dryland mountains, forests play an important role in the regional hydrological balance (FAO 2015). Additionally, the root network stabilizes the soil particles and can hence reduce erosion and landslides (Geyik 1986; FAO 2015). It should be noted that the species chosen for reforestation should be well considered, as not all tree species provide the same benefits. For example, some non-native species like eucalyptus or pine, which are frequently used for reforestation measures in the Andes, are high water-consumers and can reduce water availability and soil moisture level amongst other ecosystem services (Balthazar et al. 2015). However, Balthazar et al. (2015) further stated that this can also be used as an advantage, e.g., to reduce the water catchment of erosion gullies. Wind erosion can be mitigated especially by higher vegetation such as forests by serving as natural wind breaks (FAO 2015; Tatarko et al. 2019). Vegetation slows down wind speed and thereby reduces the wind’s erosive potential (FAO 2015; Tatarko et al. 2019). Conversely, the less vegetation, the greater the impact of rainwater and wind on the erosion rate (UNCCD 2019). Deforestation is considered a direct cause of soil erosion in the Andes and is thus contributing strongly to land degradation (Pabón-Caicedo et al. 2020). The sparse vegetation cover and often bare soil in the case study provide a surface which is sensitive to splash, sheet and rill erosion by rainwater and run-off (Geyik 1986; Brandt and Townsend 2006). This effect is intensified by reduced soil infiltration and less support of soil particles, caused by the absence of a supporting root network (Desta and Adugna 2012; FAO 2015; Pabón-Caicedo et al. 2020). The reduced soil infiltration not only affects soil moisture levels but leads to a reduction of ground water flows (Pabón-Caicedo et al. 2020). These effects are amplified in mountainous regions; droughts, desertification, biological degradation and landslides can be the consequence (Pabón-Caicedo et al. 2020). Landslides and erosion can become a threat to village infrastructure (Desta and Adugna 2012; Oyedepo 2013). Erosive processes can endanger agricultural areas by eroding valuable soil or allocating unfertile sediments and rocks on the fields and thereby decreasing the crop production (Desta and Adugna 2012). The highest risk comes thereby from barren areas in steep inclinations or the proximity to erosion gullies, all of which can cause landslides and washouts (Oyedepo 2013). The relocation of washed-out soil can further block or demolish streets, cutting-off villages from the road network (Oyedepo 2013; Ngezahayo et al. 2019). Especially unpaved roads are at high risk, as their bare surfaces render them more sensitive to rill and gully erosion, foremost during heavy precipitation events of the rainy season (Oyedepo 2013).

3.3 Drivers of Landscape Degradation

25

3.2.3 Climate Change The effects of climate change pose a significant environmental, social and economic threat in dry land regions (FAO 2015; Davies et al. 2012). Although there are uncertainties surrounding climate projections and regional varieties need to be researched by downscaling the models, all current projections forecast increasing temperatures in Bolivia and changes in the precipitation patterns (Rangecroft et al. 2013). In general, reduced precipitation rates and an increase in seasonal irregularities and extreme weather events are expected and the warming, whose rate is probably amplified with elevation, can lead to higher evaporation and evapotranspiration (Pabón-Caicedo et al. 2020). This can have an influence on growth rates and productivity of native fauna and agricultural crops, changes in species distribution and alterations in ecosystem cycles (Pabón-Caicedo et al. 2020). The first signs of the climatic changes are already noticeable at the study site today. Dry years are becoming more frequent, and the durance of the rainy season is getting shorter, shrinking from six months of time to three months (GAM SPBV 2017). The region is thus at risk of desertification (GAM SPBV 2017). According to the local population, the formerly gentle and more permanent precipitation events of the rainy season have changed and are now shorter but torrential, leading to a less favourable distribution in agricultural cycle and increased risk of soil erosion (GAM SPBV 2017). The unpredictability of the weather events further leads to losses in agricultural production (GAM SPBV 2017). The changes in the regional climate are not only due to global climate change. Changes in land cover have an influence on the radiation balance and the local hydrological cycle (FAO 2015; Pabón-Caicedo et al. 2020). When, for example, land is converted from forest to pasture, it contributes to rising average surface temperatures, lower soil moisture and lower cloud formation and precipitation rates (Pabón-Caicedo et al. 2020). Thus, forest and landscape restoration can also contribute to enhance local climate conditions. However, it is not limited to improving the local climate. Reforestation also contributes to climate change mitigation and is one of the most effective ways to store carbon (IUCN and WRI 2014; Lewis et al. 2019). Through carbon sequestration of forests, greenhouse gases in the atmosphere can be reduced. They also contribute significantly to a more stable hydrological cycle (Balthazar et al. 2015).

3.3 Drivers of Landscape Degradation Most of the previously described problems can be linked to an incremental reduction of vegetative cover and deforestation. Human activities contribute significantly to these processes, and the main drivers are expansion of agricultural areas, overgrazing and wood extraction for fuelwood (FAO and UNCCD 2019; López-Carr 2021) (Fig. 3.2).

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3 Problems of Deforestation and Its Drivers

Fig. 3.2 Drivers in the cycle of landscape degradation. The expansion of agricultural areas, overgrazing and wood extraction lead to an incremental reduction of vegetation cover. This causes higher rates of water run-off and increased wind speed, which both increase the risk of erosion. The progressing erosion leads to land degradation and reduces soil fertility and decreasing agricultural production. This leads to the demand for new fertile arable land and in turn to a reduction of vegetation cover. Grazing accelerates this process, as it disturbs natural regeneration and hinders the natural revegetation

Expansion of Agricultural Lands and Unsustainable Land Use A major influencing factor on deforestation and increased erosion originates from land use change (Brandt and Townsend 2006; Davies et al. 2012; FAO 2015; UNCCD 2019; López-Carr 2021). Especially the expansion of agricultural land is a driving force of deforestation and desertification (Brandt and Townsend 2006; Davies et al. 2012; FAO 2015; López-Carr 2021). The conversion often increases erosion as it weakens the retention of the soil through a marginalized vegetation (Davies et al. 2012; FAO 2015), especially in steep terrain (Desta and Adugna 2012). In the study area, agriculture is often practised in unsuitable locations with strong inclinations (GAM SPBV 2017). It has been observed that agricultural fields on steep slopes favour run-off, leading to washout and erosion of the fertile topsoil layers (GAM SPBV 2017). The resulting high erosive potential is increased by the practice of unsustainable farming methods, like inadequate tilling of the soil (GAM SPBV 2017). Further, the unsustainable extraction of firewood can incrementally lead to loss of forest areas and in consequence, land degradation and biodiversity loss (Davies et al. 2012). Road construction, but also unpaved animal trails and footpaths, has been identified to favour rill erosion, reduce the soil’s infiltration capacity and are thus strongly favouring erosion and the formation of gullies, which ultimately endanger the roads or trails from which they originated (Desta and Adugna 2012).

3.3 Drivers of Landscape Degradation

27

Overgrazing and Trampling The grazing of large parts of the Andean Mountain landscape is a strong driver of the decline of vegetation cover, leading to erosion, land degradation and biodiversity loss (Ellenberg 1981; Davies et al. 2012). There are two factors that influence the landscape degradation through grazing animals. Firstly, free roaming livestock reduces the natural vegetation cover (Ellenberg 1981; FAO 2010; Martins Mauricio et al. 2019; López-Carr 2021). Especially in the dry months, when less grass is available, grazing animals feed on the branches of young trees and shrubs to meet their energy needs (Ellenberg 1981). In the long run, this leads to the elimination of seedlings and young trees, which interrupts the natural regeneration of forests (Ellenberg 1981; Davies et al. 2012; FAO 2015). When the old trees eventually die, there are no young trees to fill their gaps and the forest stand declines (Fig. 3.3). The formerly forested areas are replaced by grass and shrub stands or fall barren, which leads to habitat loss and less protection to the soil against erosion (Ellenberg 1981; López-Carr 2021). Secondly, grazing animals can damage the topsoil with their hooves (Ellenberg 1981). The native species of camelids such as llamas and vicuñas have a lesser impact here, as they have soft treads and thus damage the soil less (Ellenberg 1981). However, non-native grazing animals such as goats and sheep have sharp hooves. They cause a visible network of livestock tracks, where ground vegetation is destroyed, and the soil becomes susceptible to erosion (Ellenberg 1981). These impacts are potentially influencing the case study area, as sheep and goats have a large presence (GAM SPBV 2017). In Micani, goats and sheep account to over 20,000 individuals, opposing only 66 llamas and no alpacas, which demonstrates the abundance of non-native compared

Fig. 3.3 Scheme of incremental process of deforestation in the study region. The formerly forested hillsides are partly cleared for agricultural production. The introduction of grazing animals reduces the vegetation cover and disturbs natural regeneration

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3 Problems of Deforestation and Its Drivers

to native livestock (GAM SPBV 2017). Due to the lack of pastures and the almost continuous free grazing in the communal areas, grazing pressure can be considered high (FAO 2015). The municipal government states that grazing livestock in some places reduces the vegetation cover to such an extent that for long periods of time the soil is left bare and therefore susceptible to water and wind erosion (GAM SPBV 2017). Thus, they potentially have a strong influence on the local vegetation in the study area by overgrazing and trampling. Research indicates that the pasture-driven extent of bare land is increasing rapidly and has doubled in Bolivian mountain regions in the time from 1985 to 2003 (Brandt and Townsend 2006). Incremental Deforestation Over the Last Centuries Deforestation has a long history in Bolivia, and to understand the incremental processes that lead to the landscape patterns of today, one has to take a look into the past. Some researchers hypothesized that before the beginning of agriculture and livestock farming, most parts of Bolivia were covered by forests (Ellenberg 1981; Chepstow-Lusty and Jonsson 2000). With the beginning of agricultural practices, the forest cover may have been slowly reduced to make space for fields and pastures and through wood extraction for purposes of construction, tool production and for firewood (Chepstow-Lusty and Jonsson 2000). Research indicates that the Inca had established a sustainable land management system, which included erosion control and forestry (Pampuch and Echalar 1998; Chepstow-Lusty and Jonsson 2000). Erosion was prevented by traditional terracing, referred to as ‘andenes’ (Pampuch and Echalar 1998; ChepstowLusty and Jonsson 2000), and animal husbandry of llamas and alpacas on irrigated pastures prevented overgrazing (Pampuch and Echalar 1998). The arrival of the Spanish in the sixteenth century led to a breakdown of the established land management system and accelerated the processes of deforestation (Zimmerer 1993; Chepstow-Lusty and Jonsson 2000). The colonists dispossessed the indigenous population in many places and claimed their land for themselves (Pampuch and Echalar 1998). At the same time, the diseases brought by the colonists led to a severe decimation of the population, who had no resistance or medicines against the new diseases (Pampuch and Echalar 1998). The indigenous community system of collaborative labour fell apart in many places, the traditional land management techniques, like terracing and irrigation channels, degenerated (Pampuch and Echalar 1998), and many traditional forestry practices were abandoned (Zimmerer 1993). The introduction of European farming techniques led to increased soil degradation (Zimmerer 1993; Pampuch and Echalar 1998). On the one hand, this was due to the abandonment of adapted farming techniques, like terraced farming. On the other hand, it was due to the introduction of livestock keeping, such as sheep, cattle and goats (Pampuch and Echalar 1998; Chepstow-Lusty and Jonsson 2000; Martins Mauricio et al. 2019). Further, slash-and-burn agriculture and the large demand for wood by the colonists led to severe deforestation of the landscape (Pampuch and Echalar 1998; ChepstowLusty and Jonsson 2000). As Bolivia possesses a large deposit for mineral resources, whole mountainsides were cleared of vegetation in search for mining locations, e.g., for ore and silver extraction (Pampuch and Echalar 1998).

References

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After Bolivia’s independence from colonial rule in the beginning of the nineteenth century, the mining industry as well as railroad construction and intensified timber extraction consumed large parts of the remaining forests (Pampuch and Echalar 1998). Thus, centuries of anthropogenic influences, overexploitation and unsustainable land management shaped the landscape of Bolivia and the Andes of today (Pampuch and Echalar 1998).

3.4 Summary of Regional Problems and Challenges in Relation to Land Degradation The previous analysis of the research area shows that most of the problems and challenges can be traced to the long-term regional deforestation and associated land degradation. The loss and fragmentation of forests endangers the regional biodiversity by limiting the available habitat area (Ramirez-Villegas et al. 2012; FAO 2015; Brooks 2018; WWF 2021). It also leads to a lack of wood supply for many communities that rely on wood as their main energy source (Zimmerer 1993; INE 2012). The reduction of vegetated surface leads to erosion, which poses a great risk throughout the research area (Geyik 1986; Pabón-Caicedo et al. 2020). Thereby especially affected are areas on steep slopes, as the inclination increases run-off velocity compared to plane surfaces (Lal 2001). Through erosion, the soil fertility can be reduced, leading to less productivity of arable land and can thus cause food insecurity (FAO 2015; FAO and UNCCD 2019; Smith et al. 2019). Further, the water quality can be negatively influenced by the presence of erosion gullies, as they can transport large amounts of sedimentation into rivers and other water sources (PabónCaicedo et al. 2020). Gullies and other forms of erosion can further pose a threat to village infrastructure such as roads and agricultural areas (Desta and Adugna 2012; Oyedepo 2013). Climate change is a major challenge and affects local livelihoods and ecosystem functions alike (FAO 2015; Davies et al. 2012; GAM SPBV 2017). Reforestation could help to mitigate its effects by sequestering carbon and by influencing the local micro- and macroclimate (IUCN and WRI 2014; FAO 2015; Lewis et al. 2019). Consequently, all of these problems could be mitigated by carefully planned FLR approaches.

References Balthazar V, Vanacker V, Molina A, Lambin EF (2015) Impacts of forest cover change on ecosystem services in high Andean mountains. Ecol Indic 48(1):63–75. https://doi.org/10.1016/j.ecolind. 2014.07.043

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Brandt JS, Townsend PA (2006) Land use—land cover conversion, regeneration and degradation in the high elevation Bolivian Andes. Landsc Ecol 21(4):607–623. https://doi.org/10.1007/s10980005-4120-z Brooks D (2018) South America: in the mountain valleys of southern central Bolivia | Ecoregions | WWF. World Wildlife Fund (WWF), Washington, DC. https://www.worldwildlife.org/ecoreg ions/nt0206. Accessed 19 Aug 2022 Chakravarty S, Ghosh S, Suresh CP, Dey AN, Shukla G (2012) Deforestation: causes, effects and control strategies. In: Okia CA (ed) Global perspectives on sustainable forest management. Intech, Rijeka, Shanghai. https://doi.org/10.5772/33342 Chambers R (1994) Participatory rural appraisal (PRA) analysis of experience. World Dev 1994(22):953–969. https://doi.org/10.1016/0305-750X(94)90003-5 Chepstow-Lusty A, Jonsson P (2000) Inca agroforestry: lessons from the past. AMBIO J Hum Environ 29(6):322–328. https://doi.org/10.1579/0044-7447-29.6.322 Davies J, Poulsen L, Schulte-Herbrüggen B, MacKinnon K, Crawhall N, Henwood WD, Dudley N, Smith J, Gudka M (2012) Conserving dryland biodiversity. International Union for Conservation of Nature and Natural Resources, Nairobi Desta L, Adugna B (2012) A field guide on gully prevention and control. Nile Basin Initiative— Eastern Nile Subsidiary Action Program (ENSAP), Addis Ababa Di Sacco A, Hardwick KA, Blakesley D, Brancalion PHS, Breman E, Rebola LC, Chomba S, Dixon K, Elliott S, Ruyonga G, Shaw K, Smith P, Smith R, Antonelli A (2021) Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. Glob Change Biol 27(7):1328–1348. https://doi.org/10.1111/gcb.15498 Ellenberg H (1981) Desarrollar sin destruir. Instituto de Ecologia, La Paz FAO (2010) Guidelines on sustainable forest management in drylands of sub-Saharan Africa. Arid zone forests and forestry working paper no. 1. Food and Agriculture Organization of the United Nations, Rome FAO (2015) Global guidelines for the restoration of degraded forests and landscapes in drylands: building resilience and benefiting livelihoods. In: Berrahmouni N, Regato P, Parfondry M (eds) Forestry paper no. 175. Food and Agriculture Organization of the United Nations, Rome FAO (2019) Trees, forests and land use in drylands: the first global assessment—full report. FAO forestry paper no. 184. Food and Agriculture Organization of the United Nations, Rome FAO and UNCCD (2019) Vulnerability to food insecurity in mountain regions: land degradation and other stressors. Food and Agriculture Organization of the United Nations, Bonn GAM SPBV (Gobierno Autónomo Municipal San Pedro de Buena Vista) (2017) Plan territorial de desarrollo integral 2016–2020. San Pedro De Buena Vista Geyik MP (1986) FAO watershed management field manual: gully control. FAO Conserv Guide 13(2). Food and Agriculture Organization of the United Nations, Rome INE (Instituto Nacional de Estadística) (2012) Censo de Población y Vivienda 2012. Características de Viviendas. Ine-Bolivia: Redatam (CEPAL)—Diseminación de Información Estadística. http:// datos.ine.gob.bo. Accessed 26 Sept 2021 IOG (Ingenieure Ohne Grenzen) and Fundación Sodis (2017) Bedarfserhebung in vier beispielhaften Gemeinden Micanis. Ingenieure Ohne Grenzen, Aachen IUCN and WRI (International Union for Conservation of Nature and World Resource Institute) (2014) Guide to the Restoration Opportunities Assessment Methodology (ROAM) assessing forest landscape restoration opportunities at the national or sub-national level. Working paper (road-test edition). International Union for Conservation of Nature and World Resource Institute, Gland Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12(6):519–539. https://doi.org/10. 1002/ldr.472 Lewis SL, Wheeler CE, Mitchard ETA, Koch A (2019) Regenerate natural forests to store carbon. Nature 568(7750):25–28. https://doi.org/10.1038/d41586-019-01026-8

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López-Carr DA (2021) A review of small farmer land use and deforestation in tropical forest frontiers: implications for conservation and sustainable livelihoods. Land 2021(10):1113–1136. https://doi.org/10.3390/land10111113 Martins Mauricio R, Sandin Ribeiro R, Campos Paciullo DS, Alves Cangussú M, Murgueitio E, Chará J, Flores Estrada MX (eds) (2019) Silvopastoral systems in Latin America for biodiversity, environmental, and socioeconomic improvements. In: Lemaire G, De Faccio Carvalho PC, Kronberg S, Recous S (eds) Agroecosystem diversity—reconciling contemporary agriculture and environmental quality. Elsevier, Amsterdam Ministry of Natural Resources—Rwanda (2014) Forest landscape restoration opportunity assessment for Rwanda. MINIRENA (Rwanda), IUCN, WRI Ngezahayo E, Ghataora GS, Burrow MPN (2019) Factors affecting erosion in unpaved roads. Paper presented at the 4th world congress on civil, structural, and environmental engineering (CSEE’19), Rome, Italy, 7–9 Apr 2019. https://doi.org/10.11159/icgre19.108 One Earth (2019) Bolivian Montane Dry Forests. https://www.oneearth.org/ecoregions/bolivianmontane-dry-forests/. Accessed 22 Aug 2022 Oyedepo OJ (2013) Impact of erosion on street roads: a case study of Sijuwade area Akure Ondo State Nigeria. Chem Mater Res 3(10):33–39 Pabón-Caicedo JD, Arias PA, Carril AF, Espinoza JC, Borrel LF, Goubanova K, Lavado-Casimiro W, Masiokas M, Solman S, Villalba R (2020) Observed and projected hydroclimate changes in the Andes. Front Earth Sci 8:411–440. https://doi.org/10.3389/feart.2020.00061 Pampuch T, Echalar AA (1998) Bolivien. Beck’sche Reihe; Länder, 3rd edn. Verlag C. H. Beck, München Pearson T, Walker S, Brown S (2006) Guidebook for the formulation of afforestation and reforestation projects under the clean development mechanism. Technical series 25. International Tropical Timber Organization (ITTO), Yokohama Portillo-Quintero CA, Sánchez-Azofeifa GA (2010) Extent and conservation of tropical dry forests in the Americas. Biol Conserv 143(1):144–155. https://doi.org/10.1016/j.biocon.2009.09.020 Ramirez-Villegas J, Jarvis S, Touval J (2012) Analysis of threats to South American flora and its implications for conservation. J Nat Conserv 20(6):337–348. https://doi.org/10.1016/j.jnc.2012. 07.006 Rangecroft S, Harrison S, Anderson K, Magrath J, Castel AP, Pacheco P (2013) Climate change and water resources in arid mountains: an example from the Bolivian Andes. Ambio 42(7):852–863. https://doi.org/10.1007/s13280-013-0430-6 Smith P, Nkem J, Calvin K, Campbell D, Cherubini F, Grassi G, Korotkov V, Hoang AL, Lwasa S, McElwee P (2019) Interlinkages between desertification, land degradation, food security and greenhouse gas fluxes: synergies, trade-offs and integrated response options. In: Shukla PR, Skea J, Calvo Buendia E, Masson-Delmotte V, Portner HO, Roberts DC, Zhai P, Slade R, Connors S, van Diemen, R, Ferrat M, Haughey E, Luz S, Neogi S, Pathak M, Petzold J, Portugal Pereira J, Vyas P, Huntley E, Kissick K, Belkacemi M, Malley J (eds) Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. https://www.ipcc.ch/srccl/cha pter/chapter-6/. Accessed 22 Aug 2022 Tatarko J, Trujillo W, Schipanski M (2019) Wind erosion processes and control. Colorado State University. https://extension.colostate.edu/docs/pubs/crops/xcm180.pdf. Accessed 23 Aug 2022 UNCCD (United Nations Convention to Combat Desertification) (2019) Forests and trees. At the heart of land degradation neutrality. UNCCD, Bonn UNEP (United Nations Environment Programme) (2021) Becoming #GenerationRestoration: ecosystem restoration for people, nature and climate. UNEP, Nairobi World Wildlife Fund (WWF) (2021) Tropical and subtropical dry broadleaf forests. https://www.wor ldwildlife.org/biomes/tropical-and-subtropical-dry-broadleaf-forests. Accessed 26 Sept 2021. Zimmerer K (1993) Soil erosion and labor shortages in the Andes with special reference to Bolivia, 1953–91: implications for “conservation-with-development”. World Dev 21(10):1659–1675. https://doi.org/10.1016/0305-750X(93)90100-N

Chapter 4

FLR Potentials and Spatial Allocation Parameters

Building on the previously described problems and challenges in the region, in this chapter the thematic intervention zones for reforestation are developed. They aim to mitigate the described problems and contribute to a sustainable forest and landscape restoration. To achieve this, their main influencing factors are researched and transferred to respective parameters, which enable a spatial allocation of where the problem is most pressing and where the intervention would hence be most beneficial.

4.1 Protection of Existing Forest Habitats The protection of remnant forests and the reduction of habitat fragmentation are important to conserve and potentially increase the regional biodiversity and enhance potential natural regeneration of local ecosystems (FAO 2010, 2015a; IUCN and WRI 2014; Ministry of Natural Resources—Rwanda 2014; UNCCD 2019). The establishment of buffer zones around forests can protect them from further exploitation and decrease further land degradation in the forests’ surroundings (Ministry of Natural Resources—Rwanda 2014). The reconnection of fragmented forests can be accomplished by the creation of green corridors (Portillo-Quintero and Sánchez-Azofeifa 2010; IUCN and WRI 2014). They can enhance the migrative potential of species and thereby support their distribution and protect or even increase the local biodiversity (Bond 2003; Ministry of Natural Resources—Rwanda 2014; Di Sacco et al. 2021). Approach: Establishment of Buffer Zones Around Forest Remnants Buffer zones can be an important intervention in protecting remaining forests. Since not every tree-covered surface is big enough to generate the benefits that are linked to forests, only forested areas that exceed 0.5 ha are considered for the protecting

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_4

33

34

4 FLR Potentials and Spatial Allocation Parameters

buffer zones. This corresponds to the FAO definition, which defines the minimum dimensions of an area to be considered forest at 0.5 ha (FAO 2015b). To establish their full potential, certain dimensions need to be considered, as too narrow buffers might not provide a sufficient protection. A width of 100 m has been implemented in dryland restoration planning in Rwanda (Ministry of Natural Resources—Rwanda 2014). It is used as model for the buffers of the potential reforestation zones around forest remnants in this work. Approach: Reconnection of Remnant Forest Habitats The design of the corridors is depending on the target species of the respective habitat (McKenzie 1995; Bond 2003). The connection should fulfil the species’ habitat requirements, to provide food and shelter, especially for passage in longer corridors or for corridor dwellers like smaller animals or plants, which have a slow movement or dispersal pattern (McKenzie 1995). This is favoured using native plant species in the restoration process (McKenzie 1995; Bond 2003; Beier et al. 2007, 2008; USDA 2011). The dimensions of the corridors depend on the target species’ home range dimensions (McKenzie 1995). If the corridor is destined to serve as permanent habitat, the width should be at least as wide as the minimum width of one home range, in general the wider the corridor the better (McKenzie 1995; USDA 2011). The determination of suitable target species could not be covered in this work, thus, for a more specified approach, the consultation with ecologists or biologists is recommended. Nevertheless, although the corridor design is case dependent, there are some general principles that can be applied. The minimum width recommended in literature is ranging from approx. 7.5 m (USDA 2011) to approx. 300 m (Bond 2003). Thereby sections in between the corridor should be no longer than approx. 6 m (USDA 2011). The most suitable location of the corridor is dependent on the landscape context and the target species (McKenzie 1995). Some researchers suggest that corridors that span different topographic positions support a larger number of species and ergo offer higher biodiversity benefits (McKenzie 1995). Others state that similar location factors as well as the cost of movement are critical factors (Beier et al. 2008). Beier et al. (2008) argue that similar topographic positions facilitate corridor crossing and thus increase the corridors benefits. As the montane dry forests of the Andes are characterized by considerable topographical differences and a strong fluctuation of location factors, e.g., from the northern to a southern exposed hillside, this work’s corridor design approach was based on the assumption by Beier et al. (2008). The influence of human activities plays an important role in the adoption of green corridors by wildlife and therefore needs to be considered in the planning process (McKenzie 1995; Bond 2003; Beier et al. 2008). This implies the presence of grazing livestock, wood extraction, the proximity to roads and the vicinity to settlements and/or agricultural areas (McKenzie 1995; Beier et al. 2008). Therefore, management practices should be elaborated on how to prevent negative influences on the corridors. This could, for example, include fencing pastures (McKenzie 1995)

4.2 Reduction of Erosion Potential

35

or adequate buffer zones between green corridors and settlements (McKenzie 1995; Bond 2003). The interruption of corridors through roads is acceptable if the roads are unpaved and no wider than approx. 6 m (USDA 2011). If the roads are paved or wider than 6 m, an alternative passage like a bridge or culvert is recommended (USDA 2011). As there are no paved roads in the planning area (GAM SPBV 2017) and they are usually narrower than 6 m, the construction of wildlife crossing aids can be dispensed.

4.2 Reduction of Erosion Potential To determine the areas, which potentially are most severely affected or endangered by erosion, three complementary approaches were used, each of them concentrating on one spatial factor that plays a dominant role in the progression of erosion: (1) areas with no vegetation cover, (2) areas with steep slope inclination and (3) areas with marks of gully erosion (Lal 2001). (1) Reducing Erosion Potential of Bare Lands As the lack of vegetation increases the erosion potential (FAO 2015a, 2019; PabónCaicedo et al. 2020), especially those areas with no vegetation cover throughout long periods of the year are at risk. Therefore, barren lands are classified in this study as high-priority for reforestation to reduce erosion risk and its negative effects on ecosystem functions and the livelihood of people. Approach: Reforestation of Bare Lands Locate and map areas with no protective vegetation cover. (2) Reducing Erosion Potential of Areas in Steep Inclinations Steep slopes increase the erosion potential, especially if no adequate vegetation cover is present. To protect the landscape from further erosion, it can therefore help to revegetate especially very steep slopes without protective forest cover (FAO 2015a). Approach: Reforestation of Steep Slopes To determine steep areas that are at greatest risk of erosion due to their inclination, the slope gradient classes of the Food and Agriculture Organisation of the United Nations (FAO) (2006) were applied (Table 4.1). As soils in forested areas with steep inclinations are at less risk of erosion (FAO 2015a), they were excluded from the priority mapping. (3) Halting Watershed Degradation by Erosion Gullies Gully control is one of the most important topics to combat water erosion in slopes and is therefore an important factor to be considered in watershed management and

36 Table 4.1 Slope gradient classes (based on FAO 2006)

4 FLR Potentials and Spatial Allocation Parameters Class

Description

%

01

Flat

0–0.2

02

Level

0.2–0.5

03

Nearly level

0.5–1.0

04

Very gently sloping

1.0–2.0

05

Gently sloping

2–5

06

Sloping

5–10

07

Strongly sloping

10–15

08

Moderately steep

15–30

09

Steep

30–60

10

Very steep

> 60

landscape restoration (FAO 2019). Revegetation is an integral part in this process, although depending on the severity of the gully condition, potentially other more technical approaches are additionally needed (Desta and Adugna 2012). Nevertheless, revegetation, e.g., in the catchment areas of the gullies, can strongly mitigate run-off and thereby reduce the water flow that is otherwise aggravating the gully formation (Desta and Adugna 2012). Approach: Revegetation of Catchment Areas of Erosion Gullies To locate potential zones for gully mitigation through reforestation, the existing gully formations in the area were analysed and mapped.

4.3 Sustainable Wood Supply The villagers of Andean communities depend strongly on forests for their resources like fuelwood for cooking, heating and as construction material (INE 2012). Due to the strong deforestation in some of the regions, the villagers are forced to travel multiple hours to collect wood, sometimes into bordering communities (IOG and Fundación Sodis 2017). Therefore, the reachability of the communities to forested areas is of great importance for the livelihood of the village population. Reforestation in proximity to villages with adequate forest management can facilitate sustainable wood collection (FAO 2015a). Approach: Reforestation Near Villages Which Are Potentially Facing Underprovision of Wood The assessment of the proximity of villages to the nearest forest area is taken as indicator to determine whether they lack accessible wood resources. This can serve as a basis to set a focus on the reforestation of the surroundings of these villages. Since not every tree-covered surface is big enough to supply sufficient resources and

4.4 Protection of Village Infrastructure Table 4.2 Proximity classes for the villages to the nearest forest area

37

Travel time (in min)

Distance (in m)

Class

5

Approx. 100

Very close

15

400

Close

30

800

Intermediate

60

1600

Far

> 60

> 1600

Very far

might be too small to be used sustainably by communities, only forests whose surface cover exceeds 0.5 ha (FAO 2015b) are considered as potential wood resource. The reachability of forests by the villagers was calculated based on the spatial proximity of the villages to forested areas. To assess the reachability, the analysis was based on the distances that can be travelled on average in (1) less than 5 min (very close), (2) 15 min (close), (3) 30 min (intermediate), (4) 1 h (far) and (5) more than 1 h (very far). Based on the average hiking speed in steep terrain, which applies to areas with an inclination greater than 17° (Hodgkins 2020), the villages were ranked based on the travel time of the people to reach a forested area. Calculations based on a slope map (see Sect. 7.2.3) indicated a mean inclination of 26.3° with the median at 26°, and the majority of the pixels in the area showed an inclination of 27°. A total of 83% of the planning area featured inclinations steeper than 17°. Therefore, an average maximum hiking speed of 1.6 km/h can be presumed (Hodgkins 2020). By multiplying the maximum hiking speed with the duration of the five travel-time categories, the distances that can be travelled in the according time were calculated (Table 4.2). According to this, villagers can reach forests in 100 m distance in 5 min, 400 m distances can be travelled in 15 min, 800 m in 30 min and 1.6 km in one hour. Since it was not possible during this work to carry out an analysis of the distances perceived as comfortable to collect wood by the villagers, a maximum distance of 400 m, and thus a 15-min walk, was used as an example for the following analysis. This distance can be adjusted in collaboration with the communities to refine the priority-zone assessment.

4.4 Protection of Village Infrastructure As FLR can reduce the risk of erosion, reforestation could mitigate the risk of village infrastructure being negatively affected by erosive processes (Desta and Adugna 2012; Oyedepo 2013). The revegetation of areas in the proximity to roads and agricultural areas, especially if they are barren and located in steep inclinations, could protect the village infrastructure and reduce the need of reconstruction activities (Oyedepo 2013).

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4 FLR Potentials and Spatial Allocation Parameters

Approach: Revegetation of the Surrounding Areas of Important Village Infrastructure To allocate the village infrastructure that is particularly threatened to be negatively affected by erosion processes, their proximity to erosion gullies and very steep areas exceeding 30% without retaining vegetation was analysed. Therefore, infrastructural areas within a certain risk range to erosive surfaces were detected as potential zones for reforestation. For the width of the risk range to mitigate threats to the infrastructure, no guideline values were found. Therefore, the assessment was conducted using a 20 m radius that seemed reasonable. To strengthen the informative value of the potential analysis, more research would be needed.

4.5 Mitigation of Climate Change Impacts The mitigating factors of reforestation on climate change are difficult to assign to spatial parameters. Their contribution is not location-specific but depends mainly on the size and long-term growing conditions (e.g., variation in temperature and precipitation patterns) of the forests (Calvo-Rodriguez et al. 2021). In general, the more biomass is produced, the greater the contribution to carbon storage. Therefore, those species should be considered, which are adapted to the local conditions and can be expected to thrive also under future climatic conditions (Breed et al. 2013; Havens et al. 2015). Since temperatures are expected to rise and changing precipitation patterns are anticipated, native species composition might change in the future (Havens et al. 2015). Therefore, provenancing and the introduction of species from warmer and/or dryer regions, to anticipate the future climatic conditions, could increase reforestation success and should be considered in the planning process (Lowe 2010; Breed et al. 2013; Havens et al. 2015).

4.6 Mitigation of Drivers of Landscape Degeneration The analysis of drivers showed that mainly human activities contribute to the progression of deforestation and landscape degeneration. This requires an integrated approach that addresses the underlying causes and at the same time fosters landscape regeneration. To protect existing forest as well as potentially future reforested areas, alternative agricultural and pasture practices might need to be (re)introduced, to combine reforestation and sustainable food production in the communities (UNCCD 2019; Di Sacco et al. 2021). Alternative and more sustainable agricultural practices can include agroforestry (UNCCD 2019), improved irrigation of arable land or the reestablishment of terracing (GAM SPBV 2017). This could help to mitigate the effects of wind

4.7 Summary of FLR Potentials

39

and water erosion in the region. The issue of tree loss through uncontrolled logging for fuelwood or as construction material can be met by the reforestation itself and the introduction of sustainable forest management (Ministry of Natural Resources— Rwanda 2014; UNCCD 2019). In addition, the designation of forests as protection, conservation or economic forests can facilitate the management of timber resources and increase the success of reforestation (Atmadja et al. 2019). To reduce grazing pressure, the forests could be fenced and grazing of steep slopes and other highly erosive surfaces could be restricted (Ministry of Natural Resources—Rwanda 2014). A more integrated approach lies in the implementation of silvopastoral systems (FAO 2015a; Jose and Dollinger 2019; Martins Mauricio et al. 2019; UNCCD 2019). Silvopasture is defined by a combination of pasture and forestry approaches and usually describes grazing grounds with tree coverage (Jose and Dollinger 2019; Martins Mauricio et al. 2019). It has the potential to increase fodder availability for livestock leading to a higher productivity and less space requirements and can contribute to the recovery of ecosystem services and protection of biodiversity by providing habitat for native species (Martins Mauricio et al. 2019). The local municipality of San Pedro the Buena Vista is aware of the challenges of anthropological effects on land degeneration and aims to raise awareness in the population and to establish more sustainable land use techniques (GAM SPBV 2017). Their establishment will be crucial for the FLR process in the region, and their implementation should be introduced in the respective communities before the implementation of reforestation measures (FAO 2015a). Otherwise, the effort might be in vain as the plantings might perish soon after.

4.7 Summary of FLR Potentials It can be difficult to decide on where to start FLR measures in a region heavily affected by land degradation. Regarding the high level of land degradation, there is probably almost no ‘wrong’ space for implementation; nevertheless, since forest and landscape restoration often comes at a cost, the location of the measure should be well considered, to maximize its benefits. The previously assessed approaches to locate potential areas for reforestation of thematic benefits that have been identified in this chapter can be mapped for a target region. By merging the resulting thematic maps into one combined map, it allows the identification of priority zones that help in assessing where a reforestation project can lead to the improvement of several problems. Furthermore, it can help to compare the level of threats and challenges of different regions and thus find out which communities or subcentrals would benefit most from an implementation of the FLR measures. These could then be prioritized in the planning process. The following spatially dependent benefits of reforestation have been identified:

40

4 FLR Potentials and Spatial Allocation Parameters

• Protection of forest remnants through buffer zones (FAO 2010, 2015a; IUCN and WRI 2014; UNCCD 2019) • Reconnection of remnant forest habitats through green corridors (PortilloQuintero and Sánchez-Azofeifa 2010; IUCN and WRI 2014) • Reduction of erosive risk of bare lands (FAO 2019; Pabón-Caicedo et al. 2020) • Reduction of erosive risk of steep slopes (FAO 2015a) • Watershed protection (FAO 2019; Desta and Adugna 2012) • Provisioning of villages with sufficient firewood (FAO 2015a) • Protection of village infrastructure from landslides (Desta and Adugna 2012; Oyedepo 2013). The creation of a priority map is conducted for the case study area of Micani, Bolivia. Based on the seven approaches described above for reducing problems caused by deforestation, seven thematic potential maps were elaborated. The maps were then overlaid to identify priority zones for reforestation in the area where multiple benefits can be generated for a positive ecological, social and economic development of the region (Table 4.3).

Table 4.3 Priority-zone mapping scheme Problems and Detectable challenges causes Decreasing biodiversity

Goal

Habitat loss Protection of and existing forest fragmentation

Reconnection of existing forests through green corridors

Decreasing agricultural production

Deforestation Protection of and erosion soil cover through reforestation

Spatial influencing factors

Benefits

Methods

Location of existing forested areas

Preservation of existing habitat and increase of ecosystem services

Mapping forested areas, buffer zones of 100 m

Location of existing forested areas

Reconnecting habitats, increasing movement pattern, increasing biodiversity

Mapping forested areas, planning of corridors following slope gradient, acceptable section width (6 m), minimum dimension of 300 m

Lack of vegetative cover

Improved water-saving capacity, soil fertility

Mapping of barren lands

(continued)

4.7 Summary of FLR Potentials

41

Table 4.3 (continued) Problems and Detectable challenges causes

Decreasing wood supply

Lack of forested areas in village proximity through deforestation

Damaged Landslides village and erosion infrastructure (roads, arable land)

Goal

Spatial influencing factors

Benefits

Methods

Slope inclination

Decrease of run-off, better water-saving capacity and reduction of sediment washout

Mapping of slope inclinations and categorization. The steeper the gradient, the higher the erosive potential, therefore prioritization of reforestation on very steep slopes

Erosion gullies

Decrease of Mapping of run-off, better erosion gullies water-saving capacity, and reduction of sediment washout

Reestablishment of forested areas in village proximity

Distance from villages to forested areas

Sustainable firewood supply for the communities

Evaluation of village proximity to next forested area based on possible walking distance

Protection of village infrastructure by reducing effects of erosion in close proximity

Barren lands, slope inclination and erosion gullies in close proximity to village infrastructure

Continuous usability of roads, reducing reconstruction demand

Mapping of roads and agricultural areas in proximity to barren and steep areas (more than 45°) and erosion gullies (50 m)

The table depicts the problems caused by land degradation which have been assessed in Chap. 3. To each problem, it shows the spatially detectable causes and goals that could help to mitigate the problems. The spatial influencing factors describe to what spatial conditions the development of the problems is linked. The benefits section describes how reforestation could improve the respective conditions. Building on the spatial influencing factors, the mapping parameters to locate potential zones for reforestation were defined

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4 FLR Potentials and Spatial Allocation Parameters

References Atmadja S, Eshete A, Boissière M (2019) Guidelines on sustainable forest management in drylands of Ethiopia. FAO and CIFOR, Rome Beier P, Majka D, Jenness J (2007) Conceptual steps for designing wildlife corridors. http://corrid ordesign.org/dl/docs/ConceptualStepsForDesigningCorridors.pdf. Accessed 19 Aug 2022 Beier P, Majka D, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22(4):836–851. https://doi.org/10.1111/j.1523-1739.2008.009 42.x Bond M (2003) Principles of wildlife corridor design. Center for Biological Diversity. https://www. biologicaldiversity.org/publications/papers/wild-corridors.pdf. Accessed 19 Aug 2022 Breed MF, Stead MG, Ottewell KM, Gardner MG, Lowe AJ (2013) Which provenance and where? Seed sourcing strategies for revegetation in a changing environment. Conserv Genet 14:1–10. https://doi.org/10.1007/s10592-012-0425-z Calvo-Rodriguez S, Sánchez-Azofeifa GA, Durán SM, Do Espírito-Santo MM, Ferreira Nunes YR (2021) Dynamics of carbon accumulation in tropical dry forests under climate change extremes. Forests 12(1):106–121. https://doi.org/10.3390/f12010106 Desta L, Adugna B (2012) A field guide on gully prevention and control. Nile Basin Initiative— Eastern Nile Subsidiary Action Program (ENSAP), Addis Ababa Di Sacco A, Hardwick KA, Blakesley D, Brancalion PHS, Breman E, Rebola LC, Chomba S, Dixon K, Elliott S, Ruyonga G, Shaw K, Smith P, Smith R, Antonelli A (2021) Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. Glob Change Biol 27(7):1328–1348. https://doi.org/10.1111/gcb.15498 FAO (2006) Guidelines for soil description, 4th edn. Food and Agriculture Organization of the United Nations, Rome FAO (2010) Guidelines on sustainable forest management in drylands of sub-Saharan Africa. Arid zone forests and forestry working paper no. 1. Food and Agriculture Organization of the United Nations, Rome FAO (2015a) Global guidelines for the restoration of degraded forests and landscapes in drylands: building resilience and benefiting livelihoods. In: Berrahmouni N, Regato P, Parfondry M (eds) Forestry paper no. 175. Food and Agriculture Organization of the United Nations, Rome FAO (2015b) Terms and definitions. Forest resources assessment working paper 180. Food and Agriculture Organization of the United Nations, Rome FAO (2019) Trees, forests and land use in drylands: the first global assessment—full report. FAO forestry paper no. 184. Food and Agriculture Organization of the United Nations, Rome GAM SPBV (Gobierno Autónomo Municipal San Pedro de Buena Vista) (2017) Plan territorial de desarrollo integral 2016–2020. San Pedro De Buena Vista Havens K, Vitt P, Still S, Kramer AT, Fant JB, Schatz K (2015) Seed sourcing for restoration in an era of climate change. Nat Areas J 35(1):122–133. https://doi.org/10.3375/043.035.0116 Hodgkins K (2020) What’s the average hiking speed? Calculate your pace on the trail. https://www. greenbelly.co/pages/average-hiking-speed. Accessed 29 Sept 2021 INE (Instituto Nacional de Estadística) (2012) Censo de Población y Vivienda 2012. Características de Viviendas. Ine-Bolivia: Redatam (CEPAL)—Diseminación de Información Estadística. http:// datos.ine.gob.bo. Accessed 26 Sept 2021 IOG (Ingenieure Ohne Grenzen) and Fundación Sodis (2017) Bedarfserhebung in vier beispielhaften Gemeinden Micanis. Ingenieure Ohne Grenzen, Aachen IUCN and WRI (International Union for Conservation of Nature and World Resource Institute) (2014) Guide to the Restoration Opportunities Assessment Methodology (ROAM) assessing forest landscape restoration opportunities at the national or sub-national level. Working paper (road-test edition). International Union for Conservation of Nature and World Resource Institute, Gland Jose S, Dollinger J (2019) Silvopasture: a sustainable livestock production system. Agroforest Syst 93(1):1–9. https://doi.org/10.1007/s10457-019-00366-8

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Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12(6):519–539. https://doi.org/10. 1002/ldr.472 Lowe AJ (2010) Composite provenancing of seed for restoration: progressing the ‘local is best’ paradigm for seed sourcing. In: Paton D, O’Conner J (eds) The state of Australia’s birds 2009: restoring woodland habitats for birds. Supplement to wingspan, vol 20, no 1, pp 16–17 Martins Mauricio R, Sandin Ribeiro R, Campos Paciullo DS, Alves Cangussú M, Murgueitio E, Chará J, Flores Estrada MX (eds) (2019) Silvopastoral systems in Latin America for biodiversity, environmental, and socioeconomic improvements. In: Lemaire G, De Faccio Carvalho PC, Kronberg S, Recous S (eds) Agroecosystem diversity—reconciling contemporary agriculture and environmental quality. Elsevier, Amsterdam McKenzie E (1995) Important criteria and parameters of wildlife movement corridors—a partial literature review. http://www.silvafor.org/assets/silva/PDF/Literature/LandscapeCorridors.pdf. Accessed 19 Aug 2022 Ministry of Natural Resources—Rwanda (2014) Forest landscape restoration opportunity assessment for Rwanda. MINIRENA (Rwanda), IUCN, WRI Oyedepo OJ (2013) Impact of erosion on street roads: a case study of Sijuwade area Akure Ondo State Nigeria. Chem Mater Res 3(10):33–39 Pabón-Caicedo JD, Arias PA, Carril AF, Espinoza JC, Borrel LF, Goubanova K, Lavado-Casimiro W, Masiokas M, Solman S, Villalba R (2020) Observed and projected hydroclimate changes in the Andes. Front Earth Sci 8:411–440. https://doi.org/10.3389/feart.2020.00061 Portillo-Quintero CA, Sánchez-Azofeifa GA (2010) Extent and conservation of tropical dry forests in the Americas. Biol Conserv 143(1):144–155. https://doi.org/10.1016/j.biocon.2009.09.020 UNCCD (United Nations Convention to Combat Desertification) (2019) Forests and trees. At the heart of land degradation neutrality. UNCCD, Bonn USDA (United States Department of Agriculture) (2011) Animal enhancement activity—wildlife corridors. Natural resources conservation service. USDA, Washington, DC

Chapter 5

Creation of a Land Use/Land Cover Map

For the mapping of priority zones, a base map which shows the most important land uses and land covers in the region is required. At the moment, there is no land use and land cover (LULC) classification available at a sufficient detail level for the target region; thus, first a LULC assessment had to be conducted to create a base map for further analysis. The assessment was undertaken using open-source software, to enable a potential easy and low-cost transfer of the method to other regions. This chapter therefore gives an overview of the basics of remote sensing, explains the approach of random forest (RF) classifications for the creation of LULC maps and describes the selection of suitable classes to perform the classification.

5.1 Remote Sensing Satellites can detect, record and process different intensities of electromagnetic radiation reflected or emitted by the Earth (Levin 1999; Weng 2010). This process is today usually described as remote sensing (Levin 1999; Weng 2010). The thereby acquired data can later, by selecting different wavelengths or spectral bands, be displayed as maps (Levin 1999). Since different LULC types, such as forests or water bodies, have distinct surface characteristics and thus differing radiation spectra, they can be categorized and grouped together by computer algorithms.

5.2 Random Forest Classification RF is a supervised classification method, with the algorithm being based on a large number of random decision trees (Halmy and Gessler 2015; Nguyen et al. 2018), hence the name. Using input training data, each tree in the forest is grown to train the RF classifier and ergo create a span of spectral indices that are specific to each LULC © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_5

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class (Halmy and Gessler 2015; Nguyen et al. 2018). Afterwards, test data is used to assess the classification model and calculate its accuracy. The RF classification method is defined by two phases. First a training phase, where the machine-learning classifier is trained on the basis of pixels which were assigned to the targeted classes of the classification (Levin 1999). The assignment of this training dataset is based on ‘ground truth’, as the pixels are assigned to the classes based on the knowledge from site visits, satellite images or existing, reliable map data (Levin 1999). By using machine learning algorithms, e.g., the RF algorithm, a radiation spectrum can be determined for each selected land use class with the help of the previously collected training datasets (Halmy and Gessler 2015). In the second phase, the trained classifier is applied to the whole classification area and assigns all pixels to the class with the most similar radiation spectrum (Levin 1999). This leads to an analysis of the distribution of the different land use types in the investigated area, which results in a LULC map. To test the accuracy of the classification, and thus the validity of the LULC map, the trained classifier is fed with a test dataset (DeFries and Chan 2000). This dataset, like the training dataset, consists of pixels that are assigned to the classes based on ‘ground truth’ knowledge. The accuracy is calculated from the proportion of pixels that are assigned to the correct class by the classifier versus the proportion that are assigned to an incorrect class (DeFries and Chan 2000). Following Levin (1999), accuracy can be defined as the degree of correspondence between observation and reality. The values thereby range from 0, which means no pixel was assigned to the correct class, to 1, indicating a perfect classifier. Both values are equally undesirable, as it indicates either a complete failure of the model (value 0) or hints to overfitting of the classifying algorithm (value 1). Selection of Bands In LULC classifications, bands of the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) are added to enhance the classification output. The NDVI is calculated from visible and near-infrared light and values range from − 1 to + 1. − 1 is indicating no green vegetation and + 1 very dense vegetation (Weier and Herring 2000). Green vegetation reflects near-infrared light, thus, by measuring the amount of reflected near-infrared light it allows to measure the greenness and thus vegetation density (Weier and Herring 2000; USGS 2018). This band plays an important role, as the vegetation density can contribute to differentiate the different classes. For example, barren areas show no vegetation cover for long periods during the year. Thus, they show a very low NDVI during this time (e.g., 0.1), compared for example to forest areas, which have a higher NDVI (e.g., 0.6 or more), especially during the rainy season due to their green foliage (USGS 2018). The modified normalized difference water index (MNDWI) improves the assessment of water resources. It is calculated based on a green band and a nearinfrared band and can help to increase the accuracy of water surface localization in a LULC classification (Xu 2006).

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Accuracy of Random Forest Models RF classifiers are amongst the best suited methods with the highest accuracy for LULC classifications (Talukdar et al. 2020). The evaluation of the accuracy with the help of an accuracy matrix gives an overview of possible deficiencies in the classification. The proportion of correctly and incorrectly assigned pixels can be analysed with a confusion matrix (Xu 2006). It depicts for each class into which category the test dataset pixels were correctly and falsely assigned (Halmy and Gessler 2015). Incorrect assignments occur for example through errors in the training dataset (Beier et al. 2007). In this case, a check of the quality of the training dataset should be carried out. More classes can also lead to less reliable results, as spectral indices of closely related land cover classes might be too similar or overlapping and in consequence, might lead to false assignments in the classification (Beier et al. 2007). Generally, the more classes are chosen, the more detailed and fragmented the results of the classification will be (Beier et al. 2007). Less classes facilitate the accuracy of the assigned classes but have the disadvantage that less information can be displayed on the map. Therefore, for each LULC classification, the objectives of the classification and which classes are needed should be well considered.

5.3 Determination of Classes for the Classification The classes of the classification should be selected according to the destined use of the map, in order to facilitate the further development of the priority maps and to enable a high informative value. Therefore, the classes of the classification are based on the previous analysis of problems and challenges in the planning region. The mapped classes should be able to display the priority-zone layers directly, or where this is not possible, be planned to allow for an analysis of the priority zones based on the classification results. Based on this, the following classes were selected for the classification of the case study region: Arable Land In this study, agricultural areas were defined as areas, which are used to produce field crops. Usually, they also mark the location of villages or the homes of single families, as the community members in the planning region of Micani tend to live next to their productive areas. For a majority of the year, the agricultural areas lie barren, due to the low water availability during the dry season. Partly, irrigation facilitates the growth of crops also during the dry season. For most parts, the agricultural areas are cleared of trees, with only occasional solitary trees within the areas. A special form are the terraced fields, which are historically typical to the Andean region. They are usually located on the slopes of hillsides and are due to the steep inclination mostly smaller than those on plane areas.

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Forested Areas Forested areas were characterized by their dominant tree cover. Influenced by the dry and rainy seasons, the forests in the region are mainly deciduous. Thus, during the rainy season they grow a dense foliage, which is shed during the following dry period to save water. The areas are potentially used as pastures and further provide the villagers with fuelwood. Shrub- and Grassland The class of shrub- and grassland is dominated by low vegetation. It ranges from sparse grasslands with very low green cover especially during the dry season to more densely vegetated shrub lands that are covered by low-to-medium–high woody vegetation. Shrub- and grasslands are potentially used as pastures and are therefore heavily influenced by free-roaming farm animals (GAM SPBV 2017). Barren Land The class of barren lands contains areas with no or barely any vegetative cover throughout the year. It is therefore at very high erosive risk (see Sect. 3.2.2) (Brandt and Townsend 2006; FAO 2019). In some places, this is already visible in the satellite imagery by the presence of erosion gullies, which are soil formations of linear channels or whole funnel-shaped channel systems caused by water erosion. Rivers and Riverbeds The class of riverbeds includes those areas, which are directly shaped by local rivers and seasonally covered by water. They represent the regional main hydrological network. The actual water surface varies strongly throughout the year, with higher occurrence during the rainy season and lower in the dry season. During the dry season, when the river is carrying less water, the riverbed is mainly dominated by areas of gravel, which are traversed by small meandering streams. The riverbed is mostly clear of vegetation.

References Beier P, Majka D, Jenness J (2007) Conceptual steps for designing wildlife corridors. http://corrid ordesign.org/dl/docs/ConceptualStepsForDesigningCorridors.pdf. Accessed 19 Aug 2022 Brandt JS, Townsend PA (2006) Land use—land cover conversion, regeneration and degradation in the high elevation Bolivian Andes. Landsc Ecol 21(4):607–623. https://doi.org/10.1007/s10980005-4120-z DeFries RS, Chan JCW (2000) Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sens Environ 74(3):503–515. https://doi.org/10. 1016/s0034-4257(00)00142-5 FAO (2019) Trees, forests and land use in drylands: the first global assessment—full report. FAO forestry paper no. 184. Food and Agriculture Organization of the United Nations, Rome GAM SPBV (Gobierno Autónomo Municipal San Pedro de Buena Vista) (2017) Plan territorial de desarrollo integral 2016–2020. San Pedro De Buena Vista

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Halmy MWA, Gessler PE (2015) The application of ensemble techniques for land-cover classification in arid lands. Int J Remote Sens 36(22):5613–5636. https://doi.org/10.1080/01431161.2015. 1103915 Levin N (1999) Fundamentals of remote sensing. Remote Sensing Laboratory, Geography Department, Tel Aviv University, Israel Nguyen HTT, Doan TM, Radeloff V (2018) Applying random forest classification to map land use/land using Landsat 8 OLI. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-3/W4:363– 367. https://doi.org/10.5194/isprs-archives-XLII-3-W4-363-2018 Talukdar S, Singha P, Mahato S, Shahfahad, Pal S, Liou YA, Rahman A (2020) Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens 12(7):1135–1159. https://doi.org/10.3390/rs12071135 USGS (United States Geological Survey) (2018) NDVI, the foundation for remote sensing phenology. U.S. Geological Survey. https://www.usgs.gov/core-science-systems/eros/phenol ogy/science/ndvi-foundation-remote-sensing-phenology?qt-science_center_objects=0#qt-sci ence_center_objects. Accessed 23 Aug 2022 Weier J, Herring D (2000) Measuring vegetation (NDVI and EVI). NASA Earth Observatory. https://www.earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegeta tion_2.php. Accessed 23 Aug 2022 Weng Q (2010) Remote sensing and GIS integration: theories, methods, and applications. The McGraw-Hill Companies, New York Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033. https://doi.org/10. 1080/01431160600589179

Chapter 6

Methods

This chapter presents the methods, which were used for the development of a priority map for reforestation for the case study area. First, the method to create a base map by performing a land use and land cover analysis is described. Building on the results of the LULC classification, the methods for the mapping of the thematic priority maps are explained, followed by the method on how to combine their results into one priority map.

6.1 Land Use and Land Cover Classification To build a base on which the following determination of priority zones for reforestation can be conducted, first a LULC classification was performed via remote sensing and a RF analysis using Google Earth Engine (GEE).

6.1.1 Data and Algorithm Source The classification was performed on a multispectral remote-sensing dataset of Landsat 8 (USGS Landsat 8 Surface Reflectance Tier 1), which is available in Google Earth Engine. For the classification, eleven bands were chosen: nine Landsat 8 spectral bands 1–7, 10 and 11, with additionally calculated NDVI and NDWI bands. Of the chosen bands, the mean value was calculated over the time from 1 February to 1 October 2020. The time frame from February to October was chosen to obtain a more defined radiation picture of the region compared to a single-day timeframe. The time of the rainy season was thereby excluded, as the cloud cover negatively influenced the satellite imagery in this period. This study used the random forest (RF) classifier running in Google Earth Engine to map the land use and land cover of Micani.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_6

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Creation of Training and Test Datasets The pixels for the training and test datasets were assigned based on Google Earth Engine’s high-resolution satellite imagery and a supporting typology matrix. Ideally, to obtain more in-depth ground truth, areas should be additionally analysed on-site, and thus the corresponding pixels localized and assigned to the respective classes. As a site visit was not possible during the time of this work, another solution had to be found to validate the points of the training dataset. For this purpose, a comparison matrix was set up that compared the aerial view structures in the planning area to a similar area captured by Google Street View. This facilitated the determination of whether the structures shown on the satellite image corresponded to the expected LULC type. Therefore, an area in the closest geographical proximity and with the highest optical similarity to the planning area was chosen for comparison to increase the transferability of the satellite image structures (Fig. 6.1). Subsequently, both satellite images and corresponding Google Street View images of the respective location were collected in a matrix and compared with satellite images from the planning area. On this basis, the training areas were assigned to the following five classes: (1) agricultural areas, (2) forested areas, (3) shrub and grassland, (4) barren land and (5) riverbeds. Figure 6.2 exemplarily depicts the comparison matrix for barren lands, for the other classes see Appendix.

Fig. 6.1 Map of the comparison area for this work’s classification. The area that is covered with Google Street View was used as digital site visit to compare satellite imagery with the local conditions. Image source Google, ©2021 Landsat/Copernicus, TerraMetrica [Accessed 05 Aug 2021]

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Fig. 6.2 Comparison matrix of barren land. The upper line of satellite imagery shows areas in the study site, which were anticipated to be bare. The lower lines show pictures with resemblance in the satellite imagery and the respective street-view pictures. Image source Google, ©2021 Maxar Technologies, CNES/Airbus [Accessed 05 Aug 2021]

6.1.2 Classes Agricultural Areas Classification criteria: In most parts, agricultural areas could be distinguished by their homogenous surface colour and structure. It ranged from shades of brown to light green and dark green, depending on the season and if irrigation is in place. Often the field areas were defined by a distinct boundary resulting from the transition from cultivated field area to uncultivated land. Risks: Especially in areas with little vegetation, the field boundaries were often difficult to recognize. In addition, the soil in these areas sometimes hardly stood out from the surrounding barren surface. Forested Areas Classification criteria: The classification of forests posed difficulties, as the satellite imagery was negatively influenced by high cloud covers during the rainy season. Better imagery was obtained during the dry season, but then the trees had shed their leaves to a large extent, which complicated the classification. If cloud-free images were available during the rainy season, forested areas could be identified on the deep green surface and high structuring of the tree canopy. In addition, the long shadows cast by the trees could facilitate the distinction from lower shrubs. During the dry

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season, forested areas could be identified by a dense pattern of the trunk and branch structure of individual trees. Due to the absence of leaves, forested areas were then characterized by a reddish-brown to greyish colour hue. Risks: Without a proper ground truth assessment, mix-ups could arise especially with shrub vegetation, as the habitus of the vegetation was often difficult to determine from aerial photography. In addition, heavily shaded areas showed similarities to dense forest structures due to their dark colour and could have been mistaken for forest vegetation. Shrub and Grassland Classification criteria: The class was characterized by a patchy greenish ground cover, which stood out from the lighter topsoil. Depending on the species composition, it featured high structural patterns. Grass-dominated areas were thereby less structured than areas interspersed with bushes and shrubs. The observation of shadowing was helpful for the classification, as low shadowing might have indicated lower vegetation. Risks: The assignment of grassland posed difficulties, as especially during the dry season the vegetation might not have been visible in aerial pictures due to the seasonal drying of the leaves. Then a false assignment of the training pixels to the class of barren lands could have occurred. Furthermore, to prevent misassignments, shadowing should only be used as an additional classification feature and where a comparison with trees was possible, as the length of the shadows is depending on the position of the sun. If the sun was low, the resulting longer shadows could have falsely led to the assumption of tree structures. Barren Land Classification criteria: The optical features of the class were dominated by shades of brown. The structuring was in general low, except for ‘wavy’ lines and funnel-shaped patterns. They originated from erosive processes and were therefore especially prominent on barren land. Risks: A high potential of misassignment of training pixels for the class of barren lands originated from aerial imagery which was taken during the peak of the dry season. This could have led to a mix-up with grassland, which may have seemed unvegetated and thus barren during the dry season. Dry season imagery further facilitated the confusion with agricultural areas, due to a lack of field crops and invisible field borders by missing fringe vegetation. A set of satellite imagery covering different seasons can help prevent mistakes in the assignment process. Riverbed Classification criteria: The riverbeds could be distinguished by their light colour ranging from hues of grey to beige. Darker lines within the light gravel surface indicated the river’s branches and armlets. Depending on the season, they formed a slightly darker or lighter network structure of wavy lines within the riverbed.

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Risks: The only difficulty might have originated from the spatial limitation of where a river arm was starting, as the fluent transition from erosion gullies to riverbeds was not clearly distinguishable. Aside from that, choosing training pixels for the class of riverbeds did not pose great risks of misassignment, as the areas were largely clearly recognizable from satellite imagery. For the riverbeds class, no comparison matrix was created, as the class’ optical features in the used satellite imagery were very distinct and thus easy to assign. The classification dataset of this study consisted of a total of 2306 pixels which were each assigned to one of the five classes. Thereby 550 pixels class with agricultural areas, 248 with forested areas, 831 with shrub and grassland, 530 pixels with barren land and 147 with riverbeds. The classified pixels were divided by a ratio of 80/20 into a training set and a test set, respectively. Parameter Tuning Two parameters needed to be defined as they influenced the development of the classification model: firstly, the number of decision trees that were created to prime the classifier and secondly, the number of variables per split of an RF node (Halmy and Gessler 2015; Nguyen et al. 2018). The RF parameter of number of trees was set to 500, as it delivered the best classification results in the current study, compared to 100 and 300 trees, whilst exceeding the computing power at 1000 trees. The number of variables used to split nodes is usually set to the square root of the number of variables (Halmy and Gessler 2015; Nguyen et al. 2018), which would have been 3.46 in this classification with 12 bands. In reducing the variables to split nodes, the model gets less complex, correlations between the variables are less prominent, and in total, the classifier gets more accurate (Halmy and Gessler 2015; Nguyen et al. 2018). In this LULC classification model, the number of variables to split nodes was set to 5, as it generated the highest test accuracy (0.816). For the values 1–4, the test accuracy was lower, if only for 0.026, as well as for a split value of 10, where the test accuracy was only slightly lower at 0.809. The NDVI band had the highest influence on the classification, followed by MNDWI, Band 5 and the Aerosol Band (Fig. 6.3). Bands 11, 10, 1, 6 and 7 ranged in the midfield. Bands 2, 3 and 4, indicating the visible colour spectra of blue, green and red, were depicted to be the least important bands. Nevertheless, they contributed to the overall accuracy, as their exclusion from the classification for test purposes led to a decline in the classification accuracy. On this basis, the classification was conducted by running the created algorithm model on the whole planning area. The classification results were transferred into an RGB image, visualized through a prepared colour palette, and exported as GeoTIFF. As the LULC classification map was used as base for further analysis, it was then imported into QGIS (version 3.16.3), vectorized and saved as shape file (.shp). The classes were then split up by their colour value, saved as individual shape files and rearranged into separate layers to facilitate further processing.

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Fig. 6.3 Random forest variable importance. The highest importance scored the NDVI, MNDWI and B5 bands

6.2 Methods to Determine Thematic and Priority Zones for Reforestation Based on the LULC classification, the following high-priority-zone analysis was conducted.

6.2.1 Mapping of Forest Buffer Zones To determine the areas of existing forests in the planning area and their distribution on the three subcentral regions, first the extent of remnant forests was analysed in QGIS. Thus, following the definition of forests by the FAO (2015), a QGIS query was run on the layer of forested areas, to filter for areas greater than 0.5 ha. Afterwards, the cover of the filtered layer was calculated for the whole planning area as well as for each subcentral region. To map the protective buffer zones, a 100 m buffer was created around the forested areas and depicted in the maps, following the recommendations of the Ministry of Natural Resources of Rwanda (2014).

6.2.2 Mapping of Potential Green Corridors First, the forested areas were extended by a 6 m buffer, which corresponds to the acceptable width of sections within corridors that do not interrupt its connecting function (USDA 2011). To determine the potential areas for green corridors, the forested areas from the LULC map were connected by lines that link all forest areas with their nearest

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neighbouring patch, as suggested by Beier et al. (2008). Riverbeds and mountain ridges were treated as guidelines for the linkage paths as they constitute natural barriers between areas of similar location factors (Beier et al. 2008). The cost of movement for animal species is higher in crossing mountain ridges and wide riverbeds and is therefore less favourable to the course of green corridors (Beier et al. 2008). Therefore, the connecting lines were drawn up not to trespass these areas but rather following the contour lines. This was achieved by a sublayer depicting the region’s altitudinal relief, which was generated in GEE (see Sect. 6.2.3). The connecting lines were then buffered with a 150 m radius, which accords to the minimum dimensions of green corridors recommended by Bond (2003) with a width of approximately 300 m. Afterwards, the length of the connecting lines (potential green connections) as well as the area coverage of the resulting shapes was summed up per subcentral region as well as for the whole planning area.

6.2.3 Mapping of Highly Erosive Areas The mapping of areas that are at high risk of erosion was separated into three parts: (1) the localization of barren lands, (2) the mapping of unforested areas that are located on steep slopes and (3) the spatial analysis of areas that are affected by gully erosion. (1) Barren lands were mapped directly as class of the LULC classification (see Sect. 6.1). (2) To map areas of steep slope inclinations with high erosive potential in the region, a slope classification in Google Earth Engine (GEE) was conducted using the Shuttle Radar Topography Mission (SRTM) digital elevation data (NASA SRTM Digital Elevation 30 m) (Farr et al. 2007). The dataset provides elevation data at a 30 m resolution of nearly the whole earth. It was cropped to the planning site for this analysis. Using ee.Terrain.slope(), the local gradient was computed for each pixel in the area. By this command, the altitudinal difference of the four connecting neighbours of each pixel is calculated in the planning area, resulting in an inclination degree for each pixel. The results were transferred into an RGB image, visualized in a two-tone colour palette, and exported as GeoTIFF. The image was then imported to QGIS and vectorized. The thereby generated shapefile was categorized by the colour value into classes, according to the inclination categories of the FAO (2006) (Table 4.1). To allocate areas which are at higher risk of erosion because they are unforested (FAO 2015; Pabón-Caicedo et al. 2020), the forested areas were subtracted from the inclination layer, using the difference command in QGIS. Afterwards, the coverage of the different inclination classes was calculated, both for the layers still including forested areas as well as the layers without forested areas, by adding up the polygons of the distinct classes. To get an overview over the distribution of the classes in the three subcentrals, and to be able to compare them in a later step, the coverage per

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class was calculated by clipping each inclination class to the subcentral areas and again computing their coverage. (3) To map the existing erosion gullies in the planning area, a spatial and spectral analysis was conducted via remote sensing and a random forest classifier (for more information on the function of random forest, see Sect. 5.2). As for the formation of gullies both vegetative cover and land use as well as slope inclination play a major role (see Sect. 3.2.2), a composite dataset of Landsat 8 and SRTM was created. It combines both optical (land use and land cover) and radar (topography) data. To the nine bands of the Landsat-8 dataset (B1, B2, B3, B4, B5, B6, B7, B10, B11), NDVI and MNDWI bands were added. From the SRTM dataset, a slope-inclination band was calculated and added to the Landsat-8 bands. The datasets were filtered to cover one year from the 1st of January 2020 until the 1st of January 2021 and to include data from the dry season as well as the rainy season. The classification was designed to assess three classes: (1) riverbeds (RB), (2) erosion gullies (EG) and (3) other land cover (OLC). The training and test datasets were created based on Google Earth Engine’s high-resolution satellite imagery. Ground-truth data was not available at the time of the study and should be collected in a next step to refine the analysis’ results. For the class of riverbeds 158 pixels were assigned, 547 pixels were classed with erosion gullies and 631 with other land cover types. Each class was split 80/20 into a training and test dataset. The classifier’s parameters were set to 500 trees in the forest, and the split per nodes was set to 6, as it acquired the highest user’s accuracy in the classification. Based on these setting, the classification reached a test accuracy of 85%. The accuracy within each class was depicted in a confusion matrix (Table 6.1). In the riverbeds class, the confusion matrix showed a correct assignment of 96 and 4% misassignment to EG. Erosion gullies were correctly assigned in 77%, with 22% being falsely assigned to OLC and 1% to RB. Other land covers were classed correctly for 88% of the test data, while 12% were falsely classified as EG. The most important bands for the classification model were the slope and NDVI bands, followed by B5, MNDWI, B6 and B11. Least important were the bands 2, 3, 4, 1 and 7. Table 6.1 Confusion matrix of gully cover classification Total Riverbed Erosion gullies Other land cover

Riverbed

%

23

22

96

Erosion gullies 1

% 4

Other land cover

99

1

1

76

77

22

22

121

0

0

14

12

107

88

0

% 0

The class of riverbeds is assigned correctly in 96%, erosion gullies in 77% and other land in 88%

6.3 Priority-Zone Mapping

59

6.2.4 Mapping of Village Proximity to Forests To determine the priority zones for reforestation to provide the villagers with forest resources, first the areas which account under the FAO’s definition as forest were selected (FAO 2006). Hence, mapped forested areas were selected from the LULC map in QGIS, by running a query on areas being less than 0.5 ha in size and deleting them from the layer. The forest areas were then buffered with a 100, 400, 800 and 1600 m radius, according to the distances travelled in each time segment. By intersecting the agricultural areas with the buffered forest areas, the village categories which are in 100, 400, 800, and 1600 m proximity to the nearest forest were generated. By clipping the agricultural areas with the 1600-m distance layer, the villages, which are located more than 1 h of walking distance from the closest forest, were mapped. The results were then clipped to the three subcentral areas, and their coverage was calculated per class.

6.2.5 Mapping of Endangered Village Infrastructure To locate the village infrastructure that is mostly endangered by erosion, the proximity of erosion gullies and very steep areas without retaining vegetation to agricultural areas and streets was analysed in QGIS. First, a layer that depicts areas with a high erosive potential was created. Therefore, a query was run to select areas with inclinations exceeding 30% of the layer containing slope inclinations. They were intersected with the layer depicting barren lands, and the results were joined with the layer of erosion gullies. The village infrastructure layer was created by joining the streets layer with the layer containing agricultural areas. As village infrastructure in close proximity to high erosive areas are particularly endangered by landslides and washouts, they were buffered by 20 m. By intersecting the buffered village infrastructure layer with the high erosion potential layer, the areas in proximity to village infrastructure that might be prone to be affected by erosion were allocated in the map. Their coverage was then calculated for each subcentral and the total planning area.

6.3 Priority-Zone Mapping For the generation of the final priority map, the results of the previously created thematic maps were rasterized and reclassified to carry the value ‘0’ for empty areas and ‘1’ for coloured areas. All thematic layers were then summed up into one layer using the raster-calculator tool in QGIS. This way the number of overlaps of the thematic maps could be computed, resulting in the priority-zone classes. After turning each priority-zone class to a shapefile and by clipping them to the boundaries of

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the three subcentrals, the coverage could be calculated for each subcentral and the respective priority-zone class.

References Beier P, Majka D, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22(4):836–851. https://doi.org/10.1111/j.1523-1739.2008.009 42.x Bond M (2003) Principles of wildlife corridor design. Center for Biological Diversity. https://www. biologicaldiversity.org/publications/papers/wild-corridors.pdf. Accessed 19 Aug 2022 FAO (2006) Guidelines for soil description, 4th edn. Food and Agriculture Organization of the United Nations, Rome FAO (2015) Global guidelines for the restoration of degraded forests and landscapes in drylands: building resilience and benefiting livelihoods. In: Berrahmouni N, Regato P, Parfondry M (eds) Forestry paper no. 175. Food and Agriculture Organization of the United Nations, Rome Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffe S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf DE (2007) The shuttle radar topography mission. Rev Geophys 45(2). https://doi.org/10.1029/200 5RG000183 Halmy MWA, Gessler PE (2015) The application of ensemble techniques for land-cover classification in arid lands. Int J Remote Sens 36(22):5613–5636. https://doi.org/10.1080/01431161.2015. 1103915 Ministry of Natural Resources—Rwanda (2014) Forest landscape restoration opportunity assessment for Rwanda. MINIRENA (Rwanda), IUCN, WRI Nguyen HTT, Doan TM, Radeloff V (2018) Applying random forest classification to map land use/land using Landsat 8 OLI. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-3/W4:363– 367. https://doi.org/10.5194/isprs-archives-XLII-3-W4-363-2018 Pabón-Caicedo JD, Arias PA, Carril AF, Espinoza JC, Borrel LF, Goubanova K, Lavado-Casimiro W, Masiokas M, Solman S, Villalba R (2020) Observed and projected hydroclimate changes in the Andes. Front Earth Sci 8:411–440. https://doi.org/10.3389/feart.2020.00061 USDA (United States Department of Agriculture) (2011) Animal enhancement activity—wildlife corridors. Natural resources conservation service. USDA, Washington, DC

Chapter 7

Results

In this chapter, the results are presented in three parts: First, the results of the LULC classification are presented, and the coverage of the analysed classes is shown for the whole area as well as for the three subcentrals. The second part focuses on the results of the thematic priority-zone maps. They are separated into six parts; the priority zones for the protection of existing forest in the case study area, the potential zones for the establishment of green corridors, the mapping of potential reforestation zones for areas with a high erosion risk, areas with detectable gully erosion, and lastly the two maps directly addressing anthropogenic problems, presenting potential reforestation zones for sustainable wood supply for the villages and for the protection of village infrastructure. The third part of this chapter presents the resulting high-priority-zone map, which is based on the thematic potential zones for reforestation.

7.1 Results of LULC Classification The classification of the test dataset yielded the following results which are depicted in Table 7.1. Agricultural areas were assigned correctly to 82%, with the highest misassignment to the class of shrub- and grassland and only seldomly to barren land. Barren land was assigned correctly in 87% of the test dataset, with falsely assigned pixels to shrub and grassland and only rarely to agricultural areas. Forested areas were accurately assigned in 73%, with false classifications mainly to shrubs and grasses and seldomly to agricultural areas and barren land. Shrub- and grassland had a producer’s accuracy of 81%, being falsely classified in similar amounts to either agricultural areas or barren land. The highest accuracy was reached in the class of riverbeds with 97%; the only misassignment classed with shrub and grassland. This leads to an overall producer’s/test accuracy of 83% and a user’s accuracy of 87%.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_7

61

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7 Results

Table 7.1 Model accuracy indicators Confusion matrix

AA

BL

FA

SG

RB

Producer’s accuracy (%)

Agricultural areas

0: [93,

5,

0,

16,

0]

81.6

Barren land

1: [1,

75,

0,

10,

0]

87.2

Forested areas

2: [2,

1,

35,

10,

0]

72.9

Shrub and grassland

3: [121,

17,

0,

125,

0]

81.2

Riverbeds

4: [0,

0,

0,

1,

32]

97.0

Producer’s accuracy total

82.8

The confusion matrix and the producer’s accuracy give insight into the classification’s reliability. It depicts for each class into which category the test-dataset pixels were correctly and falsely assigned (Halmy and Gessler 2015). For example, barren land was assigned correctly in 87% of the test dataset (75 pixels), with falsely assigned pixels to shrub and grassland (10 pixels) and only rarely to agricultural areas (1 pixel)

Following the results of the LULC classification, Fig. 7.1 shows the localization of the selected classes at the study site, whilst Fig. 7.2 depicts the fractions of each class in the study area. Most of the study area is covered by shrub and grassland with over 9,000 ha (41%), being followed by barren land with 7,500 ha (34%). Together they account for three quarters of the whole area. 2,400 ha (11%) are covered by agricultural land and slightly less than 2,000 ha of the surface is forested (9%). The meandering river and riverbed account for 5% of the surface with just over 1,000 ha.

7.2 Results of Thematic Priority-Zone Analysis This chapter presents the results of the thematic priority-zone analysis. They were individually developed based on the previous problem assessment of the study area. Their results were transferred to the creation of a conclusive priority-zone map, on whose base the potential benefits for FLR approaches for different regions of the study site could be derived.

7.2.1 Priority Zones for Protection of Remnant Forests Figure 7.3 depicts the forest areas in the study site (dark green) and the potential buffer zones (light green), which could help to protect the remaining forests. Of the total planning area of 220 km2 , 8% (17.4 km2 ) belong to forested areas. Thereby the forest zones are dispersed over the three subcentrals, with the majority (54%)

7.2 Results of Thematic Priority-Zone Analysis

63

Fig. 7.1 Results of the land use and land cover classification. Large contiguous forest areas were located mainly in the north and north-east of the study site. Shrub- and grassland were dominantly found on slopes and in valleys of mountains and hills, especially on their southern flanks. Bare areas were vice versa often located on the northern slopes and on ridges. Agricultural areas were dispersed throughout the region in small clusters

Fractions of land use and land cover types in the study area 5%

11%

Agricultural areas Barren land Forested areas

41% 34%

Shrub and grassland Riverbeds

9% Fig. 7.2 Fractions of the LULC classes at the study site. Following the classification, the majority of the area is covered by grassland and barren areas

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7 Results

being allocated in Micani, 36% in Ipote and 10% in Huaripampa. Micani also has the highest coverage of forest with almost 20% of the total subcentral area. Ipote is covered by 10% with forest, Huaripampa offers the lowest coverage with 2% as can be seen in Fig. 7.4. Respectively, most priority-zone areas for reforestation by the implementation of buffer zones are allocated in Micani, followed by Ipote. Huaripampa has the least surface potential for the creation of buffer zones, as there are less forested remnants for protection.

Fig. 7.3 Protection of remaining forest habitats. The buffer zones around the forest areas have the potential to protect the remaining forest habitat, increase ecosystem services and thereby protect local biodiversity and contribute to landscape restoration

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65

Fig. 7.4 Forest cover of the subcentral areas and the total study site. Micani has the highest cover of remnant forests, whilst Huaripampa has the lowest. Also, when looking at the distribution of the total forest area, over half of the total forest area of the study site can be found in Micani and only 10% in Huaripampa

7.2.2 Priority Zones for Green Corridors The map for green connections shows the potential linkages of remnant forest patches to their next neighbour, minding similar location factors and low cost of movement (Fig. 7.5). The green lines depict high-priority zones that can help to reconnect remnant forests (dark green patches) by reforestation in order to strengthen the natural regeneration of ecosystems and provide a benefit for the protection of local biodiversity (Di Sacco et al. 2021). If the whole area of potential forest corridors was to be reforested, they would account for 17.4 km2 (8%) of the total planning area. Regarding the amount of potential green corridors in the three subcentral communities, 41% account to the district of Micani, 37% to the district of Ipote and 20% to the subcentral district of Huaripampa. This can be related to the different number of forested areas within each of the three subcentral regions. Of the total 257 single forest patches, only 21% belong to Huaripampa, thus resulting in less potential connections. As the three regions contain different amounts of remnant forest vegetation, the distance in between the neighbouring forest patches varies. The average length of corridors in the planning area is 200 m. In the subcentral of Micani corridors are on average 140 m long, in Ipote 190 m and in Huaripampa 300 m. Setting the proposed corridor area with a width of 300 m in relation to the existing forest patches, they account for an increase of 83% of the total forested area. In the subcentrals, the corridors would account for an increase of 49% in Micani, 86% in Ipote and 250% in Huaripampa.

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7 Results

Fig. 7.5 Priority-zone map for habitat reconnection. The dark green areas depict existing forests, whilst the light green areas represent potential green corridors. They are linking the respective forest patches to their nearest neighbour and can contribute to enhance species migration and natural regeneration

7.2 Results of Thematic Priority-Zone Analysis

67

7.2.3 Priority Zones for Reforestation of Highly Erosive Areas Priority Zones of Areas with Low Vegetation Coverage The results of the mapping of barren areas with none or only scarcely visible vegetation cover show a coverage of 75 km2 corresponding to 34% of the whole planning area (Fig. 7.6). Amongst the three subcentrals barren lands cover 22% of the surface in Micani, 34% of Ipote and 40% of Huaripampa.

Fig. 7.6 Priority zones for reforestation of bare lands. Bare lands cover about one third of the planning area. Huaripampa has the highest ratio of the three subcentrals with a coverage of 40%

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7 Results

Priority Zones of Areas with Steep Inclinations Figure 7.7 shows the results of the inclination classification in the planning area. Of the total planning area 31% are assigned to class 10 (very steep, inclination > 60%), 52% are assigned to class 9 (steep, 30–60% inclination) and 12% to class 8 (moderately steep, 15–30%) (Fig. 7.8). The further 5% are distributed to the classes 1– 7 and are due to their small amount and lower erosion potential not further addressed. Regarding the distribution pattern in the subcentrals, Ipote has the highest coverage with 39% the class of ‘very steep’-slopes, being followed by Micani with 34%. Huaripampa has a rather low coverage with ‘only’ one quarter of the area being assigned to class 10. In all three subcentrals, class 9 scores the highest total coverage of the classes. In Micani 48% of the area is assigned to it, in Ipote 47%, and in Huaripampa 56%. Class 8 is assigned evenly to the subcentrals, ranging between 11 and 12% of surface cover. The percentile coverage of the three subcentrals with the classes 8, 9 and 10, which potentially offer a high erosive risk due to the strong inclination, shows only slight differences. These classes combined account for a moderately steep to very steep surface inclination in 94% of the area in Micani, 97% in Ipote and 94% in Huaripampa. Regarding the inclination classes in exclusion of areas that have been classified as forested, 26% of the total planning area apply to class 10, 49% to class 9 and 12% to class 8. Thus, 95% of the total planning area is steeper than 15% of which 87% is further unforested, making them prone to erosion. In Micani, the area covered by the three steepest classes accounts for 94% of the area, with 80% being unforested. Ipote shows the steepest topography with 97% coverage of classes 8–10, of which 90% are unforested. Huaripampa has the same total coverage of the three steepest classes like Micani (94%), but with 98% unforested area, the erosive risk can be assumed to be higher. The results thus show that the three subcentrals have no significant differences in the presence of steep surfaces. However, as depicted in Fig. 7.9, there are differences in the proportion of forest cover on the steep slopes. It is highest in Micani, followed by Ipote, and in Huaripampa it is lowest with only 1% of the steep to very steep areas. The mapping of the non-forested areas, whose slopes belong to one of the three steepest classes (7, 8 and 9) cover virtually the entire area (95%); hence, it would not contribute to the prioritization of potential areas for reforestation. Therefore, only areas of the steepest class with the highest erosion potential, and thus areas exceeding 60% inclination, will be integrated to the priority map for reforestation in the case study area.

7.2.4 Priority Zones for Watershed Protection from Gully Erosion Based on the random-forest classification, Fig. 7.10 shows the areas, which are affected by gully erosion. Regarding the whole planning area, 36% is classified as

7.2 Results of Thematic Priority-Zone Analysis

69

Fig. 7.7 Classification of slope inclinations in the study area. The shades of red indicate the steepness of the terrain. The darker the colour hue, the steeper. White areas indicate areas that are forested and therefore have a minor risk of erosion (FAO 2015)

being covered by erosion gullies, 4% by riverbeds and 60% by other land cover. Of the aggregated classified area affected by erosion gullies Micani accounts for 17.5%, Ipote for 20% and Huaripampa for 62.5%. In respect of the total area of the subcentrals, this corresponds to a coverage of 28% in Micani, 26% in Ipote and 46% in Huaripampa being affected by erosion gullies (Fig. 7.11). Thus, Ipote is least affected by gully formation, shortly followed by Micani. Huaripampa has the highest percentage, reaching almost 50% of the subcentral’s total area.

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7 Results

Fig. 7.8 Fractions of the inclination classes of the total case study area. Over 80% of the area are classified as steep to very steep. Only a minor part of them is covered by forests

Fig. 7.9 Slope inclinations and their fraction of forest cover in the three subcentrals

7.2.5 Priority Zones of Reforestation for Wood Supply of Communities The results of the villages’ proximity to forests are depicted in Fig. 7.12. The five classes range from very close (yellow), close (light orange), intermediate (dark orange), far (red) and to very far (dark red). The calculation of the percentages per class of the total settlement areas results in a share of 21% for settlements in very close proximity of less than 100 m, 34% are close to a forested area (< 400 m), 26% are within an intermediate radius of less than 800 m, just under 16% are more distant within a radius of 1,600 m and 3% are beyond this radius. This indicates that

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71

Fig. 7.10 Dark blue areas indicate areas that are at risk of gully erosion. The funnel-shaped catchment areas of the individual gullies are clearly recognizable in some areas. The southern part of Huaripampa seems to be particularly affected

Gully coverage of respective area (in %) 50% 40% 30% 20% 10% 0% Micani

Ipote

Huaripampa

Fig. 7.11 Gully coverage per subcentral and the total study site

Total (calc.)

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7 Results

in the entire area, about 50% of the settlement areas are within a close to very close distance of forests and thus the inhabitants have travel times of less than 15 min to the nearest forest area, given the average travel time in steep terrain of 1.6 km/h (Hodgkins 2020). 26% of the village areas are less than 30 min away from the next woodland, 16% need up to one hour by foot and 3% need more than one-hour travel time.

Fig. 7.12 Classification of villages regarding their proximity to the nearest forest area. The dark red and purple villages are located at a distance of more than 800 and 1,600 m form the nearest forest, respectively. It is striking that the majority can be found in Huaripampa. In Micani and Ipote they are in general located in closer proximity to the nearest forest area

7.2 Results of Thematic Priority-Zone Analysis

73

Regarding the proximity to forest areas for the villages in each subcentral, some differences become apparent. In Micani, the provision with woodland seems to be very high, with a total of almost 90% of the villages being located within 15 min walking distance to forested areas. Only 10% of the villages are accounted to be 15–30 min away from the nearest woodland, and almost no village is further away than 800 m to the nearest forest. In Ipote, the villages are probably slightly less well provisioned. Two-thirds of the villages in the subcentral region of Ipote are in very close to close proximity to forested areas. Almost one-third is accounted to have an intermediate distance to woodland, 6% are further away but none longer than one hour by foot. Huaripampa is the subcentral with the lowest forest accessibility. Only 7% of the villagers can reach a forest within 5 min, and only 20% are within a 15 min proximity. The majority lies within a 30-min radius with 33%, followed by 32% that can reach a forest in under an hour. 7% of the villages are located further than one hour on foot to the nearest woodland. Figure 7.13 shows the areas, in which villagers need to travel longer distances to collect wood (purple). The green zones around these villages indicate the priority zones for reforestation to facilitate wood collection for the communities.

7.2.6 Priority Zones for Protection of Village Infrastructure The results of the village infrastructure which is threatened by erosion are shown in Fig. 7.14. It depicts the streets and agricultural areas that lie within a 20 m radius of areas with a high erosive potential. Streets The results indicate that 67% of all streets in the planning area lie within an area of higher erosive potential. Regarding the fractions within the subcentral regions, in Micani 14 out of 25 km of road are affected, in Ipote 22 out of 33 km and in Huaripampa 42 out of 57 km. This results in fractions of 57%, 67% and 72%, respectively (Fig. 7.15). Arable Land For the agricultural areas, the results indicate that 22% of the total arable land is potentially threatened by erosive influences from bordering areas. Within the subcentrals, this applies to 21% in Micani, 21% in Ipote and 23% in Huaripampa (Fig. 7.15). The total area affected in each subcentral accounts to 1.3 km2 , 1.6 km2 and 2.3 km2 , respectively.

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7 Results

Fig. 7.13 Priority zones for reforestation to enable all villages the access to a forested area in under 400 m. The dark red areas show villages that lay outside a 400 m radius to the nearest forest. The green buffer zones mark potential zones for reforestation to provide the villages with fuelwood in close proximity

7.3 Results of the Priority-Zone Mapping The spatial analysis for high-priority zones for reforestation resulted in the generation of seven single thematic maps (Fig. 7.16). Figure 7.17 displays the layering of these maps, with each colour representing one analysed topic and the extent of the total planning area, which is affected by it. The more colours are overlapping, the higher the accumulated benefits of a potential reforestation measure in this area. The comparison

7.3 Results of the Priority-Zone Mapping

75

of the thematic priority maps shows that the priority layer for reforestation of erosion gullies has the highest coverage ratio (36%), being followed by the priority areas of bare land (34%) and steep inclinations (31%) (Table 7.2). The potential reforestation area for enhancing the communities’ supply of firewood accounts for one quarter of the total area. The implementation of buffer zones around remnant forest patches would result in a reforestation of 14% of the area and the potential creation of green corridors to reconnect them accounted for 7%. The protection of village infrastructure through restoration of adjacent areas accounts for the lowest coverage with 2%.

Fig. 7.14 Priority zones for reforestation to protect village infrastructure. The dark red areas show agricultural areas that are at risk to be negatively influenced by erosion. The same applies to the purple-marked streets. Reforestation in these areas could mitigate the risk of erosion and protect the infrastructure from damage

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7 Results

Fig. 7.15 Fractions of streets and agricultural areas which are at risk to be negatively affected by erosion. The graph shows the fractions of the total areas, respectively

Fig. 7.16 Single thematic priority maps. Each map describes the potential spatial benefits of a FLR approach in the coloured areas

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77

Fig. 7.17 Layering of thematical maps. The map gives insight where and which benefits can be achieved by reforestation in a respective area. Each colour represents one benefit of reforestation. The more colours are overlapping, the more benefits can be generated

In Fig. 7.18, the thematic priority maps are layered to represent the accumulated prioritization level. The darker the colour value, the higher the priority to undertake landscape restorative measures, e.g., reforestation. This results from the number of overlaps of the thematic priority maps and ranges from 1, with the lowest prioritization, to 6, with the highest.

78 Table 7.2 Coverage of the priority zones for each thematical map

7 Results Thematic maps

Coverage (in km2 ) Fraction of total area of the study site (%)

Forest buffer zones

30

Green corridors

15

7

Reforestation of bare land

75

34

Reforestation of steep inclination

68

31

Mitigation of erosion gullies

79

36

Reforestation for wood supply

54

25

5

2

Protection of village infrastructure

14

Reforestation of bare land, steep slope inclinations and mitigation of gully erosion have the highest fraction, followed by reforestation for wood supply and the area for potential forest buffer zone

The results of the high-priority-zone mapping indicate that of the total study area 79% would benefit from reforestation (Table 7.3). In Micani, the restoration could be beneficial to 70% of the subcentral area, in Ipote to 79%. Huaripampa has the highest coverage of area with 84% that could be positively influenced by reforestation. Regarding the individual classes, Ipote has the highest coverage of classes 1, 4, 5 and 6, with 34%, 28%, 0.5% and 0.012%, respectively. The classes 2 and 3 are highest in Huaripampa, with 21 and 17%. The subcentral of Micani seems to have four accumulation areas of higher priority zones, located in the central and northern part. In Ipote, there seems to be an accumulation of higher priority zones in the centre, whilst in the south the subcentral shows a relatively even coverage mainly consisting of priority zones 1 and 2. In Huaripampa, the south and southwest indicate the greatest coverage with higher classes, whilst the north-eastern part of Huaripampa is showing less high-priority zones of mainly classes 1 and 2.

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79

Fig. 7.18 Priority-zone map for Micani. The shades of green describe the priority-zone class—the darker the colour, the more benefit can be achieved by a reforestation of the area. The black circles mark high-priority zones, where dark colours are clustering—they indicate zones with high benefit of reforestation

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Table 7.3 Areas with potential benefits of reforestation for each subcentral area and the total study site Areas

Class 1 (%)

Class 2 (%)

Class 3 (%)

Class 4 (%)

Class 5 (%)

Class 6 (%)

Classes 1–6 accumulated (%)

Micani

31.4

23.1

11.5

3.4

0.4

0.015

69.7

Ipote

33.6

28.4

13.1

3.8

0.5

0.012

79.4

Huaripampa

30.9

31.9

17.2

3.7

0.3

0.006

83.9

Total study area

31.7

28.9

14.7

3.7

0.4

0.010

79.4

Of the three subcentrals, Huaripampa has the highest fraction of priority zones for reforestation with almost 84%. In comparison, Micani has the lowest fraction with 70%, Ipote ranges in the middle with 79%

References Di Sacco A, Hardwick KA, Blakesley D, Brancalion PHS, Breman E, Rebola LC, Chomba S, Dixon K, Elliott S, Ruyonga G, Shaw K, Smith P, Smith R, Antonelli A (2021) Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. Glob Change Biol 27(7):1328–1348. https://doi.org/10.1111/gcb.15498 FAO (2015) Global guidelines for the restoration of degraded forests and landscapes in drylands: building resilience and benefiting livelihoods. In: Berrahmouni N, Regato P, Parfondry M (eds) Forestry paper no. 175. Food and Agriculture Organization of the United Nations, Rome Halmy MWA, Gessler PE (2015) The application of ensemble techniques for land-cover classification in arid lands. Int J Remote Sens 36(22):5613–5636. https://doi.org/10.1080/01431161.2015. 1103915 Hodgkins K (2020) What’s the average hiking speed? Calculate your pace on the trail. https://www. greenbelly.co/pages/average-hiking-speed. Accessed 29 Sept 2021

Chapter 8

Discussion

In this chapter, the results of the LULC classification, the thematic maps and the priority-zone map are discussed. As the results build on each other, their validity is analysed in respect to previous sections and their informative value as well as limits of the information content are assessed.

8.1 Discussion LULC Classification The LULC classification builds the base for the development of the priority map in the case study area. Thereby, it has great influence on the results and misassignments can lead to the proclamation of potential zones for reforestation that might not be suitable for the purpose. The overall accuracy (producer’s accuracy 83%) of the classification indicated, according to the training and test datasets, that most areas were classed correctly. However, this still leads to a rest of 17% which has been misassigned and thus could lead to mistakes in the priority zone assessment. Another source of mistakes does not come from the classification itself, but could have happened before, in the assignment of pixels to the test and training datasets. By comparing the results of the LULC classification with the classification carried out for the assessment of gully erosion, there was an overlap detectable of an area that was classified as forests in the first classification but also as area to be affected by gully erosion. A comparison with satellite imagery revealed that the forest in the south-west of Huaripampa was in fact misassigned. The area is not covered by trees but is in contrast missing any vegetation and should have been assigned to the class of barren lands instead. However, this area was shadowed by a mountain side and thus visible as dark area in the reference satellite imagery. Thus, the mistake might have originated from a mistake in the training dataset, where other dark areas might have been assigned to forest, whilst they are in fact temporarily shadowed barren lands. This may have had consequences for the creation of the priority map. An © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_8

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area that was classified as forest but is in fact barren, would have nevertheless been integrated in the thematic priority maps for protective buffer zones, green corridor connections and forest proximity for the villages and would therefore be missing in the priority maps of erosive potential in deep inclinations, erosive risk through barren land and endangered village infrastructure. During a sampling of other areas classified as forest no additional misclassifications could be detected. Therefore, the classification should still provide a sufficient basis to test the development of the priority-zone map based on the case study region. To avoid such errors, the integration of ground-truth data by recording training points on-site should be mandatory. As mentioned, this was not possible during this work, however, if the priority-zone map was to be used by a local institution, it is advisable to repeat the classification using suitable ground-truth data in order to create a reliable basis.

8.2 Discussion of Thematic Priority-Zone Maps This chapter discusses the results of the thematic potential zone maps and their suitability for the creation of a priority-zone map.

8.2.1 Protecting Existing Forests The mapping of buffer areas aims to display areas that have the potential to protect existing forest habitats and thereby increase the regional biodiversity (Ministry of Natural Resources—Rwanda 2014; IUCN and WRI 2014; UNCCD 2019). The assessment for potential zones seems reliable, as it was based on the case study of a similar reforestation project in a mountainous dry forest zone of Rwanda (Ministry of Natural Resources—Rwanda 2014). Nevertheless, it might be useful to further discuss the buffer width of 100 m with a local stakeholder, who knows the local conditions in order to consider the characteristics of the local ecosystems and to adjust the buffer width accordingly. What further needs to be considered is that the implementation of forest buffer zones can generate potential conflict with the local farming population. As some of the forest areas border directly on agricultural land, these would fall within the area to be reforested. As a result, the buffer areas could be in competition with agricultural production. One solution could be the implementation of agroforestry on the affected areas, which would still allow agricultural production, but could also provide habitats for native species by increased tree density (UNCCD 2019). Further, the establishment of buffer zones could reduce the pressure of use on the original forest area but might not be sufficient to protect and secure the habitats if the drivers of land degeneration persist. Therefore, the establishment of a management system is an important attribution for sustainable usage that can be supported further by, e.g., the implementation of fenced protection zones to safeguard the habitats (Atmadja et al. 2019; UNCCD 2019). The map produced can help

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to allocate priority zones for reforestation, to analyse conflict zones and to provide the local population with a basis for decision-making on how to deal with the areas.

8.2.2 Green Corridors The approach to map potential green corridors in the case study area enables the planning of a forest network that could reconnect remnant forest habitats and thereby help to increase biodiversity in the region (Atmadja et al. 2019). The results can thus provide a valuable contribution to the priority map for reforestation. Yet, some of the resulting corridors raise the question of feasibility regarding their extension in relation to the size of the respective connected forest areas. Comparing the results of the different corridor lengths and the potentially reconnected forest areas, in Micani 109 connections can be established with an aggregated corridor length of 15 km, connecting 9.4 km2 of forest. In Huaripampa, by an accumulated corridor length of 16 km, only 54 patches with a total forest coverage of 1.8 km2 could be connected. With an average length of only 140 m, the corridors in Micani are less than half as long as the corridors of Huaripampa with an average of 300 m per corridor. This is due to the fact that in Huaripampa only 1.7% of the total area are covered by forest (see Sect. 7.1), resulting in the necessity for longer corridors between the dispersed patches. Further, the estimated width of 300 m might be questioned. In only sparsely forested regions like the planning site most of the remnant forest patches do not reach the minimum width of 300 m themselves and would therefore not meet the requirements of the corridors. In Huaripampa, the area of potential forest corridors even surpasses the forested areas by a factor of 2.5, which raises the question of whether the term ‘corridor’ is still appropriate in this case. A maximum length-tosize ratio of the forest areas to be connected could improve the corridor planning. The consultation of local experts could help to determine a more appropriate corridor length and width by potentially also integrating, e.g., the home range of one (Bond 2003) or multiple suitable target species (Beier et al. 2007, 2008). Further, also the quality of the forest areas that are to be connected could further refine the potential green corridor zones. Some of the forests in the area might not provide the regenerative capacity and biodiversity to serve as habitat, so their connectivity may be questioned (IUCN and WRI 2014). An assessment of forest quality could not be conducted in the course of this work, therefore, specialists, like ecologists, biologists or landscape planners need to be consulted to potentially refine the planning of green corridors. Moreover, the occurrence of agricultural areas within the corridor areas could not be considered in this approach but should be included in the future planning. Solutions can be the adaptation of the corridors’ pathways and/or a reduction of disturbances by human activities, e.g., by fencing of pastures or the implementation of silvopasture or agroforestry as part of the corridors (Atmadja et al. 2019; Martins Mauricio et al. 2019).

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In conclusion, the proposed corridors might need to be refined in collaboration with local stakeholders. Yet, the proposed areas can still provide a viable contribution to the priority map, as they can initiate an incremental reconnection of the forest areas by first serving as steppingstones and later, as more forests are reestablished, as consistent green corridors.

8.2.3 Erosive Risk Barren Lands The mapping of the priority zones of barren lands is due to their high erosive potential and wide distribution in the case study site an important factor, which needs to be considered in the development of a priority map for reforestation (Lal 2001; Desta and Adugna 2012). When comparing the distribution of mapped barren lands in the three subcentrals, it becomes apparent that Huaripampa has a very high coverage of barren land (40%), with almost twice as much as Micani (22%). Regarding the high risk of erosion of barren lands compared to vegetated surfaces (Desta and Adugna 2012), Huaripampa seems to be in urgent need to change its current land-use management to protect its soils. But Micani and Ipote with 22% and 34%, respectively, are also at high risk to lose much of their soil to erosion. If no action is taken, erosion could further increase in the coming years, both in area and intensity, threatening the agricultural production and in turn the wellbeing of the local people in the planning area (FAO and UNCCD 2019). The question could be risen, if the reforestation of barren lands is reasonable to be included in the potential high-priority zones, as their absence of any vegetation could indicate that the stands might pose insufficient resources or environmental factors for plants to thrive. In fact, the southern area of the study site is at a higher altitude on average, which would lead to lower temperatures and thus shorter growth periods, rendering the vegetation more sensitive to disturbance. Regarding the distribution of bare lands in relation to village areas, they seem to occur with a higher probability in the vicinity of village areas. Thus, their lack of vegetation might not primarily originate from unsuitable plant stands and could also be a consequence of unsustainable land-use techniques such as overgrazing. A combination of both factors is not unlikely. Therefore, further research is needed to gain insight into required site conditions for a possible reforestation and to locate suitable planting areas. The misassignments in the classification of barren lands with the class of shruband grassland (11%/12%) appear to be quite high. They could origin from mistakes in the training and test datasets. This can occur, when pixels were assigned to the class of barren lands based on satellite imagery where they appear to be free of any vegetation, whilst they are in fact seasonally covered by grassland. This could have led to mistakes in the classification model and thus, misassignments. In-field

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data collection for ground truth can help to prevent such mistakes and improve the classification outcome. Slope Inclination Since slope inclination poses a major influence especially on the potential erosion rate of scarcely or unvegetated surfaces (Ellenberg 1981; Geyik 1986; Desta and Adugna 2012), the inclusion of very steep and insufficiently vegetated surfaces into the priority map for reforestation can contribute to counter erosion in the region. Since most parts of the case study site are characterized by moderately to very steep inclinations and the overall forest coverage is low, almost the whole area can be considered a potential zone to reduce erosive risk through reforestation. In other research areas, it might be beneficial to include also less steep inclinations, but as this map is about the determination of priority zones, only the steepest areas of over 60% are integrated into the priority mapping. Regarding the distribution of potential zones in the three subcentrals, the strong topographic similarity of Huaripampa compared to the two northern subcentrals is seemingly contradictory to the significant differences of the results of the gully erosion map. Here Huaripampa shows a significantly higher coverage. This could be an indication that the strong differences in erosive risk in the region might not be induced only by topographic differences. Regarding the high coverage of barren land in Huaripampa, the higher occurrence of erosion gullies could instead be linked to less protective vegetation cover, caused by overgrazing and deforestation. Therefore, revegetation through reforestation could help to halt the progressing erosion and contribute to a sustainable restoration of the landscape (FAO 2015). Simultaneously, the current drivers of deforestation need to be mitigated, like overgrazing or practicing agriculture on steep slopes, e.g., without terracing techniques (see Sect. 4.1.6). Otherwise, it could disable the reforestation effort in the long term. Gully Erosion The halting of gully erosion is not only important to stop the reduction and degradation of local soils, but also because the high-water carrying capacity can lead to sedimentation in rivers and thus reduce regional water quality (Pabón-Caicedo et al. 2020). Reforestation of their catchment areas can help to mitigate their development (Balthazar et al. 2015) and, thus, should be integrated into the priority map for reforestation. The results of the classification indicate that Huaripampa is especially affected by the formation of erosion gullies. This may be a consequence of severe deforestation, as the subcentral region has a very high proportion of barren land and low forest cover (compare Sect. 7.2.1), both of which favour the formation of erosion gullies (Desta and Adugna 2012). But also, in Micani and Ipote over one quarter of their total area is already affected by erosion gullies. As some of the gullies already form major gully networks, they might be too large to halt them by mere revegetation and hence require technical solutions (Geyik 1986). Due to the vast masses of water and sediments that are probably transported downstream during the rainy season, the plantings would simply be washed away

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(Desta and Adugna 2012). Therefore, a next step would be an assessment of the erosion gullies, regarding their length, depth, and width. When comparing the areas, which are, according to the classification, covered by forests, it becomes apparent that some of these areas are also classified as being affected by gully erosion. This seems to contradict this work’s underlying hypothesis that forests protect the underlying soil from erosion and, thus, no erosion gullies should be able to form. There are four possible explanations for this: First, there were deficits in the classification of forested areas, with areas being falsely mapped as forested. Second, there were deficits in the assignment of erosion gullies, with gullies being mapped in places where there are none. Third, the mapping of gullies and the forest classification was correct, but the gullies are by now overgrown and thus may have lower erosive potential but are still detectable in the classification. Or last, the hypothesis is wrong, and trees do not sufficiently protect the soil from erosion. The last possible explanation can be considered as false, as there is plenty of research on the topic of the effects of vegetation on erosion. When a sample of the areas classified as both forested and highly erosion-prone was carried out, it became apparent that in some of these areas there is actually no forest vegetation to be found. The misclassification often applied to particularly dark, because shaded, areas, which were probably thus wrongly assigned to the class of forested regions. As discussed in Sect. 8.1, it might be beneficial to repeat the classification with sound ground-truth data. As the first explanation seems to apply and the classification of gullies seems to be correct, the potential zones for reforestation of gully surfaces delivers reliable results for the mitigation of erosion gullies in the priority-zone map.

8.2.4 Forest Proximity of Villages The results of the forest proximity of villages are used as an indicator to map potential zones for reforestation that would supply the villagers with enough fuelwood. Like in the previous maps, the viability was depending on the quality of the LULC classification. If forest areas or agricultural areas have been mapped incorrectly, these will lead to a decreasing reliability of the results of forest accessibility. In this approach, the impact of forest quality and size, apart from a minimum size of 0.5 ha, was not considered. As a result, there might be overlaps and a small area of forest may be accounted to potentially supply a larger number of households or villages. This may exceed the forest’s capacity resulting in insufficient wood supply despite the short distance. In this way, the map would lead to incorrect conclusions. Hence, the analysis might need to be refined, to avoid such overlaps. Nevertheless, the areas which were now assigned for a potential reforestation for fuelwood supply are with high probability in no close proximity to forests and would thus benefit from reforestation. The only exception might be villages that are located close to the border of the case study site. Forests located outside the planning area were not included in the calculation and thus could lead to a village being classified as far from the nearest forest, whilst it is just behind the study site border. This could lead to a distortion of the

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peripheral areas and their forest accessibility and show them lower than it is actually the case. Since this is probably only affecting a small number of villages, it should be minded in the planning process, but does not heavily influence the informative value of the map. Further, literature research could not fully answer, where the villagers collect their fuelwood. They might sometimes not collect wood for their daily needs directly from forests, but from places such as water drains, where wood is flushed and collected during the rainy season. Therefore, a village may be well supplied with wood despite being far from a forested area, e.g., because it is located near a major watercourse or erosion gully that passes a more forested area. However, since the goal of this research was also the sustainably managed supply of fuelwood for the villagers, the approach to establish forests in the vicinity of villages can help to implement forestry measures that include logging and replanting, to ensure a reliable and long-term supply (Ministry of Natural Resources—Rwanda 2014).

8.2.5 Endangered Village Infrastructure The results from the mapping of village infrastructure that could be endangered by erosion processes of bordering areas indicate that in the whole area approximately 60% of the roads and 20% of the agricultural land could be threatened by erosion. The impact on village infrastructure could be particularly drastic in Huaripampa, where 72% of the roads and 23% of the agricultural land are located within 20 m of highly erosion-prone areas. However, also the results in Ipote and Micani indicate severe degradation. Micani has a lower potential risk compared to Ipote and Huaripampa, with ‘only’ 57% of roads and 21% of agricultural land potentially affected. These results seem logical when considering the distribution of areas likely to be affected by erosion both due to erosion gullies and steep, unvegetated terrain (see Sect. 8.2.3), as they have the highest percentage of areas affected in Huaripampa and the lowest in Micani. It should be noted, however, that the classification is only an approximation of the most affected areas. It does not consider, for example, soil structure and composition, which can differ from area to area and, thus, also have an influence on erosion intensity (Ellenberg 1981; Lal 2001). Furthermore, the exact topographical conditions could not be considered in detail in the analysis. This means, for example, that roads located above a fallow area are classified as being just as at risk as those located below. Although the roads can theoretically also be affected by sliding earth or even erosion themselves, it is to be expected that the effects of a landslide on a road below such highly erosive zones are more serious. Revegetation might not be sufficient to protect the roads, and additional methods improving the drainage such as ditches, drains and culverts, could be important accompanying measures (Oyedepo 2013). A more in-depth analysis in cooperation with the inhabitants of the region and local authorities can further refine the results.

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In addition, the length of slopes has not been included in the analysis so far. Since long slopes generally have a significantly higher erosion risk, they should theoretically be classified as more vulnerable than shorter slopes of the same gradient (Geyik 1986; Desta and Adugna 2012). By locating long slopes, the results could be further fine-tuned in a next step. As in the other analysis maps, the results strongly depend on the preceding LULC classification. If areas were incorrectly classified as erosion gully or barren land, the map would also classify village infrastructures as high risk. However, classification errors would be worse the other way around if areas were not included in the risk-zone classification due to a misclassification and, thus, their vulnerability was not recognized. Reclassification based on field mapping results could improve the results. Nevertheless, 100% accuracy can unlikely be achieved through landuse classification. Therefore, cooperation with the local population is an important component to jointly determine the highest prioritization.

8.3 Discussion of the Priority-Zone Mapping The priority-zone map is developed to identify potential sites for reforestation that maximize the ecological and social benefits for the local population. By layering the previously created thematic maps each focusing on one benefit that can be achieved by reforestation, an accumulated priority-zone map was created. The two different ways of presentation allow different information to be gained. The thematically layered map provides information on the respective benefits of the potential areas. The second map allows the identification of the areas whose reforestation has the greatest potential for achieving several benefits. The priority map also offers the possibility for its users to decide for themselves where to focus their reforestation efforts. For example, by giving more weight to one thematic map, areas that are particularly suitable for solving this problem can be shown darker on the map to refine the area analysis of the priority zones. It further allows for a comparison of areas, regarding the priority-zone coverage. In this work’s case study region, the results of the high-priority-zone map indicate that Huaripampa offers the highest prioritization score compared to the other two subcentrals. It shows the highest percentage of accumulated prioritization area (84%) and the highest coverage of prioritization classes 3–6. This complements the results of the individual thematic maps, in which Huaripampa was mostly shown to have the highest scores for potential areas. The only exceptions are the priority-zone map of potential green corridors and the map regarding potential forest buffer zones for protection, due to the low fraction of remaining forest areas. Throughout the study site, no layering of all seven thematic maps could be observed. This is due to the similar spatial dependencies of the layers for wood supply for the villages and the forest buffer zones to protect forest remnants. The layer for wood supply integrates areas, that are in no close distance to forested areas. Therefore, they could never overlap with the layer of forest buffer zones and thus explain the absence of a priority class 7 in this study.

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The results of the thematic maps for reforestation showed that they depend strongly on the LULC map, which they are based on. Hence, this also applies to the priorityzone map as it is built on the results from the thematic priority maps. Mistakes from the classification will also persist in the priority-zone map and should be avoided by incorporating reliable ground-truth data. This was not possible in this work, therefore, if the map was to be used as framework for the initiation of FLR projects, an improved reclassification is advisable. Furthermore, some thematic maps might need refinement regarding their spatial layout, which influences the informative value of the high-priority map. This applies for the maps of green corridors (see Sect. 8.2.2), the map of erosion gullies (see Sect. 8.2.3) and the map of protection of village infrastructure (see Sect. 8.2.5). Through collaboration with the local stakeholders as well as experts, the informative value could be increased and the accuracy of the priority zones be enhanced. Some areas that would benefit from reforestation also include agricultural areas, e.g., in the thematic map regarding buffer zones for existing forests, green corridors and slope inclinations. This does, however, not imply that the agricultural land should be converted into forest. If agricultural land is located within areas of priority zones, it might be beneficial for the local famers to invest, e.g., in alternative farming methods, like agroforestry, contour farming or the rehabilitation of traditional terracing techniques (UNCCD 2019; Di Sacco et al. 2021). This way, they can locally contribute to protect their environment while potentially increasing their productive yield. Another limiting factor could be the suitability of the respective areas for reforestation. The priority map indicates the areas whose afforestation can have a positive effect on ecosystem restoration. However, site factors such as water availability and soil suitability have not yet been considered. If, for example, there is too little water available or the soils are too thin to support forest vegetation, areas designated as priority zones may not be suitable for reforestation. This analysis could not be carried out in this work due to insufficient data on stand conditions. The suitability of the areas should therefore be assessed on site and with the involvement of local experts. Another challenge might be the fragmentation of the individual thematic priority zones, as through the layering process they could fall under different priority classes. This might lead to an insufficient coverage to solve the problems of the thematic map. It may be beneficial to build a potential reforestation approach not only based on the priority map, but by including the single thematic maps to increase the impact of the intervention. Thus, the priority map should not be understood as a guideline to only restore forests and landscape of the high-priority areas, e.g., priority classes 4, 5 and 6. It rather helps to allocate areas that offer the highest benefits and to start an incremental forest and landscape restorative process from there on. The accumulation of results could thus help local stakeholders to conceptualize reforestation projects. Since the distribution of the different priority zones might be too detailed for the development of a municipal reforestation approach, a reasonable next step might be the simplification of the map, e.g., by joining prioritization levels or by reducing the resolution of the map. This way larger and more cohesive areas could be identified for the allocation of reforestation measures.

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References Atmadja S, Eshete A, Boissière M (2019) Guidelines on sustainable forest management in drylands of Ethiopia. FAO and CIFOR, Rome Balthazar V, Vanacker V, Molina A, Lambin EF (2015) Impacts of forest cover change on ecosystem services in high Andean mountains. Ecol Indic 48(1):63–75. https://doi.org/10.1016/j.ecolind. 2014.07.043 Beier P, Majka D, Jenness J (2007) Conceptual steps for designing wildlife corridors. http://corrid ordesign.org/dl/docs/ConceptualStepsForDesigningCorridors.pdf. Accessed 19 Aug 2022 Beier P, Majka D, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22(4):836–851. https://doi.org/10.1111/j.1523-1739.2008.009 42.x Bond M (2003) Principles of wildlife corridor design. Center for Biological Diversity. https://www. biologicaldiversity.org/publications/papers/wild-corridors.pdf. Accessed 19 Aug 2022 Desta L, Adugna B (2012) A field guide on gully prevention and control. Nile Basin Initiative— Eastern Nile Subsidiary Action Program (ENSAP), Addis Ababa Di Sacco A, Hardwick KA, Blakesley D, Brancalion PHS, Breman E, Rebola LC, Chomba S, Dixon K, Elliott S, Ruyonga G, Shaw K, Smith P, Smith R, Antonelli A (2021) Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits. Glob Change Biol 27(7):1328–1348. https://doi.org/10.1111/gcb.15498. Ellenberg H (1981) Desarrollar sin destruir. Instituto de Ecologia, La Paz FAO (2015) Global guidelines for the restoration of degraded forests and landscapes in drylands: building resilience and benefiting livelihoods. In: Berrahmouni N, Regato P, Parfondry M (eds) Forestry paper no. 175. Food and Agriculture Organization of the United Nations, Rome FAO and UNCCD (2019) Vulnerability to food insecurity in mountain regions: land degradation and other stressors. Food and Agriculture Organization of the United Nations, Bonn Geyik MP (1986) FAO watershed management field manual: gully control. FAO Conserv Guide 13(2). Food and Agriculture Organization of the United Nations, Rome IUCN and WRI (International Union for Conservation of Nature and World Resource Institute) (2014) Guide to the Restoration Opportunities Assessment Methodology (ROAM) assessing forest landscape restoration opportunities at the national or sub-national level. Working paper (road-test edition). International Union for Conservation of Nature and World Resource Institute, Gland Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12(6):519–539. https://doi.org/10. 1002/ldr.472 Martins Mauricio R, Sandin Ribeiro R, Campos Paciullo DS, Alves Cangussú M, Murgueitio E, Chará J, Flores Estrada MX (eds) (2019) Silvopastoral systems in Latin America for biodiversity, environmental, and socioeconomic improvements. In: Lemaire G, De Faccio Carvalho PC, Kronberg S, Recous S (eds) Agroecosystem diversity—reconciling contemporary agriculture and environmental quality. Elsevier, Amsterdam Ministry of Natural Resources—Rwanda (2014) Forest landscape restoration opportunity assessment for Rwanda. MINIRENA (Rwanda), IUCN, WRI Oyedepo OJ (2013) Impact of erosion on street roads: a case study of Sijuwade area Akure Ondo State Nigeria. Chem Mater Res 3(10):33–39 Pabón-Caicedo JD, Arias PA, Carril AF, Espinoza JC, Borrel LF, Goubanova K, Lavado-Casimiro W, Masiokas M, Solman S, Villalba R (2020) Observed and projected hydroclimate changes in the Andes. Front Earth Sci 8:411–440. https://doi.org/10.3389/feart.2020.00061 UNCCD (United Nations Convention to Combat Desertification) (2019) Forests and trees. At the heart of land degradation neutrality. UNCCD, Bonn

Chapter 9

Conclusion and Outlook

This work aimed to develop a method to locate potential sites for reforestation of the Bolivian Montane Dry Forests that maximize the ecological and social benefits for the local population. Therefore, a regional problem assessment was conducted, which showed that the main challenges of land degradation are decreasing biodiversity, changing climatic conditions, negative influences on the hydrological network, reduced agricultural production, decreasing firewood supply and the risk of damage to village infrastructure like roads and agricultural land. The analysis showed that biodiversity loss can be linked to destruction and fragmentation of habitats (RamirezVillegas et al. 2012; Brooks 2018; WWF 2021). Agricultural production is affected by erosion, which leads to the decline in soil fertility and water availability (FAO and UNCCD 2019). The low resource supply with firewood is influenced by a reduction of tree coverage in the proximity of villages (FAO 2015). The village infrastructure is at risk, because landslides and erosion pose an increasing risk to roads and arable land (Desta and Adugna 2012; Oyedepo 2013). These challenges have in common that they can be traced to deforestation and unsustainable land use. Therefore, the potentials of reforestation to mitigate the problems of the region were researched and transferred into spatial influencing factors; buffer zones around remaining forests and connecting green corridors can increase habitat availability for local biodiversity (Ministry of Natural Resources—Rwanda 2014; IUCN and WRI 2014; UNCCD 2019). Erosion is favoured on bare lands and on steep, scarcely vegetated slopes (Geyik 1986; Lal 2001); thus, revegetation of these areas contributes to erosion mitigation (FAO 2015; Pabón-Caicedo et al. 2020). The impact of existing marks of erosion, like erosion gullies, can be reduced by revegetating their catchment areas (Balthazar et al. 2015). The firewood supply of local communities can be increased through reforestation of areas in the proximity to villages and the implementation of sustainable management techniques (Ministry of Natural Resources—Rwanda 2014; UNCCD 2019). Landslides and erosion damage to village infrastructure can be reduced by revegetation of, e.g., the neighbouring areas of roads and arable land, which are at high risk of erosion (Geyik 1986; FAO 2015).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6_9

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Therefore, the problem assessment was transferred into the development of thematical priority maps. Each map indicates zones for reforestation that can mitigate one of the regional problems. The individual thematic maps were then overlaid to create a concluding priority-zone map. In areas where several thematic maps overlap, reforestation could contribute to solve several problems. The priority map for reforestation, which was exemplary developed for the case study region of Micani, gives insight into where reforestation has the greatest potential to create multiple benefits. However, there are some limiting factors that influence the informative value of the outcome. The reliability of the map depends strongly on the land use and land cover map on which it is developed. If it is outdated or lacking accuracy, it could lead to a reduced viability of the priority map. In some regions, like this work’s study site, LULC maps are often not yet available at a sufficient spatial resolution. Individual LULC classification can be conducted, as has been done in this work, however, they require knowledge of the region and technical expertise to avoid classification mistakes and achieve reliable results. A reclassification of the LULC map by consulting classification experts and conducting the classification based on sound ground-truth data could therefore increase the reliability of the priority map. Further, some approaches of the thematical maps need to be refined to improve their informative value and the reliability of the concluding priority map. Therefore, further research and the collaboration with the local population as well as classification experts can enhance the results. The selection of the most beneficial reforestation sites alone will not guarantee a sustainable forest and land restoration, as the following factors will greatly influence the outcome: This work’s approach does not answer what type of reforestation might be adequate in the distinct areas; thus, whether natural regeneration is applicable, if the land is suited for cohesive regeneration or if agroforestry and mosaic reforestation are the goals (FAO 2015). This needs to be elaborated in collaboration with local communities to meet their needs. In addition, it must be checked whether the site factors of the priority zones are suitable for reforestation, in terms of growing conditions for the forest. Further, the success of reforestation, especially in terms of protecting local biodiversity, depends strongly on species selection and composition (FAO 2015). Generally, native species should be favoured, and disturbance should be reduced (FAO 2015). Climate change, with its high probability of warming and less precipitation, should be considered in the reforestation as local vegetation might be at risk of displacement due to climatic influences (FAO 2015; Havens et al. 2015; Pabón-Caicedo et al. 2020). Hence, species composition and sustainable seed supply, e.g., through provenancing, will play an important role in the long-term success of the restoration (Lowe 2010; Havens et al. 2015). And, finally, financing and monitoring methods need to be considered that not only secure the reforestation measure, but also potentially generate economic benefits for the local population (FAO 2015). Thus, there are still some challenges ahead of the actual implementation of a sustainable restoration project. However, the priority map developed in this work could contribute to initiating the planning process. The layering of the thematic maps allows for an overview of the accumulated priority zones and can serve as discussion and planning framework for the development and implementation of reforestation

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measures. Depending on the scale focus of the implementing planning group, the map could be used in two different ways. If the planning group is formed by individual communities, e.g., who want to start their own bottom-up reforestation measure, the small scale allows them to define priority areas within their community and could therefore contribute to the planning of community forestry projects. As land use maps, which form the basis of the development of the priority map, are probably not yet available for many of the remote rural communities, they would first have to be prepared. The availability of the necessary programmes is a minor problem, as Google Earth Engine, which was used for classification in this work, and QGIS are open-source software and thus accessible to everyone. However, the requirement of technical expertise, technical equipment and knowledge to carry out a classification could represent hurdles for the creation of such a map. Local NGOs could thereby play an important role to help local communities in developing the priority maps. The second area of application could be in municipal planning for integrated forest and landscape restoration. The map not only allows the localization of the individual high-priority zones, but also enables a comparison of individual regions, such as the three subcentrals of this case study. By extending the approach to the entire territory of the municipality, it would be possible to identify those subcentrals that have so far been most affected by the consequences of landscape degradation and would thus benefit most from reforestation. By comparing the municipalities on the basis of the priority map, the decision in which region to prioritize landscape restoration can be made on a scientific basis. Hence, the map could support local institutions in planning and implementing forest and landscape restorations and thereby bridge the gap between nationally planned landscape restoration approaches and local needs. It could add a smallscale method to the current FLR approaches that focus on global (GPFLR 2013) and national areas (IUCN and WRI 2014). In this way, local institutions could be empowered to take the restoration of forests and landscapes into their own hands in order to combat the local problems of land degradation, improve their livelihoods and at the same time contribute to combating climate change (FAO 2015).

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9 Conclusion and Outlook

FAO and UNCCD (2019) Vulnerability to food insecurity in mountain regions: land degradation and other stressors. Food and Agriculture Organization of the United Nations, Bonn Geyik MP (1986) FAO watershed management field manual: gully control. FAO Conserv Guide 13(2). Food and Agriculture Organization of the United Nations, Rome GPFLR (Global Partnership on Forest and Landscape Restoration) (2013) Ideas transform landscapes. Burlington Press, Switzerland Havens K, Vitt P, Still S, Kramer AT, Fant JB, Schatz K (2015) Seed sourcing for restoration in an era of climate change. Nat Areas J 35(1):122–133. https://doi.org/10.3375/043.035.0116 IUCN and WRI (International Union for Conservation of Nature and World Resource Institute) (2014) Guide to the Restoration Opportunities Assessment Methodology (ROAM) assessing forest landscape restoration opportunities at the national or sub-national level. Working paper (road-test edition). International Union for Conservation of Nature and World Resource Institute, Gland Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12(6):519–539. https://doi.org/10. 1002/ldr.472 Lowe AJ (2010) Composite provenancing of seed for restoration: progressing the ‘local is best’ paradigm for seed sourcing. In: Paton D, O’Conner J (eds) The state of Australia’s birds 2009: restoring woodland habitats for birds. Supplement to wingspan, vol 20, no 1, pp 16–17 Ministry of Natural Resources—Rwanda (2014) Forest landscape restoration opportunity assessment for Rwanda. MINIRENA (Rwanda), IUCN, WRI Oyedepo OJ (2013) Impact of erosion on street roads: a case study of Sijuwade area Akure Ondo State Nigeria. Chem Mater Res 3(10):33–39 Pabón-Caicedo JD, Arias PA, Carril AF, Espinoza JC, Borrel LF, Goubanova K, Lavado-Casimiro W, Masiokas M, Solman S, Villalba R (2020) Observed and projected hydroclimate changes in the Andes. Front Earth Sci 8:411–440. https://doi.org/10.3389/feart.2020.00061 Ramirez-Villegas J, Jarvis S, Touval J (2012) Analysis of threats to South American flora and its implications for conservation. J Nat Conserv 20(6):337–348. https://doi.org/10.1016/j.jnc.2012. 07.006 UNCCD (United Nations Convention to Combat Desertification) (2019) Forests and trees. At the heart of land degradation neutrality. UNCCD, Bonn World Wildlife Fund (WWF) (2021) Tropical and subtropical dry broadleaf forests. https://www. worldwildlife.org/biomes/tropical-and-subtropical-dry-broadleaf-forests. Accessed 26 Sept 2021

Appendix

Comparison Matrices as Classification Ground Truth

A.1 Agricultural Area

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Böhrkircher et al., Priority-Zone Mapping for Reforestation, SpringerBriefs in Geography, https://doi.org/10.1007/978-3-031-20375-6

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A.2 Forested Area

A.3 Barren Land

Appendix: Comparison Matrices as Classification Ground Truth

Appendix: Comparison Matrices as Classification Ground Truth

A.4 Shrub- and Grassland

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