Operational remote sensing for sustainable development proceedings of the 18th EARSeL Symposium on Operational Remote Sensing for Sustainable Development, Enschede, Netherlands, 11-14 May 1998 9781000100273, 1000100278

Table of contents :
Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright Page......Page 5
Table of Contents......Page 6
Preface......Page 12
Introduction/Keynote lectures......Page 14
Remote sensing and GIS for sustainable development: An overview......Page 16
Remote sensing–A donor's view......Page 20
1 Remote sensing for sustainable development: State of the art......Page 24
The use of remote sensing to improve irrigation water management in developing countries......Page 26
Remote sensing for sustainable development State of the art......Page 42
2 Implementation strategies and cost-benefit analysis......Page 48
Sustainable use of remote sensing in developing countries: Some notes based on FAO experience......Page 50
Remote sensing in water management practice......Page 54
Implementation strategy in a multi-country context–The case of MERA project......Page 60
3 Land use and nature management......Page 66
Twelve years of vegetation cover monitoring from Landsat-TM data in Languedoc, southern France......Page 68
Reliability aspects of vegetation monitoring with high resolution remote sensing images......Page 76
Large scale vegetation mapping in mountain environments using remote sensing and plant physiology methods......Page 84
Wetland vegetation mapping for nature management Digital data integration and classification of aerial photographs......Page 90
Using Landsat TM data for poplar cultivated areas estimation in Konya-Eregli Region, Anatolia, Turkey......Page 98
Agricultural land cover monitoring in Russia using remote sensing......Page 104
Operational crop monitoring by remote sensing in Hungary......Page 112
The use of remote sensing and GIS to detect gypsiferous soils in the Ismailia Province, Egypt......Page 120
Crop yield estimation in the Hungarian meteorological service......Page 126
Land cover characterization for environmental monitoring of pan-Europe......Page 130
An economical new approach to airborne videography–Case study: Aegean region of Turkey......Page 136
Landscape sbucture derived from satellite images as indicator for sustainable landuse......Page 142
Geoinformation as an effective tool for environmental protection and landuse optimisation......Page 152
4 Water quality and pollution monitoring......Page 154
Satellite data for the air pollution mapping......Page 156
Management of water resources in megacities by using remote sensing......Page 164
Study of the floating weeds affected canals in the Western Nile Delta (Alexandria Province) of Egypt using remote sensing techniques......Page 172
Using ERS-2 SAR images for routine observation of marine pollution in European coastal waters......Page 180
SAR imagery for urban air quality......Page 188
Integrated use of RS-data for assessing environmental risks from coastal industrial plants: Examples from Sicily......Page 194
5 Coastal zone management......Page 202
Operational digital arial photography: A new airborne technique to support sustainable coastal zone management......Page 204
The LACOAST project: Land cover changes survey of European coastal zones......Page 208
lmaging of tidal flats by the SIR-C/X-SAR multi-frequency/multi-polarisation synthetic aperture radar......Page 212
A semi-empirical suspended sediment algorithm for routine monitoring of coastal waters from the North Sea to the Mediterranean......Page 216
The Bathymetry Assessment System......Page 224
RESSAC: Remote sensing support for analysis of coasts......Page 232
6 New airborne and spaceborne techniques......Page 240
Simulating data from ESA 's medium resolution imaging spectrometer for land applications......Page 242
Promoting local user access to remote sensing data: RAPIDS–A practical and affordable ground station for developing countries......Page 248
Large image format aerial cameras: Film based systems and digital perspectives......Page 254
Laser scanning for topographic applications: From fiction to reality......Page 260
The DAIS La Peyne experiment: Using airborne imaging spectrometry for land degradation survey and modelling......Page 270
Fusion of airborne and spaceborne images in visible range......Page 278
On the impact of lower atmospheric stratification on SAR images of the ocean surface......Page 284
7 Geomorphological hazards and floods......Page 290
Erosion indicators assessment using microwave remote sensing......Page 292
Monitoring of vegetation fires in Sumatra, Indonesia: A burning issue......Page 300
Satellite applications for monitoring flood hazards in Haryana, India–A case study......Page 304
The effects of urbanization on urban storm water runoff: A case study on greater Dhaka metropolitan city......Page 308
Near real-time assessment of the June 1996 flash-floods in Central Yemen......Page 318
Flood plains and lake mapping in Northern Vietnam......Page 324
8 New processing and analysing techniques......Page 330
Semi-automatic extraction of urban road network: Assessment of the quality......Page 332
Analytical processing of multitemporal SPOT and Landsat images for estuarine management in Kalimantan Indonesia......Page 338
Local statistics in supervised classification......Page 348
Simulation of high resolution imagery......Page 354
Laboratory measurements of artificial rain impinging on a water surface......Page 362
Multichannel compression for sequential image retrieval......Page 368
The use of multiresolution analysis and IHS transform for merging IRS-1C panchromatic and SPOT-XS image data around Istanbul......Page 376
Towards the development of an automated fractal cloud identification algorithm......Page 380
Imagery in mapping......Page 386
Spatial variation in SAR images of different resolution for agricultural fields......Page 390
Multiscaling in lineament pattern: Mapping between images of different resolution......Page 398
9 Climate change and interaction with landuse......Page 406
Towards operational mapping of solar radiation from Meteosat images......Page 408
Remote sensing of land surface fluxes for updating numerical weather predictions......Page 416
Application of space techniques to derive energy fluxes for water management in (semi) arid zones......Page 426
10 Sustainable development in developing countries......Page 434
Present status of geographic information system and remote sensing applications in Pakistan and their future prospects......Page 436
Study of soil salinity in the Ardakan area, Iran, based upon field observations and remote sensing......Page 442
Operational tools for monitoring regional scale landcover change in Sahelian Africa......Page 450
11 Monitoring environmental impact......Page 454
Operational remote sensing in support of forest fire monitoring: Recent experiences from Nicaragua......Page 456
Integrated monitoring and management system of lignite opencast mines using multiple remote sensing data and GIS......Page 462
Optical remote sensing in support of eutrophication monitoring in Belgian waters......Page 468
The operational remote sensing of coal in China......Page 476
Appraisal of irrigation system performance in saline irrigated command using SRS and GIS......Page 480
Meditteranean forest fire risk monitoring using NOAA AVHRR......Page 486
List of Participants......Page 494
Author index......Page 502
Colour plates......Page 504

Citation preview

OPERATIONAL REMOTE SENSING FOR SUSTAlNABLE DEVELOPMENT

PROCEEDINGS OF THE 18TH EARSeL SYMPOSIUM ON OPERATIONAL REMOTE SENSING FOR SUSTAINABLE DEVELOPMENT /ENSCHEDE/ NETHERLANDS/11-14 MAY 1998

Operational Remote Sensing

for Sustainable Development

Edited by

G.J.A. Nieuwenhuis Chairman, NSEOG

R.A.Vaughan Chairman, EARSeL

M.Molenaar Vice-Chairman, NSEOG

0

CRC Press Taylor & Francis Group Boca Ratan London New York

CRC Press is an imprint of the Taylor & Francis Group, an informa business

CRC Press Taylor & Francis Group 6000 Broken Sou nd Parkway NW, Suite 300 Boca Raton, FL 33487-2742 ©1 1999 9 9 9by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Gro up, an In forma business

No claim to original U.S. Gover nment works This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the aut hor and publisher ca nnot assume responsibility for the va lidity of all materia ls or the co nsequences of their use. The authors and publishers have attempted to trace the copyright holders of all material repro· duced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyrig ht material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of th is book may be reprinted, reproduced, transmitted, or uti li zed in any form by any electronic, mechanical, or other means, now known or hereafter inve nted, including photocopying, microfilming, and recording, or in any informatio n storage or retrieval system, without written permission from the publishers. For permission to photocopy or use materia l electronically from this work, please access www.copyright.com (http: //www.copy· right.com/) or co ntact the Copyright Clea rance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not· for· profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifica· tion and expla nation wit hout intent to infringe. Visit the Taylor & Francis Web site at http: //www.taylorandfrancis.com and the CRC Press Web site at http: //www.crcpress.com

Operational Remote Sensing for Sustainable Development, Nieuwenhuis, Vaughan & Molenaar {eds) © 1999 Balkema, Rotterdam, ISBN 90 5809 029 9

Table of contents

Preface

XI

Introduction/ Keynote lectures Remote sensing and GIS for sustainable development: An overview

XV

D. P. Rao & S. K. Subramanian Remote sensing- A donor's view

XIX

CSmit

1 Remote sensing for sustainable development: State ofthe art The use of remote sensing to improve irrigation water management in developing countries

3

W.G. M Bastiaanssen Remote sensing for sustainable development State of the art

19

G.Konecny

2 Implementation strategies and cost-benefit analysis Sustainable use of remote sensing in developing countries: Some notes based on FAO experience

27

F. L Snijders Remote sensing in water management practice

31

F.AHagman Implementation strategy in a multi-country context - The case of MERA project

37

V. Perdigiio

3 Land use and nature management Twelve years of vegetation cover monitoring from Landsat-TM data in Languedoc, southern France D. Carau.x-Garson, B.l.Acaze. C Ho/f. S. Sommer, W. M eh/ & J. Hill

45

Reliability aspects of vegetation monitoring with high resolution remote sensing images

53

lA MJanssen, E. R Kloosterman, R. W. LJordans, R.A Hartmann & J. S.Jorritsma V

Large scale vegetation mapping in mountain environments using remote sensing and plant physiology methods AM Kurnatowska

61

Wetland vegetation mapping for nature management Digital data integration and classification of aerial photographs ME. Sanders & J.G.P.W.Clevers

67

Using Landsat TM data for poplar cultivated areas estimation in Konya-Eregli Region, Anatolia, Turkey P.Sarfatti, G.Delli, LOngaro & D.Mollicone

75

Agricultural land cover monitoring in Russia using remote sensing J.G. P.W.Clevers, CA Mucher, V. P. Popov, N. M Vandysheva & G.l Vassilenko

81

Operational crop monitoring by remote sensing in Hungary G.Csornai, Cs.Wimhardt, Zr.Suba, P.Somogyi, G.Ntidor, LMartinovich, LTikasz & AKocsis

89

1be use of remote sensing and GIS to detect gypsiferous soils in the Ismailia Province, Egypt R.Goossens, E. Van Ranst, T. KGhabour & M El Badawi

97

Crop yield estimation in the Hungarian meteorological service A.Merza, J. Kerenyi & ARimOczi-Paal

103

Land cover characterization for environmental monitoring ofpan-Europe

107

CA Miicher, K Steinnocher, J. LChampeaux, S.Griguo/o, K Wester & P. Loudjani

An economical new approach to airborne videography- Case study: Aegean region of Turkey E. Saner, AH.Eronat, CBasoz & QUslu

113

Landscape sbucture derived from satellite images as indicator for sustainable landuse Th. Wrbka, K Reiter, E. Szerencsits, H. Beissmann, P. Mandl, A Bartel, W. Schneider &F.Suppan

119

Geoinformation as an effective tool for environmental protection and landuse optimisation E. Wolk-Musial & B.Zagajewski

129

4 Water quality and pollution monitoring Satellite data for the air pollution mapping LWald, LBasly & J.MBaleynaud

133

Management of water resources in megacities by using remote sensing LBruzzone, D.Fernandez Prieto & S.B.Serpico

141

Study of the floating weeds affected canals in the Western Nile Delta (Alexandria Province) ofEgypt using remote sensing techniques AR. El Noby, LDaels, R.Goossens & B. M De Vliegher

149

Using ERS-2 SAR images for routine observation of marine pollution in European coastal waters MGade, S.Ufermann, QRud, Mlshii & G.W.Jolly

157

VI

SAR imagery for urban air quality

165

LBasly, F.Cauneau, CCouvercelle, T.Ranchin & LWald Integrated use of RS-data for assessing environmental risks from coastal industrial plants: Examples from Sicily F.Bertolo, S.Folving, 1Megier& MLParacchini

171

5 Coastal zone management Operational digital arial photography: A new airborne technique to support sustainable coastal zone management CKIOditz & P.Geerders

181

The LACOAST project: Land cover changes survey of European coastal zones P. Loudjani, 1 Meyer-Roux, G. Schmuck, AAnnoni & V. Perdigao

185

lmaging of tidal flats by the SIR-C/X-SAR multi-frequency/multi-polarisation synthetic aperture radar CMelsheimer, G.Tanck, MGade & W.Alpers

189

A semi-empirical suspended sediment algorithm for routine monitoring ofcoastal waters

193

from the North Sea to the Mediterranean S.1 Shimwe/1 & G. H. F. M Hesse/mans

The Bathymetry Assessment System 1Vogelzang, G.1Wensink, C1Calkoen & G.H.F.MHesselmans

201

RESSAC: Remote sensing support for analysis of coasts

209

MViel, Y.Ginon, G.H.F.MHesselmans, MPalandri, V.Thouvenin & E.Wisse

6 New airborne and spaceborne techniques Simulating data from ESA 's medium resolution imaging spectrometer for land applications F. van der Meer, W. H. Bakker, K Scholte, A K Skidmore, 1 P.G.W. Clevers, G. F. Epema

219

&S.MdeJong Promoting local user access to remote sensing data: RAPIDS - A practical and affordable ground station for developing countries l D. Downey, 1 B. Williams, D.1Archer, 1 R. Stephenson, R. Stephenson & W. Looyen

225

Large image format aerial cameras: Film based systems and digital perspectives

231

AHinz Laser scanning for topographic applications: From fiction to reality E.1Huising, LMGomes Pereira & E.M1Vaessen

237

The DAIS La Peyne experiment: Using airborne imaging spectrometry for land degradation survey and modelling S. M de Jong, S. Sommer, B. Lacaze, K. Scholte & F. van der Meer

247

Fusion ofairborne and spaceborne images in visible range T. Ranchin & L Wald

255

VII

On the impact oflower atmospheric stratification on SAR images of the ocean surface S.Ufermann, MGade, R.Romeiser & W.Alpers

261

7 Geomorphological hawrds andfloods Erosion indicators assessment using microwave remote sensing P.G.Bressers & P.J.Van Oevelen

269

Monitoring of vegetation fires in Sumatra.Indonesia: A burning issue P.Ceccato, S. P. Flasse, LD.Downey, D. Wall & LAnderson

277

Satellite applications for monitoring flood hazards in Haryana, India- A case study B. S. Chaudhary, V. S.Arya, ABeniwal, T. P. Babu & D. S. Ruhal

281

'The effects of urbanization on urban storm water runoff: A case study on greater Dhaka metropolitan city N. Islam Khan

285

Near real-time assessment ofthe June 1996 flash-floods in Central Yemen B.H.P.Maathuis, W.J.Timmermans &AMJ.Meijerink

295

Flood plains and lake mapping in Northern Vietnam B.Moeremans, F.Berthier & S.Dautrebande

301

8 New processing and analysing techniques Semi-automatic extraction of urban road network: Assessment of the quality l Couloigner & T. Ranchin

309

Analytical processing of multitemporal SPOT and Landsat images for estuarine management 315 in Kalimantan Indonesia AG. Dekker, S. W. M Peters, MRijkeboer & H.Berghuis Local statistics in supervised classification B. G. H. Gorte

325

Simulation of high resolution imagery M Muller & KSegl

331

Laboratory measurements ofartificial rain impinging on a water surface N.Braun, MGade & P.ALange

339

Multichannel compression for sequential image retrieval D.Coltuc, MDatcu & KSeidel

345

The use of multiresolution analysis and IHS transform for merging IRS-1C panchromatic

353

and SPOT-XS image data around Istanbul H.G.COjlam & N.Musaoglu Towards the development ofan automated fractal cloud identification algorithm MW. Freeman & R.AVaughan

357

Imagery in mapping P.J.Murfitt

363

VIII

Spatial variation in SAR images ofdifferent resolution for agricultural fields l Sandholt & H. Skriver

367

Multiscaling in lineament pattern: Mapping between images ofdifferent resolution LVasiliev

375

9 Climate change and interaction with landuse Towards operational mapping ofsolar radiation from Meteosat images CRigollier & L Wald

385

Remote sensing of land surface fluxes for updating numerical weather predictions Z Su, M Menenti, H. Pelgrum, B.J.J. M Van den Hurk & W.G. M Bastiaanssen

393

Application ofspace techniques to derive energy fluxes for water management in (semi) arid zones G.Somma, CJ.de Zeeuw, ZSu & MMenenti

403

10 Sustainable development in developing countries Present status ofgeographic information system and remote sensing applications in Pakistan and their future prospects LAhmad & T.N.Qazi

413

Study ofsoil salinity in the Ardakan area, Iran, based upon field observations and remote sensing S. K.Alavi Panah, R.Goossens & M De Dapper

419

Operational tools for monitoring regional scale landcover change in Sahelian Africa E. Csaplovics & S. Hess

427

11 Monitoring environmental impact Operational remote sensing in support offorest fire monitoring: Recent experiences from Nicaragua LD.Downey, S.P.Fiasse, AJ.de Dixmude, P.Navarro, R.Aivarez, F.Uriarte, AAitamirano, ARamos, ZZuniga & l.Humphrey

433

Integrated monitoring and management system of lignite opencast mines using multiple remote sensing data and GIS CGlaesser,J.Birger & B.Herrmann

439

Optical remote sensing in support ofeutrophication monitoring in Belgian waters K.G. Ruddick, F. Ovidio, A Vasilkov, CLancelot, V. Rousseau & M Rijkeboer

445

The operational remote sensing ofcoal in China Tan Yongjie

453

Appraisal ofirrigation system performance in saline irrigated command using SRS and GIS S.K.Ambast, QP.Singh, N.K.Tyagi, MMenenti, G.J.Roerink & W.G.MBastiaanssen

457

IX

Meditteranean forest fire risk monitoring using NOAA AVHRR MT. Slater & R.A Vaughan

463

List of Participants

471

Author index

479

Colour plates

481

X

Operational Remote Sensing for Sustainable Development, Nieuwenhuis, Vaughan & Molenaar (ads) © 1999BalkBma, Rotterdam, ISBN9058090299

Preface

It is eleven years since EARSeL last held its Annual Symposium and General Assembly in the

Netherlands. In 1987, this was held in Noordwijkerhout. In 1998 we met in Enschede, the home of the International Institute for Aerospace Survey and Earth Sciences- known throughout the world as the ITC. Indeed the ITC acted as generous hosts and provided the use oftheir splendid new facilities. One departure from tradition was that we were joined by the Netherlands Society for Earth Observation and Geo-Infonnatics (NSEOG), and a wonderful job they did in the local organisation of this highly successful meeting, not least in arranging the exceptionally hot weather! We are extremely grateful to the NSEOG and the ITC, as well as to our usual sponsors ESA, the Council of Europe and the European Commission, for their support and encouragement in making this another memorable meeting. Sixty eight papers were chosen for presentation from the record number ofhigh-standard abstracts submitted, necessitating many parallel sessions, covering most aspects of the uses of remote sensing. We welcomed visitors from several Asian countries, the Middle East and North America, as well as from almost all the countries of Europe who made up the 175 or so participants. A commercial session was held in parallel with the symposium, which was followed by two one-day Workshops on 'Remote sensing and GIS fornon-renewable resources in developing countries' and 'Land use/land cover change: Methods and applications', organised jointly by the ITC and NSEOG in co-operation with Working Groups of Commission VII of the ISPRS. These Workshops attracted internationally­ known experts and were well attended. The papers presented will be published separately. Central to our meetings is the General Assembly, the business meeting of the Association, which is the opportunity for members to hear about the activities of the Association and its Special Interest Groups, and to question the Office Bearers. Also in the background were held meetings of the Bureau and the Council. The latter consists of the National Representatives of all member countries, but recently we have been inviting the Chairmen of National Remote Sensing Societies to attend. This year seven Societies were represented, demonstrating the good, complementary, relationship which now exists between EARSeL and the European Remote Sensing Community. As usual, these proceedings contain the texts of the papers presented during the Symposium, with no distinction being made between oral and poster presentations, organised according to the themes of the various sessions. Although the papers were submitted in camera-ready fonnat, some editing was necessary in a few cases, and we are extremely grateful for all the hard work and devotion of Madeleine Godefroy in assembling them into the by-now expected attractive fonnat. This is but one of the many duties so selflessly undertaken by Madeleine throughout the year, not least in the coordination of all the different aspects which are necessary to make our Annual Symposium so

XI

successful. The scientific organisation and local arrangements were carried out immaculately by Gerard Nieuwenhuis (Chainnan of the NSEOO) and Lucas Janssen, and our gratitude should be recorded to them and their band of willing helpers.

Robin Vaughan

EARSeL Chainnan

XII

& S.K.Subramanian D.P.RaoD.P.Rao & S.K.Subramanian

Operational Remote Sensing for Sustainable Development, Nieuwenhuis, Vsughan & Molenaar (eds) © 1999 Ba/kema, Rottetrlam, ISBN 90 5809 029 9

Remote sensing and GIS for sustainable development An overview D.P.Rao & S.K.Subramanian National Remote Sensing AgeiiC)I Department of Space, Hyderabad,lndia

INTRODUCTION

The evolution of mankind and the exploitation of natural resources are highly correlated. Human beings are the single largest force creating imbalances in the ecological systems of the earth in the name of development. The regard, so far, for the carrying capacity of the land, resilience of the ecosystem or ecologically safe development has been negligible. Fortunately, changes in the level of population during the last century have compelled the planners, world-wide, to realise that our natural resources are not going to be there forever and unless population growth is checked, we are moving fast from an era ofplenty to one of scarcity. Another thought currently receiving attention is conservation and development of natural resources themselves so that they could be used on a sustainable basis. Resource sustainability means that we have not only sufficient resources for all our requirements but also leave behind enough for our children and children of the children as their ecological heritage. Realising the seriousness of the problem at global level, the governments of different countries have agreed on a common commitment to work towards sustainable development at the Earth Summit of 1992 held in Rio de Janeiro, Brazil. Agenda 21, a document outlining a comprehensive action plan for the 1990s and the 21" century is the principal outcome of the Rio Conference. Agenda 21 elaborates strategies and integrated programme measures to halt and reverse the effects of environmental degradation, and to promote eco-friendly sustainable development in all countries. The following five steps embrace the Agenda 21: • Strengthen an understanding of the interacting physical, chemical, biological and socio-economic systems that regulate the human environment, • Transfer of energy-technology - and consumer-driven economic system into one that is more environmentally benign, • Stabilise population, • Re-examine societal goals with greater emphasis given to life and sustainable human development, • Reduce poverty everywhere. SUSTAINABLE DEVELOPMENT

The developmental activities of the backward regions in India had been mostly based on the criterion of economic growth. While there is no option but to produce more food and improve necessary socio­ economic conditions, it is also important to recognise that today's economic progress should not be at the expense of tomorrow's developmental prospects. Hence, the resources based development plan is a must for sustainable development. The effective use of space based remote sensing data and a suitable blend with socio-economic data helps in achieving not only a local specific prescription to achieve sustainable development of a region, but also in monitoring the impact of developmental activities taken up under various developmental schemes, especially in enhancing the rural economy. XV

By the year 2020, the Earth's population may reach 8 billion of which the developing countries alone represent 83%. So there is need to increase food production from the present 1800 million tonnes to 3000 million tonnes. The present rate of urban and industrial development also eats away the productive fann land and so the increase in production will be restricted to less fann level when compared to the present day availability. Therefore, there is need to identify sustainable techniques which should address the need in

tenns of quantity, quality and cost of satisfying market requirements for food, fuel and fodder for both

humans and animals respectively. In addition, the areas for such activities have to be earmarked. For this purpose, it is important to study transforming wastelands and marginal lands into productive land. Such lands can be identified by using space technology and the suitability of crop/pasture/fuel wood can be identified by incorporating various remote sensing methods.

Role ofRemote Sensing and GIS For the formulation of any sustainable development strategy, a clear understanding of the existing development strategies, their problems and viable alternatives that are environmentally sound is a pre­ requisite. It could be achieved by the judicious use of timely and reliable scientific and technological input for understanding the mutual interdependency of the resources, continuous monitoring and updating of related vital parameters, preparation of exhaustive databases and development of cost effective, environmentally friendly action plans and locally-specific prescriptions for active implementation. Space technology, especially Earth observation technology including remote sensing of natural resources and environment, as well as meteorological and climatic observations of the surface and atmosphere. Communications, broadcasting, navigation, search-and-rescue satellites and other space systems also contribute to sustainable development.

The Management ofNatural Resources and Environment Since the beginning of the space age, remarkable progress has been made in utilising remote sensing data, to describe, study, monitor and model the Earth's surface and interior. From the observations of catastrophic phenomena like volcanic eruptions and floods using Landsat MSS data in the early Seventies, considerable progress has been made in deriving the information on natural resources and environment from spacebome multispectral data. Beginning with the pilot projects on mapping individual natural resources and environmental parameters at regional level using Landsat MSS data, improvements in sensor technology, especially in the spatial, spectral, radiometric and temporal resolution have enabled the scientific community to operationalise the methodology. Geological. geomorphological, soil resources, land use/land cover, urban sprawl, forest cover and surface water mapping at different scales, identification of mineral and ground water potential zones, generation of derivative maps like land capability, land irrigability, etc. from soil resources maps and generation of input for forest management working plan, command area monitoring, crop acreage and production estimates, and the identification of potential fishing zones in the oceans, and monitoring of navigational channels, have been operationalised. Amongst the environmental parameters, waterlogging and subsequent salinization and alkalisation, soil erosion by water and wind, forest fires, floods and drought have been studied and monitored at operational level.

In addition to mapping and monitoring the natural resources and environmental parameters, attempts have also been made in India, for the first time, to integrate the information on various natural resources derived from remote sensing data, with the socio-economic and other ancillary data in a GIS environment to generate a locally specific action plan for sustainable development (Rao, 1993). Further, efforts have also been made to develop models for various environmental processes for making future projections that would enable planners taking up appropriate preventive an,dlcurative measures.

XVI

Integrated Mission for Sustainable Development

Having realised the potential of remote sensing data for providing the infonnation on various natural resources in a timely and cost-effective manner, India launched a unique remote sensing application project, namely 'Integrated Mission for Sustainable Development (IMSD) in 1992 covering about 84.00 m ha and spread over in 175 districts. The project aims at generating thematic maps on various natural resources like soils, ground water, surface water, land use/land cover/forest cover at 1:50,000 scale from the Indian Remote Sensing Satellite (IRS-1) linear Imaging Self Scanning Sensor (LISS-11) data and integrating them in a GIS environment to generate a locally specific action plan on a watershed basis for sustainable development. A host of activities, namely appropriate soil conservation measures, afforestation, gully plugging and construction of water harvesting structures, horticultural plantations, pisciculture, bee keeping, etc., recommended in the action plan have been implemented in part of the area. The initial results of the implementation have been quite encouraging. There has been a significant rise in the ground water table in the downstream of the watershed due to the construction of check dams I percolation tanks. It has helped, to a certain extent, in providing drinking water to rural masses. Concurrently, there has been a substantial improvement in the vegetation density and condition which has been monitored using temporal spacebome multispectral data. Further, improvement in soil conditions due to the adoption of various soil conservation measures is yet another dividend ofthis programme apart from generating rural employment. The locally specific action plan recommended under this project is based on certain assumptions. In case these assumptions do not hold good due to unforeseen changes in the climatic conditions and lack of expected co-operation from the people, the anticipated results may not be achieved. Geosphere-Biosphere Studies

The International Geosphere and Biosphere Programme (IGBP) aims at describing and understanding the interactive physical, chemical and biological processes that regulate the total Earth system, the changes that are occurring in this system and the manner in which they are influenced by anthropogenic activities. Realising the need to study the Earth as biosphere-related projects, namely study of atmospheric minor constituents and aerosols, land-air-ocean interactions, climate modelling has been carried out under the umbrella of ISRO-GBP programme in the Indian sub-continent. The continuous availability of data from our own remote sensing satellites with multiple resolution has provided the required support. The results of these studies have been presented in various fora. Communication, Meteorology and Navigation

Being cost and time effective, the satellite based communications system helps in improving the literacy of rural masses. Besides, space technology provides valuable infonnation on meteorology which is very useful in weather forecasting and disaster management. Further, it helps in search-and-rescue operations. The Indian National Satellite System (INSAT)- a multi-purpose satellite has been catering for the requirements of communication, meteorology, TV and radio broadcasting. Satellite-based navigation is yet another area wherein space technology has contributed significantly. The NAVSTAR Global Positioning System (GPS) of the USA and the GLONASS system of the Russian Federation are currently being used for navigation. The positional accuracy of about lOO m, achievable from the NAVSTAR system by non-US users, is not adequate enough for many development applications like off-shore oil exploration, hydrographic surveys, harbour approach and landing aircraft, etc. The positional accuracy could be improved using the differential GPS concept.

XVII

CONCLUSIONS Sustainability has acquired the global dimension in view of over-exploitation of natural resources to meet the growing demand ofthe ever-increasing population. Realising its importance, sustainable living has been the focal point ofAgenda 21 of the Rio de Janeiro Conference of the United Nations. Optimal utilisation of natural resources on a sustainable basis seems feasible with the integration of information on various natural resources and socio-economic conditions and subsequent generation of an action plan for their development through a holistic approach. Remote sensing and geographic information systems play a crucial role in this endeavour. Remote sensing data from IRS- 1C/1D satellite with improved spatial (5.8 m from PAN) and spectral resolution (LISS-lll) will help in refining the action plan at micro level. The planned launch of the IRS series ofsatellites, namely IRS-P4, PS, P6 and P7 from India will further enhance our capability to look at land, oceans and at the atmosphere in an integrated manner will strengthen the ongoing geosphere ­ biosphere programme and will ultimately lead to a better understanding ofthe Planet Earth as a system. REFERENCES

Rao, D.P., 1993 : Remote Sensing based integrated stw:ly for sustainable development at micro level. Presented at the National Seminar on Environment and Forest, New Delhi, 2 April1993.

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Operational Remote Sensing for Sustainable Development, Nieuwenhuis, ~ughan & Molenaar (eds) © 1999 Balkema, Rotterdam, ISBN 90 5809 029 9

Remote sensing- A donor's view C.Smit Netherlands Ministry ofForeign Affairs, The Hague, Netherlands

ABSTRACT: The author, representing the General Directorate for International Co-operation of the Netherlands Ministry of Foreign Affairs (DGIS), explains the considerations that guide the priorities of such agencies in determining their policy for the allocation of funding in the most effective and judicious manner. It is generally admitted that the technology gap between the western world and developing countries is constantly widening and donor organisations are under pressure to make efforts to ensure that the benefits ofadvancing technology are more equally shared. Such organisations, however, have learned to be cautious in promoting the introduction of advanced technologies, if certain basic preconditions are not fulfilled for a particular type technology to function as expected. Bearing this in mind, the author gives an overview of the specific efforts DGIS has supported in the field of Remote Sensing Technology and what lessons can be drawn from these exercises.

On the occasion if this symposium, the organizing committee ofhave thought it expedient to invite a donor organisation to share a few thoughts with you on the subject ofOperational Remote Sensing for Sustainable Development. I am representing here the Netherlands Ministry of Foreign affairs and more in particular the General Directorate for International Co-operation, or DGIS, which is charged with matters related to official development assistance. The question which I asked myself upon receiving the invitation for this symposium is: why would technical institutions like the 'European Association of Remote Sensing Laboratories' and the 'Netherlands Society for Earth Observation and Geo-informatics' be interested in the viewpoint of a donor on Remote Sensing and can in fact a viewpoint be expected from such a donor organisation? Donors like DGIS, by nature, are organisations that deal with policies. Generally speaking, their task is to define objectives and to find ways and means to disburse funds that have been earmarked in the national budget for development Co-operation in an effective, efficient and balanced way. Since the funds available are obviously limited and the options for spending are virtually endless it is imperative for donors to develop policies. Policies that guide these organisations in setting priorities and assist them in making choices at those junctures where various different approaches could be followed to achieve a certain set of objectives. It can therefore be quite interesting at seminars and symposia that deal with a particular policy issue to have donor representatives explain their point of view and defend the approaches that they have chosen. The question I posed just now is: are we, by discussing Operational Remote Sensing, in fact dealing with such a policy issue? My first inclination would be to say: no, Remote Sensing is a tool and not a policy issue. Is it at all possible and practical to develop a policy with respect to one particular tool? Admittedly, Remote Sensing is a versatile, costly and by no means simple tool, that requires quite some investigation before it is used. But still, the basic consideration is in fact whether or not to use this tool in a particular situation. That decision in turn may be very dependent on local circumstances and does not seem to lend itself very well to general policy considerations. The issue becomes more interesting I think when we take a wider perspective and consider not only Remote Sensing but advanced, modem technology in general and ask ourselves: is there a role for donors like DGIS to assist in the development and promotion of such technology in partner countries? XIX

I think one can safely say that there is a fairly commonly shared feeling that the technology gap between western countries and the developing world is constantly widening. Donors are therefore frequently confronted with pressure from various sides to address this issue and make efforts which ensure that the benefits ofadvancing technology are more equally shared among the western and the developing world. Donors on the other hand, although they are notoriously slow learners, do have a certain amount of experience which makes them to be cautious on this point. Many are the examples of not even tremendously sophisticated technology that has been introduced in the framework ofvarious development exercises which after a certain period oftime, or even no time at all, was found to be of little use or no use. Almost as many are the reports of review and evaluation missions that told us that we, donors, had overlooked or failed to verify some basic preconditions whose fulfilment was essential for this or that particular type technology to function as expected. Very often thereby, it turned out that it had been specifically either the institutional or the human factor, or both, that had not been given adequate attention in the preparation stages. Experience shows that most modem technology can be made to work even under third world conditions. The more crucial question however is: can people and organisations be made to work with this technology under those same circumstances? Realistically speaking, in the realm of development co-operation there is no other practical way than trying things out and learning in the process. On the occasion of this symposium it would therefore seem useful to examine what specific efforts DGIS has supported in the field of Remote Sensing Technology and what lessons can be drawn from these exercises. It is obvious that satellite and airborne data are used in many different development projects supported by DGIS. In most cases though Remote Sensing is strictly used as a tool and promotion of the technology as such does not in any sense form part of the project objectives. The current computerized database of projects that we have in The Hague unfortunately offers us no possibility of quickly scanning how frequently remotely sensed data are being used in projects and how well this technology has become established as an operational tool. To the extent that I have been able to verify in the context of this presentation, there are three concrete development activities sponsored by us that are specifically devoted to the introduction and promotion of Remote Sensing. The first of these is ARTEMIS, a project which is probably known by most of you present here. This project which is executed by FAO in Rome started in the second half of the Eighties. It went through a number of phases and extensions to come to a final close at the end of this current year. Its initial aim was to establish an operational satellite system to support agricultural production and desert locust monitoring and forecasting. For the most recent project phase though the emphasis has been shifted more towards food security and land-use planning. ARTEMIS is located in Rome and essentially provides remotely sensed weather and vegetation data to both the Rome based units like the Global Information and Early Warning System and the FAO Locust Group as well as to regional early warning centres in Africa An independent mission evaluated the results of this project during the early months of 1996. It found that the project objectives which dealt with the establishment of an operational system for acquiring, processing, archiving and distribution of satellite data products had on the whole been well accomplished. The one single objective however which did concern the user side rather than the technical supply side ofthe satellite data products was much less favourably commented upon. Here the mission observes that no market potential for the data had been established. Also, while ARTEMIS direct collaborators appear to be very familiar with the remotely sensed data products, the general level of awareness both within as well as outside FAO Headquarters could be improved. As for the sustainability of this project it can be noted that the operational cost of the data provision components of the project have now been fully absorbed into the FAO regular programme. When donor funding comes to an end however special training activities and further developmental activities aimed at widening the user base and creating effective demand for ARTEMIS products will no longer be possible. Both the mission as well as many FAO collaborators themselves consider such further developmental activities imperative if the project is to preserve its momentum and safeguard its achievements. After an exceptionally long period of 18 years of technology support. such a finding is not an encouraging message for a donor. The second DGIS-funded activity on Remote Sensing that I wanted to mention is in fact an off-shoot of the ARTEMIS programme. It is located in Harare, Zimbabwe and concentrates on the use of ARTEMIS XX

and other remote sensing data for food security and early warning purposes in the SADC, or Southern Africa region. Activities here started back in 1988 with Japanese funding. After a two-year interim phase DGIS took over as a donor from 1994 onwards until the anticipated closing date of the project at the end of June this year. The same mission which evaluated the ARTEMIS programme also reviewed this Regional Remote Sensing Project. The picture that emerged here is in fact not very dissimilar to that what had been found in Rome. Project activities have very much concentrated on the establishment ofthe data supply system both in terms of the necessary technical infrastructure as well training ofthe regional staff involved. The mission is again much more critical when it concerns the user side of the remotely sensed data products. Direct target groups of the project are the National Early Warning Units in the participating countries. With respect to these units the mission notes that they have received relatively little training, they are confronted with a high staff turnover, incorporation of remote sensing data in early warning reports is not what it might be and most importantly, these units often suffer from a diffuse institutional status which gives them little leverage on others to cooperate effectively. On the other hand it was observed that in the project region there is a growing base of potential users of Remote Sensing data products generated by the project other than the national food security units. As was the case with ARTEMIS in Rome however, relatively little effort has been made to come to a definition of various user needs and expansion of the user base. Also here it would seem that continued donor funding would be required to tap this potential in order to brighten the prospects for sustainability of project achievements. The third DGIS-supported Remote Sensing activity which I would like to mention briefly here is the Dutch National Programme on Remote Sensing, NRSP, which probably requires very little introduction to most of you. The programme is financially supported not only by DGIS but also a number of other ministries in the Netherlands. By means of a temporary stimulation programme NRSP seeks to secure the long-term integration of operational use of Remote Sensing in the user sectors of government and industry. BCRS, the Netherlands Remote Sensing Board, develops and manages a multi-year programme of activities sponsored under NRSP. Proposals for such activities are submitted from the various user sectors, one of which is development Co-operation. The programme maintains a number of criteria against which proposals are screened before financing is approved. As for the category of proposals under Development Co-operation two special criteria have been put forward. One is that there should be involvement of key persons or institutions from the recipient country. The second criterion is that an assessment should be made of future usage of Remote Sensing applications. Last year DGIS organised a mid-term review of thirteen BCRS-sponsored projects which had been classified as development Co-operation. This review study has been carried out in the Netherlands mainly to find out whether the selected projects did in fact meet the requirements set and to what extent project activities had become embedded in local structures in the recipient countries. Given the nature of the NRSP it is not surprising that the review found a fairly strong supply-driven character in the selected projects. It was disappointing to note though that in some instances no local partner at all had been identified and that only three out of the thirteen activities scored positive with respect to the assessment of future usage of Remote Sensing products. Moreover, similarly to what had been found in the FAO projects, involvement of local personnel often remains limited to specialised staff who in most cases were already to some extent familiar with Remote Sensing and its potential benefits before the activity was implemented. Now, with these experiences at hand, what kind ofconclusions can be drawn by the donor? First of all is has become clear that given a certain amount of external funding, the technology of Remote Sensing can be made to work in developing countries. Also it can be noted that both the interest in Remote Sensing products as well as the number ofpossible applications is growing. At the same time, however, one cannot escape the conclusion that despite long term involvement of donor interventions this interest still remains confined to a rather limited number of small groups in the countries concerned. Groups moreover who are primarily involved in the supply side rather than the user side ofremotely sensed data products. It would seem therefore that much remains to be done to create a more general awareness of Remote Sensing potentials and to tailor remote sensing products more accurately to the needs and capacities of potential user groups.

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Another observation ofthe evaluation exercises that I mentioned was the tremendous speed ofdevelopment in the sphere of digital data transfer and processing over the lifetime of the projects concerned. This repeatedly necessitated project staff to spend considerable effort and time to adjust systems, software and channels ofcommunication in order to keep up with technical innovations. From a donor's point of view, it becomes therefore increasingly questionable whether venturing into activities that constantly operate at the leading edge of technology is the right thing to do with scarce development funds. For a donor it would seem that as regards development initiatives to be supported, the time has now come to gradually transfer the point ofintervention from the supply side to the receiving end of the Remote Sensing data flow. This would be fully in line with a policy shift that is now taking place for example with respect to research activities that arc sponsored by DGIS. Until recently, funds budgeted for research were for the major part directly channelled to research institutions that qualified for assistance. Over the last few years, the emphasis has been changed so that funds are increasingly placed at the disposal of those who arc intended to benefit from the outputs of such research. There are strong indications that this will lead to a more user-focused output which in turn will enhance the efficiency and sustainability of development interventions. The same sustainability of development that this symposium will attempt to address in the coming days.

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D.P.Rao & S.K.Subramanian D.P.Rao & S.K.Subramanian D.P.Rao & S.K.Subramanian D.P.Rao & S.K.Subramanian

Operational Remote Sensing for Sustainable Development, Nieuwenhuis, Vsughan & Molenaar (eds) 4;.> 1999Ball 1999 Balkema, Rotterdam, ISBN 90 5809 029 9

Operational crop monitoring by remote sensing in Hungary G.Csomai, Cs.Wimhardt, Zs.Suba, P.Somogyi, G.Nador, L.Martinovich, L.Tikiisz & A.Kocsis

FOMI Remote Sensing Centre, Budapest, Hungary

ABSTRACT The Hungarian Agricultural Remote Sensing Program and primarily its final R+D segment, the National Crop Monitoring Project (1993-96) led to a concise methodology that could further be applied operationally. First the pre-operational substantial validation results are treated in the paper. The validation was retrospective; it covered a 6 county area of the total 19 in Hungary and also diverse weather conditions in a 5-year period (within 1991-96). Both the area assessment, processing Landsat and IRS-1C data and the novel Landsat/IRS + NOAA AVHRR based crop yield forecast methodology performed weU for the major crops (8) at county level. The second part deals with the overall evaluation of the first operational National Crop Monitoring Project in Hungary (I 997). A novel method that combines land use information with NOAA AVHRR time series for yield prediction is also introduced.

1 BACKGROUND Hungarian Space Office also contributed to the program. Since 1980, many consecutive projects have been carried out by the FOMI Remote Sensing Centre (FQMI RSC) which was the focal point in the implementation of HARSP. The final objective of the program was to introduce remote sensing in to the operational information system of agriculture in Hungary. The operational system should be able to monitor crops in the entire country, providing accurate, timely and reliable information on the area of the major crops, their development and problems (focusing on drought assessment), plus providing reliable yield forecast and final yield estimates. These data should be available at the country as well as the county (19) levels. The main users of the information will include, primarily the Ministry of Agriculture, the grain processing and trading companies and associations, the farmers and their different organisations, associations. Beyond the technical, scientific problems to be solved, there is still much to be done in the regulation and organisation of the system.

In the past decades, up to 1990, there were only 1300 units, huge co-operative or state farms that provided 88-95 % of the crop production in Hungary. These farms were obliged to submit data on their crop areas, crop development and yield forecast plus the final production. The whole system was legally established, fairly concise and did not change from year to year; therefore it was claimed to be accurate and timely. As soon as economic and structural changes took place in Hungary, the former crop information system became gradually inadequate. Following land privatisation, there have been dramatic changes in the holdings and parcel sizes, the number of farm owners or operators. agricultural technology and investments. In this remarkable transition period, the need for an efficient information system became even more imperative. The Hungarian Agricultural Remote Sensing Program (HARSP) was launched in 1980 as a priority R+D program, supported by the National Committee for Technological Development (NCTD) and the Ministry of Agriculture (MoA). The

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2. THE TWO MAIN PERIODS OF THE HARSP (HUNGARIAN AGRICULTURAL REMOTE SENSING PROGRAM) The main results of the 1980-96 R+D program can

be divided to two major periods:

• the development of the baseline crop area mapping and area assessment methods plus the yield models' creation and experiments {1980-90) and • the final accomplishment of the methodology to prepare and validate them for operational use (1993-96). At the earliest stage the necessary image processing and analysis system had to be developed in house in a rather isolated way. There were validation studies for 1-3 counties, up to 17000 km1 area. These preliminary results were good for the assessment of major crops. In the late 1980s, efforts were made to join to the MARS programme of the EU and also harmonisation became important in our approach.

2.1. HARSP'93-96: towards a remote sensing based crop monitoring system In the second period of HARSP a substantial, new R+D project (National Crop Monitoring Project, NCMP, 1993-96) was carried out with the objective of improving and stabilising both area estimation and crop development monitoring leading yield forecast models to a stage that can be used routinely and later operationally for the whole country. The first step in NCMP was to upgrade the computing infrastructure. The main results of the methods validation in NCMP can be grouped as foUows. 2.2. Crop survey, area assessment and their pre­ operational validation

maps, which are necessary to the crop development monitoring models. As a result of the major final validation survey in NCMP (1993-96) it was clearly found that the application and results of digital image analysis

compares weU with the data of the Central Statistical

Office, Hungary (CSOH) for a 5 years, 6 counties data set (Figs.l.a.b.). The strong relationship in the Landsat TM derived (FOMI RSC) and CSOH data for the major crops is promising to include further applications of satellite data in the inventories. 2. 3. Crop monitoring and yield estimation The most promising results of the NCMP are those related to crop monitoring and yield forecast models. The models were developed by FOMI RSC. They integrated NOAA AVHRR and Landsat or other high-resolution satellite data. This approach essentially combines the benefits of both data sources: the temporal resolution through NOAA AVHRR and spatial resolution by Landsat TM or other high resolution images (e.g. IRS-lC, SPOT). This approach requires fairly good classification for the performance with the high-resolution images. With the adaptation of a linear unrnixing model (Puyou Lascassies et. al., 1994) to NOAA AVHRR series and Landsat TM, fairly good results were achieved for the two major crops -wheat and maize­ for the same study area and period. The first results concerning the drought indication within the monitoring are good. The county wheat and maize yields, predicted by the model compared favourably to the official data (Figs.2.a.b.). Both the crop areas and the major crops development and yields were estimated by remote sensing methods. This validation provided a firm basis for the first operative crop monitoring campaign in 1997. 3. OPERATIONAL CROP AREA ASSESSMENT AND YIELD FORECAST IN 1997

The method that had been developed by FOMI RSC, used Landsat data and applied digital image analysis for the crop identification and area estimation (Csornai et al., 1983). This approach gradually expanded to 3 counties areas by 1990 (Csornai et. al., 1990). It was found that the provision of really more accurate county level data than those that had been provided by the traditional non-remote sensing systems in Hungary, was only viable through advanced digital image analysis based crop area assessment. This approach also provides reliable crop

The thorough previous validation created a firm basis to move forward an operational campaign in 1997. The crop data-reporting calendar was set by the customer, the Ministry of Agriculture. It consisted of five dates from June 30 to October 1. The area covered directly was a characteristic subsample (6) of aU the counties (I9), so that 40 % of the total cropland in Hungary was directly monitored. Beyond the counties level crop area and predicted yield data these had to be expanded to the entire area of Hungary. This expansion used a subregional 90

Filurcl.a. The satellite data coverage was incomplete for lhe9e years aDd COIDities (miaing year: 1994). The area estimation for winter wbeat shows a strong relationship between the traditional (questiOODaire) method Uld the remote sensina one. The data appear remarkably earlier from the remote sensina sy11crn.

Fiprel.a. The satellite data coverage was incomplete for lhe9e years Uld counties (llliaint! year: 1994). The wbeat yields am be predicted by remote sensina prior ID the harvest These years compriae good and extremely bad ones as well. The missing data are the same as in Fisure I.a.

Filurc I. b. The figures compare similarly ID those of wheat The reason for 90111eWbat weaker relationship is the pnctiee aDd statistics of maize for sila&e. Only a part of maize is sown originally for easila&e- Many times decisions are made if a-led for maize easilage alq the season. New methods Sllll!esl compc:osation ID this effect. The missing data are the same as in Filurcl.a.

Filurcl.b. The maize yielda can be predicted early prior ID the harvest The sample compriaes diverse years. The missing data are the same as in Filurc I. a.

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Figure 3. Crop maps for the 6 counties in Hungary derived from muhitemporal high-resolution satellite data (Landsat TM and IRS-lC LISS ill) from the early May-August period of 1997 (colour plate, seep. 484).

Figure 4. Winter wheat yield forecast for the 6 counties in Hungary using our developed Landsat!IRS + NOAA AVHRR model (colour plate, seep. 485).

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Figure 5.a.b. Crops area assessment (Fig.5.a.) and yield forecast (Figure 5.b.) in Hungary, 1997 by remote sensing (FOMI RSC) and by traditional methods (CSOH).

Figure 6.a.~ ln the S years period of 1991-96 (excluding 1994) the predicted county average yields (•) correlated vecy well with the final CSOH data. The predicted average yield to the entire countcy fitted even better (A).

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4 . ROBUST YIELD PREDICTION BY NOAA AVHRR SERIES

temporal correlation analysis plus a direct robust method (see 4.). The eight main crops monitored were winter wheat, winter and spring barley, maize, sugar beet, sunf1ower, alfalfa and maize to ensilage. These crops together represent 78-82 o/e of the entire Hungarian cropland. The crops area assessment was based on the multitemporal image analysis of Landsat TM and IRS-1C LISS m data from the early May-August period, to compensate for the cloudiness in 1997. Cloud cover was some 30% bigger than the average in the 1991-96 period. The comparison of the remote sensing results with CSOH data is obviously an indication only and the differences cannot, by any means be interpreted as an error of the remote sensing technology. The difference of crop areas estimates of FOMI RSC and the Central Statistical Office, Hungary (CSOH) ranged in the 0.8-3.7 % (Figure5.a.) for the entire cropland in Hungary. The county crop area differences occurred in the interval of 1.5-21 % depending on the crop and county. However the area weighted average difference was 4.08%. This partially can be explained by the main differences in definitions, that is the ownership based sampling of CSOH and the administrative, topographic boundary based total coverage of cropland by the satellite images (FOMI RSC}. The actual standard crop maps derived were also provided to MoA (Figure3.). The crop yield forecast was accomplished by the application of FOMI RSC developed model which combines high-resolution satellite (Landsat TM and IRS-1C LISS ill.} data and NOAA AVHRR time series. The reporting dates corresponded to those of the operative Production Forecast System of the Ministry of Agriculture. Both appeared prior to the beginning of harvest. The final official data are available after the harvest: by the end of August for wheat and barley and in December (January) for the rest. FOMI RSC provided yield estimates for the counties and expanded to Hungary. The yield data compared favourably with CSOH values, which appeared six weeks later (Figure5.b.}. The differences were less than I %for wheat and 4.5% for maize average yields in Hungary. The differences at county level averages are certainly bigger. Because of the method applied, yield spatial distribution maps could also be reported (Figurc4.) for the major crops.

The primary yield forecast model (see 3.) performed weD. There were two reasons to develop robust yield forecast model: • the need for a parallel, independent technique to control the primary yield forecast model extrapolation (see 3.) from the average yields of the directly monitored counties to the entire cropland in Hungary and • the need for a stand-alone method that uses only very basic land use information (e.g. CORINE Land Cover data base - Buttner et. al., 1995) beyond the NOAA A VHRR series and directly be applied to all the individual counties and also for national crop production forecast. The pre-processed and normalised NOAA AVHRR data set was temporally filtered . The average reflectance profile and the NDVI could be decomposed in time by a thorough spectral-temporal correlation analysis. This substantial analysis showed an extremely strong relationship between the predicted county yields by this decomposition method and the CSOH data (Figures 6.a.-d.). The county data set comprises a 5 years period in which the low and high ends of yields occurred. The model seems to be strong, independent from the year and area. Some hilly, mountainous counties or those that were covered very sparsely by the given crop had to be omitted from the analysis. Having the performance of this model by county (~,85-0,96) the country level yield prediction seems to be very reliable (rl = 0,93-0,99). These preliminary results suggest that a reliable yield prediction model can be set up.

5 CONCLUSION Both the validation of the developed remote sensing based crop area assessment and yield forecast methods plus the first operational monitoring and crop production forecast campaign (1997) in Hungary clearly demonstrated that these methods can be efficiently applied. Substantial background and investment is certainly needed. About 300 man/year was invested by FOMI RSC in the framework of the Hungarian Agricultural Remote Sensing Program (1980 to date). The first operational monitoring was designed very strictly by the Ministry of Agriculture, Hungary, according to its existing operational production forecast and monitoring system.

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Remote sensing could be very efficiently used for precise crop area estimation and provision of crop maps. The results suggest that the necessary classification perfonnance can be obtained in most cases, therefore the analysis could be cost effective. The investment to achieve this seems to be worthwhile. The new combined AVHRR and Landsat TM or IRS-1C LISS-III or SPOT based crop monitoring and yield prediction models and the approach applied perfonned properly and efficiently in a more counties' area application and also for the entire country. The second, the county level AVHRR based crop yield prediction model worked very well and seems to have a real potential on areas having quite different cropping patterns. After the first year, further assessment and a gradual extension of remote sensing into the infonnation system of the Ministry of Agriculture is under way. Together with the gradual expansion of the direct target area from 6 counties to the whole country more and even earlier reporting dates are planned. This system is supposed to operate in parallel to the existing dynamic system of MoA for monitoring area and crop development, plus yields of the most important crops in Hungary.

Digital Image Analysis, Proceedings of 5th Symp. /SSS Working Group Remote Sensing, Budapest, pp. 123-128. Csomai, G., dr. Dalia, 0 ., Farkasfaly, 1., Nidor, G., 1990, Crop Inventory Studies Using Landsat Data on Large Area in Hungary, Applications of Remote Sensing Agriculture, Butterworths, pp. 159-165. Puyou Lascassies P., Podaire A, Gay M.: Extracting Crop Radiometric Responses from Simulated Low and High Spatial Resolution Satellite Data Using a Linear Mixing Model: 1nt. Joum. of Remote Sensing, Vol. 15, no. 18, pp. 3767-3784, 1994. Biittner Gy., dr. Csat6 E., Maucha G.: The COR/NE Land Cover-Hungary Project, GISIUS'95 Central Europe, Budapest, Hungary, 12-16 June, 1995.

6. ACKNOWLEDGEMENT The whole HARSP and in particular the recent NCMP ( 1993-96) have been supported jointly by the National Committee for Technological Development and the Ministry of Agriculture, Hungary. Fonnerly, the Hungarian Academy of Sciences, since 1992 the Hungarian Space Organisation, have also given both financial and scientific support to the program. The major operational crop monitoring and production forecast program is supported by the Ministry of Agriculture. The Space Applications Institute of the EC Joint Research Centre (lspra) generously supported a natural vegetation monitoring study by supplying pr~processed NOAA data for 1991-95. REFERENCES Csomai, G., dr. Dalia, 0 ., Gothar, A., dr. Vamosi,l., 1983, Classification Method and Automated Result Testing Techniques for Differentiating Crop Types, Proceedings of Machine Processing of Remotely Sensed Data, West Lafayette, USA Csomai, G., dr. Dalia, 0., Farkasfaly, 1., dr. Vamosi, 1., Nidor, G., dr. Vamosi, 1., 1988, Regional

Vegetation Assessment Using Landsat Data and

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Operational Remote Sensing for Sustainable Development, Nieuwenhuis, Vaughan & Mo/enear (ads) © 1999Balkema, Rotterdam,ISBN9058090299

The use of remote sensing and GIS to detect gypsiferous soils in the lsmailia Province, Egypt R.Goossens University ofGent, Geography Department, Belgium

E.VanRanst

University of Gent, Department ofGeology and Soil Science, Belgium

Tharwat K.Ghabour & Mohamed El Badawi National Research Centre, Soil and Water Use Department, Cairo, Egypt

ABSTRACT: Some areas in the Ismailia Province (North Egypt) are clw'acterised by waterlogged and saline soils. They are mainly situated in the desert fringes, bordering the agricultural land. Some of these poorly drained soils are gypsiferous. These gypsiferous soils, occurring mainly in the areas situated between the Nile delta and the Suez depression were selected for agricultural reclamation. Because irrigated agriculture of gypsiferous soils is often plagued by quick dissolution of soil gypsum resulting in irregular subsidence of the land, caving in canal walls and corrosion of concrete structures. These soils can cause big damages to new public works. An appropriate inventory of the geographical extension of these soils is recommended. By aims of supervised image classification, it is possible to differentiate the gypsiferous soils from other soils by differences in surface reflectance. The image classification is done using Landsat MSS, Landsat TM and SPOT XS images. The classification of the Landsat TM image gives the best results. Reflectance values and differences ofthe gypsiferous soils are discussed in the paper. The image classification can be controlled by a DEM, since the gypsiferous soils are localised in micro depressions at the contact between the alluvial clay soils and the aeolian desert soils. It is shown that mapping of the soils containing gypsum can be done in a relatively fast and accurate way. The final output can easily be stored in a GIS for further practical applications. phase. The goal is to map and monitor soil salinity and water logging in the Ismailia Province in northern Egypt. In the study area are besides salt affected soils, gypsiferous soils present, in both wet and dry conditions. It is important that the salt affected soils and gypsiferous soils can be distinguished from each other. This paper is dealing with the research to distinguish both soil types based on remote sensing documents, and more specific Landsat TM data. The detection and mapping of soil salinity by aims of remote sensing is not frequently applied. Only some authors are dealing with this subject, but remote sensing is becoming more and more a tool to map saline and waterlogged soils. Seghal et al. (1988) were using Landsat MSS data for the mapping of salt affected soils in the frame of the reconnaissance soil map of India. Shanna et al. (1988) and Rao et al. (1991) used Landsat MSS and TM images respectively for a more detailed mapping in a limited area. Dwivedi (1992) applied Landsat MSS and TM data for mapping and monitoring salt affected soils in the lndo-Gangetic plain.

1. INTRODUCTION For a long time arid lands have been considered agriculturally and economically unimportant. A large segment of the human population, nevertheless, depends on the meagre resources arid lands provide. Exploitation of these lands by the ever-increasing population continuously degrades the soil, resulting in markedly decreased productivity. The degradation is enhanced by natural disasters and, as a result, large areas are becoming desertified, thus further decreasing the overall available resources for sustenance and economic development. This study is a part of the Belgian OSTC (Office for Scientific, Technical and Cultural Affairs) TELSAT Ill project on soil salinity monitoring by remote sensing and GIS. In a previous phase, the possibilities were examined to map and to monitor soil salinity and water logging using remote sensing and GIS data. The outcome of this research phase was promising and therefore the project was converted into an application

97

The combination of remote sensing with GIS is very promising, especially for the monitoring of

soil salinity. Soil salinity is very often interconnected with soil drainage conditions. GIS is an excellent tool to simulate waterlogging, since this phenomenon is depending on the topographical position in the landscape. GIS can be used to monitor the extension or the disappearance of saline and waterlogged soils (Goossens et al. 1993). The topic of mapping gypsiferous soil with remote sensing data remains until now practically undiscussed. Anyhow, it is important to make a difference between gypsiferous soils and saline soils. On conventional false colour composites (FCC), the spectral differences between the two soil types are not evident. In this study, more research is done using especially the thermal spectral capacities of the Landsat TM images. As it will be shown, this part of the spectrum allows to discriminate between gypsiferous and saline soils.

Figure 1. Location ofthe study area

2. ENVIRONMENTAL SETIINGS

The higher situated land is generally spoken located between 4 and 40 m above sea level. The soils are developed in desert loam and sand deposits, lying on an alluvial sand-clay complex. The aeolian sands are blown out till the level of the first alluvial clay layer was reached. In the surroundings, a dune landscape is developed. Because the clay layer is impermeable; the lowest parts in the landscape are waterlogged and salinity is develops in a natural way; the so called sebkha's orchotts. Since the population pressure is constantJy increasing in Egypt, the needs for arable land are increasing as well. The Egyptian government tries to reclaim the desert, in a similar way as was done in the western part of the Nile delta desert fringes. Efforts are made to reclaim the area between the

The lsmailia Province is located in the Northeast of Egypt, between the Nile delta and the Suez canal (Figure 1). Figure 2 shows a transect through the topography of the Ismailia area. The lower parts of the study area are situated at an altitude of -1 and 2 m under and above sea level. The soils of these low lands are clayey and used for agriculture. These lands are located along the Suez canal, in the Suez depression and along the Ismailia Channel. These clay soils are characterized by a micro topography, including slight depressions. The waterlogged and saline soils, as well as the gypsiferous soils are located in these depressions.

Figure 2. Transect through the topography ofthe Ismailia area 1 = sebkha, 2 = aeolian sand, 3 = clay layer, 4 =alluvial sand, 5 = outcropping clay, 6 = open water and waterlogged soils bordered with reed vegetation

98

eastern Nile delta and the Suez canal. Therefore, they are levelling the dune topography and they are installing irrigation and drainage channels. Pivot systems are installed to provide the irrigation water from the geological groundwater table. The excess of irrigation water will penetrate into the soil and will be stopped by the sand-clay complex. Therefore a danger exists that the waterlogged areas will extend in the near future, especially in the lower parts ofthe landscape.

In a first step the OIF is calculated. The multiband statistics for the seven spectral bands of the masked Landsat TM image show that show that the Landsat TMI 0 , TM3°, TM5° and TM7° have the highest de-correlation, and thus offer the best possibilities for discrimination. However, it is noticed that in the thermal band (TM6°) the gypsiferous soils have an intermediate reflection between soils with a salt crust and waterlogged saline soils. This indicates that this band is useful for the distinction between gypsiferous and saline soils. Therefore, two image data sets are created to investigate the separability of saline and gypsiferous soils in a feature space: - one data set composed of the spectral bands TMI, TM3, TM5 and TM7 based upon the results ofthe OIF calculation - one data set including the thermal band TM6 and thus composed of the spectral bands TM3, TM5, TM6 and TM7. In this case, the TMI (blue light) is not used because of its strong correlation with TM3 and because the blue light is strongly scattered in the atmosphere. The image sampling of the different soil types, however, is done on a conventional False Colour Composite using the spectral bands TM2, TM3 andTM4.

3. MATERIALS different and images satellite Different cartographic materials were put at our disposal. The satellite data are available for different periods and sensors, like Landsat MSS (1984), SPOT XS (1992) and Landsat TM (1994). The cartographic materials consist out of: - topographical maps dating from 1947 at a scale of 1/25,000 with a contour line interval of 0 .5m, and topographical maps dating from 1985 at a scale of 1/50,000 with a contour line interval of 2.5m. - a geological map at a scale of 1/500,000 - a soil map at a scale of 1/500,000 The maps were digitized and integrated in a GIS (ILWIS: Integrated Land and Watershed Information System, Enschede, The Netherlands). Especially the topographical maps of 1947 are very useful because of their detailed information about topography with an interval of 0.5 m. This means that the interpolation could be done every 0. 1 m. This resulted in a Digital Elevation Model (DEM) which represents the micro relief in a detailed way. This is important to assess waterlogging and soil salinity (Goossens R. et al, 1993) The image processing, digitizing and GIS­ functions were done with ILWIS software. (ILWIS, 1992)

5. IMAGE SAMPLING The definition of the different classes is based on field observations. The sampling sites on the images correspond with the locations where observations are made on the terrain. The classes are sampled on a conventional false colour composite. The defined classes are listed in table I. The feature space for the sampling set of both band combinations clearly show different clusters corresponding to different soil types. The feature space between bands TM3 and TM5 indicates the presence of a so-called "soil line". However, the term "soil line" is used conventionally in the case of a feature space between the red and the near infrared band. The line indicated in this feature space is formed by the desert soils with different stages of destruction of the desert crust (nr.6, nr.7, nr.l 0 and nr.ll ). Salt crusts (nr.S), wet and dry gypsiferous soils (nr.3 and nr.4) are situated below the soil line. These clusters are clearly isolated from each other and from the other soil clusters. Only the cluster representing the salt crust and those representing the classes desert (nr.6, nr.7, nr.IO and nr.ll) are located side by side. The classes, water (nr.2) and waterlogged! (nr.1), are located in the lower left part of the feature space, but are well distinguishable. The vegetation is not interfering with the different soil types.

4. 1MAGE INTERPRETATION

If the possibility exists that gypsiferous and saline soils can be differentiated on satellite data, it is obvious that the Landsat TM with its high ground resolution and its wide spectral range offers the best opportunities. Prior to the inventory of the gypsiferous soils and saline soils, a masking technique is applied to exclude all areas where vegetation occurs. Therefore, a vegetation index is calculated. If the resulting value is higher than 0, meaning that some photosynthetic active material is present, the area is masked and thus not taken into account. The suffix o following the number of the spectral band (e.g. TM2°) indicates that the corresponding band is masked for vegetation. 99

Table I - Class definitions for the image classification

CLASS NAME

CLASS

CLASS DESCRIPTION

NUMBER Water

2

Relatively deep water: mainly the Suez Canal, but also some extreme waterlogged areas. In the last case, the soils are also very saline. Shallow water: still some background reflection of the soil influencing the pixel values.

Waterlogged 1 Waterlogged2

12

Poorly drained soils with a shallow groundwater table in winter time; during summer time, when the groundwater table is lower salt crusts will be formed.

Gypsiferous soils I

3

Soils with gypsum in relatively dry soil conditions. The groundwatertable is situated between O.S and 1 m.

Gypsiferous soils2

4

Soils with gypsum (Poorly drained). The groundwater table is close to the surface (less than O.S m).

Salt crusts

s

Desert I

6

Sandy loam desert soils with an intact desert crust t

Desert2

10

Sandy loam desert soils with a slightly disturbed desert crust

Desert3

11

Sandy loam desert soils with a moderately disturbed desert crust

Desert4

7

Sandy loam desert soils with a completely destroyed desert crust

Vegetation

g

Including different types of vegetation, such as crops, orchards and desert shrubs :

Reed vegetation

9

Occurring exclusively on waterlogged and saline soils. This is possible because the absolute growth reduction for reed is situated around 14 to IS dS/m. It can thus be concluded that these waterlogged soils have a soil salinity less than the above mentioned EC-values.

Slightly poorly drained soils. The groundwater table is situated at a lower level than in the previous classes (lower than I m). The whole year through, salt crusts are present on the soil surface

t Places with an intact desert crust are characterized by a very high reflection. The more the desert crust is

destroyed, the more the reflection of the original material will dominate the overall reflection. Generally, this corresponds with a lower reflection. : Because different types of vegetation are sampled in one class, a high standard deviation in all bands is obtained. However, this will not influence the result ofthe classification because the cluster corresponding to vegetation is located in the feature space at remote distance from the clusters representing the different classes of soil. A more detailed sampling to include different types of vegetation is possible with the inclusion of the spectral band TM4. The feature space between the bands TM6 and TM7 gives a totally different image (Figure 3), indicating the effect of the thermal band. No 'soil line' is formed, but the clusters are located along a

parabola. The different stages of destruction of ihe desert crust are located at the top. This suggests that these four classes have more or less the same temperature. The classes, water (nr.2) and

100

Legend: see table I

Figure 3. 2D-feature space between TM band 6 and TM band 7 (colour plate, see p. 486).

Figure 4. Maximum likelihood classification based upon the combination TM3, TM5, TM6 and TM7 (colour plate, see p. 486).

101

waterlogged1 (nr.1), are located in the lower left corner of the feature space. Both classes are not warmed up at the moment of the image recording. The salt crusts (nr.S). the two classes of gypsiferous soils (nr.3 and nr.4) and the class waterlogged2 (nr.2) .-e located between the desert soils and the wettest aoils. It is remarkable to find the class waterlogged2 located in-between the salt crusts and the gypsiferous soils. This is importaDt because it can be deduced that the salt crusts warm up more rapidly than the gypsiferous soils. It is thus possible to separate as well these two soil typeJ using the band combination TM3, TMS, TM6 and TM7. Also the cluster representing the class salt crusts (nr.S) is clearly separated from the different classes "desert crust", making an overlap between these classes more unlikely.

7. CONCLUSIONS

It can be concluded that Landsat TM images offer possibilities for the detection of gypsiferous soils. To achieve this. it is necessary that a good field data set is available. Especially the location of the different soil types is important, because this is the base for the image sampling. The choice of the different bands seems to be a very importaDt factor. The accuracy of the results is strongly depending on the band combination to perform the image classification. Landsat TM bands 3, 5, 6 and 7 seem to offer the maximum possibilities. The thermal band (TM6) plays a key role in separating the saline soils from the gypsiferous soils. TM6 is also importaDt to separate the gypsiferous soils in dry and wet conditions respectively , as a result of the temperature difference.

6. IMAGE CLASSIDCATION Using the same training set, two maximum likelihood classification are made: one using the band combination TM1, TM3, TM5 and TM7 and one using the band combination TM3, TM5, TM6 and TM7. The resulting classified images for the band combination TM3, TMS, TM6 and TM7 is shown in figure 4. For most classes, both classifications are similar. The band combination TM3, TMS, TM6 and TM7 gives a confusion between the classes, water (nr.2) and waterloggedl (nr.l) and it is clearly visible in the Suez Canal where both classes occur what is surely a mis classification. This error does not occur when using the band combination TM1, TM3, TM5 and TM7. This band combination, however, resuhs in numerous pixels classified as salt C1Usts in the upper right corner of the image. In reality, this is not the case. The mis classification is due to the fact that the class salt crusts (nr.5) is located relatively close to the classes desert. Using the other combination (TM3, TMS, TM6 and TM7), the pixels are classified correct as being desert soils with a disturbed soil crust. Although the use of the combination TM3, TMS, TM6 and TM7 may cause a problem of mis classification of the water in the Suez canal, it offers the best results for the different soil types. The salt crusts are present in their correct proportion. For both classification. the result ofthe gypsiferous soils can be accepted and both results are comparable. It can thus be concluded that both combinations of spectral bands are suitable for the detection and classification of gypsiferous soils. In the case of salt crusts, the band combination TM3, TM5, TM6 and TM7 is preferred.

REFERENCES Dwivedi R.S. 1992. Monitoring and the study ofthe effects of image scale on delineation of salt­ affected soils in the Indo-Gangetic plains. International Journal of Remote Sensing, 13-1 pp. 1527-1536 Goossens R., De Dapper M., Gad A and Ghabour Th., 1993. A model for monitoring and prediction of soil salinity and waterlogging in the Ismailia area (Egypt), based on remote sensing and GIS. Proceedings of the International Symposium on 'Operationa/ization of remote sensing' vol 6 ITC Enschede, The Netherlands, pp.97-107 ILWIS. 1992. Ilwis 1.3 User's Manual ITC Enschede, The Netherlands Rao B.R.M., Dwivedi R.S., Venkataratnam L., Ravishankar T.• and Thammappa S.S.. 1991. Mapping the magnitude of sodicity in part the lndo-Gangetic plains Uttar Pradesh, northern India using Landsat TM data, International Journal of Remote Sensing, 11-3, pp. 419-425 Segal J.L., Saxena R.K. and Verma K.S. 1988. Soil resource inventory of India using image interpretation techniques, Remote Sensing is a tool for soil scientists, Proceedings of the 5-th symposium of the working group remote sensing ISSS, Budapest, Hungary. pp. 17-31 Sharma R.C. and Bbargava G.P. 1988. Landsat imagery for mapping saline soils and wet lands in north-west India, lntemational Journal of Remote Sensing, 9-1, pp. 39-44

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Operational Remote Sensing for Sustainable Development, Nieuwenhuis, ~ughan & Molenaar (eds) e 1999 Ba/ksma, Rottetdam, ISBN 90 5809 029 9

Crop yield estimation in the Hungarian meteorological service

A.

Merza, J. Kerenyi & A. Rirn6czi-Paal Hungarian Meteorological Service, Hungary

ABSTRACT: In this study we estimate county level yield in Hungary with a statistical method using surface meteorological data (average daily temperature, relative humidity, precipitation and cloudiness) from the meteorological station network and satellite data (surface temperature, normalized difference vegetation index) from NOAA AVHRR. The surface data and satellite data are provided by the Hungarian Meteorological Service and the measured county level yield are provided by the Hungarian Statistical Office. The trend function is determined with nitrogen and hauling power data in order to remove systematic yield increase. The effect ofweather is considered by multilinear regression. actiVIty. Recently, the Meteorological restarted producing yield estimations.

1 INTRODUCTION Yield estimation is important for harvest, storage, transport and working up planning and for the decision-makers to facilitate export-import and marketing planning. There are several different methods for yield estimation or forecasting. Simple methods often used by the statistical offices are for example field surveys or questionnaires (Oiofsson, 1994). Statistical models are more sophisticated, a more precise estimation can be carried out with them, but they cannot explain the causal relations (Palm, 1995). Dynamical models such as crop growth models take into account many parameters affecting crop growth and final yield. However, this is also their disadventages, since these parameters are not always determinable in large regions like counties. There are also combined methods which try to have the adventage of both dynamical and statistical models at the same time: they are relatively simple to handle, and they also explain the growth of the plants. All of these models can be used with remote sensed data (Hayes and Decker, 1996; Bouman, 1992) The yield estimation at the Hungarian Meteorolgical Service began developping in the 1970's, when sufficient amount of phenological and yield data was available (Varga-Haszonits, 1979). In the 1980's dynamical crop growth model was also applied to calculate final yield (Dunkel, 1984). At that time, the need for including remote sensing emerged, but the economic changes in Hungary and the reorganization of the Service delayed this

Service

2 DATA AND METHOD We used measured yield data provided by the Hungarian Statistical Office for the years 1971-1996. The meteorological data used in this study were on one hand taken from the surface meteorological station network (e.g. daily mean temperature, mean relative humidity, precipitation and cloudiness). On the other hand data from NOAA AVHRR were also available such as atmospherically corrected normalized difference vegetation index

Figure 1. Country-wide average amount of applied nitrogene fertilizer ( 1000 kg)

103

(Putsay et al. 1997} and surface temperature for the year 1996. The examined period is between 1971 and 1996. The plant is part of the soil-plant-atmosphere system, so the elements of these system can detennine its development During a longer period each of the three subsystems can change. The soil and the different plant hybrids change more slowly than the meteorological elements. But in examining longer periods, these changes must be included. This inclusion can be done with a trend function, which is essential since in Hungary and in other Eastern European countries the political changes occurred in paralel to agricultural reorganization. Authors used linear, second order, etc. functions. The structure of the agricultural production has changed, agricultural cooperatives and state farms were divided into smaller private parcels. Changes have occurred also in the applied agrotechnical methods. Figure 1. shows the applied nitrogen fertilizer in Hungary between 1971 and 1996. Obviously, decreased rate of applied nitrogen fertilizer has affected the production and the final crop yields. Figure 2. shows the maize and winter wheat yield. In order to find a parameter changing similarly to the yields, it is better to fit an analitical curve to determine the trend function. Since there is a relation between the applied nitrogen fertilizer and yield (as can be seen in Figure l. and Figure 2.) the other method to determine the trend function is the use of nitrogen fertilizer data (Thompson, 1969). We calculated the trend function with the latter method, but we also used data of machine hauling power for 1000 hectare. The determined trend function is the simple mean of nitrogen detennined and hauling power determined trend function.

Figure 2. Measured county-wide average wheat and maize yields ( 1000 kg)

104

The next step is the examination ofthe effect of the meteorological elements. This can be taken into account with the consideration of trend difference or trend ratio. We chose the second one. Then the trend ratios were regressed with various meteorological variables. This multilinear regression contained the monthly temperature, precipitation, global radiation and an aridity index, which was the difference of potential evapotranspiration and precipitation. The potential evapotranspiration was calculated after the foUowing equation (Bussay et al., 1993): 100-' = - -) · T · n PE ( 'JJXJ-f 100 - ' ) ·(1+ T) ·n PE .. (

zoo-1

21iJ

1fT :Z:.1°C 1fT< l°C

where PE is the potential evaporation (mm), f the relative humidity (1'/o), T the daily mean temperature (GC), n the number ofdays. The global radiation was calculated using the relative global radiation calculated on the basis of daily mean cloud coverage in tenth (PaaJ, 1987) 3 RESULTS

The calculations were made for maize and wheat. Maize requires much water for its growth. Its water consumption culminates 14 days before and after flowering, which is in July. Therefore maiz: yields and drought indices in July or August resulted in high correlation coefficients. The convenient soil moisture content in May is essential for good emergence. We examined three counties: Bekes, Hajdu­ Bihar and Gy6r-Moson-Sopron. In the case of Bekes and HajdU-Bihar counties, we calculated the multilinear regression on the basis ofthe precipitation in May, the global radiation in July and drought index in May, July and August. In the third county the global radiation in August and drought index in May and August were applied. The estimated yields i n Bekes are shown on Figure 3/a. The mean deviation is 9.2%. Wheat is sensible to precipitation in the beginning of the year (January, Febnwy, March), because the precipitation of these months essentiaUy determine the soil moisture in April, when wheat begins shooting and requires much water. The temperature is important in May and June, too hot weather is not favourable. The estimated yields in Gyor-Moson-Sopron can be seen on Figure 3/b., the mean deviation is 7.65%.

Figure 3a. Measured vs. estimated maize yields in Bekes county

Figure 3c. Measured vs. estimated potato yields in Gyor-Moson-Sopron county

Figure 3b. Measured vs. estimated wheat yields in Gyor-Moson Sopron county

The calculations were made also on potato and sugarbeet. The potato yield in Gyor-Moson-Sopron county was found to correlate with global radiation in July and drougt index in August and September, since the potato doesn't like too dry soil because its root is in the higher part of the soil. The results are on the Figure 3/c, the mean deviation is I 0.4%. The yield of sugarbeet in Hajdu-Bihar is on the Figure 3/d, the estimation was based on summer temperature (May and July) and global radiation in September. The mean deviation is 12%. Considering NDVI and surface temperature pictures from 1996, during the early spring NDVI showed dry conditions, which was mainly due to the unusually long winter and late start of the vegetative period. During summer, moisture conditions were

Figure 3d. Measured vs. estimated sugat beet yields in Hajdu-Bihar county more preferable in June and July, but in August severe rains resuted in yield losses. Wheat yield was reduced by the long and cold winter conditions.

4 SUMMARY The yield of wheat, maize, potato and sugarbeeat was analysed between 1971 and 1996. Statistical estimation was done based on meteorological data and NDVI and surface temperature maps was analysed to understand better the thermal and moisture contitions during the vegetation period. Since satellite images show an area! view of the landscape it is useful to applicate them with the surface data together.

105

REFERENCES Bournan, B. A M. 1992. Linking physical remote sensing models with crop growth simulation models, applied for sugar beet, In/. J. ofRemote Sens., 13:2565-2581 Bussay A, Varga-Haszonits Z. & Lambert K., 1993. Calculation of evaporative power of air on the basis of long series, Reports on scientific researches carried out in 1989, Hungarian Meteorological Service, 136-143 (In Hungarian) Dunkel Z., Hunkar M., & Zarbok Zs. 1984. Description of the development of maize using dynarnic-symulation plant-growth mode~ Idojimis, 91 :197-208 (In Hungarian) Hayes, M. J. & Decker, W. L. 1996. Using NOAA AVHRR data to estimate maize production in the United States Corn Belt, lnt. J. Remote Sens., 17:3189-3200 OlofSson, P. 0. 1994. Field surveys as a tool for crop yield forecasts, In: Proceedings of the Yield Forcasting Seminar, Villefranche-sur-mer, 24-27 October, Eurostat-JRC-DGVI-FAO, pp. 165­ 193 Pail, A 1987. Investigation of relationship between cloudiness and global radiation using METEOSAT Images, Pageoph, 125:109-120 Palm, R. 1995. Regression methods including the EUROSTAT AGROMET model and time trends, In: Dallemand and Vossen (eds), Proceedings of the Workshop for Central and Eastern Europe on Agrometeorological Models: Theory and Applications in the Mars Project, Ispra, Italy, 21-25 November 1994, pp.61-72., Publication EUR 16008 ofthe Office for Official Publications ofthe E. C. Luxembourg, 246 pp. Putsay M., Szenyan 1. & Kerenyi J. 1997. Atmospheric correction of NOAA/AVHRR visible and near-IR. channels in Hungary, Proceedings of 1997 Meteorological Satellite Data Users' Conference, Brussels, Belgium, 29th September-3rd October 1997, 503-501 Thompson, 1969. Weather and technology in the production of corn in the U.S. Corn Belt, Agronomy Journal, 61 :453-456 Varga-Haszonits Z. 1979. Estimation of winter wheat yield based on meteorological parameters, ldojdrils, 83:332-340 (In Hungarian)

106

Operational Remote Sensing for Sustainable Development, Nieuwenhuis, "'ughan & Molenaar (eds) e 1999 BallIVII liUJII 1 9.5.5 _ to 1996 .. Dacriptloa

Urbaa

or

Ara

IIICIUR

Type

Es,...­

Aru(Ha.)

1955 1955-1915

3.926.61 4.647.40 4.l4B5 2.078.89 2.847.50 10,110.58 668.25 17,742.92 28,521.74

..

Cum­ ud•e

hrlod

Urban Urban Urban Urban Urban Rwal

Wale< Bodic:l TOIIII Urban TOIIII Area

1975-19&3 1983-1989 1989-1996 1955- 1996

22.13

22.13

26.19 23.91

48.32 72.23 83.95 100.00

11 .72

16.05

Figure 1. The Grealer Dhaka Metropolitan City Urban expansion: Past, Present and FulllrC. Table 2(b). Projected urban expansion for Grealer Dhaka -------010 --·

- --- ---- Projec Projected

Aerial

Emat

·led Year

Urbaa Aftll(lla.)

.. or

Projected Urbaa

.........

Ara

Urbea TOIIIIUlt!M

1996­

2010

2010

7.984.35

22,001.22

24.00 77.14

the urbanization process. The Table 3 is giving a very clear indication of drastic reduction of the natural storage areas by the urbanization process over the time period. Thus the first urban development took place at the relatively higher terrace, elevation ranging between 10-12 AMSL. Within a range of 15 Km from the city center most of the high land is already built-up and occupied by man's activities (Islam, 1996). The second level of urban development took place in the relatively medium terrace elevation between 6 - 9 AMSL. Due to the high rate of population increase and shortage of higher land, recent and the third level of urban development is taking place on the low terrace, depression, backswamps having elevations between 2 - 5 AMSL. which is susceptible and vulnerable to riverine flooding and act as a natural storage area of urban storm water runoff for a longer period annually. This process started from the mid 80'ies and continuing without proper planning. Which is causing serious waterlogging problem in Dhaka City during the monsoon.

Cama·

ladY• ..

ar

Projected UrbM Ara

124.00

Metropolitan City. Figure 1 is showing the past, present and future (projected) urban expansion scenarios of the Greater Dhaka Metropolitan City. Dhaka is having horizontal expansion along with the vertical intensification. This vertical intensification has started late 80's and added a new dimension in the city growing process. This recent phenomenon of high rise building in both commercial and residential sectors, clearly manifest the city to adopt this process to cope with the ever increasing pressure in Dhaka city. In Dhaka vertical growth is taking place of horizontal growth in the higher and medium terraces, which is the indication of unavailability of land within central part of the city and to accommodate a large number of people in a piece of land to get more economic return from the land. This is also adding another dimension and trend in present and future urbanization process.

Table 3. Relationship Between Urbanization and \JCOffi()fJ)hOIOI(' ounnR 1'.1))- L--· 996 Geomorpblo: Unit

2.2 Urbanization Process and Waterlogging In this section attempt has been made to quantify the influence of topographical settings in the urbanization process, which in turn aggravating the present waterlogging situation in Dhaka City. The geomorphological map was overlaid with the urban expansion trend map in order to quantify the rate of urbanization per geomorphic unit. Result of this analysis giving the clear indication that topographical settings have the direct influence in

Hisb.

Year

1996

1989

1983

1975

1955

86%

91%

94%

94%

9~%

14%

9%

6%

6%

5%

medium, low Mldbupur terrace and natural

levee

Depression,

abandoned channel, point bar

287

In this section another relationship was established between geomorphological settings and waterlogging condition based on following hypothesis: Which geomorphic units are more vulnerable to waterlogging. In this respect waterlogging distribution map was overlaid with the geomorphological map. Results of this analysis presented in Table 4. Medium and low terrace units are more vulnerable to the present waterlogging situation. Which is also related with the present trend of urban expansion. Since 1980 urban expansion is rapidly stretching towards the lowlying areas. On the other hand higher terrace is also facing the same problem of waterlogging, because of obstruction of the natural flow, inefficient performance of the drainage system, high population density, ineffective solid waste management and uncontrolled land development in the lowlying areas. The depression storage in the urbanized basin plays a significant role to reduce the internal flooding to retain the urban storm runoff and maintained ecological and environmental balance of the city. But in Dhaka city, natural channels and depressions are being disappeared and reducing continuously by the unplanned and uncontrolled urbanization process. Therefore, localized flooding I waterlogging is experienced in the previously well drained and flood free areas as a consequence of the construction of buildings interfering the existing corridors for the overland flow. This scenario needs special attention and coordinated efforts of policy, decision makers and urban planners for sustainable urban development of Greater Dhaka Metropolitan City. 3 GIS MODEL FOR ASSESSING THE EFFECT OFURBANUATIONONURBANSTORM RUNOFF 3.1 Description and Selection Criteria for the Model Area

Tab I

~ "'

·y,

_

.......

._..~U'VOO

V'O

GeoiiMOrpbk: UDII

YY .....-

••

~..!:!

..

~

Numborol OCCUrrtDCt

IU

~-

omorphic Unit.

of

Wal

(de) in this case is a regional representative value (0.000 -0.020), and additionally in the spectral 10.5­ to 12.5 urn spectral region, we have £

.= 0.985 ± 0.007

£,=0.960±0.010

3.2.5. Roughness The total roughness is calculated as a superposition of vegetation and orograohic roughness according to the following:

Zo=~~+z!, The orographic roughness is calculated as I

Zo.,. =--·V

p•

where v is the variance of a neighborhood of a group of DEM grid points and Pnu the size of a DEM grid. The vegetation roughness can be derived from land use classification (each land use is assigned a specific roughness value, e.g. Wieringa, 1993) or from an empirical function of the following form: Zo.,

=f(NDV[)

(e.g.1.o., =exp[-6.65+6.38·NDVI])

Figure 6 Total roughness length calculated as a combination due to orography and vegetation (left) and vegetation only (right). Bright color indicates bigger roughness. 3.3 The improved SEBAL algorithm The Surface Energy Balance Algorithm for Land (SEBAL) has been developed at DLO Winand Staring Centre (Menenti and Bastiaanssen, 1997; Bastiaanssen, 1995). An improved version is reported in this study. In determining the regional resistance for heat, we employ the potential air temperature at the blending height derived from either radiosonde data or NWP predictions. The energy balance is described by

R" =G0 +H+AE where the symbols indicate net radiation, soil heat flux, sensible heat flux and latent flux, respectively. The net radiation is derived from the radiation balance

R. = K~ -Kt +L~ -Lt

=(1 - a)R,. +E(E'dT.4 -o'l'04 )

The roughness length for Europe on 23 March 1995 is given in Figure 6. The importance of orography on the total roughness can be clearly seen in most parts of Europe. However it needs to be pointed out that the first method is applicable to homogeneous terrain, while the second is generally derived empirically and thus only valid locally. A better determination of roughness lengths remains a current research issue (e.g. Molder, 1998). A promising technique to measure the aerodynamic roughness using air-borne laser altimeter measurements has been proposed by Menenti and Ritchie (1994). Its extension to space borne measurements may provide a definite solution.

where K ~,Kt, LJ., Lt indicate incoming, outgoing short wave and incoming, outgoing long wave radiation, t', T. denote apparent air emissivity and temperature, and o is the Stefen-Boltzmann constant. The incoming long wave radiation can be derived by NWP predictions. Other quantities have been explained previously. The sensible heat flux is obtained using a resistance formulation

.,

H = pC To-T, ro~o

where p., C, are the air density and isobaric heat specific heat, r.. is the resistance for heat transfer

397

between the surface and a certain reference height at which the air temperature is specified. The soil heat flux is parameterized by an empirical function

G0 =R.~To~.32N+0.62ii 1 XI-0.98NDVI 4 )-Io-1 where

a

is a daily mean albedo value. Here T0 is

expressed in °C . The latent heat flux is then derived as a residue of the energy balance as

AE = R,. - G0 - H In order to determine the sensible heat flux, we need

information on both the vertical temperature difference =T0 - T. and the resistance for heat r.,, which can be derived by examining the extreme conditions present in the images. Two conditions will be distinguished, namely dry areas and wet areas. In order to determine which pixel belongs to dry areas, a linearised theory proposed by Menenti and Bastiaanssen ( 1989) is used. It has been observed that surface temperature and reflectance of inhomogeneous areas are correlated and that the relationships can be applied to determine the effective land surface properties (Menenti et al, 1989; Feddes et al., 1989). Using a simple parameterization of the relationship between soil heat flux with net radiation, and by assuming constant net radiation, air temperature and latent heat flux, a formal explanation can be given to the observed surface reflectance and temperature. At low reflectance, surface temperature increases with increasing reflectance. In this case, surface temperature may be termed as evaporation controlled' because the increase of the temperature is a result of the decrease of the evaporation as a consequence of less soil moisture availability. Here the increase in excess sensible heat flux exceeds the decrease in net radiation due to increase of reflectance. Beyond a certain threshold value of reflectance, surface temperature decreases with increasing reflectance. This is due to the fact that the soil moisture has decreased to such an extent that no evaporation can take place in this case. Hence the available energy is purely used to heat up the surface. However, due to the increase of reflectance, the available energy decreases as a result of the

ar.

398

Figure 7. Schematic representation of the relationship between surface reflectance and temperature: The solid line denotes the relationship between temperature (and also sensible heat) and reflectance, and the dashed line represents the relationship between available energy and reflectance. The difference between the dashed and the solid line gives the latent heat flux according to the energy balance.

decrease of net radiation (more is reflected away). This process leads to the decrease of temperature with increasing reflectance. Here the temperature is said to be 'radiation-controlled'. A schematic representation is given in Figure 7. The threshold value can then be used to determine the pixels belonging to dry areas where the reflectance is bigger than the threshold value. For dry areas, we can approximately write

AE:O H:R.-G0

.

1.1!.

R• - G o -- P. C, To-T. -­ r.,.

For a given regional T., the area average resistance for heat transfer,(r.,.), can be derived for the dry areas. Having obtained (r.,.), we can invert for the maximum vertical temperature difference, from the equation for sensible heat flux

~

ar•._ = P.C,{H_,}

ar..... '

By applying the concept of the blending height (Wieringa. 1986 and Mason, 1988), the resistance to heat for each pixe1 (at a surface reference height), r.M , can be obtained. The blending height is defined as a height at which the flow is approximately in equilibrium with the local surface and independent of horizontal position. By means of aggregating of pixel-wise roughness heights, using the heuristic rules of Wood and Mason (1991) and Blyth and Dolman ( 1995), an effective roughness for dry areas and a regional representative roughness (for the whole area) can be obtained. The former is used to obtain over dry areas the effective friction velocity that is in turn used to derive the wind speed at the blending height. The later is used, together with the wind speed at the blending height, to derive the local wind velocity at a near surface reference height (typically 5 meters above the roughness height) for each pixel. With the local wind velocity, the resistance for heat can be derived for each pixel. This later procedure is a disaggregating process by means of similarity flux profile relationships. The scheme is shown in Figure 8. The following relationships are utilized (e.g. Brutsaert, 1982)

r>M =~[m(~.} 'l'.(z,L)+ 'I'~(Zo•• L)] (s·m-•)

u=~ [~~• }'I'Jz,L)+'I'M(Zo•• L)] (m.s-t) kB-l =ln Zo.

z.

where u, u • denote the wind velocity and friction velocity respectively. 'I'M '¥ ~ are the stability corrections for momentum and heat, respectively, and can be described by the Businger-Dyer function for unstable and neutral conditions (Brutsaert, 1982) and the Beljaars-Holtslg expression for stable conditions (Beljaars and Hotalg, 1991; Van den Hurk and Holtslg, 1995). L is the Obukhov length. z. z_, z. are respectively the reference height, the roughness for momentum and the roughness for heat, k = 0.4 is the von Kaman constant.

0 0

Figure 8, Schematic illustration of the aggregating ­ disaggregating process - the wind velocity u 11 at the blending height z11 is utilized to obtain the wind velocity u.., at the surface reference height z.,., which in turn is used to obtain u • , the friction velocity for each pixel (corresponding to the pixel­ wise roughness length Zo ).

At present,

z_

is assigned the value of

Z0

and

z.

is derived by assigning the value of 2.3 for kB- 1 (Brutseart, 1982). This is clearly an oversimplification of a rather complicated phenomenon that involves different sources for momentum and heat transfers. Quite different ranges of kB- 1 values have been reported for heterogeneous terrain (Kustas et al., 1989, Beljaars and Holtslag, 1991). Recently the validity of a constant kB- 1 has again been strongly questioned (Verhoef et al., 1997). Ultimately this value must depend on the flow itself and should be expressed in terms of the roughness Reynolds number (which of course also depends on the flow characteristics). This remains a future research issue. The wet areas are simply taken as the areas with low reflectance associated with low temperature. For which, we have:

AE::R. -G0 H::O dTa"" ::0 By making further an assumption that the vertical temperature difference is linearly related to the 399

Figure 10. Evaporation fraction derived for 23 March 1995. Figure 9. The linear relationship between dT. (.x, y) and T0 surface temperature, as indicated in Figure 9, we can obtain apixel-wise dT.(.x,y). The sensible heat flux is then calculated using dT. and r.. for each pixel

4 CONCLUSIONS

H=p.(z.~)C, dT. ro~o

where p(z.~ ) is the air density at a certain elevation z.~ as given by a DEM. The latent heat flux results from the residual of the surface energy balance, by assuming that local advection is non-existent below the surface reference height. Finally the evaporative fraction can be obtained as

AE H +AE

SEBAL system as described previously. The method has been applied to the Iberian Peninsula during a 7­ day period in the summer of 1994. The results indicated that the evaporative fractions derived from the satellite data contain a signal that may be used to assimilate soil moisture in NWPs. Currently this method is being verified in the whole of Europe.

AE R. -G0

A=--=-­ Figure 10 shows an evaporative fraction map derived from the surface parameters as described previously. The pattern shown is as expected, though the actual accuracy still needs further validation.

In this study, we have developed a set of methods for deriving land surface parameters. An improved SEBAL model has also been described. Application of these methods allows us to study land surface processes at scales ranging from local to continental using satellite remote sensing data. Together these methods form the SEBAL system. By assimilating the remotely sensed land surface parameters, it is anticipated that the NWPs can be improved. Currently data NOAA/AVHRR data from the whole year of 1995 are being processed, in combination with NWPs simulations. First results of this effort show that remote sensing data contain signal comparable to SYNOPS data when operated on European scale. However both the accuracy of the remotely sensed fluxes and the data assimilation procedure deserve further attention and research efforts. We hope to present further results to the scientific community in the near future (Van den Hurk et al., 1998). REFERENCES

3.4 Update the NWPs using the remote sensing products

Basstiaanssen W.G.M. 1995. Regionalization of surface flux densities and moisture indicators in composite terrain, Ph.D. Thesis, Agricultural University Wageningen, 273 pp. Becker, F. & Z-L. Li 1990. Temperature independent spectral indices in thermal infrared bands, Remote Sens. Environ. 32: 17-33.

Van den Hurk et al. (1997) have developed a method for assimilation of initial soil moisture fields in NWPs. A correction to initial soil moisture was calculated from a comparison between the evaporative fraction produced by RACMO and 400

Becker, F. & Z-L. Li 1990. Surface temperature and emissivity at various scales: definition, measurements and related problems, Remote Sens. Rev. 12:225-253. Beljaars, A.C.M. & A.A.M. Holtslag 1991. The parameterization of surface fluxes in large scale models under free convection, Q.J.R. Meter. Soc., 121 :255-270. Bird, R. & R.L. Hulstrom 1980. Direct insolation models, Trans. ASME J. Sol. Energy Eng., 103, 182-192. Bird, R. & R.L. Hulstrom 1981. A simplified clear sky model for direct and diffuse insolation on horizontal surfaces, SERVI'R-642-761, Solar Energy Research Institute, Golden, Colorado. Blyth, E.M. & Dolman, A.J. 1995. The roughness length for heat of sparse vegetation, J. App. Meteor. 34:583-585. Brutsaert, W. 1982. Evaporation into the atmosphere, Reidel, Dordrecht, 299 pp. Caselles, V. & J.A. Sobrino 1989. Determination of frosts in orange groves from NOAA-9 A VHRR data, RSE, 29: 135-146. Christensen, J .H. & E. van Meijgaard 1992. On the construction of a regional atmospheric climate model, KNMI scientific Report No. 147, 22 pp. Coli, C. & V. Caselles 1997. A split-window algorithm for land surface temperature from A VHRR Data: validation and algorithm comparison, JGR, in press. Feddes, R.A., M. Menenti, & P. Kabat 1989. Modelling the soil water and surface energy balance in relation to climate models, Proc. European Coord. Meeting in Land Surface processes: Barcelona, 12-15 March, 1989. lqbal. M. 1983. An introduction to solar radiation, Academic Press, Toronto. 390 pp. Kustas, W.P., B.J. Choudhury, M.S. Moran, R.J. Reginato, R.D. Jackson, L.W. Gay & H.L.Weaver 1989. Determination of sensible heat flux over sparse canopy using thermal infrared data, Agr. Forest Meteor. 44: 197-216. Li, Z-L. & F. Becker 1993. Feasibility of land surface temperature and emissivity determination from AVHRR data, Remote Sens. Environ. 43: 67-85. Mason, P.J. 1988. The formation of areally-averaged roughness lengths, Q.J. Royal Meterology Society, 114:399-420. Menenti, M. & W.G.M. Bastiaanssen 1989. Estimation of effective properties of non­ homogeneous land surfaces with measurements of surface reflectance and temperature, Proc. 40th

congress, Int. Astronautical Fed. Paris, 7-12 Oct., 1989. Menenti, M. & W.G.M. Bastiaanssen (eds) 1997. Mesoscale climate hydrology: Earth Observation System-Definition Phase, Report 106, DLO-Winand Staring Centre, Wageningen, the Netherlands (appeared also as BCRS Report NRSP-2 95-15): 197 pp. Menenti, M., W.G.M. Bastiaanssen, D. van Eick & M.H. Abd el Karim 1989. Linear relationships between surface reflectance and temperature and their application to map actual evaporation of groundwater, Adv. Space Res., 9(1): 165-176. Menenti, M. & J.C. Ritchie 1994. estimation of effective aerodynamic roughness of Walnut Gulch watershed with laser altimeter measurements, Water Resources Research, 30(5): 1329-1337. Molder, M. 1998. Roughness lengths and roughness sublayer corrections in partly forested regions, Ph.D. dissertation, No. 345, Uppsala University, 45pp. Paltridge, G.W. & R.M. Mitchell 1990. Atmospheric and viewing angle correction of vegetation indices and grassland fuel moisture content derived from NOAA /AVHRR, Remote Sens. Env. 31 :121-135. Valiente, J.A, Nunez, M., Lopez-Baeza, E. & Mereno, J.F. 1995. Narrow-band to broad-band conversion for Meteosat-visible channel and broad-band albedo using both AVHRR-1 and -2 channels, lnt. J. Remote Sens. 16(6): 1147-1166. Valor, E. & V. Caselles 1995. Mapping land surface emissivity from NDVI: Application to European, African, and Sourth American Areas, Remote Sens.Env.57: 167-184. Van den Hurk, B.J.J.M & A.A.M. Holtslag 1995. On the bulk parameterization of surface fluxes for various conditions and parameter ranges, Boundary-Layer Met. 82: 199-134. Van den Hurk, B.J.J.M, W. Bastiaanssen, H. Pelgrum & E. van Meijgaard 1997. A new methodology for initialization of soil moisture fields in numerical weather prediction models using METEOSAT and NOAA data, J. Appl. Met. 36: 1271-1283. Van den Hurk, B.J.J.M, E. van Meijgaard, Z. Su & A.A.M. Holtslag 1998. Soil moisture assimilation over Europe using satellite derived surface fluxes, to be presented at 2Dd Conf. On BALTEX, Island ofRUgen, 25-29 May 1998. Verhoef, A., K.G. McNaughton & A.F.G. Jacobs 1997. A parameterization of momentum

401

roughness length and displacement height for a wide range of canopy densities, Hydro). Earth Sys. Sci. 1:81-91. Wieringa, J. 1986. Roughness-dependent geographical interpolation of surface wind speed averages, Q.J.R. Meteor. Soc. 112:867-889. Wieringa, J. 1993. Representative roughness parameters for homogeneous terrain, Boundary Layer Met. 63:323-363. Wood, N. & P.J. Mason 1991. The influence of static stability on the effective roughness lengths for momentum and heat transfer, Q.J.R. Meteor. Soc. 117: 1025-1056.

402

Operational Remote Sensing for SustsNble DevBiopmflnt, Nleuwenhuis, l.tlughsn & MolensBr (eds)

e

1999Balems, Rotterr:Jsm,/SBN9058090299

Application of space techniques to derive energy fluxes for water management in (semi) arid zones G.Somrna Institute ofHydraulics, Hydrology and Water Management (IIIGA), University ofCatania,llllly

C.J.de Zeeuw, ZSu & M. Menenti DW Winand Slllring Centre for Integrated Land, Soil and Water Research (SC-DW), Wageningen, Netherlands

ABSTRACf: The results of a research work period carried out in the framework of the ASTIMwR (Application of Space Techniques to the Integrated Management of a river basin water Resources) project are presented. Promoted by the EU within the CEO (Centre for Earth Observation) programme for the development of Space Techniques applied to environmental monitoring and research, the domain of the ASTIMwR is the Application Support, i.e. the branch in which the production of information and related services oriented to the customer requirements is intended to be enhanced. Partial objectives of the project are to apply Remote Sensing techniques to calculate surface energy balance and actual evapotranspiration maps (ETa), and to monitor water resources use in overexploited aquifer areas. A modified SEBAL (Surface Energy Balance Algorithm for Land) model was used to calculate ETa maps for the selected test site, the Torre de Abraham irrigation district in Spain. These maps were used to analyse exploitation regimes and evaluate irrigation performance through the use of a set of three indicators. The results indicate a moderate efficiency of the irrigation practice within the district, and reveal the presence of some irrigated areas outside the district, for which no irrigation water was delivered by the water authorities. Key words: Remote Sensing, surface energy balance, actual evapotranspiration maps, SEBAL, exploitation regimes, irrigation performance.

1 INTRODUCfiON

The availability and demand for water, at the appropriate time and place, are frequently imbalanced, thus encouraging over-exploitation and degradation of water reserves. This imbalance of availability and demand threatens the environment and the long-term security of supplying water at an economical price. Given the increasing limitations in availability of water resources, irrigation schemes should be improved by formulating a water management system for an optimal distribution of available water. One possible way to assess the efficiency of water distribution in large irrigation schemes is using performance indicators. The difference between actual and target values of the indicators provides the data to make a diagnosis of the real functioning of the selected scheme.

To determine the actual values of these indicators one needs to quantify evapotranspiration rates. Unfortunately, field measurements are seldom representative. In particular, determining the evaporation rates of large scale irrigation schemes by field work is practically impossible, due to the difference in regional conditions. Currently, the use of satellite remotely sensed data, coupled with the support of a Geographical Information System (GIS) and existing tables and maps, has proven to be a more feasible alternative, compared to conventional ground survey, allowing for regular and timely updating of information. With the GIS system, multispectral satellite images for manipulating large data sets can be used to appraise irrigation management at a relatively low cost (Menenti et al. 1990, D'Urso et al. 1992).

403

In Table I the extension of the total and irrigated areas for each section as declared by the water management organisation are presented.

2 OBJECITVES Main objective of this study is to calculate surface energy balance and actual evapotranspiration (ETa) maps, for a fixed date and time. This objective is achieved by using the SEBAL remote sensing algorithm (Bastiaanssen 1995). Results from SEBAL are used to evaluate exploitation regimes for past years in over-exploited aquifer areas within the selected subset image of the Guadiana basin. This second objective wiii be fulfilled by comparing exploitation regimes with irrigation performance through the utilisation of three indicators (see section 5.3).

Table I. Total and declared irrigated areas in the Torrc de Abraham irrigation district.

Year

Sector

635.7 449.4 1237.5

3&4

1481 1220 2851

1103.7 563.3 1602.2

1 2 3&4

1481 1220 2851

785.9 421.6 1169.7

2 3&4 1

2 1996

3 STUDY AREA Covering a surface of 5552 hectares, the Torre de Abraham irrigation district has been selected as study area (Fig. 2) within the Guadiana river basin (Fig. 1). The geographical coordinates of the centre of the district are: 39°15'00"N latitude, and 04°)5'00"W longitude. The main crop in the area is maize, cultivated on about 95% of the area The irrigation system is designed to accommodate the so called "on-demand" scheme, in which farmers control both timing and quantity of irrigation without any restriction. Water is conveyed from a dam to three water towers, attending to one sector of the district (except the one which serves sectors 3 & 4) and including a set of pumps and a small elevated water reservoir for regulating the pressure in the pipe system.

Irrigated area (ha)

1481 1220 2851

1989

1991

Total area (ha)

Figure 1. The Guadiana river basin.

4 AVAILABLEDATA The climatological data for the selected test site were acquired from the National Meteorological Institute (http://www.inm.es) and from an automatic weather station installed in the Torre de Abraham area. The availability of cloud free Landsat-5 TM data for the selected area was explored. Three cloud free images were available for the said period, and therefore acquired for analysis purpose (07 July 1989; 21 July 1991; 18 July 1996) and a sub-scene encompassing the Torre de Abraham irrigation district was extracted. Apart from the digital images, each data set contains also an extensive amount of auxiliary information, such as time of acquisition, scene location, solar elevation angle and calibration constants.

Figure 2. Torre de Abraham irrigation district.

5 METHODOLOGY 5.1 The SEBAL model The exchange processes occurring at the land surface control the distribution of moisture and heat in soil

404

and atmosphere. In the moisture and the heat balances, the land surface acts as the interface between the soil and the atmosphere. Assuming no horizontal advection, the energy fluxes are one­ dimensional away or towards the surface. Therefore the thermodynamic equilibrium between dominantly turbulent transport processes in the atmosphere and dominantly laminar processes in the soil manifests itself in the energy balance which for the land surfaces reads as: Q•

= G0 +H+lE

(Wm-2)

( 1)

where Q• is the net radiation flux density; Go is the soil heat flux density; H is the sensible heat flux density; lE is the latent heat flux density. Q• is considered positive when radiation is directed towards the land surface, while Go. H and lE are considered positive when directed away from land surface. Using an iterative procedure, SEBAL solves the surface energy balance for each pixel of the input imagery and on instantaneous time basis. Net radiation is obtained from distributed hemispherical surface reflectance and surface radiation temperature data in combination with spatially variable zenith angles for the determination of the clear sky incoming shortwave radiation. The incoming Iongwave radiation is assumed to be areally constant. Soil heat flux density is computed from an empirical soil heat I net radiation flux density ratio. The area effective momentum flux density is obtained from the area effective aerodynamic resistance for dry land surface elements, using the slope between surface temperature and surface hemispherical reflectance. The vertical near-surface air temperature difference is related linearly to surface temperature. The vertical difference between two horizontal layers of different air temperature is obtained by an inversion of the equation for sensible heat transfer at specific partial areas corresponding with two extreme conditions, i.e. one where H = 0 (wet condition) and one where ).£ 0 (dry condition). These partial areas can be allocated by means of the To-ro relationship. The momentum and sensible heat flux densities are based on the resistance version of the flux-profile relationships. Finally, latent heat flux density is obtained as the residue of the land surface energy balance (Bastiaanssen 1995). The model has been modified (Su et al., in this proceedings) so that the resistance to heat for each image pixel (at a surface reference height) can be obtained desagregating the regional flux, utilising

similarity theory and the roughness length, and assuming that the regional momentum flux (at the blending height) can be represented by that of the dry areas. Satellite imagery constitutes the main input to the model, but analytical relationships are also used, in combination with empirical relationships. Ground truth data may be used for calibration. The SEBAL algorithm is applied in two parts, so that output images from the first part can be analysed and corrected and used as input data for second one. For the first part, all the bands of TM image are used to generate output images for each of the relevant variables, i.e. surface albedo, r0 , incoming shortwave radiation, JC, Normalised Difference Vegetation Index, NDVI, surface emissivity, e0 , surface temperature, To. 5.2 Actual evapotranspirationmapping, ET, The SEBAL model calculates instantaneous energy fluxes from which daily evapotranspiration rates (expressed in mm d" 1) need to be calculated. Due to the strong fluctuations that can occur during the day, extrapolation of the latent heat flux over the day is not possible. Nevertheless, the energy partitioning between H and lE on a daily basis is hydrologically controlled and changes slowly. Therefore, instantaneous H and lE maps from SEBAL can be then fruitfully employed to derive terms with a constant partitioning of surface flux densities among their components during daytime (Brutsaert & Sugita 1992). Among others, the evaporative fraction, A, is a moisture indicator commonly used in large-scale hydro-meteorological studies to represent the magnitude of evapotranspiration. Because of its quasi-constancy and because it can be computed from H and ).E without any auxiliary data, it has been chosen to calculate ET, daily values:

=

).E

A

= ).E + H

lE = Q• - G0

(-)

( 2)

A has been experimentally justified (Bastiaanssen 1995) as an adequate tool to describe surface energy partitioning for time scales of one day or less, and daily integrated values of Q•-G0 can be determined with remote sensing data Then actual daily evaporation rates can be obtained as:

405

A Q" Ef=~

•

(mrnday- 1)

A.

production potential of that crop under the given growing environment (Doorenbos & Pruitt 1975). It is calculated by means of the modified Penman method and corrected for the effective precipitation providing the crop irrigation water requirements. Ere""' can be also calculated, by means of the SEBAL model, per unit time, given the parameters that define the local situation (such as: solar radiation, surface albedo, temperature, roughness length, etc.). Finalll, the available amount of water, Vw (mm d. ), consists, among others, of precipitation water and of irrigation water provided by the river basin management organisation, per unit area and per unit time. Concerning the calculation of the third indicator, A, ETc""'' was not estimated with the modified Penman method because its determination on heterogeneous land surfaces with such method is usually hampered by the assumption of areally constant hydro-meteorological parameters and their non-representativeness for wet conditions. Bastiaanssen (1995) suggested to consider Q"-Go as a suitable replacement for ETc""'' and (Bastiaanssen et al. 1995) showed how A maps can be used also to diagnose crop stress and evaluate the performance of regional irrigation water management.

( 3)

where A. = 2.454 MJ kg" 1 is the latent heat of vaporisation for water, and is the net radiation flux density over a 24 hour period (MJ m"2).

Q;...

5.3 Aquifer areas exploitation peiformiJIICe indicators

regimes

and

To fulfil the second objective, a comparison was made including both spatial and temporal aspects. The spatial aspect mainly concerns the differences between different cropped areas: within the study area uneven ETa distribution was expected. The temporal aspect implies a comparison of the situation over different years. This is accomplished by selecting images taken in the same month (July) for different years, giving a multiannual base for comparison (only one image within the irrigation season was available in each year, therefore a multitemporal comparison within the irrigation season could not be performed). A set of three indicators, described in Table 2, is used to carry out the comparison. The actual evapotranspiration, ETa (mm d" 1), is defined as the quantity of water that evaporates from a land surface per unit area, per unit time, given the present hydro-meteorological situation, and it's calculated by means of SEBAL. The potential crop evapotranspiration, ETcrop (mm d"\ refers to evapotranspiration of a disease­ free crop, growing in a large field (one or more hectares} under optimal soil conditions including sufficient water and fertility and achieving full

6 RESULTS The frequency distribution for years '89, '91, and '96 is shown in Figure 4. For the major crop (maize), values as determined by SEBAL are in the range of 4-8 mm day·•.

Table 2. Irrigation Water Indicators.

Indicator

Formulation

water use efficiency

e.= ~ El'...,

(%)

Effective water ratio

w,,. =

ET.

vw

(%)

~-------~ ---- ~ - - - ----------------

It allows to evaluate if the amount of water delivered by the managing organisation