ࡱ> q 0bjbjt+t+ @AA(%.S]( $P`LD؛\ (FDTXk UWWWWWW$|{]uuu{V\ VVVuUuUVV2:U@IV H  VEGETATION 2000 Belgirate, April 3-6, 2000 PRELIMINARY AGENDA (03/23/2000) TIME TABLE Monday April 3rd 14:00 Opening and Welcome JRC/SAI Representative14:15KEYNOTE ADDRESSB Moore Chair SC-IGBP15:00  The VEGETATION Programme, status and future. D Faivre, A Ghazi, Cochairmen of the VEGETATION Steering Committee15:45VEGETATION standard pre-processing functions X Passot (Integrated Project Team)16:15Product quality performance P Henry (CNES)16:45Production entity D Van Speybroek (CTIV)17:15Distribution entity S Moreau (SPOT Image) PLENARY SESSIONS Tuesday April 4th 09:00 Session 1  TM \o "1-3" UTILIZATION OF VEGETATION TO EXTRACT EFFECTIVE SURFACE PARAMETERS  RENVOIPAGE _Toc478441190 \h 9 Jiaguo Qi,  RENVOIPAGE _Toc478441191 \h 9 Estimation of land surface albedo and vegetation biophysical properties using SPOT-4 VGT and semi-empirical BRDF models.  RENVOIPAGE _Toc478441192 \h 10 M. J. Barnsley, T. L. Quaife, P. D. Hobson and J. Shaw,  RENVOIPAGE _Toc478441193 \h 10 VEGETATION/SPOT for Northern Applications: Assessment of Utility and Examples of Products  RENVOIPAGE _Toc478441194 \h 11 Jing Chen and Josef Cihlar  RENVOIPAGE _Toc478441195 \h 11 Validation of neural network techniques to estimate canopy biophysical variables from VEGETATION data  RENVOIPAGE _Toc478441196 \h 12 M.Weiss(1) , F.Baret(1), M.Leroy(2), O.Hautecur(2), L.Prvot(1), and N.Bruguier(1)  RENVOIPAGE _Toc478441197 \h 12 11:00 Session 2 Estimation of Net Primary and Net Ecosystem Productivity of European terrestrial ecosystems by means of the C-Fix model and NOAA/AVHRR data.  RENVOIPAGE _Toc478441198 \h 13 Frank Veroustraete and Hendrik Sabbe  RENVOIPAGE _Toc478441199 \h 13 Monitoring North American Grasslands Dynamics with VEGETATION  RENVOIPAGE _Toc478441200 \h 14 David J. Meyer *,  RENVOIPAGE _Toc478441201 \h 14 Multitemporal analysis of the VEGETATION data for landcover assessment and monitoring in Indochina.  RENVOIPAGE _Toc478441202 \h 15 Chandra Giri & Surendra Shrestha  RENVOIPAGE _Toc478441203 \h 15 The Suitability of VEGETATION for Mediterranean Land Degradation and Desertification Monitoring  RENVOIPAGE _Toc478441204 \h 16 W. Mehl, P. Strobl, S. Sommer, H. Bohbot  RENVOIPAGE _Toc478441205 \h 16 14:00 Session 3 Monitoring of forest ecosystems at regional scale using VEGETATION daily-data: First results on the Landes maritime pine forest (SW France)  RENVOIPAGE _Toc478441206 \h 17 Jean-Pierre Lagouarde 1,Dominique Guyon 1, Benot Duchemin 2  RENVOIPAGE _Toc478441207 \h 17 The potential contribution of SPOT 4/VEGETATION data for mapping Siberian forest cover at the continental scale  RENVOIPAGE _Toc478441208 \h 18 S. Bartalev (1), F. Achard (1), D. Erchov (2) and V. Gond (1  RENVOIPAGE _Toc478441209 \h 18 Fire Scar Detection in the Canadian Boreal Forest  RENVOIPAGE _Toc478441210 \h 19 Plummer, S.E., Gerard, F.F. and Wyatt, B.K.  RENVOIPAGE _Toc478441211 \h 19 Monitoring Boreal Forest Resources in Northern Europe from the VEGETATION instrument  RENVOIPAGE _Toc478441212 \h 20 Bernard Pinty(1),  RENVOIPAGE _Toc478441213 \h 20 16:00 Session 4 SPATEM: The analysis of annual sequences of VEGETATION data at the landscape scale.  RENVOIPAGE _Toc478441214 \h 21 Agustin Lobo and Nicolau Pineda  RENVOIPAGE _Toc478441215 \h 21 VEGETATION/SPOT4 applications for macro-regional landscape mapping  RENVOIPAGE _Toc478441216 \h 22 Lioubimtseva E. (1),  RENVOIPAGE _Toc478441217 \h 22 Fire patches in natural vegetation in southern Africa  RENVOIPAGE _Toc478441218 \h 23 Swinnen E. *, Verwimp R. **, Gulinck H. ***  RENVOIPAGE _Toc478441219 \h 23 Application of SPOT 4-VEGETATION data for mapping the forest-cover of Madagascar  RENVOIPAGE _Toc478441220 \h 24 Mayaux Philippe, Gond Valry and Bartholom Etienne  RENVOIPAGE _Toc478441221 \h 24 Wednesday April 5th 09:00 Session 5 STEM-VGT : Satellite measurements and terrestrial ecosystem modelling using VEGETATION instrument  RENVOIPAGE _Toc478441222 \h 25 G. DEDIEU (LERTS/ CESBIO Toulouse France)  RENVOIPAGE _Toc478441223 \h 25 Intermediate Scale Approach for Estimating Vegetation Canopy Leaf Area Index using SPOT4/VGT Spectral Bands.  RENVOIPAGE _Toc478441224 \h 26 F. Cipriani, E. Cubero-Castan  RENVOIPAGE _Toc478441225 \h 26 A new vegetation map of Central Africa  RENVOIPAGE _Toc478441226 \h 27 Herman Eerens, Bart Deronde & Jan Van Rensbergen  RENVOIPAGE _Toc478441227 \h 27 Sub-pixel mapping of Sahelian wetlands using multi-temporal SPOT-VEGETATION images  RENVOIPAGE _Toc478441228 \h 28 Jan Verhoeye, Robert De Wulf  RENVOIPAGE _Toc478441229 \h 28 Estimation of surface variables at the subpixel level for use as input to climate and hydrological models  RENVOIPAGE _Toc478441230 \h 29 JeanPierre Fortin*,Monique Bernier , Ali El Battay, Yves Gauthier and Richard Turcotte  RENVOIPAGE _Toc478441231 \h 29 11:00 Session 6 Integration of VEGETATION and HRVIR data into yield estimation approach.  RENVOIPAGE _Toc478441232 \h 30 Andr HUSSON  RENVOIPAGE _Toc478441233 \h 30 Interest of MIR data from VEGETATION for the monitoring of climatic phenomena impact on crops, a case study  RENVOIPAGE _Toc478441234 \h 31 Thierry Fourty  RENVOIPAGE _Toc478441235 \h 31 VEGETATION contribution to the desert locust habitat monitoring  RENVOIPAGE _Toc478441236 \h 32 Cherlet Michael* , Mathoux Pierre**, Bartholom Etienne*** and Defourny Pierre**  RENVOIPAGE _Toc478441237 \h 32 Mediterranean habitats: a multi-variate analysis of VEGETATION data.  RENVOIPAGE _Toc478441238 \h 33 Agustn Lobo1, Jordi Carreras2 and Josep-Maria Ninot2  RENVOIPAGE _Toc478441239 \h 33 14:00 Session 7 SENSITIVITY ANALYSIS OF COMPOSITING STRATEGIES: MODELLING AND EXPERIMENTAL INVESTIGATIONS  RENVOIPAGE _Toc478441240 \h 34 de Wasseige Carlos*, Lissens Gil**, Vancutsem Christelle*, Veroustraete Frank** and Defourny Pierre*  RENVOIPAGE _Toc478441241 \h 34 Modeling directional reflectance in rugged terrain using VEGETATION products  RENVOIPAGE _Toc478441242 \h 35 Lihong Su, Xiaowen Li, Jindi Wang  RENVOIPAGE _Toc478441243 \h 35 Development of a spectral index optimized for the VEGETATION Instrument  RENVOIPAGE _Toc478441244 \h 36 Michel M. Verstraete, Nadine Gobron and Bernard Pinty  RENVOIPAGE _Toc478441245 \h 36 MC-FUME: A new method for compositing individual reflective channels  RENVOIPAGE _Toc478441246 \h 37 GiL Lissens, Els Brems and Frank Veroustraete  RENVOIPAGE _Toc478441247 \h 37 SPACE-VEGETATION SOFTWARE : A software for pre-processing VEGETATION L-Band Data  RENVOIPAGE _Toc478441248 \h 38 Csar CARMONA-MORENO  RENVOIPAGE _Toc478441249 \h 38 16:00 Session 8 Detection of Clouds and Cloud-Shadows for VEGETATION images  RENVOIPAGE _Toc478441250 \h 39 Pieter KEMPENEERS, Gilbert LISSENS, Freddy FIERENS, Jan VAN RENSBERGEN  RENVOIPAGE _Toc478441251 \h 39 NEW ALGORITHMIC CONCEPT FOR ATMOSPHERIC AND DIRECTIONAL CORRECTION OF THE SURFACE REFLECTANCES  RENVOIPAGE _Toc478441252 \h 40 P. MAISONGRANDE  RENVOIPAGE _Toc478441253 \h 40 Round Table on New Standard Products Thursday April 6th 9:00 Session 9 European Forest Mapping using VEGETATION data  RENVOIPAGE _Toc478441254 \h 41 Herv Jeanjean,  RENVOIPAGE _Toc478441255 \h 41 Mapping and monitoring small ponds in dryland with the VEGETATION instrument application to West Africa  RENVOIPAGE _Toc478441256 \h 42 V. Gond*, E. Bartholom*, F. Ouattara, A. Nonguierma+  RENVOIPAGE _Toc478441257 \h 42 Detection and mapping of burnt areas and active fires in tropical woodland ecosystems with the VEGETATION sensor: the SMOKO-FRACTAL case study over Northern Australia  RENVOIPAGE _Toc478441258 \h 43 D. Stroppiana1, M. Maggi1, J-M. Pereira2, D. Graetz3, J-M. Grgoire1, J.  RENVOIPAGE _Toc478441259 \h 43 DISTURBED ECOSYSTEMS DYNAMICS IN THE ARAL SEA REGION BY REMOTE SENSING AND GIS METHODS.  RENVOIPAGE _Toc478441260 \h 44 R. Ressl, A. Ptichnikov, G. Kapustin, P. Reimov, D. Forstman.  RENVOIPAGE _Toc478441261 \h 44 11:00 Session 10 Crop Growth Monitoring with Coupling of AVHRR and VEGETATION  RENVOIPAGE _Toc478441262 \h 45 Wu Bingfeng  RENVOIPAGE _Toc478441263 \h 45 Use of medium-resolution imagery in the Belgian Crop Growth Monitoring System (B-CGMS)  RENVOIPAGE _Toc478441264 \h 46 K. Wouters*, H. Eerens*, D. Dehem**, B. Tychon**, D. Buffet*** & B. Oger***  RENVOIPAGE _Toc478441265 \h 46 Combined use of VEGETATION and RADARSAT data for updating snowpack cover and water equivalent in the HYDROTEL hydrological forecasting model  RENVOIPAGE _Toc478441266 \h 47 Monique Bernier*, JeanPierre Fortin, Yves Gauthier, Richard Turcotte and Ali El Battay  RENVOIPAGE _Toc478441267 \h 47 Antarctic snow characteristics from POLDER and VEGETATION data  RENVOIPAGE _Toc478441268 \h 48 Michel FILY, Olivier MANSE, Jean-Pierre BENOIST  RENVOIPAGE _Toc478441269 \h 48 14:00 Session 11 Applications of VEGETATION data to resource management in arid and semi-arid rangelands  RENVOIPAGE _Toc478441270 \h 49 Bgu (CIRAD),G. Chehbouni (IRD), R. Escadafal (IRD)  RENVOIPAGE _Toc478441271 \h 49 VEGETATION potentialities in food early warning systems in the Sahelian region  RENVOIPAGE _Toc478441272 \h 50 TYCHON Bernard , OZER Pierre and TOURE Souleymane  RENVOIPAGE _Toc478441273 \h 50 Incorporating the use of VEGETATION data in FAOs programmes  RENVOIPAGE _Toc478441274 \h 51 F.L. Snijders  RENVOIPAGE _Toc478441275 \h 51 The contribution of VEGETATION/ SPOT 4 products to Remote Sensing Applications for Food Security, Early Warning and Environmental Monitoring in the IGAD sub-region.  RENVOIPAGE _Toc478441276 \h 52 Guy PIERRE SCOT  RENVOIPAGE _Toc478441277 \h 52 The Millennium Land Cover Assessment initiative  RENVOIPAGE _Toc478441278 \h 53 S. Belward  RENVOIPAGE _Toc478441279 \h 53 16:00 Conclusion Jean Paul Malingreau (Chairman of the VEGETATION International Users Committee) Rudolf WINTER (Director, Space Application Institute, JRC) POSTER PAPERS Monitoring natural disasters and hot spots of land-cover change with SPOT VEGETATION data to assess regions at risk and vulnerability  RENVOIPAGE _Toc478441280 \h 55 Prof. E. F. Lambin, Dr. I. Reginster & F. Lupo Sartor  RENVOIPAGE _Toc478441281 \h 55 Improved atmospheric corrections and data compositing methods for surface reflectance retrieval  RENVOIPAGE _Toc478441282 \h 56 Ph. Maisongrande (1), B. Duchemin (1), M. Leroy (1) (P.I.), G. Dedieu (1), J.L. Roujean (2), B. Berthelot (1), Ch. Dubegny (1), R. Lacaze (2)  RENVOIPAGE _Toc478441283 \h 56 VALIDATION OF BIOPHYSICAL PRODUCTS DERIVED FROM LARGE SWATH SENSORS FOR GLOBAL BIOSPHERE MONITORING  RENVOIPAGE _Toc478441284 \h 57 F. Baret(1)  RENVOIPAGE _Toc478441285 \h 57 Improving access to VEGETATION data: some results of on-going experiments  RENVOIPAGE _Toc478441286 \h 58 E. Bartholom*, V. Gond*, S. Morimondi*  RENVOIPAGE _Toc478441287 \h 58 INTERCOMPARISON OF DEKADAL VEGETATION INDEX FROM NOAA/AVHRR AND SPOT4/VEGETATION OVER THE IGAD REGION  RENVOIPAGE _Toc478441288 \h 59 T. Bennouna and P. Bicheron  RENVOIPAGE _Toc478441289 \h 59 Vegetation Action & Demonstration Plan for desertification monitoring in China  RENVOIPAGE _Toc478441290 \h 60 Christian CREPEAU SCOT  RENVOIPAGE _Toc478441291 \h 60 Vegetation Action & Demonstration Plan for dry grassland monitoring in Senegal  RENVOIPAGE _Toc478441292 \h 61 Christian CREPEAU SCOT  RENVOIPAGE _Toc478441293 \h 61 Classifying land cover types with VEGETATION data in dryland: A case study in Burkina Faso  RENVOIPAGE _Toc478441294 \h 62 V. Gond, E. Bartholom  RENVOIPAGE _Toc478441295 \h 62 UTILISATION DES DONNEES VEGETATION POUR LE SUIVI DE LA CAMPAGNE AGROPASTORALE SUR LA ZONE CILSS  RENVOIPAGE _Toc478441296 \h 63 A ROYER  RENVOIPAGE _Toc478441297 \h 63 Generating fine spatial resolution VEGETATION derived imagery using SAR  RENVOIPAGE _Toc478441298 \h 64 Conrad M. Bielski, Franois Cavayas and Langis Gagnon  RENVOIPAGE _Toc478441299 \h 64 Sub-pixel characterization of land cover at the global scale using SPOT-VEGETATION imagery ()  RENVOIPAGE _Toc478441300 \h 65 Else Swinnen (*), Frank Canters(**) & Herman Eerens (*)  RENVOIPAGE _Toc478441301 \h 65 L'tablissement de Nomenclatures Vgtation partir d'Images SPOT  RENVOIPAGE _Toc478441302 \h 66 D. Blamont, M. Raffy  RENVOIPAGE _Toc478441303 \h 66 UTILISATION DE SPOT4-VEGETATION POUR LETUDE DU CHANGEMENT DECHELLE.  RENVOIPAGE _Toc478441304 \h 67 M. Raffy, D. Blamont  RENVOIPAGE _Toc478441305 \h 67 VEGETATION data for monitoring woody vegetation in landscape frameworks  RENVOIPAGE _Toc478441306 \h 68 Hubert Gulinck and Tim Wagendorp  RENVOIPAGE _Toc478441307 \h 68 VEGETATION data for regional forest cover mapping of Southeast Asia  RENVOIPAGE _Toc478441308 \h 69 H-J Stibig, R. Beuchle, V. Gond  RENVOIPAGE _Toc478441309 \h 69 An evaluation of SPOT-VEGETATION, for land cover mapping and the evaluation of forest resources, I: Mato Grosso, Brasil.  RENVOIPAGE _Toc478441310 \h 70 Jones, S. D. Eva, H. D.,  RENVOIPAGE _Toc478441311 \h 70 BRDF correction in SPOT 4/VEGETATION ten-days composite imagery for mapping of boreal forest  RENVOIPAGE _Toc478441312 \h 71 D. Erchov (1), S. Bartalev (2), M. Deshayes (3), J. R. Dymond (4)  RENVOIPAGE _Toc478441313 \h 71 Detecting active fires with the VEGETATION instrument  RENVOIPAGE _Toc478441314 \h 72 V. Gond*, M. Maggi*, P. Henry, J.-M. Grgoire*, E. Bartholom*  RENVOIPAGE _Toc478441315 \h 72 Drawbacks and advantages of the VEGETATION and AVHRR instruments for burnt area detection in Northern Australia.  RENVOIPAGE _Toc478441316 \h 73 D. Stroppiana1, M. Maggi1, D. Graetz2, S. Campbell2, I. Balzer2, J-M. Grgoire1 and J.M.C Pereira3.  RENVOIPAGE _Toc478441317 \h 73 Using SPOT 4 HRVIR and "VEGETATION " sensors to assess impact of tropical forest fires in Roraima  RENVOIPAGE _Toc478441318 \h 74 T. PHULPIN, F. LAVENU, M. F. BELLAN, B. MOUGENOT and F. BLASCO  RENVOIPAGE _Toc478441319 \h 74 A Large Forest Fire in the Mediterranean region as seen by VEGETATION  RENVOIPAGE _Toc478441320 \h 75 Agustin Lobo and Nicolau Pineda  RENVOIPAGE _Toc478441321 \h 75 ATMOSPHERIC MESOCYCLONES OVER POLAR SEAS AND THEIR INFLUENCE ON ECOLOGICAL REGIME FORMATION  RENVOIPAGE _Toc478441322 \h 76 Lagun V.E., Lutsenko E.I.  RENVOIPAGE _Toc478441323 \h 76 INVESTIGATION OF RUSSIAN ARCTIC ECOSYSTEMS VARIATION CAUSED BY ANTROPOGENIC ACTIVITY AND CLIMATE CHANGE  RENVOIPAGE _Toc478441324 \h 77 Ivanov V.V., Lagun V.E.  RENVOIPAGE _Toc478441325 \h 77 Mapping Biological Diveristy in Boreal Forest From Space Using Ecological Models  RENVOIPAGE _Toc478441326 \h 78 Anthony J. Warren, Michael J. Collins  RENVOIPAGE _Toc478441327 \h 78 EVALUATION OF VEGETATION CLOUD MASKS FOR CLIMATOLOGY STUDIES AND DESIGN OF SATELLITE SYSTEMS  RENVOIPAGE _Toc478441328 \h 79 J. Hamon, L. Harang, A. Rodot  RENVOIPAGE _Toc478441329 \h 79 An ICT-based course in Earth Observation with emphasis on VGT-data  RENVOIPAGE _Toc478441330 \h 80 Rombout Verwimp1c, Ann Willekens1, Jos Van Orshoven1 and Jan Elen2  RENVOIPAGE _Toc478441331 \h 80  PLENARY SESSIONS UTILIZATION OF VEGETATION TO EXTRACT EFFECTIVE SURFACE PARAMETERS Jiaguo Qi, Michigan State University Gerard Dedieu, Yann Kerr, CESBIO, Toulouse, France, Ghani Chehbouni, IMADES/ORSTOM, Mexico Initial effort of this investigation was to characterize biophysical properties in the arid and semiarid southwest United States with VEGETATION data. Soon after the imagery became available, its applications expanded to include areas of Great Lakes and tropical Amazon regions where deforestation is drawing attention worldwide. Although continued effort is in process, some results from these VEGETATION images have been achieved and showed a great potential of the VEGETATION imagery in characterizing terrestrial surfaces. This report contains past and ongoing field activities and some results from VEGETATION imagery analysis. In particular, we will report in the following three areas: 1) practical techniques for correcting bidirectional effect found in the VEGETATION imagery with a focus on field activities and modeling effort, 2) feasible and practical alternatives to circumvent atmospheric effects, and 3) operational use of the data to retrieve biophysical variables such as fractional green and senescent vegetation cover and green leaf area index at variable spatial scales. Research effort is continuing and some peer-reviewed articles have been or are being published. Estimation of land surface albedo and vegetation biophysical properties using SPOT-4 VGT and semi-empirical BRDF models. M. J. Barnsley, T. L. Quaife, P. D. Hobson and J. Shaw, Earth Observation & Environmental Monitoring Group, Department of Geography, University of Wales Swansea, U.K. P. Lewis and M. I. Disney, Remote Sensing Unit, Department of Geography, University College London, U.K. The VGT instrument facilitates analysis of the land-surface angular signature by virtue of its wide swath width ( multiple over-passes of a location provide image data at varying sun-sensor geometries. In recent years considerable effort has been directed towards devising methods to assess the information content of such data, most notably in the formulation of models of the Bidirectional Reflectance Distribution Function (BRDF). The three commonly cited potential products of BRDF model inversion are 1) the radiometric normalisation of off-nadir pixels, 2) the improved estimation of narrow- and broadband albedo and 3) the estimation of land-surface biophysical properties such as Leaf Area Index (LAI). Semi-empirical linear models of the BRDF such as the AMBRALS suite proposed for use with the MODIS sensor are rapidly invertible and have the potential to provide the aforementioned outputs. Data have been collected from the VGT sensor for the period 17th of May to1st of October 1999, covering Western Europe. This constitutes a total of 478 scenes (all P level products). The data were atmospherically corrected in-house using the Simplified Method for Atmospheric Correction (SMAC) and cloud masked using the reflectance information in the blue channel. Three areas have been selected for detailed study: 1) East Anglia, UK a MODLAND validation site with which some of the authors are involved, and for which field data is available. The area is flat with large arable farming areas. 2) Bordeaux, France Focusing on the Les Landes area, and 3) SE Spain a semi-arid area with low cloud-contamination. The AMBRALS BRDF model was inverted using VGT data for the above scenes. The potential for angular normalisation and estimation of spectral albedo is clearly demonstrated. Investigation of the potential for biophysical parameter and broad band albedo estimation is ongoing -- the main focus of which is fieldwork in East Anglia, including investigations with plant growth models and the temporal trajectories of the BRDF model parameters. VEGETATION/SPOT for Northern Applications: Assessment of Utility and Examples of Products Jing Chen and Josef Cihlar Canada Centre for Remote Sensing 588 Booth Street Ottawa, Ontario K1A 0Y7, Canada Tel: (613)947-1266 Fax: (613)947-1406 Email: jing.chen@ccrs.nrcan.gc.ca and Larry Band, University of Toronto, Canada Raymond Desjardins, Agriculture Canada Samual Goward, University of Maryland, USA Zhanqing Li, Canada Centre for Remote Sensing Alain Royer, University of Sherbrooke, Canada Robert Fraser, Intermap Technologies Ltd. Rasim Latifovic, Canada Centre for Remote Sensing Goran Pavlic, Intermap Technologies Ltd. 10-day Canada-wide synthesis images of VEGETATION acquired in the growing season (April 1 to November 30) in 1998 have been assessed for northern applications. The images received from the VITO centre after atmospheric corrections have been normalized to a common illumination and observation geometry (45( solar zenith angle and nadir view) after an angular correction procedure. Radiometry and atmospheric correction have been analysed using a dense dark vegetation inversion. Pixels contaminated by subpixel clouds were detected and replaced through temporal interpolation. The corrected images have been used for various applications including landcover classification, leaf area index (LAI) retrieval, fire scar area and age estimation, and net primary productivity (NPP) modelling. The utility of the VEGETATION sensor is assessed in comparison with AVHRR and Landsat TM sensors, with emphasis on the usefulness of the shortwave infrared (SWIR) channel. It is found that SWIR reflectance is very useful for improving landcover classification. SWIR information can also be used to suppress the background (understory, litter and moss) effects on forest LAI retrieval. These improvements in landcover and LAI mapping using VEGETATION images are significant in pixel-based NPP modelling. Mapping of new fire scar areas is also improved with the use of the SWIR band. The ratio of SWIR to near-infrared (NIR) reflectance was found to be highly correlated to fire scar age up to 50 years. This ability of mapping fire scar age is critical in estimating the spatial distribution of carbon sources and sinks in the northern ecosystems as the age determines the amounts of regrowth and heterotrophic respiration. Validation of neural network techniques to estimate canopy biophysical variables from VEGETATION data M.Weiss(1) , F.Baret(1), M.Leroy(2), O.Hautecur(2), L.Prvot(1), and N.Bruguier(1) (1) INRA Bioclimatologie, Domaine Saint-Paul, 84914 Avignon Cdex 9, France (2) CESBIO, 18 avenue E.Belin, BP 2801, 31041 Toulouse Cdex 4, France The objective of this study is to develop a global algorithm to monitor the vegetation, applicable to cultivated as well as natural vegetation areas. The monitoring is performed through the estimation of vegetation biophysical variables from 26-day VEGETATION data (fraction cover Fc, leaf area index LAI and fraction of absorbed photosynthetically active radiation fAPAR). Those variables are thus closely linked to the radiative transfer within the canopy and pertinent with regards to possible applications such as canopy primary production modeling or prediction of flux transfer of mass and at the soil-vegetation-atmosphere interface. The learning phase of the neural networks is achieved by using a synthetic catalog of VEGETATION BRDF. The latter is built thanks to well-known radiative transfer models and a wide range of model parameters for different dates and latitudes. In this study, we do not take into account atmospheric effects and work only with top of canopy reflectance data. As the number of VEGETATION reflectance data during 26 days depends mainly from the latitude and cloud occurrence, it is necessary to pre-process these data to get a constant number of inputs required by neural networks. This is achieved by inverting a linear BRDF model to estimate the nadir and hemispherical reflectances in the 4 VEGETATION wavebands. Three neural networks are then calibrated using these inputs to estimate Fc, LAI, and fAPAR. The optimal architecture is found to be one layer with four sigmoid neurons and one output layer with one linear neuron. A first validation is performed using a synthetic BRDF catalog of homogeneous and mixed pixels. Results show good performances on Fc and fAPAR. The LAI estimation is less satisfactory for dense canopies due to the saturation of the canopy reflectance. Moreover, LAI estimation is sensitive to the pixel heterogeneity. A second validation is then performed on experimental data sets provided by the ReSeDA (Remote Sensing Data Assimilation, 1997). The ReSeDA site is a 4km*5km agricultural area (mainly wheat, sunflower, alfalfa and maize) near Avignon (France). Ground measurements (LAI, Fc) were performed during the whole crop cycles. Reflectance data were acquired with the airborne POLDER sensor at 15 dates during the year. The neural network technique is first modified to be consistent with POLDER measurements and then applied to retrieve biophysical variables. The comparison between estimated variables and in situ measurements is quite consistent with the results obtained with synthetic data Estimation of Net Primary and Net Ecosystem Productivity of European terrestrial ecosystems by means of the C-Fix model and NOAA/AVHRR data. Frank Veroustraete and Hendrik Sabbe Flemish Institute for Technological Research (Vito) Centre for Remote Sensing and Atmospheric Processes (TAP) In recent years, a suite of primary productivity models has been developed, to address issues related to food security and biotic responses to climatic warming. Traditional approaches to assess primary productivity range from empirical climate correlation models to mechanistic ecophysiological models. These approaches all operate on point measurements that are extrapolated in space and time. Since landscapes are quite heterogeneous, the spatial scaling (upscaling) of point measurements is problematic, relative to the sampling density. Diagnostic or inverse (top-down) approaches to primary production like C-Fix have received a great deal of attention in recent years, owing to their avoidance of spatial extrapolation or land-cover classification, and the unique use made of remotely sensed observational data. The family of models to which C-Fix belongs to are genuinely called PEMs (Production Efficiency Models). This means that C-Fix inherently is spatially explicit, relying on observations that are specific for a given time and location, and hence the model is driven by satellite observations. C-Fix is a tool to create geo-referenced NPP (Net Primary Productivity) data layers for use in durable development planning and decision-making processes (Kyoto protocol). State-of-the-art algorithms describing carbon uptake and release mechanisms of carbon dioxide from vegetation cover in relation to meteorological conditions and satellite based quantification of the fraction of absorbed photosynthetically active radiation (fAPAR), allow for the build-up of yearly, geo-referenced datasets of NPP and NEP. Specific regions of interest (ROIs) can be specified for use in GIS and land management planning environments. In the work presented here, the modelling concepts of C-Fix will be discussed. The NOAA/AVHRR pre-processing steps to derive a pan- European fAPAR data set will be discussed. C-Fix NEP pixel simulations will be compared with data obtained from the EUROFLUX carbon exchange measurement sites. Hence the plausibility of the approach to determine NEP will be demonstrated. Finally a pan-European carbon budget will be presented and discussed within the framework of results obtained during the EUROFLUX project. Keywords : NOAA/AVHRR, Net Primary Productivity, Net Ecosystem Productivity, C-Fix, Carbon exchange, fAPAR. Acknowledgements: We acknowledge the financial support granted by the EUs 4th Framework Programme under contract ENV4-CT97-0577 and the Belgian OSTC contract CG/DD/05F. Monitoring North American Grasslands Dynamics with VEGETATION David J. Meyer *, Bradley K. Reed *, Bruce K Wylie *, Larry L. Tieszen +, Cullen R. Robbins *, Allison L. Scherff * *Raytheon Systems Company, EROS Data Center, Sioux Falls, SD 57198, USA + U.S. Geological Survey, EROS Data Center, Sioux Falls, SD 57198, USA This report culminates a multi-year study of the utility of the Systeme pour lObservation de la Terre (SPOT) Haute Resolution Visible Infra Rouge (HRVIR) and Vegetation instruments for multi-scale monitoring vegetation dynamics in the Great Plains of North America. The proposed study included (1) a simulation of the Vegetation viewing geometry, including pixel size variability, (2) the use of Vegetation for monitoring biophysical parameters over grasslands, (3) the use of Vegetation for monitoring seasonal dynamics within the grasslands, and (4) the utility of having spectrally matched, simultaneously acquired multi-resolution images to study spatial scaling processes within the Great Plains. The pre-launch study focused on the simulations, refinement of field validation techniques and development of seasonal and monitoring scaling strategies for the post-launch phase; the pre-launch work is described in a previous report. The post-launch study proceeded with biophysical, seasonal dynamics and multi-scale mapping studies using the HRVIR and Vegetation data sets. The biophysical component includes the mapping of surface relationship developed between reflectance, leaf area index (LAI), fraction absorbed photosynthetically active radiation (fAPAR) and green biomass to the HRVIR scale, then onto the Vegetation scale. The surface scaling methodology involves developing relationships between reflectance and biophysics at a quadrat scale (~0.5 m2), using extended reflectance grids large enough to be seen by the HRVIR to distribute the relationships across areas representative of HRVIR pixels, estimating the biophysical parameters at the grids scale using geostatistical techniques, then correlating the geostatistical estimates with HRVIR pixels. The scaling from HRVIR to Vegetation is done on daily syntheses acquired simultaneously with the HRVIR, the multi-date syntheses are used to distribute the measurements over time. The seasonal dynamics component of the study focused exclusively on the 10-day synetheses, using seasonal metrics develop for the Advanced Very High Resolution Radiometer (AVHRR). The results of the dynamics component of the study with Vegetation are compared to results retrieved from AVHRR data. Multitemporal analysis of the VEGETATION data for landcover assessment and monitoring in Indochina. Chandra Giri & Surendra Shrestha UNEP Environment Assessment Programme for Asia and the Pacific Asian Institute of Technology P.O. Box 4, Klongluang, Pathumthani 12120, Thailand Email:  LIENHYPERTEXTE "mailto:cpgiri@ait.ac.th" cpgiri@ait.ac.th &  LIENHYPERTEXTE "mailto:surendra@ait.ac.th" surendra@ait.ac.th Josef Aschbacher Directorate General Joint Research Centre (DG/JRC) pace Applications Institute (SAI) I-21020 ISPRA (Va) Italy Email:  LIENHYPERTEXTE "mailto:josef.aschbacher@jrc.it" josef.aschbacher@jrc.it Land use/land cover changes are occurring at an unprecedented rate and scale in Indochina. Accurate and reliable data, however, have not been available in the past. The current study aims at improving this situation. SPOT VEGETATION and NOAA AVHRR data were used to assess the usefulness of the data for accurate delineation and demarcation of major land cover types in the region. Land cover maps of 1985/86 and 1992/93 were prepared using NOAA AVHRR and a land cover map of 2000 was prepared using VEGETATION data. The paper presents a synopsis of this exercise focusing on the usefulness of VEGETATION data and its comparative advantage over NOAA AVHRR data. The ultimate purpose is to integrate the use of VEGETATION data into a regular assessment and monitoring operation of land cover types in Asia. The Suitability of VEGETATION for Mediterranean Land Degradation and Desertification Monitoring W. Mehl, P. Strobl, S. Sommer, H. Bohbot (JRC Ispra), R. Escadafal (IRD), J. Hill (Univ. Trier) After launch of the VEGETATION instrument the operational parameters of the instrument and their applicability and validity for the purpose of operational monitoring of short term events as well as of pluriennal trends and indicators for land degradation are assessed. Investigated topics include: Assessment of the product chain: accuracy of spatial positioning a strategy for identification of radiometrically stable areas, and their use for verifying the reliability of calibration and operational atmospheric correction Assessment of alternative preprocessing steps (follow-up on prelaunch topics): comparison of strategies for 10 days composites impact of DEM-derived illumination correction on interpretability Applications linked to high time resolution observation of aftereffects of rainfall on soil and vegetation in arid ecosystems mapping of aeolian sand transport events Extraction of indicators relevant for long term environmental monitoring: seasonal evolution of sparse vegetation cover - comparison between NDVI and spectral mixture analysis methods in particular, assessment of state of vegetation degradation in semiarid areas based on the relationship between annual herbaceous species and perennial shrubs. differentiation between movable and fixed surface materials (continuation of prelaunch assessment based on simulated data) Monitoring of forest ecosystems at regional scale using VEGETATION daily-data: First results on the Landes maritime pine forest (SW France) Jean-Pierre Lagouarde 1,Dominique Guyon 1, Benot Duchemin 2 (1) INRA, Unit de Recherche en Bioclimatologie, BP81, F-33883 Villenave dOrnon Cedex (2) CESBIO, 18 Av. Ed.Belin, F-31055 Toulouse Cedex Previous studies carried out within the framework of Vegetation Preparatory Program on coniferous and deciduous forests using VEGETATION data simulated from AVHRR/NOAA and Landsat TM time-series. They demonstrated the potential of VEGETATION daily-data for monitoring large-scale spatial heterogeneity and temporal changes of biophysical variables which determine surface-vegetation-atmosphere transfer, net primary production (NPP), forest growth, forest yield and other environmental processes: phenological cycle duration, albedo, fraction of photosynthetic radiation absorbed (fAPAR) by forest canopy, trees-cover fraction We present here applications performed with actual VEGETATION daily-data for estimating these variables. The study is based on a VEGETATION daily-data set acquired during one cycle of vegetation (31 March to 7 November 1998) on the Landes maritime pine forest. It is complemented by ground measurements of forest biophysical variables and land-use geographical data base, on the test site of NEZER which covers about 3000 hectares. The directional variations of reflectance in visible (VIS), near infrared (NIR) and middle infrared (SWIR) are analysed. A semi-empirical model of BRDF (Rahman model) already tested in the pre launch phase of the preparatory program is fitted. Applications for the normalisation of reflectances, the estimation of albedo and fAPAR are presented and the consequences of the sampling of the BRDF during only one year discussed. The spatial variations of reflectance and their sensitivity to forest structure parameters are also studied. VEGETATION reflectances are compared to tree-cover fraction estimated at 1km scale from ground measurements. The practical interest of vegetation indices including the SWIR reflectance to reduce the seasonal sensitivity to phenological properties of undergrowth is analysed. The potential contribution of SPOT 4/VEGETATION data for mapping Siberian forest cover at the continental scale S. Bartalev (1), F. Achard (1), D. Erchov (2) and V. Gond (1 (1) Joint Research Centre of the European Commission I-21020 Ispra (VA), Italy TP 440 ( +39-0332-786396, fax +39-0332-789073,LIENHYPERTEXTE "mailto:sergey.bartalev@jrc.it" E-mail: sergey.bartalev@jrc.it (2) International Forest Institute 117418, 69 Novocheriomushkinskaya str., Moscow, Russia ( +7-095-332 68 77, fax +7-095-332 29 17, E-mail: erchov@ifi.rssi.ru The most recent national Russian vegetation map (i.e. covering the full territory of Russia) was published in 1990 at the scale of 1:2,500,000 (Isaev et al., 90). This map was produced from the compilation of more detailed information (inventories or maps) from various sources at different dates and with heterogeneous accuracy. For example for the Siberian North-eastern regions, the map has been derived from the spatial aggregation of visual interpretations made from aerial surveys taken in the 1950s. Many changes in the forest cover have occurred since this period, as a consequence of natural or human-made fires, clear-cutting, insect damages and following regrowth/regeneration processes. In spite that these forests have not a large economical value, their role on the biosphere, including in the carbon cycle, is thought to be rather important. New opportunities of getting a consistent and up-to-date forest map of Siberia are provided by the SPOT-4/VEGETATION data due to their spectral, repetitiveness and geometric characteristics well related to the issues to be solved. A feasability study for forest mapping at the Siberian scale has been carried out using a long time series of S-10 products during the vegetative growing season: from beginning of March 1999 untill end of November 1999 covering the boreal zone of Siberia (from 42 N to 75 N and 5 E to 180E). A preliminary visual comparison between the existing Russian vegetation map and Summer mosaics of S-10 products images has already shown significant discrepancies in the map due either to initial map inaccuracies or to changes which occurred from the date of the map. For this boreal forest mapping study at continental scale, the separability of the main forest types is investigated. These main forest types are identified by dominant tree species, such as larch (Larix spp.), spruce (Picea sibirica) and scots pine (Pinus sylvestris), A set of locations representative of these main forest classes has been selected over the Siberian region from a few georeferenced forest maps derived from SPOT HRV imagery. The spectral (temporal signatures) and ancillary (from inventory databases) attributes over these locations are identified and compared to each other. The separability of the classes is assessed from this comparison. The feasibility of boreal forest mapping at continental scale is further discussed. Fire Scar Detection in the Canadian Boreal Forest Plummer, S.E., Gerard, F.F. and Wyatt, B.K. Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Cambs, PE17 2LS, UK Tel: +44 1487 772475, Fax: +44 1487 773467, Email: sp@wpo.nerc.ac.uk The boreal ecosystem stretches across the Northern Hemispheres circumpolar countries. It covers approximately 10% of the Earths land surface, ranking second in terms of total plant mass to the tropical forest belt. Because it contains approximately 40% of terrestrial carbon, it plays an extremely important role in the global carbon budget. It is therefore important to identify anything that perturbs this ecosystem, for example, fire. However, global terrestrial carbon cycle models generally do not take into account loss of carbon through disturbance. Further, disturbance has a strong influence on succession in the boreal ecosystem through the effect on opportunities for change from boreal biotopes to others typical of biomes to the south and north. Since climate change may alter the frequency and size of disturbance events, it is vital to monitor changes in their spatial and temporal occurrence if we are to predict the impacts of global environmental change. Yet, currently there is no comprehensive database of disturbance across the entire boreal ecosystem and, where efforts to collate information have been attempted, they have usually been either spatially restricted or a snapshot in time. Air photo interpretation and visual annotation of base maps from light aircraft are the primary methods for fire mapping. This is an extremely labour intensive method of fire scar mapping which requires considerable financial investment. For the boreal forest, the large area of individual burns makes coarse resolution remote sensing an attractive alternative although it is limited to the last 25 years. This paper extends the work of Eastwood at al. (1998) on fire scar detection through the comparison of new indices, thresholding and segmentation of VEGETATION data over the BOREAS experimental region. Imagery on a monthly time-step was acquired through the 1998 active fire season (May-September). Segmentation was performed on a total of 24 VEGETATION images to assess sensitivity of the approach to segmentation criteria and the variability of fire scar detection as a function of image geometry and atmospheric state. The results were compared against hot spot observations recorded for the time period in the FIRE-M3 detection system (http://fms.nofc.cfs.nrcan.gc.ca/FireM3/) (Li et al. 1997). Older fire scars were identified with reference to the Canadian Forestry Service GIS database of large fires covering the period 1980-92 as used by Eastwood et al. (1998). The results are in accordance with the observations by Eastwood at al. (1998) that the middle infrared waveband provides better spectral differentiation of fire scars than methods based on NDVI. Monitoring Boreal Forest Resources in Northern Europe from the VEGETATION instrument Bernard Pinty(1), Jean-Luc Widlowski(1), Nadine Gobron(1), Michel M. Verstraete(1), Ola Engelsen(2), Harald Johnsen(2) and Yves Govaerts(3) (1) Space Applications Institute EC Joint Research Centre I--21020 Ispra (VA), Italy (2) Norut Information Technology N-9005 Tromso, Norway (3) EUMETSAT, Am Kavalleriesand 31 D-64295 Darmstadt, Germany Boreal forests constitute a major renewable economic resource of north European countries. These ecosystems are threatened by the combined effects of increasing demand for timber and wood products, and by likely changes of climate resulting from the well known increase in greenhouse gases in the atmosphere. The VEGETATION sensor offers new opportunities to monitor the status and evolution of these resources, thanks to its four spectral bands. The synergy between these data and the existence of advanced models and techniques of data analysis has permitted the full exploitation of data generated by this instrument, despite the difficulties arising from low solar zenith angles and a variable atmospheric aerosol load. This project provides a map showing the likelihood of the presence of dense boreal forest in the Barents region. This information should be useful for the pre-operational management of northern Europe's boreal forest resources. The approach developed here will also be applicable to the operational management of other ecosystems. SPATEM: The analysis of annual sequences of VEGETATION data at the landscape scale. Agustin Lobo and Nicolau Pineda Instituto de Ciencias de la Tierra "Jaume Almera" (CSIC) Lluis Sol Sabars s/n, 08028 Barcelona, Spain alobo@ija.csic.es The instrument VEGETATION acquires images with global and daily coverage of the Earth surface at 1 km2 resolution from the same SPOT platform that hosts HRVIR, the sensor acquiring high spatial resolution images of selected areas. VEGETATION and HRVIR imagery have similar spectral characteristics, are acquired with similar angles and can be geo-corrected to the same projections. In SPATEM we have approached the integration of multi-temporal VEGETATION images into products derived from the high spatial resolution images in a forested Mediterranean landscape. Our approach has included both methodological aspects as well as the implications of such integration for the applications of Earth Observation. We used the high spatial resolution imagery and a method based on image segmentation and discriminant analysis to produce a detailed land-cover map, emphasizing on forest types, and we calculated the cover fractions of the classes within each VEGETATION pixel. After a general analysis of the geometric and optical quality of the multi-temporal sequence of VEGETATION images and an optimization of the annual sequence of vegetation indices, we modeled the annual cycle of the vegetation indices of each class. We also performed an inverse analysis, in which we estimated the cover fractions from the multi-temporal sequences of the VEGETATION pixels and the models, using an Spectral Mixture Analysis. Results were poor at the pixel level, probably as a result of our models not being at extremes in the feature space, but the average estimate for the entire area of study was very accurate. We discuss some possible improvements to increase accuracy at the 1 km2 pixel level. We also discuss the characteristics of the different annual cycles considering both the ecological attributes of the classes and their projections on a reduced plane of phenologic variability. In order to obtain a more comprehensive view of different annual cycles, we also enlarged our area to cover 17 digital vegetation maps at 1:50 000 scale within the area covered by our VEGETATION imagery (0 to 5 E, 40 to 45 N). In this case, we selected those 1 km2 pixels that were at least 90% contained within one single type of vegetation according to the maps. We found that VEGETATION data can define distinct annual cycles of vegetation indices for very detailed vegetation types. This ability will have important consequences, not only for improving global-scale land-cover mapping, but also because a better understanding of the annual cycles will let us formulate and test improved models of vegetation dynamics, beyond the elementary vegetation units that are currently used. VEGETATION/SPOT4 applications for macro-regional landscape mapping (Land Cover/ Land Use mapping and monitoring of Russia, PI - E.V. Milanova) Lioubimtseva E. (1), E.V Milanova (2), P.Tcherkashin(2), and V.N.Solntsev (2) (1) Dept. of Geography and Land Studies, Central Washington University (2) Dept. of Physical Geography and Geoecology, Moscow Lomonosov State University The final (post-launch) phase of this study has been focused on analysis of the potential of VEGETATION/SPOT 4 for medium scale land-cover mapping and, more specifically for mapping and landscape mosaics of different heterogeneity and graininess. The area of study comprises various combinations of coniferous and mixed forests with forest-steppe, pastures and agricultural lands on the Russian plain. During the preparatory phase data from AVHRR/ NOAA, RESURS01-3/MSU-SK, and COSMOS/MK-4 were processed to explore the requirements of landscape ecological mapping in terms of spectral channels and ground resolution of the satellite instrument. Evaluation of 16 most common types of landscape mosaics identified within the study area demonstrated that despite its relatively coarse ground resolution, VEGETATION provides an excellent tool not only for land-use/ cover mapping but also, and especially, for landscape ecological applications. Available spectral windows allow particularly fine discrimination between patches within complex heterogeneous landscapes (i.e. forest-steppe mosaics with agricultural lands) as well as discrimination between different types of forest vegetation. The data also represent considerable interest for monitoring riparian ecosystems and water content analyses in vegetation cover. Fire patches in natural vegetation in southern Africa Swinnen E. *, Verwimp R. **, Gulinck H. *** * KULeuven / VITO (since 15/9/1999) ** Ground for GIS, KULeuven *** KULeuven The objective of this study is to explore the feasibility of SPOT-VEGETATION for burnt surface mapping in savanna ecosystems. The study site is located in the Chobe area, northern Botswana. It is mainly covered by a mosaic of open and dense woody savanna and woodland. A time series with an interval of 2 weeks of VEGETATION images over one dry season (1998) is used for the signature analysis and the change monitoring. End member analysis is applied on a single date image with aid of a high resolution SPOT-XS image of even date and classified by means of field survey recordings. The spectral behaviour of burnt and unburnt savanna is examined in the RED, NIR and SWIR bands on a single image and for a time series. The reflectance of a burnt surface is lower than for savanna: a decrease in reflectance of 25-30% in NIR and 15-20% in SWIR was observed immediately after burning. The signal variability is largest in SWIR compared to NIR and RED. An increase of variability is observed in all examined bands immediately after a fire occurred. NIR has the most discriminative power. The change detection analysis focussed on mapping fire scars (change) and savanna (no change). Two change algorithms are employed to extract the change information: standardised differencing and 2-dimensional principal component analysis (PCA) (time1 vs time2). A statistical threshold is applied to classify the output of these algorithms to a burnt/non-burnt information layer. Accuracy is assessed using the field work recordings. Both change algorithms are successfully applied. All kappa coefficients are higher than 80%. No significant difference can be proved between standardised differencing and PCA. Although the slower decrease of the SWIR band after the burn-event the change monitoring process performed no significant better results for NIR. For areal extent measurement, two sub-pixel classifiers are examined, linear spectral unmixing (LSU) and artificial neural networks (ANN). The former is based on typical class signatures, but omitting the spectral heterogeneity within one class for a given band, whereas the latter takes this heterogeneity into account by training the network with examples. Different band combinations are explored. Results are evaluated by comparing the areal estimates of the classes, the rank correlation coefficient with the reference high resolution classification and the distributions of the errors. Both methods give satisfactory results, although ANN performs slightly better. Band combinations including NIR always yield better results. When SWIR is added, the results are less accurate for LSU, because of the large variability of the reflectance of both classes in this spectrum. The best combination is RED-NIR for both techniques. Rank correlations obtained range between .85 and .90 for LSU and exceed .90 for ANN. Largest estimation errors are logically found for the mixed pixels. The result of ANN contains more small errors than LSU (resp. 75% and 35% of the pixels with an error <5%). VEGETATION images prove to be an excellent tool for monitoring fire scars and complementary themes as flood monitoring. Application of SPOT 4-VEGETATION data for mapping the forest-cover of Madagascar Mayaux Philippe, Gond Valry and Bartholom Etienne Global Vegetation Monitoring Unit Space Applications Institute TP 440 - Joint Research Centre 21020 Ispra (VA) - Italy The objective of this poster is to demonstrate the possibility of updating forest-cover maps in ecosystems affected by rapid changes using VEGETATION data in a limited time. In this poster, an efficient technique for cloud decontamination of the ten-day composites is presented. A 36 composites were used in this study covering the period from October 98 to September 99. These composite images were still too contaminated by clouds and haze to allow for direct classification. Monthly images were produced in order to reduce the remaining clouds. Two different criteria of second stage compositing were tested: the maximum NDVI and the minimum SWIR. A forest-cover classification was derived from the 12 monthly composite images. The 36-band image was classified into 40 clusters using the Isodata unsupervised method. The algorithm based on the minimum SWIR was selected because it produced more spatially homogeneous monthly composites. Then the monthly profiles are interpreted in terms of vegetation phenology. The class labeling was done based on available field knowledge, ancillary information and visual analysis. The accuracy of the resulting map was assessed by comparison with Landsat classifications interpreted by local experts over three sites. Compared to previous similar exercises with AVHRR, VEGETATION offered 4 main comparative advantages: high capacity to update the forest-cover maps in a fast manner fast access to ready-to-analyze preprocessed data high geometric accuracy systematic data acquisition STEM-VGT : Satellite measurements and terrestrial ecosystem modelling using VEGETATION instrument G. DEDIEU (LERTS/ CESBIO Toulouse France) Co investigators: JC Grard (LPAP / IAL Lige), D. Graetz (Gondwana Lab, CSIRO, Canberra, Australia) Participants : B. Berthelot, P. Cayrol, S. Lafont, P.Maisongrande (CESBIO) L. Kergoat (LET) A. Chebouni (IRD) Our general objective is to develop process models of vegetation functioning that can be used at regional and global scales for predicting carbon and water exchanges between land surface and the atmosphere. The specific objective of this project is to develop and validate VEGETATION based methods for validating, driving, or calibrating such vegetation process models. During the VEGETATION pre-launch phase, we used NOAA/AVHRR data as a surrogate of VEGETATION at both global and regional scales. The algorithms developed have been applied to actual VEGETATION data to address global and regional topics. Atmospherically corrected VEGETATION measurements were used to estimate daily and seasonal vegetation net primary productivity (NPP) at the global scale with 0.5x0.5 resolution. The TURC model (Ruimy et al., 1996) is driven by NDVI through estimation of the fraction of Photosynthetically Active Radiation absorbed by plant canopies. Soil respiration, SR, is computed as a function of temperature and soil humidity. Net Ecosystem Productivity, the difference between NPP and SR, is used as input of an atmospheric transport model in order to check results through comparison to atmospheric measurements of CO2. At local and regional scale, ground experiments acquired in 1998 and 1999 during the SALSA experiment, held at the border of USA and Mexico, were used to prepare the use of VEGETATIOn for calibrating a vegetation functioning model coupled to a SVAT. The coupled models are used to predict leaf area index (LAI), from which specral reflectances are estimated and compared to actual ones. Consistent results are obtained over this semi-arid area, even if further work is require to better understand the driving parameters of SWIR reflectances. Despite the weak vegetation cover in the SALSA area, VEGETATION data allowed to monitor the seasonal variation of LAI. The main benefit of VEGETATION data relies in its high geometric and radiometric quality and its capability to maintain nearly constant space resolution in the field of view, resulting in an important saving of time when handling the data. In addition, parallel studies indicate that the blue channel could help to improve atmospheric correction, while overall quality of the system should shortly lead to operational normalization of bidirectional effects. Intermediate Scale Approach for Estimating Vegetation Canopy Leaf Area Index using SPOT4/VGT Spectral Bands. F. Cipriani, E. Cubero-Castan MEDIAS Toulouse France The Leaf Area Index, defined as the one-sided green leaf area per unit ground area, has until now been either globally estimated, with rather coarse ground resolution data (typically about five kilometers, with NOAA14/AVHRR), or locally estimated, upon forest sized areas, and unmixed kind of vegetation species, through high resolution data (SPOT/HRV or SPOT/HRVIR or LANDSAT/TM). This type of land surface parameter need though being also estimated on an intermediate scale, typically one kilometer, in order to increase, with dedicated sensors as SPOT4/VGT, the precision of previous large scale estimations of vegetation behaviour, as climate-atmosphere-vegetation interactions models are now reaching the meso-scale. Moreover, the ability of daily coverage of the vegetation cover remains a powerful means of dynamical modelling, which is worth exploiting, in the case of derived parameters as the LAI, when vegetation phenology is involved. Eventually, this intermediate scale is worth considering, in order to reach the combination of local experiments consistency with the large remote sensed both temporal and spatial coverage abilities. From this point of view, a one kilometer scale processing approach is being developped, using SPOT4/VGT decade synthesis (one kilometer ground resolution), covering one year of data (april 1998 - april 1999), over France. The decade synthesis provide both a sufficient amount of data over the year, for a temporal evolution study, as well as an already corrected data set, from the geometric, radiometric, and atmospherical effects. These data sets are associated with the Corine Land Cover data set raster map (250 meters resolution), which provides a coarse description of the land cover, both static, and inaccurate in terms of vegetation species description, but sufficient for a first approach. The VGT data sets and the CLC map are first commonly geo-referenced, using USGS map projection routines. Thus each VGT pixel can be associated with a composition of land occupation components. The Red and Near-infra-red signals of the sensor are then unmixed (Faivre, 1996), in order to calculate the contribution of each land cover component, within the VGT pixel. The Normalized Differenced Vegetation Index can therefore be computed, for each component, and the LAI estimated from the NDVI, trough a global scale parametric model (Sellers, 1994). The parameters used are the NDVI temporal extrema values for each land cover class, computed from the VGT data sets, and the maximum value of the LAI, over the year. The NDVI extrema are taking into account a North-South gradient over France, being computed within one degree latitude bands. The maximum LAI values depends on the forest composition gradient, over the studied area (Solmon, LA), the other class values remaining constant. The LAI values computed are eventually integrated, in order to obtain a one kilometer LAI data set, over France. LAI maps are produced for each decade of the year where a VGT synthesis exists, allowing thus temporal profiles to be constructed. The process actually follows the validation procedure. Straight on ameliorations could be yielded by data pre-analyzing, the improvement of the LAI model used, and a precision growth by taking into account the LAI-NDVI saturation (and limits), and error due to the integrated surfaces. Moreover, a classification procedure has to be engaged to replace the CLC map. This procedure would involve high-resolution SPOT4/HRVIR data crossing with SPOT4/VGT data. Key Words : Remote sensing, Land surface parameters, Meso-scale, SPOT4/VGT, NDVI, LAI, Spatial heterogeneity, Signal unmixing, Land cover classfication. A new vegetation map of Central Africa Update of the JRC-TREES map of 1992 with SPOT-VEGETATION imagery of 1998 Herman Eerens, Bart Deronde & Jan Van Rensbergen Centre of Expertise on Remote Sensing and Atmospheric Processes (Vito - TAP) Boeretang 200, B-2400 Mol, Belgium. Tel:(+32) 14 336844 Fax: (+32) 14 322795 Internet: http://www.vito.be E-mail: jan.vanrensbergen@vito.be In the frame of a short-term feasibility study (2.5 man-months), the land cover in the Central-African region was mapped with recent imagery of the 1km-resolution sensor SPOT4-VEGETATION. The work was performed on behalf of METAFRO-InfoSys, the information system of the Belgian Royal Museum of Central Africa. The image classification was calibrated with information from the well-known TREES-map, which was established by the EU-Joint Research Centre on the base of (mainly) NOAA-AVHRR imagery of 1992. In this light, the actual map should not be considered as a new, stand-alone product, but rather as an update of this TREES-map. The used VGT-imagery comprised the 36 decadal syntheses ("VGT-S10" products) ranging from April 1998 until March 1999. This yearly image set was pre-processed in the following way. First, for each decade, the Red and NIR reflectances were combined into a modified version of the "Soil Adjusted Vegetation Index" (SAVI), which is less sensitive to variations in the reflectance of the soil background than the classical NDVI. In order to remove the cloud perturbations, the SAVI time series were then submitted to a cleaning procedure and from the smoothed curves, monthly mean SAVI-values were computed. Finally, these monthly profiles were used to derive "phenological" images, which quantify the general shape of each pixel's growth curve by means of parameters such as: the annual SAVI-mean, -extremes and -range, a seasonality index (mean-weighted amplitude), the start and length of the growing season, etc. The image classification was realized by means of a Maximum Likelihood algorithm, applied on a subset of these phenological images and supervised with training areas selected from the JRC-TREES map of 1992. The resulting land cover map was embellished with vector information (boundaries, rivers and roads) and plotted on scale 1:4.000.000. Statistical tables with the acreage distribution of the land cover classes were also derived for the national and regional levels, and for both years (1992: TREES vs. 1998: VGT-update). Mutual comparison pointed out that both maps agree fairly well (89% of the concerned acreage), which implies that no dramatic changes have taken place in the course of the last six years. However, as the updated map was not checked on the field, it remains unknown to what extent the observed deviations (11% of the pixels) are due to misclassifications or to real changes. Although part of the observed deviations are certainly artefacts, a number of probably significant land cover changes were revealed which deserve further inspection, either by field controls or by the analysis of high resolution imagery. Sub-pixel mapping of Sahelian wetlands using multi-temporal SPOT-VEGETATION images Jan Verhoeye, Robert De Wulf University of Gent Faculty of Agricultural and Applied Biological Sciences Coupure Links 653 9000 Gent, Belgium Africa supports some of the worlds largest swamps. Some of the most extensive of these occur within the Sahelian zone: the floodplains of the Senegal River, the Interior Niger Delta, the mid Niger floodplain, the Chad Basin (comprising Lake Chad, the Logone-Chari floodplains and Hadejia, Jamaare and Komadugu floodplains). During the last 40 years the growth of the human population and the associated increased demand for irrigation water and arable land, has put increasing pressure on these wetland ecosystems. In an effort to improve the economic situation of the local populations, large-scale hydro-agricultural projects are being planned, comprising large dams and irrigation schemes, which threaten the wetlands. These wetlands are very extensive and they constitute very dynamic eco-systems. These characteristics make the wetlands suitable objects for study using satellite images with coarse spatial but high temporal resolution, such as SPOT-VEGETATION images. At coarse spatial resolutions pixels inevitably become mixed. Traditional classification techniques are hard in the sense that a single pixel is assigned to a single land cover class. For mixed pixels soft classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. The result consists of a number of fraction images. This step will be called sub-pixel classification. The next step, sub-pixel mapping, consists of assigning the land cover fractions to the sub-pixels and results in a hard classification at a higher resolution than the original SPOT-VEGETATION images. The sub-pixel classification is based upon the hypothesis of the linear spectral unmixing model: the image spectra are the result of mixtures of surface materials, shade and clouds, and each of these components is linearly independent from the other. In fact the value of each pixel can be modelled as a linear combination of the land cover spectra present in the image and their respective fractions. Multiple linear regression techniques can be used to solve these models. This technique has been applied to the combination of a high-resolution SPOT-XS image and a time-series of low-resolution SPOT-VEGETATION images. The results show that presented method is capable of accurately estimating the sub-pixel fractions. The key problem of sub-pixel mapping is determining the most likely locations of the fractions of each land cover class within the pixel. Assuming a spatial dependence within and between pixels can solve this. The coarse pixels are divided into smaller units and the land cover is allocated to the smaller cells within the larger pixels, in such a way that spatial dependence is maximised. This problem can be cast into a traditional linear programming format. The spatial dependence can be represented by various measures, varying from simple averages to values calculated using spatial statistics. Preliminary results indicate that the classification accuracy at 500 m resolution closely approaches the original accuracy at 1000 m. Estimation of surface variables at the subpixel level for use as input to climate and hydrological models JeanPierre Fortin*,Monique Bernier , Ali El Battay, Yves Gauthier and Richard Turcotte *INRSEau, 2800 rue Einstein, C.P. 7500, SainteFoy (Qubec) G1V 4C7 Canada Tel.: (418)6542591; Fax: (418) 6542600; email: jpf@inrseau.uquebec.ca For hydrological simulation and forecasting, estimation of the spatiotemporal variability of watershed variables like albedo, soil moisture and snow cover is very important. The needed information cannot come uniquely from gound survey data, as the number of sample sites required and the frequenxy of measurements would be prohibitive. Remote sensing can help to find out a solution. As a new medium resolution but high frequency sensor, having spectral bands in the visible and near infrared,was to be on board the SPOT4 satellite, together with an enhanced high resolution sensor, we have proposed an investigation having as objectives (a)the estimation at the subpixel level of physical variables of the surface, namely the reflectances of each land cover within the pixel, (b) the estimation at the subpixel level of the spatial distribution of snow cover and, finally, (c)as accurate as possible registration of the images for multitemporal input into a spatially distributed hydrological model using geocoded data. During the prelaunch phase of the investigation, we have developed the methodology, using simulated VEGETATION and HRVIR data from TM data. Our results were very encouraging. Using a methodology based on the theory of spectral mixture, we were able to estimate from VGT pixels the reflectances of the broad land use classes present on a summer image with a very good accuracy, the estimations being well within one standard deviation from the true values estimated from corresponding simulated HRVIR pixels. Also, we have defined two snow indices allowing estimation of snow cover at the subpixel level with a very good accuracy, more than 70% of the estimations being within 10% of the snow cover value estimated from HRVIR pixels. Finally, we were able to obtain registrations of VGT pixels within less than 100m from the simulated true location. For the postlaunch phase, we were able to have access to 15 VGT images, from January to June of 1999, as well as to one HRVIR image and one panchromatic image from SPOT2, both during the snowmelt period. The idea was to verify the methodology with actual VGT and HRVIR data. In this communication, after recalling the results obtained in the prelaunch phase of the investigation, we will discuss the results obtained with the actual data. In short, in 1999, the snowmelt period in the selected region did not coincide very much with the available high resolution data necessary to test our methodology. We were however able to map the snow cover and estimate the reflectances of land covers present in the pixels. Also, the VGT images used were localised within the expected accuracy with respect to each other and with the HRVIR image. Integration of VEGETATION and HRVIR data into yield estimation approach. Andr HUSSON SCOT Robert FAIVRE INRA The possibility of obtaining reliable and relevant information on crop evolution and crop production is a key factor for decision making and defining strategy. Originally based nearly exclusively on meteorological data and on significant gathering of ground information, forecasting systems became rapidly reliant on statistical models which allows yields to be linked in a simple way to some explanatory variables. These preliminary tools proved to be limited in particular as regards exceptional years for which trends estimates have to be corrected of cyclical effects. As earth observation data can monitor crop conditions of an on-going campaign during all the growing season, measure parameters related to plant functioning and improve the detection of spatial features of phenomena affecting crops, various studies have been carried out in these last years to investigate the contribution of remote sensing data in these fields. They have shown that using models seems to be a necessary step in obtaining information which, such as crop yields, cannot be directly derived from space technology. This project, which fits into the general context of the VEGETATION Preparatory Programme for the International User Community (IUC), aims at improving the methodology for integrating remote sensing data into crop models. The proposed investigation, based on the performance of the VEGETATION instrument, was focused on the following main issues: a methodological approach: to define a strategy for integrating remote sensing data into crop growth models. a research approach: the combination and the complementarily between VEGETATION instrument and HRVIR instrument, and the capability of retrieving individual spectral response of each main crops on the site from VEGETATION signal unmixing. an operational approach: the possible strategies, in the context of an operational programme, to combine Earth observation derived information and crop models in order to forecast regional crop production. As part of the pre-launch phase of the preparatory programme, a methodology for processing coarse information (on a scale of 1km) on vegetation cover to derive more specific information on a given crop was tested on pseudo- Vegetation data, in fact a small number of Spot/HRV images which had been downgraded to Vegetation resolution. The purpose of the second phase (post launch phase) is to validate the methodology on an actual series of Vegetation imagery. This validation was done for the Beauce Chartraine (an area of about 1600km around Chartres). The applied methodology is based on three main stages: 1. Deconvolution application of the method of estimating components in mixed data for each date and for each channel of interest (this disaggregation model requires prior knowledge of the land cover). deducting a vegetation index (or a leaf area index) on the basis of the radiometric information thus obtained for each crop theme. 2. Simulation Use of the spatio-temporal evolution of this index to calibrate a crop simulation model pixel by pixel (as part of this work, the STICS model developed by INRA Avignon will be used). Mapping of simulated yields for the studied region, and comparison with the official production statistics. This approach should lead to forecasting of regional yields for the crop of interest (wheat) and its spatial distribution. Interest of MIR data from VEGETATION for the monitoring of climatic phenomena impact on crops, a case study Thierry Fourty Herve Kerdiles, Frederic Biard (Geosys, France) Remote sensing approaches applied to agriculture are undergoing profound changes. Actually, two kinds of approaches, requiring both different technologies or tools, are developed. Precision farming is the most promising approach. However, mainly due to lacks in the understanding of radiative transfer processes occurring inside the crops (vegetation/soil) and atmosphere, results remain approximate. The second approach is the monitoring of crops on a global basis in both time and space dimensions. This approach is very interesting for private companies since it doesnt require new tools or sensors to respond to the emergence of economic markets. To show and test an operational use of such an approach, we propose to investigate the use of data acquired with the VEGETATION sensor to monitor crops at a regional scale. This study will focus on the potential of the MIR band to show crop water stress in a region located around the Aude in south west of France, showing an important difference in rainfall between 1998 and 1999. From an industrial point of view, crop monitoring based on remote sensing requires 2 mains stages. 1) The first stage consists in determining and delimiting homogeneous zones in terms of crop or their behavior. Three different approaches will be investigated. The first one consists in a photo-interpretation based on color compositions. The second one consists in fitting linear relationships between bands to show a possible significance of residuals in terms of type of crops (or land cover). The last one consists in doing an unsupervised classification. For each approach, the strengths and weaknesses of the additional use of MIR information will be evaluated. 2) The second stage consists in processing previously defined zones to detect abnormalities in crop behavior, related to the crop water status due to rainfall. For this purpose, based on the results of the first stage, defined zones provided by the best approach will be selected. Then, possible relations between radiometric information, including or not MIR (crop water sensitive) and the measured data of cumulative rainfalls between 1998 and 1999 will be researched. Results will be discussed with regards to industrial applications, sensibility and accuracy. VEGETATION contribution to the desert locust habitat monitoring Cherlet Michael* , Mathoux Pierre**, Bartholom Etienne*** and Defourny Pierre** *FAO Locust Group, Plant Protection Service ** Department of Environmental Sciences - Universit catholique de Louvain Croix du Sud, 2 bte 16 B-1348 Louvain-la-Neuve ***JRC-SAI - Global Vegetation Monitoring Unit I-21020 Ispra The Desert Locust, Schistocerca gregaria, is a continuous threat to agriculture, subsistence farming and pastures in the arid areas of northern Africa, the Middle East and south-west Asia. Control of the Desert Locust is an important part of the general effort in ensuring food security. To further improve the combating of this migrant pest and to improve its routine global locust monitoring and forecasting activities, the FAO is implementing a regional Emergency Prevention programme, EMPRES. One aspect of the programme focuses on increasing national early warning capacities in view of optimising monitoring through more efficient survey and control planning . Early detection and control of initial locust populations in the recession area are critical in order to prevent the development of outbreaks and plagues. Traditionally, Plant Protection Services organise field surveys to try to obtain an idea of the locust populations and the condition of the locust habitats in terms of soil moisture and vegetation. Low resolution satellite remote sensing, integrated with other field information, offers a cheap means to obtain a synoptic overview on the conditions of the habitats in near real time. Rainfall is the first indicator of potentially good habitat conditions, but estimates based on satellite data are often not adequate over desert areas . However, the important secondary effects of rainfall such as soil moisture and vegetation growth, can be more reliably observed and monitored by low resolution satellites. Past projects developed methodologies to increase the reliability of the AVHRR NDVI time series. However, the limits were reached for using NOAA to detect very sparse vegetation due to poor image positioning, poor calibration and high sensitivity of the NDVI-MVC compositing technique to the directional perturbing factors. This research aims to provide demonstrative results for Desert Locust habitat monitoring using the SPOT VEGETATION that can be readily implemented into actual operations. The current work focuses on three main technical issues. The sensitivity of the VEGETATION signal to soil moisture change and to very low levels of green biomass. The detection of soil humidity is based on the SWIR band. A feasibility study investigates the impact of actual rainfall events on the observed SWIR signal over bare soil areas. The detection of the light greening of the desert is essential and requires selection of a sensitive vegetation index which is not disturbed by soil variability. To improve the consistency of the selected index, correction of bi-directional effect is performed. These analyses are carried out using daily S1 VEGETATION product. (ii) However, handling daily synthesis is a major constraint for operational monitoring. Therefore the S10 product is also investigated with regard to optimising interpretation reliability by looking at: the influence of the temporal sampling on the monitoring performances, the impact of the MVC-NDVI compositing strategy on these performances and the index calibration using frequent and targeted field observations provided by national locust teams. (iii) The VEGETATION central processing and distribution principle offers a sound alternative to solve the existing problems of timely data delivery provided some specific work is done on transformation of data. This project will consider developing dedicated methods for the transformation of data into information appropriate for fast delivery. Intelligent compressing methods, i. e. transfer of only the significant information from the thematic point of view, will be considered in order to keep data flow as low as possible. Optimised and reliable final information, e.g. a habitat risk-map, can then be efficiently transferred to the final users which are the national service in charge of desert locust control in affected countries. Mediterranean habitats: a multi-variate analysis of VEGETATION data. Agustn Lobo1, Jordi Carreras2 and Josep-Maria Ninot2 1Institut de Cincies de la Terra "Jaume Almera" (CSIC) Lluis Sol Sabars s/n, 08028 Barcelona, Spain alobo@ija.csic.es 2Departament de Biologia Vegetal, Facultat de Biologia, Universitat de Barcelona Diagonal, 645, 08028 Barcelona, Spain Annual cycles of greenness, as observed from satellite imagery at resolutions ranging from 1 to 8 km, have proven to be a useful proxy of the phenology of light interception. Such data have been applied to calculate primary production, to parameterize dynamic vegetation models and to produce digital land cover charts at continental and global scales. VEGETATION imagery has superior radiometric and geometric specifications compared to the commonly used NOAA-AVHRR images, which should result into a more accurate description of phenolgy. We have analyzed the annual cycles of VEGETATION reflectance quotients for a number of very detailed habitat types in the NW Mediterranean basin. We have processed 36 S10 VEGETATION images and 18 digital maps of habitats at scale 1:50 000, which legend is based on the Directive 92/43 of the European Union with specific improvements for Catalonia (NE Spain). We selected all VEGETATION pixels (1 km2) that were included (at least in a 90%) within one single habitat patch, and extracted their time series of reflectance data and ancillary information. We calculated normalized differences of near-infrarred minus red and of near-infrarred minus medium-infrarred reflectance values. We run a principal component analysis for each set of time series and projected the observations on the planes defined by the first two principal components. We found that individual time series tended to cluster by habitat type. The distribution of habitats on the principal component plane indicated that the first axis is related to the average value of the time series while the second one is related to the position of the maximum value (spring or summer). In few cases some individual series were segregated from their clusters, but we could track back those observations to the 1:50 000 habitat cartography and to the 1:25 000 ortho-imagery and explain their behavior, normally due to land use change or to mixing in the habitat cartography. In two cases our results suggest the need of modifying the legend of the habitats cartography. Our work shows the interest of detailed studies to fully understand the dynamics of multi-temporal VEGETATION imagery and favors the opinion that an analogous sensor with 1 ha resolution would be relevant for the Mediterranean basin. SENSITIVITY ANALYSIS OF COMPOSITING STRATEGIES: MODELLING AND EXPERIMENTAL INVESTIGATIONS de Wasseige Carlos*, Lissens Gil**, Vancutsem Christelle*, Veroustraete Frank** and Defourny Pierre* * Department of Environmental Sciences - Universit catholique de Louvain Croix du Sud, 2 bte 16 B-1348 Louvain-la-Neuve ** Vito - Flemish Institute for Technological Research Boeretang, 200 B-2400 Mol High temporal resolution satellites, such as VEGETATION provide multiple images of the same site over short periods of time. Time series constituted of these individual images are characterised by a lack of signal consistency since measured radiances generally result from various cloudiness, atmospheric and geometric conditions. To reduce the related noise, various compositing techniques are available. The pre-launch phase went into a systematic investigation of the main issues related to the temporal synthesis production using a one-year time series simulated for the VEGETATION sensor spectral and geometric configuration. The aim of that investigation was to test globally the sensitivity of the compositing process to different factors that perturb the signal, i.e. the sun-target-sensor geometry, the atmospheric conditions and the surface anisotropy. Perturbing factors have been ranked according to their impact on the sensor signal. This sensitivity analysis highlighted the large effect of the viewing angle as opposed to atmosphere variability with regard to day-to-day variations. However, the perturbing factors were always manifested as a coupled effect on the sensor signal. The analysis of the one-year simulated time series showed three nested scales of variation. A five-day cycle related to the viewing angle and due to the wide swath of the sensor. A 26-day cycle corresponding to the satellite orbit revisit time, and the sun annual cycle changing according to latitude. The conclusions drawn from the pre-launch phase of the VEGETATION programme have resulted in a proposal for two new image compositing strategies. The approach pursued in the pre-launch phase was repeated using actual VEGETATION data in the post-launch phase. Three decades of global daily VGT-P were used. Decade 1 from 11/06/98 to 20/06/98, decade 2 from 21/07/98 to 31/07/98 and decade 3 from 11/10/98 to 20/10/98 were selected. A sampling approach based on the global dataset of VGT-P segments was designed with 50 x 50km chips to asses the performances of the existing compositing strategies for the various sun-target-sensor geometries and the different surfaces of the main terrestrial biomes. The spatial and temporal variability of the signal was first analysed for the various chips with regard to the simulation results. The current compositing technique for VEGETATION data (VGT-S10 product) shows radiometric artefacts in the reflective bands that may cause a significant noise for subsequent retrievals of surface parameters. The performances of various compositing strategies are assessed as well for the reflective bands as for the NDVI composites. Dedicated indicators and statistical analysis are computed to provide quantitative results by zone and by band. An innovative strategy such as the Median Composite of FUzzy Multispectral Estimate (MC-FUME) has been developed as well, to produce composites with reflectance values independent of the observation/illumination geometry at the time of measurement. Potential improvements were first tested based on selected NOAA AVHRR multitemporal time series. The results obtained using actual VEGETATION data are compared to the current MVC-NDVI approach and to other documented alternatives. A discussion of the results will provide suggestions for possible improvements in the VEGETATION processing chain compositing algorithms. Modeling directional reflectance in rugged terrain using VEGETATION products Lihong Su, Xiaowen Li, Jindi Wang The center of remote sensing and GIS, Beijing Normal University Beijing, 100875, China. Center of remote sensing, Boston University Boston, MA, 02215, USA Part of the Three-Gorge reservoir area is chose as research region. The area is plenty of mountains. We use Ambrals algorithm to inverse BRDF from VGT P product( reflectance). Thus spectral albedo, and BRDF-corrected NDVI be obtained. We will compare the results with VGT S product (NDVI) on same area. At VGT 1km resolution, VGT P product and its BRDF/Albedo results will also be used to evaluate with-in pixel spatial heterogeneity of remote sensed surface. Thus we can get some idea on their topographic roughness. We will validate the parameter with landuse and topography map of the area. With these results, we can make topographic corrected NDVI, and compared with VGT S product. Development of a spectral index optimized for the VEGETATION Instrument Michel M. Verstraete, Nadine Gobron and Bernard Pinty Space Applications Institute EC Joint Research Centre I--21020 Ispra (VA), Italy Vegetation indices have traditionally been used to interpret satellite remote sensing data, for example in terms of land cover types or to estimate simple vegetation characteristics. To the extent that the radiances measured by the sensors at the top of the atmosphere are affected not only by the plant cover but also by the atmosphere and the underlying soil, these indices may be significantly affected by these undesirable perturbations. One approach would be to correct the data to take these effects into account, but this requires access to considerable additional data sets (e.g., the analysis of large amounts of weather data). An alternative approach consists in developing new indices designed to remain sensitive to the presence of vegetation, but also to be less sensitive to these perturbing factors. This research proposal permitted the developement of a spectral index optimized to monitor vegetation on the basis of the radiometric data which are produced by the VEGETATION instrument. This index is designed to represent the Fraction of Absorbed Photosynthetically Active Radiation. The performance of this index has been evaluated and compared to existing indices. This evaluation has been based on a large set of simulations including various atmospheric type and composition as well as surface conditions. A preliminary application of this optimized index against actual VEGETATION data will be shown. MC-FUME: A new method for compositing individual reflective channels GiL Lissens, Els Brems and Frank Veroustraete Flemish Institute for Technological Research (Vito) Centre for Remote Sensing and Atmospheric Processes (TAP) MC-FUME stands for Median Composite of FUzzy Multispectral Estimate. It is the name of a newly developed method for compositing individual reflective channels of the VEGETATION sensor (VGT) onboard the SPOT-4 platform. It was developed in the framework of the VGT Preparatory Programme and is based on an extensive database of model simulated TOA (Top-Of-Atmosphere) reflectance values in the BLUE, RED and NIR spectral range of the VGT instrument. Considering the atmospheric influence to be stochastic, the database is used to estimate for each pixel the TOC (Top-Of-Canopy) reflectance at a reference geometry based on the observed TOA reflectance. Taking the median of the estimated TOC reflectance values does compositing over a specific time period. The method is tested on simulated time series at different latitudes. Its ability to correctly reproduce TOCnadir reflectance was evaluated with respect to a number of existing compositing methods. Results are encouraging. The MC-FUME method was in a first instance also tested on real satellite images in particular on a time series of nine NOAA AVHRR images over Belgium. The results obtained with VGT images will be presented to demonstrate the plausibility of this methodology for this sensor as well. These results are compared with composites obtained with the most commonly or frequently used algorithm that is, the MVC-NDVI (Maximum Value Composite of the NDVI). For the individual reflective channels (i.e. RED and NIR), the newly developed MC-FUME algorithm produces speckle-free composite reflectance values that are independent of observation/illumination geometry at the time of measurement. It is shown by means of some simple statistics that this is not the case for the classic MVC method. Keywords: MC-FUME, multitemporal compositing, speckle reduction, MVC-NDVI, observation/illumination geometry, VEGETATION. Acknowledgements: We acknowledge the financial support offered by the CNES under contract N95/CNES/0449 and the Belgian Science Policy Office under contract.SP67008. SPACE-VEGETATION SOFTWARE : A software for pre-processing VEGETATION L-Band Data Csar CARMONA-MORENO European Commission, Joint Research Center, Space Applications Institute Global Vegetation Monitoring Unit e-mail : cesar.carmona-moreno@jrc.it ; http://www.mtv.sai.jrc.it/ The VEGETATION Programme allows a daily monitoring of terrestrial vegetation cover through remote sensing, at regional and global levels. The instrument, funded by the EC at 50%, is part of the SPOT 4 payload. This satellite, launched the 24th Mars 1998, deliver measurements which were specifically tailored to monitor land surfaces parameters with a frequency of about once a day on a global basis and a medium spatial resolution of 1 kilometre (2200 Km swath width and 300m of geolocation accuracy). The associated ground services for processing and distribution are full operational since the end of 1998 where two ground processing services: The Centralise/Global Processing is performed by the CTIV (Centre de Traitement dImages VEGETATION at Mol Belgium). This Centralise Ground Segment will process instrument measurements at global scale to offer standard high quality products to the general users community. The daily global VEGETATION data (VGT) are downlinked via VEGETATION X-Band transmission in Kiruna (Sweden) and transmit them to the CTIV for processing. The Local Processing is performed by the local VEGETATION L-Band receiving stations with a specialised pre-processing system called SPACE-VGT. The VGT L-Band product has 1Km of geolocation accuracy and the radiometry accuracy is equivalent to that of VGT-P products. These products are mainly addressed to a user community with particular thematic needs where the (near-) real-time access to VEGETATION data drive their applications. This presentation deals with a description (functional and architectural) of the SPACE-VGT software. The Joint Research Centre Space Application Institute has full developed the local L-Band pre-processing chain and this software will be delivered free of charge to the VEGETATION programme. This will be distributed to the officially VEGETATION registered users by SPOT-Image. In a general way, local VEGETATION L-Band receiving stations demodulate and store local VGT Level 0 image data files from the L-Band transmission of the VEGETATION instrument. The software processes these local VGT Level 0 data files to produce a pre-processed product. The solar and instrument (zenith and azimuth) angles associated to each pixel are also stored. A detailed presentation of this system, its scientific objectives, data input/output description, image radiometric/geometric accuracy and pre-processing algorithms will be made. During the VGT-2000 meeting a demonstration of this system will be available. Detection of Clouds and Cloud-Shadows for VEGETATION images Pieter KEMPENEERS, Gilbert LISSENS, Freddy FIERENS, Jan VAN RENSBERGEN Cloud detection is an essential part of the pre-processing chain at the CTIV (Centre de Traitement dImages Vegetation) for various products of the VEGETATION sensor. State of the art techniques have been developed to construct a 3 level cloud mask and a binary cloud shadow mask. Several approaches to the cloud masking problem can be found in literature on other sensors (AVHRR, MODIS), but the challenge in case of the VEGETATION sensor is the absence of channels in the thermal infrared section of the spectrum. This severely complicates cloud-masking efforts, because the thermal information is of crucial importance: clouds in general are far colder than the earth's surface. A genetic algorithm has been developed to optimize thresholds on the VEGETATION spectral bands. The increase in performance is dramatic, and improves the quality of VEGETATION products (atmospheric correction, synthesis). Clouds also cast shadows on the earth's surface. This can lead up to a 40% bias of the true reflectance of the underlying terrain element. A new technique has been developed to provide a cloud shadow mask. The position of shadows can be theoretically computed using geometry. Sun and viewing angles are known but cloud height is not. The innovating aspect of the proposed method is to predict cloud height, by measuring the distance from cloud to shadow edge (detected from differences in radiometry). Shadows then, are calculated by means of geometry. This differs from existing techniques that predominantly rely on radiometry and are therefore very sensitive to the reflectivity of the surface. The results for the cloud shadow mask are very promising. NEW ALGORITHMIC CONCEPT FOR ATMOSPHERIC AND DIRECTIONAL CORRECTION OF THE SURFACE REFLECTANCES P. MAISONGRANDE B. DUCHEMIN, C. DUBEGNY,.G. DEDIEU , M. LEROY. CESBIO (Centre dEtudes Spatiales de la Biosphre) - CNES (Centre National DEtudes Spatiales) 18, Avenue E. Belin, 31401 TOULOUSE, cedex 4 tel: 33 5 61 55 85 16 ---- fax: 33 5 61 55 85 00 In the frame of the Project for Improvement and Continuity of the VEGETATION Mission, our goal was twofold. We have investigated new algorithmic schemes in order to improve the evaluation of Aerosol Optical Thickness (AOT to be used in the atmospheric correction) and for introduction of directional models in compositing methods. These new approaches are planned to replace respectively AOT climatology and the Maximum Value Composite (MVC) technique that are used in the production line of VEGETATION data set at Centre de Traitement des Images Vegetation. The current algorithm already accounts for the SMAC method, which corrects the top of atmosphere reflectances for absorption by gases and scattering by molecules and aerosols. While ozone and water vapor are documented by remote sensing and meteorological models, AOT still requires real time estimations. Taking advantage of the VEGETATION spectral range (Blue[0.415-0.455mm], Red[0.580-0.680mm], NIR[0.730-0.840mm] and SWIR[1.520-1.660mm]), we present a new approach for AOT retrieval. This procedure is based on the use of a time and target dependent ratio between blue and SWIR reflectances. After presenting the algorithm, we compare the newly processed reflectances time profiles and images with results of the current method. Data provided by wide field of view optical sensors also present a strong dependency on the three dimensional geometry (source-target-sensor). Despite substantial reduction of these effects through calculation of Vegetation Indices (VIs), residual directional impacts still remain on reflectances time series. In the 10 days MVC products, the association of different orbital tracks on a same image creates patchwork artifacts while the time behavior of reflectances and VIs show erratic changes. This noise-like fluctuation is important since the currently applied MVC tends to select large angle configurations, where directional and atmospheric effects are pronounced. Kernels driven semi-empirical Bidirectionnal Reflectance Distribution Functions (BRDF) offer a good efficiency/complexity compromise to quantify and model surface reflectance anisotropy. Taking such a model for granted, our purpose becomes its operational use in the real-time processing of global data set at full spatial resolution. The first originality of the method we developed, lies in the data selection scheme applied during the fit of the BRDF. Furthermore, we combined the smoothing effect of the average with noise reduction due to the normalization of the data to a standard viewing geometry. After presentation of the algorithmic outlines, new results will be compared with those obtained with the MVC technique. European Forest Mapping using VEGETATION data Herv Jeanjean, Forest Group Coordinator SCOT, 8-10 rue Herms 31526 Ramonville cedex herve.jeanjean@scot.cnes.fr Hubert Glinck, Professor Landscape Analysis & Rural Planning Laboratory for Forest, Nature and Landscape Research Katholieke Universiteit Leuven, Vital Decosterstraat 102, 3000 Leuven, Belgium Hgulinck@agr.kuleuven.ac.be The past decade has seen a dramatic increase of the concern about global climate change issues. It is now recognised that human activities are responsible, to some extent, of the emission of green house gas, e.g. carbon dioxide. The 1997 Kyoto Protocol to the Climate Convention has set a number of measures aimed at mitigating the emission of greenhouse gas. In this context, forests are considered as part of the problem (release of CO with deforestation, forest fires), but also represent a possible solution to climate change (afforestation, reforestation, appropriate forest management measures). Forests can play an important role in carbon sink (up to 200 t/ha) and should be given attention as far as environment monitoring is concerned. The Kyoto Protocol is clearly stipulating that any land-use changes related to forestry activities since 1990 should be measured and quantified "in a transparent and verifiable manner" for all parties involved in protocol. Land-cover and land-use change is also a major issue of the IGBP-LUCC programme. There is therefore a need to assess and monitor forest resources at national, regional and above all global scale. Europe has started to set up its own observation and control tools in the framework of the "Baveno manifesto". The state of forest in Europe has been given much attention during the past few years with several efforts aimed at assessing its resources and its biodiversity. Beside the well advanced mapping effort carried out by CORINE Land Cover, the Joint Research Centre and the Directorate General of Agriculture of the European Commission have launched several significant projects on forest mapping and forest change detection, as well as on national forest inventory harmonisation. At global scale, the USFS/NASA has produced a forest map of the world, with evergreen, deciduous and broad-leaved dominance. Despite these numerous and valuable efforts, there is still a need to set up a forest monitoring system over Europe aimed at providing to European decision and policy makers relevant information on the status of forests resources and changes. VEGETATION data from the Spot 4 satellite represents a unique and valuable source of information for meeting such objectives. This study has been launched in the context of testing the feasibility of VGT data, and more specifically the 10 days synthesis, for mapping forest over Europe. Funded by Cnes, the project has collected and processed 21 VGT synthesis from April till October 1999. Two directions have been investigated for extracting information : firstly, a monthly composite has been derived in order to discriminate forest clusters using an algorithm developed and tested by VTT (Finland) in the context of the JRC/FMERS study (Forest Monitoring by Remote Sensing). Secondly, temporal indices based on the normalised vegetation indices NDVI have been developed and analysed over a sample of forest types to verify the soundness and stability of this information as compared to other land cover types. A stratification of forest ecosystems has been applied in both approaches. This stratification has been derived from the JRC/FIRS foundation action (regionalisation and stratification of European forest ecosystems), but another stratification has also been developed and tested using landscape fragmentation indices. The results of this study indicate that VEGETATION data are showing a great potential for forest mapping at regional to global scales, and that the NDVI temporal profiles contain extremely rich information for forest types discrimination, i.e. dominance of coniferous or broad-leaved species and evergreen species. Despite remaining directional and clouds effects, 10 days synthesis data can be considered as appropriate products for vegetation monitoring. Mapping and monitoring small ponds in dryland with the VEGETATION instrument application to West Africa V. Gond*, E. Bartholom*, F. Ouattara, A. Nonguierma+ *Joint Research Ispra, Italy Direction de la Mtorologie Nationale, Ouagadougou, Burkina Faso +Centre Rgional AGRHYMET, Niamey, Niger Monitoring the state of small ponds is very useful in dry regions, as most of them are non-permanent and entirely constrained by the rhythm of local rainfall. These ponds determine various human activities such as watering herds, production of vegetables and other plants, and even water supply of local populations. Finally these water bodies are important for biodiversity, both of plant species and animals such as migratory birds. There is an information need at national as well as at regional levels. On VEGETATION image colour composites water bodies and marshy vegetation show up clearly. Yet this does not mean that these features can easily be extracted, as their spectral signature may vary largely according to their specific ecological properties. In addition several other issues make the problem more difficult: atmospheric haze, bi-directional effects, and the strong N-S ecological gradient This in confirmed by the poor scores obtained when using classical image classification. To overcome this problem several procedures have been tested on both VEGETATION S1 and S10 standard products over one window centred on Burkina Faso and including parts of the neighbouring countries. The time series extends from September till December 1999, which was a particularly humid end of rainy season in this region. Finally the most successful procedure is based on a classical photo interpretation criterion, i. e. the local contrast. In arid lands ponds and marshes are small objects scattered in the landscape. Because of their specific nature their spectral properties are clearly different to their environment. Thus derived channels are produced to reinforce local contrasts. These derived channels are obtained by computing regional averages on sliding windows of large size (>30 pixels) to capture the average landscape signature. Several derived channels are computed to eliminate possible confusions. Finally, simple and robust thresholds allow easy object delineation. The tests show that S1 data should be screened with a more effective cloud mask that the standard one, and that the algorithm developed in first place for S1 products also work fine on S10 products. Although validation could be carried out only in a limited manner with historical data and maps, it shows that commission errors are probably very few. Detection and mapping of burnt areas and active fires in tropical woodland ecosystems with the VEGETATION sensor: the SMOKO-FRACTAL case study over Northern Australia D. Stroppiana1, M. Maggi1, J-M. Pereira2, D. Graetz3, J-M. Grgoire1, J. Silva2, A. S2, P. Henry3, V. Gond1 and E. Bartholom1 1 JRC-Space Applications Institute, Ispra, Italy 2 Instituto Superior de Agronomia, Lisbon, Portugal 3 CSIRO-Earth Observation Centre, Canberra, Australia 4 CNES, Toulouse, France The SMOKO-FRACTAL field campaign was conducted during the dry season [June 1999] in Kakadu National Park, Northern Territory, Australia, by four partners: the CSIRO Earth Observation Center [EOC], the Technical University of Lisbon [TUL], the Centre National dEtudes Spatiales [CNES] and the Space Applications Institute (SAI) of the European Commission Joint Research Center. The central scientific objective of the experiment is to develop and test methodologies for burnt areas assessment, from satellite imagery, in order to quantify the contribution of vegetation fires to gas and particulate emissions, as well as to improve knowledge of the regional carbon budget. A complete data set of SPOT-VEGETATION imagery, S1 products, was acquired from May 15th to July 15th. The imagery is composed of daily four channel images of ground reflectances over the study area of one million square kilometers [10-17 S, 125-135 E]. In addition, some VEGETATION night images were acquired to assess MIR channel sensitivity to active fires. The vegetation cover is mainly characterized by woodland formations with tall grassy understoreys. Other important vegetation types include mangroves, monsoon rainforest, freshwater wetlands and heathlands. Ground and helicopter observations were collected on the extent of the main burns, the characteristics of the vegetation cover [specific composition and vegetation structure] and fire behaviour [cold and hot fires]. Using a classification trees approach, an algorithm has been developed to detect burnt areas from temporal composites of daily SPOT-VGT images. Burnt area maps have been produced for the 2 months period of the time series. High resolution images [Landsat TM] have been used to validate the results: to assess the performance of the algorithm and to estimate the total and per each vegetation type commission and omission errors. In order to evaluate the specificities of the VEGETATION sensor for burnt area mapping, three other satellite coverages were acquired for the experimental period [AVHRR-HRPT, ATSR, and ERS2-SAR] and processed to burnt area maps, and the results compared with those obtained from SPOT4-VGT. The main conclusions of the study are: SPOT-VEGETATION images performed well for burnt area detection and mapping, when a temporal change detection approach is used, due mainly to the presence of the NIR and SWIR channels and to the excellent geometry of the system. The detection of burnt areas must be done on composited images, using a modified MinNIR criteria, rather than on single date images. The analysis and interpretation of the results obtained with the other types of imagery collected for the experiment show that each sensor provides a specific contribution to what could be a multi-sensor approach to burnt area mapping. The main elements of such an approach are proposed. The results obtained over the SMOKO-FRACTAL experimental site will be further developed in the framework of a global scale initiative for burnt area mapping from VEGETATION data: a network of partners is currently being built to develop a set of methodologies adapted to a range of ecological conditions over the globe. The acquisition of a daily global coverage, of VEGETATION imagery, has been initiated on October 1999 and will continue until December 2000. The resulting time series will be the basic experimental data set for the development of a prototype system for mapping burnt areas at medium resolution globally, and will contribute to the Millenium Assessment intitiative. Features observed on night images are confirmed to be fires that can be easily retrieved by simple threshold. Given the small area size and possible cloud coverage it is difficult to draw conclusions on fire counts. More observations would be necessary to carry out an accurate benchmark of VEGETATION night image efficiency for active fire detection DISTURBED ECOSYSTEMS DYNAMICS IN THE ARAL SEA REGION BY REMOTE SENSING AND GIS METHODS. R. Ressl, A. Ptichnikov, G. Kapustin, P. Reimov, D. Forstman. DLR Oberpfaffenhofen Germany The ecological situation in the Aral Sea region changed dramatically in direction of catastrophe during last three decades. Ecosystems around Aral sea, including deltaic ones, are in a state of dynamic disequilibrium as they adjust to rapid and intense internal and external impact-factors. The aim of the project is to provide careful ecological assessment of current state, level and rate of degradation, possible seasonal and perennial trends of ecosystems dynamics of Amudarya and Syrdarya delta and dry bottom of the Aral sea, using integration of Remote Sensing data into existing layers of Aral GIS. The research focuses on the development of GIS-project for the Amudarya delta of the Aral Sea region. GIS tools in association with computer processing and spatial analyses of digital satellite imagery of different resolution, has played an especially important role in meso- and macro scale land cover/landuse analysis, planning and decision making where the constantly changing land cover/landuse patterns require implementation of a flexible and fast response information/data entry, analysis and output system. The important part of the presented research was evaluation of VEGETATION (VEG) data to support Aral sea GIS development. Particularly were investigated: Approaches to use VEG data in classifications in comparison with NOAA AVHRR and other sensors Possibilities to obtain accurate land cover/land use classifications using VEG data Monitoring of NDVI index for natural ecosystems Ranking natural ecosystems by different parameters Detection of vegetation period length and growing differences between several agricultural crops. After testing of VEG data we found that: VEG data exceeds NOAA AVHHR data for classification of natural vegetation, detection of ecosystem parameters, monitoring and time/series analysis. It provides additional strength in application of specific algorithms, such as different indexes, tassled cup transformation, principal component analysis etc. VEG data seems promising substitute of more expensive data (Landsat TM) for detection and fine-tuning of ecosystems dynamics, especially in between period of observation, based on more expensive data. We didnt found major advantages of VEG data in agricultural related analysis, except classification of crops. Crop Growth Monitoring with Coupling of AVHRR and VEGETATION Wu Bingfeng Head, Agriculture and Environment Institute of Remote Sensing Application P.O. Box 9718, Beijing 100101 Wubf@irsa.irsa.ac.cn Feng Renguo Head, Land Resources and Remote Sensing Science and Technology for Resources and Environment Chinese Academy of Sciences Sanlihe Road 52, Beijing 100864 In agriculture, monitoring of crop growth and development, and early estimates of the final production to be expected are of general interest. Traditionally, the monitoring of crop growth and yield forecasts are made on the basis of samples by field visits or written inquiries. Problems encountered concern subjectivity in responses, respondent differences and non-response. On national scale, the processing of these sample data is an expensive and time-consuming procedure. In general, there is a need for an objective, standardized and possibly cheaper and faster methodology for crop growth monitoring and yield forecasts. China started to use remote sensing to monitor crop health and to forecast production in as early as 1983, but failed to put into operation. In 1997, Chinese Academy of Sciences initiates Crop Monitoring project with its purposes to realize operational crop monitoring on wheat, rice, corn and bean over the whole country. Both NOAA AVHRR and SPOT VEGETATION data are used to monitor the crop growth over the entire country at dekad period during growing season from March to October. It includes: comparison of any dekad of the current growing season with the previous dekad of the same growing season; the current dekad with the same dekad of the previous growing season; 3) the current dekad with the same dekad of the normal; and 4) percent comparison of the current dekad with the maximum NDVI value within the normal. AVHRR data were received by our own receiving station. VEGETATION data was obtained through a cooperation established with the Joint Research Centre of the European Commission in the framework of the Share-cost action on the improvement of the VEGETATION mission. The red and near-infrared bands of the AVHRR data were calibrated to reflectance and the two thermal bands were calibrated to surface temperature. Geometrical rectification was done using orbit information from the TLE data allowing a registration accuracy at the sub pixel level. Clouds were masked interactively with individual verification of all field sites, and noises were detected and removed interactively. The ten days Maximum Value Composite (MVC) were produced. In order to focus on crop growth information, only farming land pixels were kept. This was done by combining land cover database at a resolution of 1km, gridded from vector maps at a scale of 1:100,000. A detailed, quantitative and statistic analysis within the GIS system is accomplished by calculating the percentages of 5 categories, as well as the mean, maximum and minimum of the NDVI value, on a dekad basis, for crop masks, for each of the 31 provincial-level administrative zone. Pixels influenced by cloud are excluded from the calculation of the mean NDVI statistics. Each mean NDVI curve by selected administration can be viewed, analyzed and compared to other years within the statistical archive. Users have the flexibility to choose the comparison years and can electronically export the data or the NDVI curves into reports or presentations. The differential image is a color image by assigning red, yellow, green, cyan and blue to five categories together with statistic table. This should be explained in the term of crop growth and at the same time, the reason as well as the recommendation should be addressed too, with ancillary information, such as the majority crop, the crop phonology, and the climate data. All products are in a GIS digital format. The users can access a password-protected account containing the historical and current crop growth information as well as natural disaster via a Web browser on the Internet. The benefits are obvious for users and provider; the client saves money by not having to invest in a GIS package to view the products, while the provider can expand the client base provided the client has access to the Internet. Changes, revisions and updates are transparent to the client improving efficiency, ease of access and program flexibility. For some important decision makers, who may not find time to look at the website, but has the time to quick look the paper, we printed the products together with explanation on the hard copy and delivery to their desk within one day. Use of medium-resolution imagery in the Belgian Crop Growth Monitoring System (B-CGMS) K. Wouters*, H. Eerens*, D. Dehem**, B. Tychon**, D. Buffet*** & B. Oger*** *Centre of Expertise on Remote Sensing and Atmospheric Processes (Vito-TAP) Boeretang 200, B-2400 Mol, Belgium. E-mail: jan.vanrensbergen@vito.be **Fondation Universitaire Luxembourgoise (FUL) Avenue de Longwy 185, B-6700 Arlon, Belgium. E-mail: tychon@ful.ac.be ***Centre de Recherches Agronomiques (CRA) Rue de Liroux 9, B-5030 Gembloux, Belgium. E-mail: oger@cragx.fgov.be The yield forecasting system CGMS (Crop Growth Monitoring System) was one of the major achievements of the European MARS-programme (Monitoring Agriculture with Remote Sensing). Since a few years, CGMS is used on a continuous base to predict harvests of the main crops at the level of the EU member states. In July 1998, a 2-year pilot project was started up in order to implement a specific version of the CGMS in Belgium, the so-called "B-CGMS". In this way the Belgian Ministry of Agriculture will be able to generate its own harvest predictions in a timely way. This communication presents intermediate results after 1.5 year of project work. The main task of the project consisted in the adaptation of the CGMS to the Belgian conditions. First, the spatial scale of the system was enhanced, such that the agrometeorological model now runs on 5km x 5km cells (instead of 50km x 50km before). Second, at the level of pedological and meteorological input, more detailed information was collected and introduced into the model. Crop parameters were also chosen typically for the Belgian territory. And finally, the model output is now specified per agricultural region and "circumscription". Currently, the adapted forecasting system is being validated on a 15-years series of yield data, obtained from the National Institute for Statistics. Another important project task aimed at the incorporation of 1km-resolution remote sensing imagery in order to improve the yield forecasts. The strategy developed so far first estimates the reflectances of the pure classes (crops) by application of a spectral unmixing procedure on the imagery. The required land use data per parcel were obtained from the (yearly updated) "Integrated Administration and Control System" of the Ministry of Agriculture. When this procedure is repeated on the multitemporal image set, one obtains pure and crop-specific time series, which can be converted into fAPAR-profiles (fraction of absorbed PAR). Finally, biomass accumulation is assessed by means of a Monteith-based algorithm which combines the satellite-derived fAPAR-values with meteorological data (solar irradiance, air temperature). The additional value of the obtained crop-specific estimates is currently being evaluated. Subsequently, they can be used in an integrated approach together with the B-CGMS output to come to a refined yield prediction. The methodology was developed with historical images of NOAA-AVHRR, but in the near future it will be applied as well on the data of the SPOT-VEGETATION sensor system. Combined use of VEGETATION and RADARSAT data for updating snowpack cover and water equivalent in the HYDROTEL hydrological forecasting model Monique Bernier*, JeanPierre Fortin, Yves Gauthier, Richard Turcotte and Ali El Battay *INRSEau, 2800 rue Einstein, C.P. 7500, SainteFoy (Qubec) G1V 4C7 Canada Tel.: (418)6542585; Fax: (418) 6542600; email: monique_bernier@inrseau.uquebec.ca Accurate forecasting of snowmelt in Spring is a very important component of any flood prevention strategy. Yet, the use of remotely sensed data and even hydrological models is far from being a daily practice in most forecasting agencies, for various reasons mostly related to a strong reluctance to change. Other agencies, like HydroQubce, are prepared and willing to make the necessary steps. In the meantime, researchers at INRSEau have been working for many years on the development of both application of remote sensing to hydrology and distributed hydrological models able to use remotely sensed and GIS data. Within the framework of the VEGETATION Preparatory Program, we have developed a snow mapping methodology at the subpixel level as well as a reflectance estimation of individual land use classes from the Vegetation data. Concurrently, in the framework of similar RADARSAT programs, we have developed a software package called EQeau, for the distributed estimation of the water equivalent of a dry snowpack. Also, the HYDROTEL hydrological model has been adapted to hydrological forecasting. In this communication, we will first explain why hydrological models do need updating of their state variables for more accurate streamflow forecasts. During the winter and for complete snow cover over the studied watershed, VEGETATION data can help to monitor changes of reflectance in the various bands as a result of new snowfalls while RADARSAT data will be used to furnish a new distribution of snow water equivalents. The albedo values as well as the water equivalents as simulated by the model will be updated using that information. During the snowmelt period, liquid water in the snowpack will reduce the reflectance in each VEGETATION band and the backscattering in the RADARSAT data. The distinction between dry and wet snow could be facilitated using both sensors. At the same time, VEGETATION data will be used to monitor the areal reduction of the snow cover over the watershed. Again, the available information will be very useful to verify if the model is making the proper distinction between hydrological units on which melt has begun and those on which snow is still dry. Also, VEGETATION data will be used to monitor snow cover in the model during the melt period. Examples will be taken from applications of VEGETATION and RADARSAT data over Qubec territory and from application of the HYDROTEL model over watersheds located in Qubec. We will conclude with comments on the interest of using remotely sensed data for hydrological forecasting. Antarctic snow characteristics from POLDER and VEGETATION data Michel FILY, Olivier MANSE, Jean-Pierre BENOIST Laboratoire de Glaciologie et Gophysique de l'Environnement, CNRS-UJF, Grenoble, France fily@glaciog.ujf-grenoble.fr The energy balance at the surface of the ice sheets is mainly dependent on the radiative fluxes. During daylight the albedo of snow is one of the main parameters which control these fluxes. As the albedo is high (about 0.8) a small difference can change drastically the amount of absorbed energy and then the surface temperature. Therefore a good parameterization of the snow albedo is necessary when modelling the polar climate over the ice sheets. The snow albedo depends on its pollution in the visible part of the spectrum and on the grain characteristics (size, shape) in the short wave infrared (SWIR). The sun incidence angles and the surface roughness also modify the snow albedo. For radiative effects, the snow can be considered as clean on the ice sheets. Good parameterization of the grain size and of of the sun incidence angle effects are found in the litterature but, so far, there is very poor knowledge about the surface grain size and about the roughness of the ice sheets at a global scale. The objective of our investigation is to derive the snow grain size from the Vegetation SWIR (1.6 m) channel which is very sensitive to this parameter. For this purpose Vegetation data were acquired above Antarctica during the 98-99 and 99-00 austral summers. The cloud selection is based on the facts that the grain size is usually smaller in clouds than in snow and that the cloud surface is often rougher than the snow one. From POLDER data it was found that the reflectance obtained at large angle are affected by the surface roughness and only view angles less than 35 are used. The results of a radiative transfer model is used to derive the grain size from the reflectance and finally the albedo is computed. Applications of VEGETATION data to resource management in arid and semi-arid rangelands Bgu (CIRAD),G. Chehbouni (IRD), R. Escadafal (IRD) P. Heilman (USDA), B. Mougenot (IRD), Y. Nouvellon (CIRAD/USDA), J. Qi (USDA), A. Royer (AGRHYMET), C. Watts (IMADES) The main objective of this proposal was to investigate a strategy to use the VEGETATION data to provide rangeland managers with critical information on vegetation and soil to help them in making management decision. For assessing the VEGETATION data on summer grazing, a database of vegetation measurements taken over 3 summers (97-98-99) has been built for 3 countries : The grassland plateau of southwest United States (Arizona) and northern Mexico (Sonora), mainly composed of perennial plants, and the Sahelian grassland in Niger mainly composed of annual grass. The work is still under progress, and despite an incomplete analysis of the data, the research axis and the main results are illustrated through examples acquired over the different sites. First, temporal ground, AVHRR and VGT time profiles and compositing techniques are presented and compared. From satellite data, assessment of floristic composition could be done for Niger, allowing the production of species maps. We then derived several time indicators (various Vegetation Indices summation, maximum, amplitude ) to assess biomass production through statistical relationships over Mexico and Niger. The atmospheric and directional effects are discussed. To improve the empirical relationships, different stratifications of Niger grassland have been tested : stratification of soil and vegetation types according to existing maps or to image classification. To provide timely estimates of biomass during dry seasons, the US and Mexican teams have begun development of an algorithm to estimate a vegetation index that is also sensitive to senescent vegetation. By using SWIR to enhance the vegetation signals of senescent vegetation, a better estimate of total biomass appears possible. Some preliminary results showed that the use of SWIR band might provide critical information for estimating biomass in the arid and semiarid regions. An operational algorithm was also developed in collaboration with Qis VEGETATION project to estimate green vegetation fraction cover at VEGETATION scales validated with multiscale TM and ETM+ data. This algorithm can provide a rapid computation of fractional cover, as a practical technique to circumvent the atmospheric effect. The third and last research axis concerns the use of VGT data to calibrate ecosystem models developed for semi-arid grasslands. The results obtained for Mexico and US sites are mainly presented in Dedieu's investigation. Unfortunately, Niger modeling work is not achieved at this time. VEGETATION potentialities in food early warning systems in the Sahelian region TYCHON Bernard , OZER Pierre and TOURE Souleymane Fondation Universitaire Luxembourgeoise 185, Avenue de Longwy, B-6700 Arlon - Belgium Email: tychon@ful.ac.be Developing countries in the Sahelian region dedicate most of their activities to Agriculture. A year with rainfall deficits or unevenly distributed rainfall during the rainy season often leads to food insecurity in this part of the world. This food status has to be foreseen early enough to allow decision-makers or financial backers to react fast enough to prevent or reduce the effects of potential famines. Among the available tools to foresee this type of disaster, the combined use of the low resolution NOAA-AVHRR sensor with the IR band of METEOSAT has served up to now in the global vegetation monitoring in Africa. This sensor association is presently used in routine by FAO inside its ARTEMIS Programme. However, the AVHRR sensor has shown some limitations that did not always allow reaching the initial expectation of such an earth observation sensor. Some of these limits have been reduced even removed with VEGETATION sensor placed on SPOT 4. The general objective of this study is to verify that VEGETATION instrument actually provides information of better quality than this derived from NOAA satellite specifically for the Sahelian region and within the scope of early warning system on food and agriculture of FAO. Two North-South transects were chosen inside the Sahelian region as study-area: the first one from the Malian Sahel to southern Burkina Faso; the second one inside Niger only concerns the Sahelian band. Four criteria were selected to compare remote observations from the two satellites: The first criterion is based on the correlation between local yields of millet calculated with an agrometeorological model (DHC-CP) and the NDVI from NOAA and VGT. The second criterion checks the potentiality of spatial extrapolation of agrometeorological parameters. Here, the chosen criterion allows determining the start of the vegetation season with remote sensing based on a set of image series. Results from the two remote sources are compared with a map calculated with an agrometerological approach that fixes sowing date according to a given quantity of rainfall per 10-day period. A third criterion checked the saturation level of both sensors in regions with high vegetation density. Finally, pixel location was analysed inside a window crossed by the Niger River. The VEGETATION instrument demonstrated its higher capacities for vegetation monitoring in the Sahelian region within the scope of agricultural campaign monitoring and food early warning systems in comparison with the NOAA-AVHRR sensor presently used for this topic. These potentialities should now be used to replace NOAA images in the different monitoring systems. Moreover, we think that VGT should be used for other applications than those presently run by NOAA. Especially, it should be necessary to look at the potentialities of the sensor in the monitoring of small areas (< 100 km). It should also be worth improving the integration of remote sensing information inside agrometeorological models in order to valorise this new type of information in a quantitative way. Incorporating the use of VEGETATION data in FAOs programmes F.L. Snijders Remote Sensing Officer Food and Agriculture Organization of the United Nations Data from low-resolution earth observation satellites have been used operationally by FAO in the fields of early warning for food security and desert locust plague prevention for many years. Originally, this focussed on data at 4-10 km resolution, which proved very suitable for the monitoring of crop growing conditions over large areas, in particular when a long-year archive of historic data was available. In the field of desert locust control, this was less successful and efforts were undertaken to obtain data at 1 km, from local HRPT stations. However, operational use of data from a variety of sources proved very difficult and the quality and timeliness was highly variable among sources. The launch of the VEGETATION instrument onboard SPOT offered an excellent opportunity to change this situation as it offered a global coverage processed at a single Centre. In order to explore the possible uses of VEGETATION data, an agreement was established between FAO and the EU Joint Research Centre. Within the framework of this agreement, a number of activities were undertaken. First, some tools had to be developed to facilitate access to the data, which is in HDF format. This resulted in a simple information extractor and a conversion utility that has been put in the public domain and allows the use of the data on simple PCs. Second, the use of 10-daily composits has been incorporated in the operations of the FAO Global Information and Early Warning System (GIEWS) and the locust control group, through the ARTEMIS system. Third, new applications of the data are being explored. The experiences obtained so far clearly indicate that VEGETATION is a unique and very valuable instrument that has a very wide field of applications, although some improvements are still needed to both the processing and the delivery system. While in the field of locust control the benefits are more direct, in the field of early warning for food security the importance will grow more steadily, as this application relies strongly on the availability of historic data. Other applications, such as in the field of irrigation monitoring, are still under investigation. The contribution of VEGETATION/ SPOT 4 products to Remote Sensing Applications for Food Security, Early Warning and Environmental Monitoring in the IGAD sub-region. (Project No.: 7 ACP RPR 532/ EDF 7) Guy PIERRE SCOT BENEFICIARIES: IGAD - Intergovernmental Authority on Development, P.O. Box 2653 Djibouti. + IGAD Member-states: Djibouti, Ethiopia, Eritrea, Kenya, Uganda, Somalia, Sudan 1.Introduction and background The strengthening of remote sensing applications for early warning, food security and environmental monitoring in the IGAD sub-region is a regional project financed by the 7th European Development Fund. It envisages supporting the National Meteorological Services of the IGAD member-states in better responding to the reception and interpretation of low resolution satellite data and thus contributing to the compilation of regional strategies for tackling vital emergencies, as well as nutritional and environmental problems of the population of the region. The Regional Authorising Officer (RAO) for this project is the IGAD Secretariat and the Supervising authority is the Kenya Meteorological Department (KMD) in Nairobi where the project is based at. 2.Summary of activities undertaken by the Remote Sensing project Following a restricted tender, the technical assistance for the execution of the project has been awarded to the consulting firm SCOT (FR) which started its activities in August 1998. The project office was established in Nairobi within KMD and two permanent staff, the Regional Co-ordinator and the Remote Sensing Officer were installed. Between October 1998 and March 1999, the team has conducted visits in all IGAD countries to evaluate the remote sensing situation and to assess the needs of National Meteorological Services, Early Warning Units and Environment agencies. Several co-ordination meetings were held so far in Djibouti, Nairobi and Brussels to monitor the progress of the project and discuss the consultants preliminary recommendations. Taking into consideration the needs assessment results in the IGAD member states, the consultant presented a development plan with a number of technical options, that were approved by all IGAD member states during a workshop held in Nairobi in last January 2000. These include the selection of equipment to be installed in the different National Meteorological Services (NMS), Ministries of Agriculture and of Environment with all the support activities required, as well as the regional dissemination of regional and national VEGETATION/ SPOT 4 products, through the regional hub of Nairobi, towards the other NMS, that shall further disseminate to other national public users. The Millennium Land Cover Assessment initiative S. Belward European Commission Joint Research Centre - Space Applications Institute 21020 Ispra, VA, Italy alan.belward@jrc.it The Kyoto Protocol to the United Nations Framework Convention on Climate Change contains legally binding commitments to either reduce or limit the emissions of six major greenhouse gases (Carbon dioxide [CO2], methane [CH4], nitrous oxide [N2O], hydrofluorocarbons [HFCs], perfluorocarbons [PFCs] and sulphur hexafluoride [SF6]). The Protocol contains agreed targets for the Annex I countries (essentially the already industrialised nations) that collectively amount to a 5% reduction on 1990 levels. The Protocol makes provision for the use of biological sources and sinks to meet commitments and requires national systems for verification, reporting and accountability; specifically changes in carbon stocks resulting from human-induced land-use change and forestry activities (Article 3). Changes in forest cover can occur rapidly. The difficulties of monitoring and measuring changes in sinks become even greater if land use and forestry projects are included in carbon trading activities. If forestry projects are allowed under the terms of the Clean Development Mechanism (Article 12) then world-wide assessment of changes in land use, especially afforestation, reforestation and deforestation becomes a requirement. Deforested areas quickly revegetate (although it would take hundreds of years to return to the original levels of carbon as stored in primary forest). Detection of afforestation and reforestation too will call for annual measurements of forest area. The cyclical nature of satellite based observations is ideally suited to such repetitive tasks. Deforestation is almost always accompanied by fire. Biomass burning associated with deforestation, naturally occurring fires and activities such as rangeland management and hunting is a major source of greenhouse gases, and one with huge uncertainties associated with it. Fire, along with other disturbances (such as wind throw or disease) can also result in unplanned changes in carbon stocks that will have to be monitored and accounted for. On the global scale the lack of consistent, coherent baseline inventories of forest resources is keenly felt. The spatial, spectral and temporal characteristics of the VEGETATION system are well suited to the provision of such an inventory. With this in mind the Joint Research Centre (JRC) in partnership with others in the VEGETATION programme have launched the Millennium Land Cover Assessment initiative. Using the daily S1 product as a base the project intends to create a uniform land cover inventory for the globe for the year 2000. This will be accompanied by a global assessment of burnt area per land cover class. The land cover classification will build on the classes recommended by the Intergovernmental Panel on Climate Change for Green House Gas inventories, and mapping will be achieved through a network of regional experts, co-ordinated by the Global Vegetation Monitoring Unit (GVM) of the JRC. The S1 archive has been assembled since 20th October 1999 and will continue until 31st December 2000. The aim is to complete the mapping exercise by the end of 2001. European organisations are welcome to participate in the exercise, and modalities for participation will be co-ordinated by the VEGETATION Steering Committee. POSTER PAPERS Monitoring natural disasters and hot spots of land-cover change with SPOT VEGETATION data to assess regions at risk and vulnerability Prof. E. F. Lambin, Dr. I. Reginster & F. Lupo Sartor Department of Geography Universite catholique de Louvain place Louis Pasteur, 3 B-1348 Louvain-la-Neuve, Belgium All recent scientific evidences clearly point to the fact that the impact of global change on land surface attributes will not be uniformly distributed geographically. Assessing the regions at risk of rapid land-cover changes and/or natural disasters is therefore a priority for global change research and for policies aimed at mitigating the impact of these changes. The objectives of this project are to: (1) Use SPOT VEGETATION data to monitor over large regions the impact on ecosystems of natural disasters such as droughts, fires, floods and vegetation diseases, as well as land-cover change hot spots; (2) Validate and interpret SPOT VEGETATION-based maps of natural disasters and extreme land-cover changes with collateral data on natural disasters and hot spots of land-cover change; (3) Integrate this validated product in the current efforts of the global change scientific community, sponsored by the International Geosphere-Biosphere Programme (IGBP) and International Human Dimensions Programme on Global Environmental Change (IHDP), to assess regions at risk of rapid environmental change in order to focus research on most vulnerable areas and support the design of appropriate mitigation policies. The product can only be generated once two full years of SPOT VEGETATION data will be acquired (i.e. by April 2000). In the meantime, a data processing chain with the change analysis algorithm has been designed, and initial tests over two growing seasons in West Africa (June to November 1998 and 1999) have been conducted. Through these tests, we evaluated the level of data pre-processing required (i.e. geometric registration, compositing period and criteria, combination of spectral bands) to detect different processes of land-cover change. We also evaluated whether the change detection algorithm needs to be adapted to different situations and different biomes. In parrallel, we are assembling a database of collateral data on natural disasters and rapid land-cover changes during the period April 1998-April 2000 for validation and interpretation of the VEGETATION-based change maps. Our first result include a map (available in digital format) which represents the impact of the natural disasters and rapid land-cover changes which have occurred during the growing seasons of 1998 and 1999 in West Africa. For every 'hot spot' of change detected (flooding, decrease or increase in vegetation cover), we have compiled information on the type of change, their cause (exceptional rain events, drought conditions, migratory pests or fires) and their environmental significance. While the ultimate objective of the project is to generate a product at a global scale, during the first year, we are only processing data over the whole of Europe and Africa. The main end-users of this project are the International Geosphere-Biosphere Programme (IGBP) and International Human Dimensions Programme on Global Environmental Change (IHDP), for the cross-cutting activity on Vulnerability and regions at risk. Moreover, the project provides a geographic product for the concluding year of the International Decade for Natural Disaster Reduction (IDNDR), sponsored by the United Nations. Improved atmospheric corrections and data compositing methods for surface reflectance retrieval Ph. Maisongrande (1), B. Duchemin (1), M. Leroy (1) (P.I.), G. Dedieu (1), J.L. Roujean (2), B. Berthelot (1), Ch. Dubegny (1), R. Lacaze (2) CESBIO, 18, avenue E. Belin, 31401 Toulouse Cedex 4, France CNRM/Mto-France, 42 av. Gustave Coriolis, 31057 Toulouse Cedex, France This investigation aims at an improvement of the accuracy of surface reflectance products delivered by the VEGETATION system. The foreseen improvement of products concerns the algorithms of atmospheric corrections and removal of anisotropy effects. Atmospheric corrections: Inaccuracies of atmospheric correction procedures lead to significant errors on surface reflectances retrieved from the sensor measurements. The nominal VEGETATION products are corrected for atmospheric effects using the SMAC code (Rahman and Dedieu, 1994), with the water vapor, ozone and aerosol contents given as inputs. It is clear that any error on these contents translates into a corresponding error on retrieved surface reflectances. In the prelaunch phase, we have seeked to improve the accuracy of these atmospheric contents. One of the issues concerned the choice of sources for the water vapor and ozone concentrations. The conclusion was that while a climatology is sufficient for ozone, daily water vapor contents derived from meteorological models have to be used. This modification has been implemented operationally in the VEGETATION products. So far, a crude correction for aerosol effects is made with VEGETATION data, using fixed aerosol amounts per band of latitude. We have proposed an original method of retrieval of aerosol content based mainly on the use of the B0 VEGETATION blue channel. A version of this method was partially validated during the prelaunch phase with radiative transfer simulations of TOA reflectances and with time series of POLDER/ADEOS data on sites equipped with AERONET sunphotometer measurements. In the postlaunch phase, VEGETATION data were acquired during one year on 15 areas worldwide equipped with AERONET sunphotometers. These data were used to investigate the potential of self consistent estimates of the ratio of surface reflectances SWIR/B0 to improve the retrieval of the atmospheric part of the top of atmosphere B0 signal. It has been shown that the use of this ratio generally improves the atmospheric correction performances, even on areas without dense vegetation cover. The algorithms of retrieval of this ratio and implementation in an atmospheric correction scheme, and their overall accuracy assessment with actual VEGETATION data are presented elsewhere (see the other paper by Maisongrande et al. in this conference). Anisotropy removal : The nominal VEGETATION products use the Maximum Value Composite (MVC) technique as a compositing method of temporal series of data. This compositing attenuates but does not suppress undesirable effects due to directional effects (and also cloud contamination of pixels, and atmospheric effects). The basic idea developed here is to remove the directional effects with a BRDF estimated self consistently with a time series of VEGETATION data. In the prelaunch phase, the problem was analyzed with time series of AVHRR data on 3 selected sites : semi-arid (HAPEX 92), boreal forest (BOREAS 94), and agricultural temperate site (Alpilles 96). Three BRDF models adjusted against the data were intercompared on the basis of their ability to provide smooth temporal profiles of normalized reflectances, and to reconstruct BRDFs as close as possible from reference BRDFs measured with the airborne POLDER instrument on each of the 3 sites. It was concluded that Walthalls model satisfies better the first criterion, and Roujeans model the second. It was recommended that the ouput variables produced by the operational processing center be spectral hemispherical reflectances. A period of composition of 30 days was also recommended. The problem was revisited in the post-launch phase 1) by means of numerical simulations; the simulations were conducted so as to test various cloud cover situations (say, 20%, 40% cover) and various ways of obtaining the BRDF shape and of applying this knowledge for normalizing the daily data; 2) by designing and testing an algorithm of anisotropy removal on actual VEGETATION data. This presentation focuses on the numerical simulation aspect, while the presentation by Duchemin et al. describes the results obtained with the VEGETATION data and the algorithmic synthesis. The main outcome of the simulations are1) that the concept of a fixed 30-day compositing period can not be operative in many cloudy regions of the world (due to lack of data for the estimation of the BRDF shape) and should be replaced by a more flexible compositing period concept, 2) to recommend that the compositing should be first to correct every daily data for the shape of the BRDF, and second produce a 10-day composite by simple average of the directionally corrected daily data. VALIDATION OF BIOPHYSICAL PRODUCTS DERIVED FROM LARGE SWATH SENSORS FOR GLOBAL BIOSPHERE MONITORING F. Baret(1) D. Allard(2), A. Begue(3), G. Dewispelaere(3), E. Dufrene(4), J.P. Gastellu(5), D. Guyon(6), A. Kuusk(7), J.P. Lagouarde(6), T. Leonidas(8), M.Leroy(5), P. Lewis(9), D. Lo Seen(3), W. Mauser(10), J. Moreno(11), E. Mougin(5), T. Nilson(7), S. Rambal(12), J.L. Roujean(13), M. Weiss(1) INRA Bioclimatologie, Domaine Saint-Paul, 84 914 Avignon Cedex 9, France INRA Biomtrie, Domaine Saint Paul, 84 914 Avignon Cedex 9 France CIRAD, Maison de la tldtection, 34 093 Montpellier cedex 05, France Laboratoire dcologie Vgtale, Universit Paris sud, 91 400, Orsay, France CESBIO, 18 avenue E.Belin, BP 2801, 31041 Toulouse Cdex 4, France INRA Bioclimatologie, Domaine de la grande Ferrade, BP 81, 33 883 Villenave-dOrnon cedex, France Tartu Observatory, 61 602, Toravere, Estonia NAGREF, Institute for soil classification and mapping, 1, Theophrastos street, 413 35 Larissa, Greece University College London, Department of geography, 26 Bedford way, WC1H OAP, London, UK Institut fr Geography, Luisentr. 37, 80 333 Muenchen, Germany Dept. Termodinamica, Facultad de Fisicas, C/Dr. Moliner 50, 46 100 Burjassot (Valencia), Spain CEFE, Route de Mende, BP 5051 34 033 Montpellier, cedex 1, France CNRM, 42, Av. Coriolis, 31 057 Toulouse Cedex, France Several large swath sensors (VEGETATION, AVHRR, POLDER, SEAWIFS, MSG, MERIS, AATSR, MODIS, GLI) will provide concurrently global monitoring of the Earth's surface. The radiometric data collected by these sensors are then transformed in biophysical products (albedo, LAI, fAPAR, and fCover) used both in ecosystem models and in GCMs to define the surface boundary conditions. However, very little work is dedicated to the evaluation of the actual accuracy of these products as well as to the inter-comparison of products between possible algorithms and sensors that could be exploited to combine their data and get enhanced products (spatial resolution, revisit frequency, accuracy on biophysical products). The objective of the VALERI project is to develop a network of sites and a methodology designed to evaluate the accuracy of the biophysical products derived from large swath satellites and propose ways to combine them and improve their performances. The VALERI project is based on a network of 10 to 15 sites covering the Earth's surface and representing a wide range of conditions. Each site is about 100km, a dimension consistent with large swath satellite sensors such as VEGETATION, AVHRR, POLDER, SEAWIFS, MSG, MERIS, AATSR, MODIS, GLI. The biophysical products envisioned are either instantaneous quantities or result from a temporal synthesis spanning over a maximum one month period. A methodology is developed to allow accurate measurements of ground level measurements of the biophysical variables of interest: LAI, fAPAR, fCover, albedo representative both in time and space. It is based on the following steps: 1- Selection of a set of elementary places (around 30 to 50) based on a previous high resolution satellite image (SPOT, TM) thanks to geostatistical methods. 2- Ground measurement of the biophysical quantities using the LAI2000 instrument. 3- Extrapolation of the set of local biophysical quantities measurements to the whole site thanks to a high spatial resolution image acquired during the period of interest and geostatistical methods. 4- Estimation of the biophysical quantities at the resolution of the large swath sensors by agregation, and evaluation of the associated uncertainty. 5- Comparison of the values computed from the large swath sensor data thanks to a given algorithm and the values measured from ground level over the ensemble of sites and the several period of measurements. The project will therefore provide the basic information to evaluate the absolute accuracy of the proposed sensor/algorithms. It will also provide inter-comparison between products derived from several sensors and thus allow to propose possible ways to combine the data collected concurrently by several large swath sensors. The VALERI project is complementary to the validation effort conducted in the USA (MODLAND), and exchanges of satellite and ground level data will insure efficient synergy between both projects. Improving access to VEGETATION data: some results of on-going experiments E. Bartholom*, V. Gond*, S. Morimondi* *Joint Research Ispra, Italy Distribution of VEGETATION data is currently based on two different procedures: express courier and ftp. The purpose of the study, carried out in the framework of the Share Cost Action improvements for the VEGETATION mission, is to find ways by which access to data can be facilitated and speeded up especially for operational users. The driving lines of the experiment are as follows: 1) make better use of broadly available techniques 2) keep extra operational costs as small as possible, and anyway compatible with data prices, 3) facilitate rather than complicate the use of VEGETATION data for final analysis The actions are focussing on 3 topics: improving data transfer, automating procedures, conditioning data Improving data transfer Several long duration tests are being carried out to assess data accessibility for users working in constraining telecommunication network conditions, national and sub-continental applications in Africa, continental applications in Asia and global applications in Europe. Both ftp and email attachment have been successfully tested. Alternative methods to standard Internet are also being assessed. automation procedures One of the elements of an efficient operational system is automation of repeated actions. Tools are developed to automate ftp file retrieval and file expedition as email attachment. data conditioning Tests are carried out produce and evaluate with end-users simple derived products that facilitate both data transfer and data analysis. This includes the production compressed colour composites for visual analysis, of useful band combinations to provide extended information in a highly compacted manner, etc This experience shows that for VEGETATION data distribution the end-user service can easily be improved at marginal cost by improving the use of available telecommunication infrastructure. This can be achieved with limited impact on the current central processing facility architecture. Users can be served all over the world with such techniques. By doing so delivery times are fully compatible with most operational applications. INTERCOMPARISON OF DEKADAL VEGETATION INDEX FROM NOAA/AVHRR AND SPOT4/VEGETATION OVER THE IGAD REGION T. Bennouna and P. Bicheron SCOT, 8-10 rue Hermes, 31526 Ramonville Cedex, France Email: bicheron@scot.cnes.fr Numerous operational systems (MARS, World Fire Web) are supplied with data acquired by the AVHRR instrument. An important archive has been thus constituted but with a critical data quality. Since 2 years, the VEGETATION instrument delivers data with a quality largely greater, but do no offer AVHRR temporal depth. In this framework, the AVHRR/VEGETATION intercomparison has all its interest, since the determination of transfer functions between both instruments products will allow to constitute a virtual VEGETATION archive. This study will thus allow on one hand to improve the quality of the existing archive and on the other hand to guarantee a transition and a substitution of AVHRR data with VEGETATION data in operational projects. We will focus more precisely on the comparison of temporal dekadal NDVI series derived from both instruments. The study area locates in Oriental Africa (IGAD region) over a region of 1500 x 1500 km (19N, 25E; 5N, 39E). This area covers a part of Eritrea, Sudan, and the eastern part of Ethiopia, offering a broad vegetation gradient from North to South. The land cover is characterised by 27 classes following the USGS land cover classification realised with LANDSAT data. Over our study area, 10 vegetation classes are covered (grazing, cropland and grazing, grazing and cropland, Shrub Savannah, Other types of savannah, Forest, Dry vegetation), more two non vegetation classes (Urban, Water). The first step of the study consist of processing the AVHRR and VEGETATION Dataset to make them comparable. This data processing includes geometrical and atmospherical standard corrections, and the synthesis of NDVI according to the MVC method. According to a regular sampling by step of 1, we extract the NDVI temporal signature from AVHRR and VEGETATION Dataset for 175 pixels belonging to several classes. After an analysis of the different signatures, we propose different filtering techniques to discard erroneous values taking into account or not the temporal evolution of the pixel. We present then the processing of simple regression coefficients between AVHRR and VEGETATION NDVI. Vegetation Action & Demonstration Plan for desertification monitoring in China Christian CREPEAU SCOT Partners of the study CNDMC: The China National Desertification Monitoring Centre (CNDMC) is a scientific organisation which is responsible to the Academy of Forest Inventory Planning (AFIP) and the Forest Information Centre (FIC), which are themselves responsible to the Forest State Administration. CARSA: China Association for Remote Sensing Application. Xinjiang Autonomous Region Authority CNES, SCOT This study aims to demonstrate the contribution of Vegetation data for monitoring land cover in areas threatened with desertification in China and to identify the optimum conditions to allow Chinese users to use Vegetation data and derived products in an operational mode. Two study areas, threatened with desertification, have been chosen by the CNDMC: Inner Mongolia: 46N, 97E - 31N, 123E (3.6 millions km) and Xinjiang: 49N,73E - 34N,97E (3.4 millions km). Actions carried out Acquisition, processing and photo-interpretation of VGT data concerning the 1998 and 1999 agricultural seasons. These data were provided by the CTIV in the form of ten-day synthesis (S10). Validation using ground survey data collected by the CNDMC and production of chronological and differential NDVI-VGT maps. Acquisition and processing of several AVHRR images concerning the 1998 summer (Inner Mongolia area) in the form of ten-day synthesis provided by the Beijing receiving station. Large ground survey campaign (750 sites) during the 1998 summer and delivery to SCOT of a climate and cartographic data base (road network, drainage network) concerning the two study areas. First Results In inner Mongolia. In the area where the NDVI value is the highest, land cover is composed of annual crops, generally planted with trees or of dense rangeland type with woody species. Crops are alternated in tight bands. In the area where the NDVI value is low (Korqin desert) the soil is generally sandy and land cover is of rangeland type with local xerophytic woody species or leguminous plants adapted to drought. The quality of the NDVI layer is excellent in spite of some technical problems (insufficient cloud masking, too large coastal buffer). In Xinjiang. Land cover data provided to SCOT by Xinjiang Region Authority show that vegetation vertical distribution illustrates the existence of different climates: high mountain tundra (between 4 100 and 3 200 m), grazing lands (from 3 500 to 2 300 m), forested belt (2800 1 600 m), altitude steppes (2 400 to 1 300 m), steppelike and/or bushy or woody vegetation (from 1 300 to 300 m), grassland and crops on piedmont areas. In irrigated areas, NDVI values are very high in August (in dark green). These areas are mainly located in the Tien Shan and Heerku Shan northern side and the piedmont basins where irrigated crops are prevailing (Northern Manas and southern Yanqi). Glaciers, rocks, altitude or piedmont lakes are in pink. Arid areas are in yellow and hyper-arid areas in light orange. Between the green mountainous area and the green piedmont area, there is an area for which NDVI values are lower. This area, located at the foot of the mountains, is covered by dry xerophytic vegetation. The quality of VGT geometry and radiometry allows to identify very small features. Conclusions To develop the VGT data market in Asia, a second phase should be dedicated to the development of new methodologies able to produce desertification indicators using VGT data plus climatological and socio-economical data. This new phase should take advantage of the important operational capabilities of the Chinese partners, in technical and logistical terms. Vegetation Action & Demonstration Plan for dry grassland monitoring in Senegal Christian CREPEAU SCOT Partners of the study CSE (Centre de Suivi Ecologique), Dakar, Sngal. CNES, SCOT Objectives This study aims to demonstrate the contribution of Vegetation data for monitoring land cover in dry grassland areas in Sngal and to identify the optimum conditions to allow Senegalese and Sahelian users to use Vegetation data and derived products in an operational mode. Actions carried out Acquisition, processing and photo-interpretation of VGT data concerning the 1998 and 1999 agricultural seasons. These data were provided by the CTIV in the form of ten-day synthesis (S10). Validation using ground survey data collected by the CSE and production of chronological and differential NDVI-VGT maps. Acquisition and processing of several AVHRR images concerning the 1998 summer in the form of ten-day synthesis provided by the CSE HRPT receiving station. Ground survey campaign (36 sites) during the 1998 summer. First Results Users needs assessment, and evaluation of the AVHRR-derived products elaborated in Sngal and in the Sahel region, Design of new VGT-derived mapping products : Item 1 : chronological analysis (synoptic view of the NDVI of all the dekads) Item 2 : differential analysis (comparison between a dekade d, the previous one d-1, the same decade the year before). Item 3 : thematical ancillary data (land use, rainfall, soils, population, altitude, crops, ) aiming to facilitate NDVI interpretation. Quality assessment of the S10-NDVI layer. Excellent in spite of some technical problems (insufficient cloud masking, too large coastal buffer). Interpretation of the VGT images. In the area where the NDVI value is the highest, land cover is composed of annual crops, savannah and wet forests (mangrove). In the area where the NDVI value is low (North and East) the soil is generally sandy and land cover is of rangeland type with local xerophytic woody species adapted to drought. The quality of VGT geometry and radiometry allows to identify very small features. Conclusions To develop the VGT data market in Africa and within the international organisations, a second phase should be dedicated to the development of new methodologies able to produce dry lands monitoring indicators using VGT data plus climatological and socio-economical data. Classifying land cover types with VEGETATION data in dryland: A case study in Burkina Faso V. Gond, E. Bartholom Joint Research Ispra, Italy Land cover mapping is an essential component of environmental assessment. In the framework of the international conventions each signatory country must prepare reports with reliable figures for a series of items, including surfaces allocated to various land-cover types. This is for instance the case for the Framework Convention on Climate Change where potential natural carbon pools and sinks (e. g. forest surfaces) should be properly assessed. The principle applies also to the other conventions. The need is thus clearly defined: land cover mapping should provide accurate figures at the national level, and the location accuracy should be high enough to help year-to-year monitoring and to guarantee credibility to the information provided. The objective of the present study is to assess the capacities of VEGETATION data to provide the best possible detail both spatially and thematically. To carry out the study S1 as well as S10 products were used, together with derived products, such as the NDVI and what we call here the NDWI (Normalised Difference Water content Index, after Gao 1996): (IR-MIR)/(IR+MIR). The time-series analysis shows that these 2 indices give slightly different information regarding season duration. In addition there are differences in signal intensities that can possibly be interpreted either as differential atmospheric effect on the indices, or as a specific environmental information. In dry regions, land-cover mapping can more easily than elsewhere be carried out with cloud-free single-date images at various stages of vegetation development. Furthermore, in these environments vegetation types can be discriminated through soil type spectral properties rather than through biomass amount. In single date images bi-directional effects can be minimised both by using band combinations and local contrast techniques. As simple classifiers, such as the ISODATA algorithm, applied either on time series or on multispectral data sets do not lead to very satisfactory results compared to what can be photo-interpreted from the images, alternative procedures were evaluated. This is especially the case for instance for linear features such as valley bottoms A series of examples are analysed in various environmental conditions and compared to reference material such as vegetation and soil maps, and high resolution satellite data. UTILISATION DES DONNEES VEGETATION POUR LE SUIVI DE LA CAMPAGNE AGROPASTORALE SUR LA ZONE CILSS A ROYER AGRHYMET NIAMEY Depuis 1989 le Centre Rgional AGRHYMET(CRA) utilise les donnes NOAA-LAC de sa propre station de rception Niamey pour le suivi de la campagne agropastorale sur les 9 pays du CILSS. A la recherche de nouveaux supports d'analyse lui permettant d'amliorer les indicateurs d'alerte prcoce le CRA s'est associ avec le CCR pour valuer l'apport des donnes VEGETATION(VGT) sur dans un contexte oprationnel sur toute la zone CILSS. L'tude comprend 4 objectifs : ester les possibilits de transmission des donnes en temps rel Niamey; contrler les qualits radiomtrique et gomtrique des images; vrifier l'intgration des images VGT dans les chanes de traitement du CRA pour la gnration des indicateurs de suivi de la campagne; enfin comparer dans la mesure du possible les cartes de conjoncture obtenues avec les 2 capteurs. Dans un contexte d'alerte prcoce, l'information satellitale n'a d'intrt que si elle est diffuse en quasi temps rel. Au niveau du CRA cette information est utilise en attendant la remonte d'information de terrain pour le suivi du front de vgtation et est publie dans un bulletin dcadaire produit au plus tard 5 jours aprs la fin de la dcade. Avec VGT l'utilisation du mail s'avre la solution la plus efficace sur Niamey en prenant soin de trononner les images en plusieurs fichiers. Au niveau des dlais de transmission les images sont reues en moyenne 7 jours aprs la dcade. Les images NOAA acquises partir de la station de rception du Centre Rgional AGRHYMET sont prtraites automatiquement l'aide du logiciel LAS de l'USGS suivant le protocole du projet GLOBAL 1KM.Pour contrler les ventuelles drives radiomtriques chaque indice dcadaire est calibr sur une zone invariante en zone dsertique. Ces contrles effectus sur les donnes VGT montrent une grande stabilit radiomtrique qui ne ncessitent pas d'ajustement. Cependant on a pu remarquer quelques sauts radiomtriques en bordure de mosaques. Au niveau gomtrique aucun dcalage significatif n'a t observ et la qualit de la superposition multitemporelle permet de faire des analyses plus fines en particulier dans les petites valles. L'intgration des donnes VGT s'est faite sans problme du fait du format binaire fourni par le CCR. La gnration des indicateurs se faite sous ERDAS 8.3 et un simple changement de variables a t ncessaire pour extraire les diffrents indicateurs. Les indicateurs calculs au cours de la campagne sont : les diffrences interdcadaires de 1999 et par rapport une anne de rfrence (1998 pour VGT); les diffrences interdcadaires sur 3 dcades mettant en vidence les rgressions conscutives; les diffrences de cumul d'indice de vgtation par rapport une anne de rfrence (1998). Les indicateurs de fin de campagne regroupent: les dates d'mergence de la vgtation; les dates du maximum d'indice de vgtation; les cumuls d'indice de vgtation; les cumuls d'amplitude d'indice de vgtation. la stratification en fonction des volutions d'indice de vgtation. Ces derniers indicateurs font l'objet de comparaisons inter-annuelles de 1999 par rapport 1998. Il ressort que les donnes VEGETATION sont bien adaptes aux analyses de suivi de la campagne telles que dfinies avec les donnes NOAA LAC. On remarque aussi : une dynamique de l'indice de vgtation importante; une bonne homognit des donnes interdcadaires; une trs bonne prcison gomtrique qui permet une analyse plus fine des volutions d'indices de vgtation; Ce dernier facteur permet une meilleure apprciation de la localisation et de l'extension des poches de dficit et de rgression de l'volution de la vgtation. Generating fine spatial resolution VEGETATION derived imagery using SAR Conrad M. Bielski, Franois Cavayas and Langis Gagnon Geography Dept University of Montreal Quebec Canada The VEGETATION platform can record observations of the entire Earth at a high temporal frequency thereby making it possible to continuously monitor areas ranging from regional to global in extent. The wanted detail however for land cover analysis is not possible due to the very coarse spatial resolution of the sensor. Thus land cover estimates are very general and prone to large estimation errors. The lack of data is apparent as the need for more detailed information for both researchers and policy makers becomes more in demand at the global scale. Presently there are two possibilities to fill this requirement, use sample fine spatial resolution data and scale upwards or use coarse spatial resolution data and scale downwards using fine spatial resolution samples. Our approach is based on scaling downwards because the VEGETATION sensor has a high spatial resolution counterpart (SPOT XS) that is spectrally identical thereby making validation of derived data and information possible. Thus our objective is the creation of a satellite based remote sensing derived product whose information content is compatible to regional scale data requirements, covers a variable sized area up to the global extent and has a fine temporal resolution with a minimum of missing pixels. Our approach is based on the use of two different types of high temporal frequency remotely sensed data: multispectral SPOT VEGETATION and SAR RADARSAT. The coarse spatial resolution SPOT VEGETATION imagery provides spectral data better suited for deriving land cover classification while ScanSAR RADARSAT data provides finer spatial resolution (100m) imagery without atmospheric interference. We intend to combine these two data sources by decomposing the SPOT VEGETATION imagery using the spatial dependence from both types of imagery derived from the computed semivariograms. Image decomposition will be done using stochastic imaging, a geostatistical tool. The anticipated result is a better delineation of scene objects in the visible and near infrared spectral region providing better data for land cover analysis and monitoring over large extents. Validation of the decomposed VEGETATION will be made using SPOT XS imagery. Our analysis is centred around the city of Montreal in Quebec, Canada. Preliminary results show that among general land cover classes such as urban, agriculture and forest, both the ScanSAR RADARSAT data and the VEGETATION data provide different spatial relationships. It is these differences between and similarities within land cover classes that we intend to harness. Furthermore, the SAR imagery will be used as an indicator of spatial complexity within the coarse spatial resolution VEGETATION pixels. We intend to present our initial findings of this research into this type of procedure. Sub-pixel characterization of land cover at the global scale using SPOT-VEGETATION imagery () Else Swinnen (*), Frank Canters(**) & Herman Eerens (*) *Centre of Expertise on Remote Sensing and Atmospheric Processes (Vito-TAP) Boeretang 200, B-2400 Mol, Belgium. Tel: (+32) 14 336844 Fax: (+32) 14 322795 E-mail: jan.vanrensbergen@vito.be **Centre for Cartography and GIS - Brussels Free University (VUB) Pleinlaan 2, B-1050 Brussel Tel: (+32) 2 6293556 Fax: (+32) 2 6293378 E-mail: fcanters@vub.ac.be Accurate information about the spatial distribution of different land cover types is of utmost importance for the modelling of ecological and environmental processes at the regional and global scale. The objectives of the project, that will be presented in this poster, consist in the development of an easily reproducible method to derive global land cover maps from 1km VEGETATION imagery (full year sets of 10-daily syntheses), and in the inference of accurate estimates of the areal proportion of the different cover types at the level of typical cell sizes used in continental and global scale modelling. The general idea is to develop a supervised method for sub-pixel classification of major land cover types, using high-resolution data for the training. By defining calibration models, relating class proportions obtained from the classification to class proportions obtained from the high-resolution reference data, local estimates of the proportion of different land cover types will be derived for different levels of aggregation. At present various strategies for sub-pixel classification are tested on a small section of the global VEGETATION data set of 1998-1999, covering most of the European continent. The definition of land cover types is based on the IGBP Global Land Cover classification scheme. For the training, use is made of the ETC/LC production database of CORINE Land Cover, containing data collected between 1989 and 1997. All co-ordinate information in the CORINE database was geometrically transformed to the same reference system as the VEGETATION imagery. Extra adjustments were made to achieve an optimal fit. After spatial aggregation of the CORINE data (44 land cover classes) to the agreed set of major land cover types, the transformed version of the database was gridded at a resolution of 20m. From this detailed information, land cover proportions were calculated for each 1km2 pixel in the VEGETATION imagery. This proportional data forms the input for the training and verification of the classification tests that are currently performed. Once a suitable classification strategy has been defined, the same high-resolution data will be used to develop an appropriate calibration method to improve the quality of land cover area estimates derived from the classification. L'tablissement de Nomenclatures Vgtation partir d'Images SPOT D. Blamont, M. Raffy GRTS/LSIIT/CNRS/ULP 5, Bd. S. Brant 67400 Illkirch courriel: denis@lune.u-strasbg.fr Pour tablir la nature des objets thmatiques dtectables sur des images basse rsolution spatiale, telles que celles produites par l'outil Vgtation, une des mthodes possibles est de passer par l'utilisation de donnes rsolution plus fine, qui peuvent tre une image satellitale ou des donnes de terrain. condition de rsoudre les problmes de calage gographiques des images et de connatre l'erreur commise en considrant la rflectance mesure par Vgtation comme la moyenne des rflectances mesures par SPOT, le couplage SPOT-Vgtation, par exemple, doit permettre de construire des objets thmatiques d'une chelle gographique compatible avec la rsolution de Vgtation. Or, les lois physiques rgissant les changements de rsolution diffrant de celles gouvernant les changements dchelle thmatique, il n'existe pas aujourdhui de mthode systmatique pour passer d'une nomenclature haute rsolution la dfinition d'une nomenclature correspondante basse rsolution. L'objet de cette prsentation est de poser le problme et de donner une mthode pour la construction des nomenclatures radiomtriques que l'on dfinira prcisment. Les rgles des changements de rsolution et dchelle ainsi labores peuvent s'appliquer des domaines autres que ceux de la tldtection. Ne sont abords que les problmes concernant spcifiquement la radiomtrie, les informations de type structural ou textural obissant dautres lois. Les rgles prsentes permettent de dterminer les objets thmatiques dont on peut affirmer la prsence avec certitude sur l'image Vgtation: il s'agit des objets correspondant aux extrmes du domaine radiomtrique de l'image haute rsolution. Les objets ainsi construits doivent tre suffisamment importants (en termes la fois thmatiques, surfaciques et radiomtriques) pour rduire au maximum les rgions de l'image sur lesquelles on ne pourra rien dire avec certitude. On donne un moyen de calculer un indice de la qualit de la nomenclature basse rsolution, exprimant l'incertitude moyenne par rapport au critre ci-dessus. Dans le cas prsent, nous prsentons l'utilisation d'une image SPOT4 pour l'tablissement d'une taxonomie d'une portion d'image Vgtation du plateau tibtain UTILISATION DE SPOT4-VEGETATION POUR LETUDE DU CHANGEMENT DECHELLE. M. Raffy, D. Blamont GRTS/LSIIT/CNRS/ULP 5, Bd. S. Brant 67400 Illkirch courriel: raffy@sepia.u-strasbg.fr La modlisation physique des processus de surface, de plus en plus ncessaire lutilisation des donnes de tldtection, impose le choix dune chelle dans les paramtres entrant en jeu. Ce choix est lui-mme impos par lomniprsente htrognit des milieux naturels. Or la rsolution spatiale des donnes de la tldtection correspond des chelles rarement compatibles avec celle adopte par les modles, gnralement associe aux validations in situ. La question du changement dchelle est ainsi fortement justifie et souvent tudie. Lun des points cl de ltude thorique est de connatre le lien entre la radiomtrie satellitaire basse rsolution et celle haute rsolution. En particulier, les conditions temporelles, gomtriques, atmosphriques ainsi que les bandes spectrales tant les mmes haute et basse rsolution, la radiomtrie basse rsolution est-elle la moyenne de la radiomtrie haute rsolutionsur la mme zone? Si ce nest pas le cas, quel est lordre de grandeur de lapproximation? Linstrument Spot4-Vgtation permet une comparabilit des mesures haute et basse rsolution jamais atteinte par lutilisation des capteurs existants qui ne possdent pas les proprits de comparabilit satisfaisantes. Nous prsentons quelques premiers rsultats sur cette question, aprs avoir abord l'tude prliminaire du recalage des images auxdeux rsolutions. VEGETATION data for monitoring woody vegetation in landscape frameworks Hubert Gulinck and Tim Wagendorp KULeuven This project tries to relate the VEGETATION sensor data to areal information about wood cover. In order to find out how coarse resolution images react to the target features (wood) in highly fragmented cultural landscapes several approaches were envisaged. The CORINE land cover database was taken as the prime reference datalayer because of its extent and consistent legend. Areal estimation of this reference were calibrated for the basic resolution and aggregation effects, by comparison with a geographic sample and subsamples of 20 and 1m resolution respectively. over Flanders. Seven VEGETATION images over a section of north-western Europe were analysed. In a sample of segments, wood cover as defined by the reference dataset was regressed (stepwise multiple regression) to image characteristics, in order to be able to assess the contribution of the individual bands and periods. The SWIR seems to provide extra information for quantifying the contribution of coniferous versus deciduous wood. Seasonal combinations yield additional information. The concept of landscape stratification was introduced. 5km land squares were classified according to land cover context, fragmentation and gross soil characteristics. Each of the retained land classes serves as stratum for an independent sampling of 5km images quadrats. The other way round, the contribution of VGT imagery in current concepts of wide-scale landscape stratifications is discussed. VEGETATION data for regional forest cover mapping of Southeast Asia H-J Stibig, R. Beuchle, V. Gond Joint Research Centre , GVM, TP440 I-21020 Ispra (Va), Italy Mapping of forest cover of Southeast Asia from satellite images is hampered by several factors characteristic of the region. Most significant is the persistent cloud cover over insular Southeast Asia where all satellite image acquisitions are contaminated by cloud and haze. For most of continental Southeast Asia good satellite images can be obtained during the dry season. However, in this part of the region the monsoon regime causes high variation in vegetation phenology. Land use patterns are small and intermingled (e.g. shifting cultivation) and particularly in the deciduous forest domain transitions from forest to woodland are gradual. In addition, in the mainly mountainous area the impact of shadow on the spectral reflectance is considerable. The VEGETATION instrument of SPOT 4 can offer a valuable tool for forest cover mapping at regional scale. The improved spatial resolution and geometric quality of the new generation of coarse resolution satellite images make a more distinct mapping of forest cover feasible. The daily coverage of the whole continent allows an image selection according to best quality, lowest cloud cover and the optimal phenological stage of vegetation cover. Generating mosaics from series of images could provide a possibility for coping with persistent cloud cover. VEGETATION 10-day standard composites (S10 products - with pixel extraction based on the maximum NDVI value) were tested for regional mapping of forest cover. For continental Southeast Asia twelve S10 mosaics were extracted during the dry season from December to March for the years 1999 and 2000. For the insular part of Southeast Asia 42 composites were acquired from May to November 1998 and 1999. A further mosaicing procedure was applied for each sub-regional set of images, extracting pixels from the S10 composites by the minimum value in the short wave infrared band. Such selection would tend to give preference to cloud free or less cloud contaminated pixels and would reduce the artificial mosaicing effects in the original S10 products. Looking at vegetation phenology green stages of vegetation would be chosen rather than leafless stages. The mosaics and first classification results obtained open up some promising perspectives when based on an optimal selection of the seasonal and phenological time window. VEGETATION 10-day-composites appear to offer a possibility for a rapid assessment and first level mapping of forest cover in Southeast Asia at continental scale. Such products are useful not only for producing a regional overview of forest cover, but also for stratification purposes and monitoring of large-scale forest conversion and trends of change. Limitations to be expected are due to the nature of the data, the scale of the exercise and the characteristics of vegetation cover and land use in the region. Overestimation of forest cover can be expected due to shadow effects in mountain areas, the inclusion of woodland formations within deciduous forest cover and of small-scale plantations within evergreen forest cover. On the other hand underestimation may occur in areas of extensive shifting cultivation by missing small forest lots. An evaluation of SPOT-VEGETATION, for land cover mapping and the evaluation of forest resources, I: Mato Grosso, Brasil. Jones, S. D. Eva, H. D., SAI, JRC Italy The need for timely, low-cost, high quality land cover information from earth observation is of particular importance in the humid and sub-humid tropics. This paper focuses on the state of Mato Grosso, Brasil, which lies at the heart of the arc of deforestation in the Southern Amazon. In this context SPOT VGT S-10 and S-1 products are evaluated along side the commonly used NOAA-AVHRR LAC product and the ERS-2 ATSR GBT. The evaluation focuses first on sensor-platform attributes including possible bi-directional capabilities and effects, geo-locational accuracy, near-real time monitoring, cost, radiometric and spectral characteristics. Secondly, several land cover assessment methodologies are attempted: (i) unsupervised classifications (ISOCLUSTER) were performed and expert interpretation solicited; (ii) several vegetation indices were then performed and results compared; and (iii) analysis of the spectral phenology, derived from temporal composites, was undertaken. These results were then validated by Landsat TM coverages and collaboration with Brasilian agencies. Preliminary results suggest that VGT (and LAC) data are more suitable for rapid continental-scale evaluations and seasonality studies. VEGETATION data consistently producing high quality image products but being let down by its geolocational accuracy and internal sampling routines. More detailed studies, at a regional-scale (the case in Mato Grosso), benefited greatly from the increased spectral (smaller band-pass widths) and geometric accuracies of the ATSR-2 sensor. Possible synergies between these three data sources are then explored. BRDF correction in SPOT 4/VEGETATION ten-days composite imagery for mapping of boreal forest D. Erchov (1), S. Bartalev (2), M. Deshayes (3), J. R. Dymond (4) (1) International Forest Institute, Moscow, Russia ( +7-095-332 68 77, fax +7-095-332 29 17,  BOUTONATTEINDRE BM_1_ erchov@ifi.rssi.ru (2) Joint Research Center of the European Commission, Ispra, Italy ( +39-332-786396, fax +39-332-789073,  BOUTONATTEINDRE BM_2_ sergey.bartalev@jrc.it (3) Systemes et Structures Spatiaux Cemagref-ENGREF, Montpellier, France ( +33-(0)4 67 54 87 51, fax +33-(0)4 67 54 87 00,  BOUTONATTEINDRE BM_3_ deshayes@teledetection.fr (4) Landcare Research, Palmerston North, New Zealand ( +64 6 356 7154, fax +64 6 355 9230,  BOUTONATTEINDRE BM_4_ DymondJ@landcare.cri.nz The appearance of the SPOT 4/VEGETATION instrument opens new capabilities of forest mapping at continental and sub-continental scales. The examination of ten-day synthesis imagery, acquired over Russias boreal forests, has revealed the strong influence of Sun-Earth-sensor geometry. This non-Lambertian vegetation reflectance can be described and corrected with the use of BRDF models. In the present research, we used a physically based and simple BRDF model (the WAK model) to correct ten-day synthesis imagery for non-Lambertian reflectance. Observation conditions were standardised to nadir view and 45o solar zenith angle. Model parameters were derivable directly from the VEGETATION imagery, for predominant vegetation groups of the condition-zoning map of Russia. Model parameter determination is based on the solution of a system of equations describing the reflectance of reference sites viewed from different directions. Not less than three pairs of reference sites for each vegetation type, or land cover class, are required. Each reference site was located across the boundary between two series passes to provide different view directions. The efficiency of the BRDF correction with reference to boreal forest mapping is quantified. The limitations of the given approach and direction of further improvement of BRDF correction in the SPOT 4/VEGETATION imagery are discussed. Detecting active fires with the VEGETATION instrument V. Gond*, M. Maggi*, P. Henry, J.-M. Grgoire*, E. Bartholom* *Joint Research Ispra, Italy CNES, Toulouse, France Active fires may, in certain conditions, be observed on VEGETATION images. Indeed fires generate a surplus of energy that can be detected with the Middle Infrared channel if they are sufficiently large and warm. To assess instrument sensitivity, VEGETATION S1 data have been analysed over 3 different windows, one over Northern Australia (Darwin), and the 2 others over West and Central Africa (Burkina Faso and Central African Republic). The area covered in Australia is typified by open forest, woodlands and some swamps in the coastal region. The combination of both African windows includes a wide range of ecological conditions, stretching from the sub-arid conditions of Northern Sahel, to the moist evergreen forest of the Congo basin. In addition to the S1 day images, night images were acquired over N. Australia by CNES on an ad-hoc basis for calibration purposes. The World Fire Web network provides external validation material by giving access to fire geo-location retrieved from AVHRR data processed locally: by CSIR-EOC for Australia and PRGIE-OFB for Central Africa. Some ATSR images were also used over the N. Australia site for comparison. VEGETATION data are also used for consistency assessment: smoke plumes of active fires show up clearly in the Blue channel (B0), day-to-day image difference of the Middle Infrared channel (MIR) allow newly burnt surfaces to be delineated. This is easily achieved thanks to the excellent image co-location. For day images the conclusions of this study may be summarised as follows: 1) suspected features are confirmed to be fires, 2) fires influence exclusively the MIR channel 3) these features cannot be extracted by simple threshold, but rather by taking advantage of their local contrast (contextual analysis), 4) they can also be detected using day-to-day image comparison thanks to excellent image co-registration, 5) detectable fires occur only in specific ecological conditions, typically in humid and sub-humid rather than in arid regions 6) fires detected with VEGETATION are only a small fraction of those detected with AVHRR. For night images the conclusions are the following: 1) features observed are confirmed to be fires, 2) they can be easily retrieved by simple threshold, 3) given the small size of area it is difficult to draw conclusions on the efficiency in terms of fire count, 4) there is further uncertainty due to lack of cloud coverage information, 5) fire activity (including gas flares of off-shore oil platforms) should be taken into account when MIR calibration campaigns are performed 6) more observations would be necessary to carry out an accurate benchmark of VEGETATION night image efficiency for active fire detection. Drawbacks and advantages of the VEGETATION and AVHRR instruments for burnt area detection in Northern Australia. D. Stroppiana1, M. Maggi1, D. Graetz2, S. Campbell2, I. Balzer2, J-M. Grgoire1 and J.M.C Pereira3. 1 JRC-Space Applications Institute, Ispra, Italy 2 CSIRO-Earth Observation Centre, Canberra, Australia 3 Instituto Superior de Agronomia, Lisbon, Portugal Until recently, the detection and mapping of burnt areas from low resolution satellite data, has been done exclusively with the imagery provided by the ATSR and AVHRR instruments. The VEGETATION instrument is now opening new perspectives for the detection of burnt areas. In order to assess these perspectives and to investigate the specific advantages of the VEGETATION instrument in a multi-sensors approach, burnt area maps were derived from NOAA-AVHRR and SPOT-VEGETATION images for a study site in Northern Australia for the dry season in 1999. Daily data acquired from both sensors were composed into 10 day images using a two steps compositing criterion for AVHRR (minimum channel two and maximum channel four) and a single step criterion for VGT (minimum NIR). A burnt area detection algorithm for each sensor was then derived using a Classification Trees approach. The algorithm was directly applied to the VGT composites to classify them into burnt/unburnt. For what concerns the AVHRR data set, active fire maps produced by the World Fire Web were used as inputs in a seed-growing approach. The burnt area detection algorithm derived for AVHRR is applied starting from the fire points and let them grow untill no neighbouring pixels satisfy the burnt conditions. Finally Landsat TM and ATSR images were used for a first evaluation of the results. Preliminary conclusions are: 1)the lack of a thermal channel on the VEGETATION instrument is certainly a drawback, specially during the compositing process 2)the excellent geometry of the VEGETATION imagery, compared to the AVHRR one, allows to improve the performance of a burnt area algorithm based on a change detection approach 3)the MIR channel improves the detectability of the burnt areas 4)the seed-growing method, which combines active fire locations and burnt area algorithm, seems very promising and could be the basis of a combine VEGETATION-AVHRR approach to the detection and mapping of burnt areas at global level. Using SPOT 4 HRVIR and "VEGETATION " sensors to assess impact of tropical forest fires in Roraima T. PHULPIN, F. LAVENU, M. F. BELLAN, B. MOUGENOT and F. BLASCO Centre National d'Etudes Spatiales (CNES, DSO/OT/QTIS), Bpi 2801 31401 Toulouse cedex 4, France Centre d'Etudes Spatiales de la Biosphre (CESBIO), Toulouse, France Institut de Recherche pour le Dveloppement, presently at CESBIO, Toulouse, France Laboratoire d'Ecologie Terrestre (LET), Toulouse, FRANCE Due to El Nio phenomenon, the 1997-1998 dry season in Roraima (Brazil, Amazonia) was particularly pronounced. Consequently, vegetation fires have spread widely and have been monitored by many satellites in real time. Satellite images are currently being used to monitor vegetation fires either globally for climate studies or more regionally for impact assessment. After reviewing different satellite data used for impact assessment, this paper focuses on the contribution of SPOT 4's imagery provided by high resolution HRVIR and coarse resolution VEGETATION sensors. These sensors are described with emphasis on those characteristics of potential benefit for forest mapping and fire detection. Early images of Roraima from SPOT 4 are analyzed and interpreted to delineate the areas already subject to fire damage. VEGETATION images provide a first estimate of damaged areas on a regional scale and an indication of main ecosystems affected. SPOT HRVIR is used to establish a much more precise classification of various ecosystems. Each vegetation class is associated with a biomass density. From the known burned areas, an estimate of burned biomass during the 1998 dry season is computed, as well as total carbon release. Areas derived from VEGETATION are then cross-validated with HRVIR and thus an attempt is made to extrapolate the burned biomass with the help of VEGETATION on a regional A Large Forest Fire in the Mediterranean region as seen by VEGETATION Agustin Lobo and Nicolau Pineda Instituto de Ciencias de la Tierra "Jaume Almera" (CSIC) Lluis Sol Sabars s/n, 08028 Barcelona, Spain alobo@ija.csic.es In 18-07-1998 a large forest fire started in the South foothills of the Pyrenees. After four days, a total of 27 000 ha had been burnt. In this poster, we show how this event was observed with VEGETATION imagery and discuss the utility of this sensor at detecting and recording wild fires as well as the possibility to use it for the assessment of fire risk. ATMOSPHERIC MESOCYCLONES OVER POLAR SEAS AND THEIR INFLUENCE ON ECOLOGICAL REGIME FORMATION Lagun V.E., Lutsenko E.I. Mesoscale cyclonic eddy disturbances (Polar low, Arctic low etc.) are the unique phenomena of both Arctic and Antarctic areas atmosphere. The study of mesocyclones formation is very important for ensuring of human activity in polar regions because these disturbances induce extraordinary weather condition over Arctic and Antarctic seas (icing-up of ships, storm wind, strong snowfall etc.) This phenomena are subgrid events for surface measurement network and cannot be forecasted successfully using traditional synoptic and numerical prediction methods. Therefore, the only reliable method of mesoeddies genesis and evolution over polar seas presently is the processing of satellite imageries of cloudiness and ice edge distribution. This method describes the cloudy cover over Northern-European basin, Barents and Kara seas, and several Antarctic seas for 1981-1996 period. On the base of this archive the catalogue of cloudy subsynoptic eddies is formed. It contains more than 500 of mesocyclones pictures. Their dimensions are from 50-70 km to 500-700 km. For mesocyclonic genesis period the quantitative estimations of cloudy eddies parameters (time frequency, linear size, shape, duration and trajectory) are made. The morphologic description of mesocyclones (comma, spiral and strap forms) is executed. Also the classification by mesoeddies origins is made. This includes polar mesocyclones (Polar low), summer cloudy mesoeddies and secondary cyclones. The map of intensive polar mesocyclogenesis areas for both hemispheres is constacted. The seasonal and interannual variability of polar low formation is studied. Thus, two pronounced mesocyclone frequency maxima in December and April are detected. The last decade is characterized by increasing of polar mesoeddies number and intensity. For investigations of subsynoptic eddies formation, structure, evolution and of their interaction with surrounding air masses and underlying surface the specific meteorological archive is created. This archive contains the high space-time resolution data for most typical cases of mesoscale cyclogenesis over seas and coastal areas. Nevertheless, the resolution of presently available meteorological information used for dynamic and energetic atmospheric parameter diagnosis does not let to describe the formation and moving of Polar low reliably. Since the Polar lows displacement often causes the local natural catastrophes (for instance, sea accidents) the inaccuracy of these events forecasting can have got the very negative consequences for human activity. Therefore the utilization of regular homogeneous satellite data about the cloudy, snow and ice covers received in frames of Programme VEGETATION can play the important role for quantitative mapping and diagnosis, for statistical analysis and improving of intensive mesocyclone forecast development in polar regions. INVESTIGATION OF RUSSIAN ARCTIC ECOSYSTEMS VARIATION CAUSED BY ANTROPOGENIC ACTIVITY AND CLIMATE CHANGE Ivanov V.V., Lagun V.E. Arctic ecosystems are characterized by instability to external forcing, by low ability to reconstruction and self-clearing due to extraordinary climatic conditions there. Therefore, the ignore of ecological requirements accompanying by weak study of Arctic environment and influence of local and transboundary anthropogenic processes cause the ecosystems degradation, especially in several impact areas. The main impact areas are Norilsk and Kola industrial regions, Ob-Yamal deposits area, Pechora coal basin, the plants of timber industry. Systematic approach oriented to the study of Arctic ecosystems, including varied connections between natural elements and anthropogenic factors is applied. The main goals of this study are the ecological mapping of Arctic territories based on landscape, hydrometeorological, hydrochemical and biological parameters set, and investigation of interannual variability of ecosystem parameters. The data about terrestrial, river, estuaries, and atmospheric components of ecosystems are used. For studying of current ecosystem condition the methods of analytical analysis and mathematical modeling are applied. These methods use the measurement of pollutant concentration contained in soil, plant, biological component, freshwater, air, ice and sea water. Numerical modeling takes into account emission, transport and deposition pollutant processes. The results of this study are utilized in applied investigations. These investigations are based on application of more correct environment parameters data and on predicted tendency of such parameters variations. The results of investigations will be taken into account for rational utilization of natural resources. The data about vegetation cover geographical distribution from VEGETATION Program are the reliable indicator of ecosystem conditions, especially for Arctic impact areas and hot-points. Mapping Biological Diveristy in Boreal Forest From Space Using Ecological Models Anthony J. Warren, Michael J. Collins Department of Geomatic Engineering University of Calgary The primary objective of this work was to map biological diversity at the southern extent of the boreal forest in Prince Albert National Park, Saskatchewan, Canada. This was accomplished by using remote sensing and GIS techniques to spatially estimate (map) the four input variables of an ecological model which is able to predict biological diversity. The variables of interest were (1) the distance from a forest stand to a drainage basin ridgeline, (2) the time elapsed since the last forest fire, (3) the canopy species type and (4) the canopy stem density. The methods used to map each variable over the extent of The Park are discussed in detail. The data used to map these variables included spaceborne imagery (electro-optical and synthetic aperture radar) as well as vector format elevation contours, lakes and streams. The four variables were input into the model to predict biodiveristy. Close attention was paid to the estimation of uncertainty associated each of these input variables. The uncertainty in from each variable was propagated through the model. The results are presented in the form of three maps of biological diversity in the park. These include a map of predicted diveristy as well as maps of the upper and lower bound on the predicted diveristy as a result of uncertainty propagation. The results show that combining spatially estimated input parameters with such a model was reasonably successful and is an innovative use of remote sensing and GIS. EVALUATION OF VEGETATION CLOUD MASKS FOR CLIMATOLOGY STUDIES AND DESIGN OF SATELLITE SYSTEMS J. Hamon, L. Harang, A. Rodot Centre de mtorologie Spatiale, 22302 LANNION, France J.M Lachiver, T. Phulpin CNES, 31401 TOULOUSE, France VEGETATION offers an opportunity to get everyday in quasi real time cloud cover images at global scale. Provided a good representativity and reliability of this product, the cloud masks could be used in a contribution to cloud climatology studies. They can also be used in the exploitation of current satellite data, e.g to re-program quickly acquisition of fully overcast SPOT scenes, or in engineering studies for the design of new spaceborne systems, (compression techniques, pointing in cloud free areas). This study aims at building up a data set for validation of the automatic cloud screening procedures. The data set and the way it. is established is presented. This data set is used to test the quality of the cloud mask provided in the status map or alternative masks, in a comparison with other cloud masks obtained with meteorological geostationnary satellites. Based on it. , attempts are made to validate the quality of compressed cloud masks and masks made with degraded resolution data. An ICT-based course in Earth Observation with emphasis on VGT-data Rombout Verwimp1c, Ann Willekens1, Jos Van Orshoven1 and Jan Elen2 1 Leuven Earth Observation, Katholieke Universiteit Leuven 2 DUO, Katholieke Universiteit Leuven 1c Address of corresponding author: Vital Decosterstraat 102, 3000 Leuven, Belgium The use of Earth Observation (EO)-data in general and VEGETATION (VGT)-data in particular is often limited by lack of knowledge on principles of image processing and information extraction and on types of and access to available data, ... Tailored use of ICT (Information and Communication Technology) presents scope for enhancing dramatically the efficiency of related training programs. Leuven Earth observation (University of Leuven - Belgium), supported by the OSTC (Belgian Federal Office for Scientific, Technical and Cultural affairs) is currently preparing such an ICT based modular Remote Sensing/VGT course. A general EO module and one focussing on the use of VGT and its organization are currently being developed. Modules for other sensors, data types, processing techniques are scheduled. Participants should be able to choose their own non-linear course-pathway. Each way should fulfill as much as possible the specific needs of the student. For example, decision makers and planners, who mostly are more interested in the application possibilities of the VGT-data rather than in the technical details should be able to choose a different path compared to people focussing on processing VGT data. Furthermore through the VGT-module students should get familiar with the organisation and different products the VGT-program is offering nowadays. Along with the aim of offering a non-linear course-pathway, two additional objectives -enhancement of the concept of guided self-study and rientation towards a professional public (i.e. vocational training) - and boundary conditions were set. Due to the digital nature of EO-product handling, ICT is almost a natural partner for EO-education and training. In a class-room setting, ICT can be clearly present. Time/place constraints however remain pertinent for vocational trainings. The internet and world wide web are a very appropriate medium to comply with the specific properties of the proposed type of eductation and to overcome these time/place constraints. Recent studies (NUA Internet Surveys) show that there are worldwide almost 250 million internet users online. Also The EITO (European Information and Technology Observatorium) recently reported that last year the European ICT-market grew over 10 percent and that access to the internet within the EC increased over 25 percent. These facts confirm the growing possibilities of this new medium for education, training and promotion. The main added value we aim at is the access to exercise material through the web. Our ICT based course will be able to make students familiar with several processing algorithms, parameters and other image processing techniques. Current GIS and RS web based technologies (cfr. ESRI IMS en SDE products, Oracle SDO, ), make this dynamic approach possible. Main restriction is the time-intensity of development of such a course program. . Project financed by the Belgian Federal Office for Scientific, Technical and Cultural Affairs (Contract T4/67/57). . METAFRO-InfoSys: http://metafro.africamuseum.be . Mayaux P, Janodet E, Blair-Myers C & Legeay-Janvier P, 1997, Vegetation map of Central Africa at 1:5,000,000, TREES Series D: thematic outputs N 1, Space Applications Institute, EC-JRC, EUR 17322 EN. . Major D, Baret F & Guyot G, 1990, A ratio vegetation index adjusted for soil brightness, Int. J. Rem. Sens., 11(5), 727-740. Project financed by the Belgian Ministry of Agriculture, the Federal Office for Scientific, Technical and Cultural Affairs, and Vito-TAP. 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