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Remote sensing methods for biomass productivity assessment

A project has been set up to develop remote sensing based methods of biomass productivity assessment in Sahelian countries at scales ranging from the regional to the subcontinental level. The satellite data set over Niger and Burkina Faso contains over 1000 images which were pre processed for geometric and radiometric correction, and for geophysical parameter computation. Extensive sets of ground data were analysed in order to assess the relationship between green biomass, as perceived by the satellite, and grain production, which is the significance information for the end user. The study was carried out on a series of more than 2800 measurements collected directly in farmers fields. These data were collected in a wide range of ecological conditions, in 3 different countries and during 4 agricultural seasons. The main conclusion of the analysis was that there is a very wide variability between observations. The observed differences cannot be fully explained by environmental or agricultural conditions, and may be due to experimental conditions. The normalized difference vegetation index (NDVI) values derived from satellite measurements show very little difference between the agricultural and the nonagricultural domains. it was found that field data collection carried out on a village basis does significantly improve the calibration of NDVI for yield mapping. It is better to collect average yield figures at regional level, and to compare them to NDVI averages from cloud free pixels which can be found more easily over broader areas. A preliminary evaluation was carried out on the possible assessment of actual evapotranspiration (ET) from advanced very high resolution radiometer (AVHRR) data. Any accuracy assessment was carried out on a satellite derived digital elevation model, in order to better assess its usefulness for topographic description in moderately hilly regions. The resulting quality not only depends upon the geometric properties of the satellite data, but also upon the intrinsic qualities of the algorithms used. Flexibility in the processing of AVHRR data has been introduced by developing a new data and image management environment and by porting available routines into it.


Record Number: 12430 / Last updated on: 1995-06-19
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Collaboration sought: Information exchange/Training
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