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Table 1. Capabilities for Vegetation Monitoring 
Characteristics 
Landsat MSS 
Landsat TM 
NOAA/AVHRR 
SPOT 
Spatial resolution 
80 m 
30 m 
1,100 m 
20/10 m 
Spectral resolution 
4 bands 
7 bands 
5 bands 
3 bands 
Useful bands for 
vegetation monitoring 
2 
4 
2 
2 
Radiometric resolution 
(quantitative levels) 
64 
256 
1,024 
256 
Temporal resolution 
16 days 
16 days 
2-3 days 
every 9 days 
9 days 
Swath width 
185 km 
185 km 
2,700 km 
92 km 
Single frame cover 
34,000 km 2 
34,000 km 2 
2.10 6 km 2 
8,500 km 2 
Scenes/orbits required 
for Desert Locust 
recession area coverage 
700 scenes 
700 scenes 
7 orbits 
2,800 scenes 
The choice of suitable satellites for remote sensing observations for particular applications depends upon 
satellite spectral, spatial, temporal and radiometric resolutions, orbital considerations and the 
spatial-spectral-temporal characteristics of the objects to be observed. 
Moreover, successful monitoring of crop conditions and Desert Locust habitats, relevant to FAO’s programmes, 
requires a satellite that can, at any one time, detect the presence and assess the quantity of green-leaf 
vegetation biomass with a suitable spatial resolution over very large areas on continental and inter-continental 
scales, while radiometrically maintaining the integrity of spectral contrast of the surface materials, and do so 
at frequent intervals (Tucker et al. 1985). Furthermore, satellite detection of vegetation cover at high temporal 
intervals, requires elimination of often considerable cloudcover which is generally present during the typical 
monitoring periods for above applications, i.e. the rainy seasons. A technique for compositing largely cloud-free 
NOAA AVHRR vegetation index data from multiple daily NOAA orbits over ten-day and monthly periods, 
while maintaining data/ information integrity, was successfully developed and tested by NASA Goddard Space 
Right Center (GSFC) (Holben 1986). 
Landsat Multispectral Scanner (MSS) data has been evaluated for mapping and monitoring desert locust 
habitats by Pedgley (1973) and Hielkema (1977, 1979, 1980), by using both visual and digital image analysis 
techniques. These studies showed that Landsat MSS data could accurately detect the presence of green 
vegetation and monitor its phytodynamics. In addition, Landsat MSS data can make an important contribution 
to the systematic mapping of potential desert locust habitats on through visual interpretation of suitably 
enhanced imagery at a scale of 1:250,000. The imagery provides synoptic and yet detailed information on the 
geomorphology and soil characteristics of an area. At present, FAO is undertaking, following the 1986-1988 
desert locust plague, a systematic mapping of potential desert locust habitats in the recession area, by using 
a methodology developed by Popov, in which the use of Landsat MSS data is paramount.