GLOBAL DISCRIMINATION OF LAND-COVER TYPES FROM METRICS DERIVED FROM AVHRR PATHFINDER DATA

Seasonal changes in the greeness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index ( NDVI) throughout the year have been the basis for discriminating betwe en cover types in previous attempts to derive land cover from AVHRR da ta at global and continental scales. Several researchers have suggeste d and applied the use of metrics, such as maximum NDVI or length of gr owing season derived from a temporal profile of 10-day or monthly NDVI values, as an alternative to classifying cover types front the monthl y NDVI values directly. This study examines the use of metrics derived from the NDVI temporal profile, as well as metrics derived from obser vations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability m easures calculated front Bhattacharya distances, average separabilitie s improved by using 12 of the 16 metrics tested (1.97) compared to sep arabilities using 12 monthly NDVI values alone (1.88). Separabilities improved from poor to good in 20 out of 25 pairs of cover types with p oor separability. Percentage of pixels correctly classified in a maxim um likelihood classifications also improved by using the metrics from 76% to 86%. Overall, the most robust metrics for discriminating betwee n cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Ban d 2 (near-infrared reflectance) and Band 1 (red reflectance) correspon ding to the time of maximum NDVI, and maximum land surface temperature . Deciduous and evergreen vegetation can be distinguished by mean NDVI , maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mea n NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.