NDVI-DERIVED LAND-COVER CLASSIFICATIONS AT A GLOBAL-SCALE

Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) deriv ed from satellite data, have been used to classify land cover at conti nental scales. Extending this technique to global scales raises severa l issues: identifying land cover types that are spectrally distinct an d applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of re liable information on global land cover; and acquiring high quality gl obal data sets of satellite sensor data for input to land cover classi fications. For this study, a coarse spatial resolution (one by one deg ree) data set of monthly NDVI values for 1987 was used to explore thes e methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the fe asibility of using satellite sensor data to increase the accuracy of g lobal land cover information, although the result has not been validat ed systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derive d from satellite sensor data.