A new approach for using time-series remote-sensing images to detect changes in vegetation cover and composition in drylands

Abstract

Vegetation cover and composition are important aspects of the dryland environment because they provide livelihood to humans and also protect soil resources against erosion. Currently, scientists are advancing various techniques for detecting vegetation degradation in the drylands and the possibilities for its control. This study contributed through the testing of time-series mixed-effects modelling of the normalized difference vegetation index (NDVI) and rainfall relationship to trace the footprints of vegetation dynamics in the drylands. The approach aimed at providing guidelines for quick diagnosis of the changes in vegetation cover and composition to trigger necessary action. The mixed-effects technique used in this study is a novel regression approach for simultaneous modelling of the NDVI–rainfall relationship in different dominant vegetation types. Its time-series application with Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images between 1982 and 2008 was tested in eastern Kenya. The results show how the original dominant vegetation types had been converted to cereal croplands, open grasslands, or reduced to bare ground in a span of 27 years. In some places, it shows how the changes in vegetation composition resulted in the overall loss of vegetation cover. Field validation positively confirmed these observations; thus, indicating that the method was a promising tool for tracing vegetation dynamics in the drylands. In spite of its success, the method was found to be only useful in detecting changes in large areas with dominant vegetation types. The technique can therefore be recommended for regional analysis, and can be used as a first approximation to guide more detailed subsequent analysis.