Application of SPOT Imagery for Landcover Mapping and Assessing Indicators of Erosion and Proportion of Bareground in Arid and Semi-arid Environment

Abstract

Inappropriate land-use on a fragile ecological condition have greater impact on the natural state of rangelands making land degradation a common phenomenon. Usage of remote sensing has become an ideal choice for monitoring these natural resources. SPOT 5 imagery was used, in this study for characterizing land cover classes and mapping vegetation distribution in the North West Province, South Africa by employing the maximum likelihood classification technique. Regression technique was also used to assess relationship between rainfall distribution and proportion of bare ground. Water body, bare ground, indicators of erosion, built-up area, grass and shrubs were the LULC classes in the image classification. Except for indicators of erosion, all the land-cover classes were classified with higher accuracies (in average, >0.78 overall accuracies and 0.70 for Kappa). However, SPOT 5 imagery yielded low overall accuracy (< 0.3) for indicators of erosion. Strong coefficient of determination (r²=0.80) was detected between average rainfall and proportion of bare ground indicating that rainfall is the most important factor in controlling the spatial distribution of vegetation in the study sites.