A GIS-based Local Spatial Autocorrelation for Drought Risk Assessment in Arid and Semi-Arid Environments: a Case Study in Ejin Oasis, Western China

Drought risk assessment is an important issue of environmental monitoring and assessment in arid environments. Using remote sensing and GIS techniques, this study quantified cumulative vegetative and hydrological drought risks in Ejin Oasis, western China. Analyses of spatial distributions in drought are often influenced by spatial autocorrelation. The use of the Getis statistic (Gi*) provides insights on the spatial relationships of land cover changes to drought risk assessment. Specially, the location of significant Gi* values identified areas where the differences in cumulative vegetative and hydrological drought risks occur and are spatially clustered. Analyzing the local spatial autocorrelation of the differences between vegetative and hydrological drought risks identified those areas that have systematic sensitivity to areas of high drought risk. This information may then be used to map the high drought risk areas and help governments to improve the use of local water resources.