Department of Tourism, Aletheia University, 32, Zhenli St., Danshui Town, Taipei County 25103, Taiwan
Department of Geography, National Taiwan University, No. 1 Sec. 4 Roosevelt Road, Taipei 10617, Taiwan
The objective of this study is to develop a novel methodology integrating remote sensing, geographic information system technology and local spatial autocorrelation geo-computation for quick drought assessment. One group of drought indices, based on the condition of the vegetation, includes the Normalized Difference Vegetation Index (NDVI), Anomaly of Normalized Difference Vegetation Index (NDVIA) and Standardized Vegetation Index (SVI). The other group, based on the moisture conditions, includes the Normalized Difference Moisture Index (NDMI) and Standardized Moisture Index (SMI). The local G-statistic (Gi*) provides insight into the spatial relationships of the drought indices for drought risk assessment. Specifically, locations with significant Gi* values indicate spatial clusters where there are differences between the vegetative and hydrological drought indices. The results of spatial co-occurrence analysis indicate the existence of hot spots where the drought indices are spatially stable. This spatial information can be used to identify high drought risk areas as a first step towards helping local administrators improve the allocation of local water resources in arid environments. Finally, the novel methodology, integrating remote sensing, geocomputation and geographic information techniques, is demonstrated. The results indicate its effectiveness for quick drought assessment.