Satellite Image-Based Drought Monitoring: Vision to Enhance Drought Resilience

Drought is the most challenging climate-related issue in the examined area because of the vast changes in the land-cover, the dynamics of the plants, and the climates. The vegetation and environment in India’s arid region have changed significantly during the previous two decades. A process for yearly analysis and monitoring the progress of land desertification utilising a long-term series of earth observation satellite data should be developed, as well as the potential applications of remote sensing analysis for desertification monitoring. This study’s objective is to assess how well GIS and remote sensing methods work for determining the spatiotemporal extent of drought. Using the SPI index, the drought in the Jodhpur district has been assessed. Precipitation statistics are utilised to evaluate droughts. A 7-year (2015–2021) examine how monsoonal patterns affect the vegetation indices in the desert zone, a temporal series of Moderate Resolution Image Spectrometer (MODIS) data for Pre & Post Monsoon were used to compute the Normalised Difference Vegetation Index (NDVI). The association between the Jodhpur District’s rainfall pattern and vegetation changes is investigated using the cloud-free NDVI time series data. Three categories of NDVI maps were classified viz. class-1: no vegetation, class-2: Low vegetation and class-3: dense vegetation cover, which includes agricultural fields. The yearly evaluation and monitoring of the vegetation includes the use of ENVI and ArcGIS image processing tools. When comparing pre- and post-monsoon temporal analyses, enormous differences were found. In this study, the values of the SPI (Standardized Precipitation Index) and NDVI (Normalized Difference Vegetation Index) were correlated. The findings show that NDVI and SPI values in low vegetation areas have a significant association. This study demonstrates that the best data for quick vegetation assessment in arid conditions is MODIS NDVI data.