Geospatial Surveillance of Climate-Driven Desert Locust Dynamics in Arid Rajasthan, India

Abstract

Desert locusts (Schistocerca gregaria) pose a persistent threat to agricultural stability in the arid landscapes of western Rajasthan, where climatic fluctuations and ecological fragility amplify outbreak risks. The 2019–2020 upsurge, intensified by extreme weather anomalies such as Cyclone Amphan and a strong Indian Ocean Dipole, highlighted the need for advanced spatial surveillance tools. This study employs an integrated geospatial framework combining satellite imagery, remote sensing, and GIS-based analytics to monitor breeding habitats, assess environmental suitability, and predict locust infestation zones across the Thar Desert. Multi-source datasets from MODIS, VIIRS, SMAP, and ERA5 were analyzed to derive soil moisture, vegetation indices (NDVI), and wind and temperature profiles, providing a synoptic understanding of the ecological drivers of locust dynamics. The Random Forest model demonstrated robust predictive capability (R² = 0.87; Cohen’s Kappa = 0.82) in accurately delineating potential breeding and migration corridors, validated against ground surveillance data from the Locust Warning Department. Results identified low-elevation districts such as Jaisalmer, Barmer, and Bikaner as persistent hotspots driven by post-monsoon greening, transient soil moisture, and favourable wind trajectories. Conversely, elevated zones along the Aravalli range were less conducive to breeding, acting as natural barriers to swarm movement. Climate change may increase suitable habitats by ~25% through altered rainfall and temperature patterns. Geospatial tools, complemented by field monitoring and sustainable interventions, offer effective early-warning capabilities, with future adoption of hyperspectral sensors and AI-based forecasting can further enhance preparedness and resilience in climate-sensitive arid ecosystems.