Semi-arid ecosystems often exhibit irregular spatial vegetation patterns that result from complex ecological feedback. Research suggests that these patterns may serve as indicators of ecosystem resilience. While theoretical and modelling studies have explored these patterns at length, empirical validation, especially using temporal data, is limited. This thesis aims to bridge that gap by examining the patterns and dynamics of vegetation clusters across African and Indian drylands.

      In the first chapter, we analyse semi-arid sites in sub-Saharan Africa using high-resolution satellite-derived NDVI data. We look into steady-state spatial patterns. And also look into temporal changes in vegetation clusters, and fit statistical models to cluster-size distributions.
      In the second chapter, we extend this approach to 20 semi-arid sites in India. Additionally, I examine how rainfall, fire, and soil properties influence cluster dynamics and whether similar patterns evolve across continents.
      In the third chapter, we plan to evaluate the effectiveness of freely available versus paid high-resolution satellite datasets for spatial early warning detection. Then, compare metrics derived from both data types and assess their utility in accurately characterising vegetation patterns. This analysis aims to determine whether low-cost solutions can be scaled up for global dryland monitoring.

Finally, we plan to propose a predictive framework for ecosystem resilience that integrates high-resolution vegetation cluster statistics with environmental data. We aim to predict the vegetation patterns based on future climate change scenarios, with the aim of identifying and prioritising areas for management and conservation purposes.