Explainable GeoAI-Based Multi-Temporal Remote Sensing Framework for Early Detection of Climate-Induced Land Cover Transformation - Bibhu Prasad Ganthia; Subash Ranjan Kabat (International Journal of Satellite Remote Sensing, 2026)
Climate change has emerged as one of the primary drivers of rapid land cover transformation, affecting ecosystems, agricultural productivity, biodiversity, and regional sustainability. Traditional remote sensing approaches often face challenges in detecting subtle and early-stage land cover changes due to limitations in temporal analysis and model interpretability. This study proposes an Explainable GeoAI-based multi-temporal remote sensing framework for the early detection of climate-induced land cover transformation using multi-source satellite imagery and geospatial datasets. The framework integrates advanced deep learning architectures with explainable artificial intelligence techniques to identify spatial and temporal patterns associated with vegetation degradation, urban expansion, desertification, wetland loss, and changes in agricultural landscapes. Multi-temporal observations from optical, hyperspectral, and synthetic aperture radar datasets are processed through feature extraction and spatio-temporal learning modules to improve detection accuracy under varying climatic conditions. Explainability methods such as SHAP and attention visualization are incorporated to provide transparent decision-making and to identify the dominant climatic and environmental factors influencing land cover dynamics. Experimental evaluation demonstrates that the proposed framework enables earlier detection of environmental changes, enhances classification reliability, and supports policymakers and environmental agencies in implementing proactive climate adaptation and land management strategies through scalable, robust, interpretable, data-driven, sustainable, region-specific, predictive environmental monitoring systems globally.
Keywords: Climate Change Detection; Explainable GeoAI; Geospatial Artificial Intelligence; Land Cover Transformation; Multi-Temporal Remote Sensing