Abstract

Geospatial measurement of carbon is required for hotspot identification and precise quantification of carbon sinks across various ecosystems. The evolution of GIS, remote sensing, LiDAR, and spatial modeling using AI has significantly improved the precision and extent of carbon monitoring. The chapter describes techniques of examining forest biomass, soil carbon sequestration, and ocean carbon sinks through satellite data, geospatial computation, and machine learning models. Integration of big data enhances carbon flux estimation and land-use impact assessment on sequestration capacity. Significant challenges such as data resolution, model uncertainty, and computational complexity are addressed, along with new solutions. Geospatial analysis augmented by AI is at the core of carbon sequestration activities maximization, enabling climate change mitigation, sustainable land management, and transparent carbon credit systems.