GEOSPATIAL TECHNIQUES FOR ASSESSING CARBON SEQUESTRATION IN MANGO ORCHARDS

Abstract

Carbon sequestration in mango (Mangifera indica L.) orchards represents a promising nature-based strategy for climate change mitigation, yet accurate large-scale assessment remains challenging using conventional field methods. This review examines the application of geospatial technologies—including Geographic Information Systems (GIS), remote sensing, Global Positioning Systems (GPS), Unmanned Aerial Vehicles (UAVs), and Light Detection and Ranging (LiDAR)—for estimating carbon stocks in mango orchards. Satellite platforms such as Sentinel-2 and Landsat provide multispectral imagery enabling derivation of vegetation indices (NDVI, EVI, NDRE, GNDVI) strongly correlated with biomass and carbon storage. Integration of these spectral datasets with machine learning algorithms, particularly Random Forest, Support Vector Machine, and Artificial Neural Networks, substantially improves prediction accuracy. Species-specific allometric models relating diameter, height, and wood density to biomass further enhance carbon estimation. UAV and LiDAR technologies provide high-resolution three-dimensional canopy information for individual tree-level assessment. Despite advances, challenges persist regarding data availability, sensor integration, model transferability, and standardized measurement protocols. Future research should prioritize multisensor data fusion, artificial intelligence applications, cloud-based platforms, and robust measurement, reporting, and verification frameworks. Integrating advanced geospatial techniques with sustainable orchard management offers a reliable pathway for enhancing carbon accounting, supporting precision agriculture, and contributing to global climate mitigation efforts.