Integrating Remote Sensing Techniques and Allometric Models for Sustainable Carbon Sequestration Estimation in Prosopis cineraria-Druce Trees

This study emphasizes the role of Prosopis cineraria (Druce) in promoting sustainability through its contribution to carbon sequestration and climate change mitigation. The accurate quantification of the aboveground biomass (AGB) of Druce trees is essential for assessing their potential in reducing carbon emissions, yet remains a significant challenge. To address this, the study aimed to (1) estimate the AGB using destructive sampling; (2) analyze variability in existing allometric biomass equations; (3) evaluate remote sensing and machine learning techniques for estimating AGB and carbon sequestration; and (4) develop and validate new allometric equations based on field and remote sensing data. The Druce trees, with diameters at breast height ranging from 20.7 to 28.97 cm, exhibited an AGB of 208.3 kg per tree, which corresponds with a carbon sequestration stock of 97.89 kg C/tree. This translates to an annual carbon dioxide sequestration potential of 0.36 t C/tree. The newly developed allometric model (Model-2) was found to demonstrate superior accuracy, with performance metrics including a mean absolute percentage error (MAPE) of 2.6%, relative bias of 5.3%, R2 of 0.906, mean absolute error (MAE) of 0.151, and root mean square error (RMSE) of 0.189. These improvements highlight the significant role of remote sensing technologies in advancing sustainable carbon monitoring and offer a more precise tool for enhancing global carbon sequestration models. By integrating field-based measurements and advanced technologies, this study strengthens our ability to assess the carbon sequestration potential of trees, contributing to more sustainable management and climate resilience strategies.