Integrating forest cover change and carbon storage dynamics: Leveraging Google Earth Engine and InVEST model to inform conservation in hilly regions

Abstract

Forests are vital in combating climate change by storing and sequestrating CO2 from the atmosphere. Measuring the influence of land use/land cover (LULC) changes on the capacity of carbon storage (CS) within forest ecosystems presents a significant challenge. This study employs remote sensing techniques to examine the changes in spatiotemporal patterns of CS in the Chittagong Hill Tracts (CHT), resulting from LULC alterations between 1996 and 2021. LULC change patterns were identified for six different years utilizing the Google Earth Engine (GEE). The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was combined with GEE to evaluate the changing patterns of CS. The study discovered that the CHT region experienced a loss of 21.65 × 106 Mg of CS, owing to a 21% reduction in vegetation cover (2862.85 km^2) during the study period. The central city area (Chittagong) accounted for the most significant loss of CS (7.99 × 106 Mg), while the suburban areas of Khagrachari (0.92 × 106 Mg) and Rangamati (3.53 × 106 Mg) contributed the least. The multiple regression model revealed that elevation and vegetation characteristics significantly influenced CS. The findings underscore the importance of developing policies and strategies that mitigate the adverse effects of land cover change on CS, and advocate for sustainable forest management practices that strike a balance between ecological, social, and economic concerns.