Forests are vital regulators of global carbon balance, yet accelerating deforestation and land-use conversion continue to erode their capacity to sequester carbon. This research quantifies forest restoration and carbon sequestration potential across Visakhapatnam, India, by integrating imaging spectroscopy with machine learning at medium spatial resolution. Using 33 spectral and environmental predictors, an ensemble Random Forest model was developed and benchmarked against a K-Nearest Neighbors algorithm. The Random Forest approach demonstrated markedly higher predictive strength, explaining 87% of the spatial variability in tree cover, while maintaining low error margins. By excluding agricultural and urban areas, the analysis identified approximately 104,800 hectares of restorable land. The restorable area corresponds to an estimated carbon sequestration potential of about 0.12 petagrams, underscoring the district’s significant yet underutilized capacity to contribute to regional and national climate goals. The research highlights how integrating spectroscopy-derived vegetation metrics with ensemble learning enables spatially precise, policy-relevant restoration planning. By linking medium-resolution environmental data with carbon accounting, this framework advances a scalable pathway for data-driven forest recovery and nature-based climate mitigation, bridging the gap between site-specific ecological assessments and large-scale sustainability initiatives.
Anupoju et al. (Tue,) studied this question.