Satellite Image and Tree Canopy Height Analysis Using Machine Learning on Google Earth Engine with Carbon Stock Estimation

This research presents a comprehensive investigation into the dynamics of forested ecosystems using advanced geospatial techniques and machine learning applications, focusing on the University of Malaya study area. The study aims to contribute crucial data for informed decision-making aligned with sustainable development goals. It encompasses canopy height estimation, aboveground biomass density prediction, and carbon stock estimation. Machine learning algorithms, including Random Forest, Gradient Boost Tree Regression, and Support Vector Machine, are employed for canopy height estimation. Their performance is evaluated with and without Principal Component Analysis using metrics such as Root Mean Squared Error and R-squared. Results, summarized in Table 1 and Table 2, highlight the variability in canopy height predictions across different models and feature selection methods. The research explores challenges associated with GEDI Aboveground Biomass Density data, emphasizing spatial variability in model performance across different strata. Results, detailed in Table 3, underscore the importance of tailoring the model, especially in areas characterized by high biomass canopy forests. The integration of Aboveground Biomass Density data with tree cover datasets forms the basis for aboveground carbon stock estimation. Carbon stock is calculated considering forest area, land-use types, and specific carbon content factors. Findings, presented in Table 5, reveal a spectrum of aboveground carbon stock estimates, reflecting the complexity of the University of Malaya study area. This research advances remote sensing and machine learning in forestry and environmental monitoring. Its insights support informed decision-making and policy formulation.