Carbon Stock Estimation Using SPOT-7 Imagery: A Study of Taman Botani Negara Shah Alam

This study explores the use of NDVI derived from SPOT-7 satellite imagery to estimate carbon stock in the tropical forest of Taman Botani Negara Shah Alam, by estimating aboveground biomass (AGB). Traditional field-based methods for estimating biomass and carbon stocks are time-consuming, labor-intensive, and often limited in spatial coverage. This study aims to demonstrate the potential of remote sensing, specifically NDVI, as a non-destructive tool for biomass estimation across large forested areas. A moderate positive linear relationship between AGB and NDVI was found, with an $\mathbf{R}^{\mathbf{2}}$ value of 0.318, suggesting that NDVI can provide useful information for aboveground biomass estimation despite some variability. The study also highlights the spatial distribution of biomass and carbon stock across the study area, with an estimated average AGB of $10,795.37 \mathrm{~kg} / \mathrm{m}^{2}$ and an average carbon stock of $5,073.824 \mathrm{~kg}$ of carbon per $\mathrm{m}^{2}$. These estimates are based on an NDVI-AGB regression model and indicate significant spatial variability in both biomass and carbon stock across the landscape. The findings underscore the utility of remote sensing in large-scale forest monitoring and carbon management, especially in the context of climate change mitigation strategies. This study contributes to the growing body of knowledge on remote sensing applications for forest carbon estimation and provides insights that could inform policies related to forest conservation, sustainable land management, and initiatives such as REDD+ in Malaysia.