Application of Remotely Sensed Data in Estimating Aboveground Biomass and Carbon Stocks

Land degradation directly impacts the aboveground carbon pools, influencing carbon emissions, making its assessment crucial for managing a city's biomass and carbon storage. Although traditional field techniques are accurate, they require a significant amount of time and labor. This study explored a reliable and affordable option for measuring aboveground biomass and carbon stocks (AGBC) with remote sensing. The performance of five different vegetation indices (VIs) – namely simple ratio (SR), difference vegetation index (DVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) – for biomass prediction was assessed using Landsat 8 OLI imagery. Models were created to estimate AGBC by using data from various land cover types like grassland, rice, corn cropland, and forestland in Naga City, the Philippines. By combining measured aboveground biomass data from different land cover types (grassland, rice, corn cropland, and forestland) in the city of Naga, models were developed to estimate AGBC. The optimal models for each land cover type were selected based on their statistical performance, specifically the highest coefficient of determination (R²) and lowest root mean square error (RMSE). The key findings are: [1] a significant correlation exists between field-measured AGBC and the five VIs, with SR performing best for grasslands, DVI for rice cropland, and EVI for both corn cropland and forestland; [2] the city's total AGB is about 2.7 million megagrams (Mg), with an estimated 1.2 million megagrams of carbon (C Mg); [3] forestland stores the highest average AGB, followed by grassland, corn cropland, and rice cropland. The results enhance our understanding of carbon inventories, offering critical information for the development of programs and approaches aimed at addressing land degradation.