Biophysical Model of Mount Babaris for Predicting Carbon Potential using Remote Sensing - Jauhari, Ahmad; Syauqiah, Isna; Taati, La; Munsyi, Munsyi (Bright Publisher, 2024)
The biophysical model of Mount Babaris aims to predict carbon potential using remote sensing technology to address high levels of greenhouse gases, particularly CO2. This study combines satellite data with field measurements to create a validated model analyzing Forest Canopy Height (FCH), Normalized Difference Vegetation Index (NDVI), Vegetation Density (VD), and Land Surface Temperature (LST). A multiple regression analysis shows a strong correlation between these parameters and VD, with an R² value of 0.8673, indicating that 86.73% of the variation in vegetation density can be explained by these variables. Field validation, including drone photographs, crown and stem base density measurements, and plant size, ensures the accuracy of the satellite-derived data. The model uses the equation VD = 123.295486 x NDVI - 0.413961 x LST - 0.410253 x FCH - 3.173195, validated through field data. For processing field measurements, the equation LBDstemCor = 0.007645 x LBDcrown + 0.034348 x VD - 1.575439, with an R² value of 0.9564, further demonstrates its accuracy. To estimate carbon potential in kilograms per pixel (CPP), the equation CPP = LBDstemCor x FCHcor x 0.7 x 680 x 1.34 x 0.47 was used. The predicted carbon potential for Mount Babaris (1,576 ha) ranges from 607,767.55 to 607,829.54 tons, reflecting the model's precision in estimating carbon storage. This model plays a crucial role in monitoring and predicting carbon potential, supporting environmental management and climate change mitigation efforts. By integrating GIS and remote sensing, the model offers a scalable, replicable methodology adaptable to other regions with similar characteristics. It enhances the accuracy of carbon stock estimations and provides essential data for developing strategies to increase carbon sequestration, contributing to global climate change mitigation. The combination of satellite data, field measurements, and statistical analysis makes this model an invaluable tool for effective ecosystem conservation and restoration.