Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning

Abstract

As agricultural land expansion is the primary driver of deforestation, agroforestry could be an optimal land use strategy for climate change mitigation and reducing pressure on forests. Agroforestry is a promising method for carbon sequestration. With recent advancements in geospatial and data science technology, the ability to predict aboveground biomass (AGB) and assess ecosystem services in agroforestry is rapidly expanding. This study was conducted in the Belpada Block of Balangir, Odisha, a forest-dominated region of eastern India. We recorded species occurrence and measured plant parameters, including Circumference at Breast Height (CBH), height, and geolocation, in 196 plots (0.09 ha) in agroforestry intervention sites and noted the tree species. This study used Sentinel-1 and Sentinel-2 multi sensor data to achieve data synergy in AGB estimation. Three machine learning models were used: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The RF model exhibited the highest level of prediction accuracy (R2 = 0.69 and RMSE = 17.07 Mg/ha), followed by the ANN model (R2 = 0.63 and RMSE = 19.35 Mg/ha), SVM model (R2 = 0.54, RMSE = 21.97 Mg/ha. The spectral vegetation indices that are (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Modified Simple Ratio (MSR), Modified Soil-Adjusted Vegetation Index (MSAVI), Difference Vegetation Index (DVI), and SAR backscatter values, were found important variables for AGB prediction. The findings revealed that agroforestry interventions and plantations resulted in an average carbon stock increase of 15 Mg/ha over five years in the study area. The Plant Value Index (PVI), which indicates the importance of species in the local economy and biomass carbon storage, showed that Tectona grandis was the dominant species with the highest PVI value (88.35), followed by Eucalyptus globulus (56.87), Mangifera indica (53.75), and Azadirachta indica (15.45). This approach enables the expansion of monitoring efforts to assess carbon stock in agroforestry systems, thereby promoting effective management strategies.