| Summary: | Forests play a significant role as forest sources and have been commonly used to measure carbon stocks within the international carbon cycle and biomass of the forest. Land biomass is an essential element in determining the carbon and carbon balance capabilities of forest ecosystems. This study aimed to estimate forest biomass carbon stocks from the field, Airborne LiDAR, and WorldView-3 data using an Artificial Neural Network and Random Forest. In total, 245 observations and 5 variables including independent variables, the total height of tree measured in field (hF), diameter at breast height (DBH), height extracted from Lidar (hL), crown projection area (CPA) and dependent variables (CS) at which based on the data used, multiple regression has been carried out to estimate the forest carbon stocks. ANN has been tested with different hidden layers by trying and error and for Random Forest, two parameters which are the number of randomly picked variables for each node of the tree (Mtry) and the number of trees to grow (Ntree), which was 500 have been used in this study. The best model obtained from both methods was used to generate the carbon stocks map prediction. This study result shows that Model 5 of the ANN algorithm obtains (RMSE = 92.248 Mg ha-1 and R2 = 0.916). From this study, RF can be concluded as the best model that can be used for the estimation of biomass and carbon stocks as for this study as Model 3 of RF shows the lowest error compared to ANN (RMSE = 49.417 Mgha-1 and R2 = 0.976) and the effectiveness of R as the best model for biomass estimation has been proven from the previous research. © 2023 UMT Press. All Rights Reserved.
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