원문정보
초록
영어
The purpose of this study was to estimate the forest biomass using satellite imagery and machine learning techniques. In this study, Random Forest, XGBoost, SVM, Multiple Linear Regression were used for forest biomass estimation. Research Forest management plan(8th) data and Sentinel-2 imagery information were used to analysis. As the dependent variables, forest biomass was calculated using volume information, and the biomass expansion factor. The 10 bands of Sentinel-2 were used as independent variable. The optimal forest biomass estimation model was selected by comparing the calculated value based on the Research Forest management plan data and the estimate based on the machine learning techniques. MAE, RMSE, and R2 were calculated for comparison of estimated biomass statistics. As a result, the XGBoost model showed the highest RMSE(61.63ton/ha), MAE(44.16ton/ha), and the highest R2(0.48) value, and was evaluated as the optimal biomass estimation model. The average amount of biomass for sub compartments estimated using the XGBoost model was 225.3tons/ha, which was underestimated by 3.4 tons/ha compared to the average amount of biomass calculated using the Research Forest management plan.