Total and component forest aboveground biomass inversion via LiDAR-derived features and machine learning algorithms - Ma, Jiamin; Zhang, Wangfei; Ji, Yongjie; Huang, Jimao; Huang, Guoran; Wang, Lu (Frontiers in Plant Science, 2023)
Forest above ground biomass (AGB) and its biomass components are key indicators for assessing forest ecosystem health, productivity and carbon stocks. Light Detection and Ranging (LiDAR) technology has great advantages in acquiring the vertical structure of forests and the spatial distribution characteristics of vegetation. In this study, the 56 features extracted from airborne LiDAR point cloud data were used to estimate forest total and component above ground biomass. VIP (Variable Importance in Projection) values calculated through a partial least squares regression algorithm were utilized for LiDAR derived feature ranking and optimization. Both leave-one-out cross validation (LOOCV) and crossvalidation method were applied for validation of the estimated results. The results showed that four cumulative height percentiles (AIH30, AIH40, AIH20, AIH25), two height percentiles (H8, H6) and four height-related variables (Hmean, Hsqrt, Hmad, and Hcurt) are ranked more frequently in top 10 sensitive features for total and component forest AGB retrievals. Best performance was acquired by Random Forest (RF) algorithm, with R 2 =0.75, RMSE = 22.93 Mg/ha, rRMSE = 25.30%, and MAE = 19.26 Mg/ha validated by LOOCV method. For cross validation results, R 2 is 0.67, RMSE is 24.56 Mg/ha, rRMSE is 25.67%. The performance of Support Vector Regression (SVR) for total AGB estimation is R 2 = 0.66, RMSE = 26.75 Mg/ha, rRMSE = 28.62%, and MAE = 22.00 Mg/ha using LOOCV validation and R 2 = 0.56, RMSE = 30.88 Mg/ha, rRMSE = 31.41% by cross validation. For the component AGB estimation, the accuracy from both RF and SVR algorithms was arranged as stem > bark > branch > leaf. The results confirmed the sensitivity of LiDAR derived features to forest total and component AGBs. They also demonstrated the worse performance of these features for retrieval of leaf component AGB. RF outperformed SVR for both total and component AGB estimation, the validation difference from LOOCV and cross validation is less than 5% for both total and component AGB estimated results.