Total and component forest aboveground biomass inversion via LiDAR-derived features and machine learning algorithms

作者：Jiamin Ma, Wangfei Zhang, Yongjie Ji, Jimao Huang, Guoran Huang, Lu Wang · 发表于：Frontiers in Plant Science · 年份：2023 · DOI：10.3389/fpls.2023.1258521 · 被引用次数：29 · 研究领域：Remote Sensing and LiDAR Applications、Forest ecology and management、Remote Sensing in Agriculture

Forest aboveground 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 AGB. 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 cross-validation methods were applied for validation of the estimated results. The results showed that four cumulative height percentiles ( AIH 30, AIH 40 , AIH 20 , and AIH 25 ), two height percentiles ( H 8 and H 6 ), and four height-related variables ( H mean , H sqrt , H mad , and H curt ) are ranked more frequently in the 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, root mean square error (RMSE) = 22.93 Mg/ha, relative RMSE (rRMSE) = 25.30%, and mean absolute error (MAE) = 19.26 Mg/ha validated by the LOOCV method. For cross-validation results, R 2 is 0.67, RMSE is 24.56 Mg/ha, and rRMSE is 25.67%. The performance of support vector regression (SVR) for total AGB estimation is R 2 = 0.66, RMSE = 26.75 ...