Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data

• Published in: Ecological indicators Vol. 108; p. 105747
• Main Authors: Xu, Kexin, Su, Yanjun, Liu, Jin, Hu, Tianyu, Jin, Shichao, Ma, Qin, Zhai, Qiuping, Wang, Rui, Zhang, Jing, Li, Yumei, Liu, Hongyan, Guo, Qinghua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2020
• Subjects: Aboveground biomass (AGB); Degraded grassland; Machine learning; Northern agro-pastoral ecotone; Terrestrial laser scanning (TLS); Degraded grassland; Northern agro-pastoral ecotone; Machine learning; Aboveground biomass (AGB); Terrestrial laser scanning (TLS)
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2019.105747

•Evaluated the performance of TLS data on deriving degraded grassland AGB.•The SMR model yields the highest accuracy for predicting AGB (R2 = 0.84).•Canopy cover and minimum height are the two best predictors for estimating AGB.•AGB prediction accuracy increases with the TLS point density. Aboveground biomass (AGB) is an important indicator for grassland ecosystem assessment, management and utilization. Remote sensing technologies have driven the development of grassland AGB estimation from labor-intensive to highly-efficient. However, optical image-based remote sensing methods are fraught with uncertainty issues due to the saturation effects. In this study, we evaluated the capability of the emerging terrestrial laser scanning (TLS) technique in estimating grassland AGB in the northern agro-pastoral ecotone of China. Seven variables (i.e., canopy cover, canopy volume, mean height, maximum height, minimum height, range of height, and standard deviation of height) were extracted from the TLS data of 30 plots across the northern agro-pastoral ecotone of China, and were used to build regression models with field measured AGB using four regression methods, which are simple regression (SR) model, stepwise multiple regression (SMR) model, random forest (RF) model and artificial neural network (ANN) model. The results demonstrate that TLS is a feasible technique for extracting grassland structural parameters. Mean grass height and canopy cover obtained from TLS data have good correspondence with field measurements (R2 > 0.7, p-values < 0.001). Among the four regression models, the SMR model yields the highest prediction accuracy (R2 = 0.84, RMSE = 48.89 g/m2), followed by the RF model (R2 = 0.78, RMSE = 68.72 g/m2), the SR model (R2 = 0.80, RMSE = 86.4 g/m2), and the ANN model (R2 = 0.73, RMSE = 101.40 g/m2). Minimum grass height and canopy coverage are the two most important variables influencing the prediction accuracy of the SMR model, and the prediction accuracy of the SMR model increases with the increase of point density. The results of this study can provide guidance for choosing the optimal model and data collection method for estimating degraded grassland AGB using TLS in agro-pastoral ecotone.