Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data
| Autor: | Jing Zhang, Rui Wang, Qinghua Guo, Kexin Xu, Qiuping Zhai, Yumei Li, Yanjun Su, Hongyan Liu, Jin Liu, Qin Ma, Shichao Jin, Tianyu Hu |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Canopy geography geography.geographical_feature_category Ecology General Decision Sciences Regression analysis Ecotone 010501 environmental sciences Stepwise regression 010603 evolutionary biology 01 natural sciences Standard deviation Grassland Random forest Environmental science Simple linear regression Ecology Evolution Behavior and Systematics 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Ecological Indicators. 108:105747 |
| ISSN: | 1470-160X |
| DOI: | 10.1016/j.ecolind.2019.105747 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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