Chinese Journal of Plant Ecology >
Validation and uncertainty analysis of satellite remote sensing products for monitoring China’s forest ecosystems—Based on massive UAV LiDAR data
Received date: 2022-04-24
Accepted date: 2022-09-05
Online published: 2022-09-28
Supported by
Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080301);National Natural Science Foundation of China(31971575);2020 Industry Management Special Business Fund of National Forestry and Grassland Administration(2020-21-89);Independent Research and Development Plan Project of National Forestry and Grassland Administration(LC-1-01)
Aims Accurately obtaining forest structural attributes is important for forest ecosystem research and protection. As a key data source, satellite remote sensing data are used to derive various regional and global products of forest structure and conditions, which are widely used in forest condition evaluation, forest biomass estimation, and forest disturbance and biodiversity monitoring. However, these products derived from satellite remote sensing data lack verification for Chinaʼs forested areas, and their accuracy and uncertainty under different forest structure and terrain conditions is not clear. Light detection and ranging (LiDAR) has the advantage of acquiring high-precision three-dimensional information. It has been widely used in monitoring forest ecosystems and validating various datasets of forest structure derived from remote sensing data. This study focused on evaluating the accuracy of Global Land Surface Satellite Products System-Leaf Area Index (GLASS LAI), Global Land Cover Facility-Tree Canopy Cover (GLCF TCC), and Global Forest Canopy Height (GFCH) products in China based on massive unmanned aerial vehicle (UAV) LiDAR data.
Methods We collected nationwide LiDAR point cloud data at 114 sites in China’s forested areas to build the benchmark validation dataset including canopy cover, canopy height and LAI. The corresponding pixel values of the above three products were extracted using the geolocation from UAV LiDAR data. The coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the accuracy and uncertainty of the three products. The uncertainty under different forest types, canopy cover and terrain conditions were also analyzed.
Important findings The results indicate that compared to the LAI, canopy cover and canopy height derived from UAV LiDAR data, GLASS LAI (R2 = 0.29, RMSE= 2.1 m2·m-2), GLCF TCC (R2= 0.47, RMSE= 31%), GFCH (R2= 0.37, RMSE = 5 m) all exhibit large uncertainties and suffer from saturation problems in China’s forested areas, and their accuracy varies significantly across forest types, canopy cover and terrain conditions. In general, the GLASS LAI and GLCF TCC are mainly influenced by forest types and canopy cover, respectively. In contrast, both slope and canopy cover have large influences on the accuracy of GFCH.
Key words： validation; UAV lidar; leaf area index; canopy cover; canopy height
LIU Bing-Bing, WEI Jian-Xin, HU Tian-Yu, YANG Qiu-Li, LIU Xiao-Qiang, WU Fa-Yun, SU Yan-Jun, GUO Qing-Hua . Validation and uncertainty analysis of satellite remote sensing products for monitoring China’s forest ecosystems—Based on massive UAV LiDAR data[J]. Chinese Journal of Plant Ecology, 2022 , 46(10) : 1305 -1316 . DOI: 10.17521/cjpe.2022.0158
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