首页|Forest terrain and canopy height estimation using stereo images and spaceborne LiDAR data from GF-7 satellite

Forest terrain and canopy height estimation using stereo images and spaceborne LiDAR data from GF-7 satellite

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Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China's first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accu-racy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu'er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m x 20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R2=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.

Gao Fen-7(GF-7)spaceborne LiDARstereo imageAirborne Laser Scanning(ALS)forest heightPu'er

Liming Du、Yong Pang、Wenjian Ni、Xiaojun Liang、Zengyuan Li、Juan Suarez、Wei Wei

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Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing,China

Key Laboratory of Forestry Remote Sensing and Information System,National Forestry and Grassland Administration,Beijing,China

State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing,China

College of Resources and Environment,University of Chinese Academy of Sciences,Beijing,China

Forest Research,Northern Research Station,Midlothian,UK

Institute of Science and Technology Information,Yunnan Academy of Forestry and Grassland,Kunming,China

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2024

10.1080/10095020.2023.2249037

地球空间信息科学学报(英文版)
武汉大学（原武汉测绘科技大学）

地球空间信息科学学报(英文版)

影响因子：0.207
ISSN：1009-5020
年,卷(期)：2024.27(3)