Canopy Height Integration for Precise Forest Aboveground Biomass Estimation in Natural Secondary Forests of Northeast China Using Gaofen-7 Stereo Satellite Data

被引:1
|
作者
Liu, Caixia [1 ]
Huang, Huabing [2 ,3 ]
Zhang, Zhiyu [1 ]
Fan, Wenyi [4 ,5 ]
Wu, Di [6 ,7 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Int Res Ctr Big Data Sustainable Dev Goals, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[2] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 510275, Peoples R China
[3] Sun Yat Sen Univ, Key Lab Comprehens Observat Polar Environm, Minist Educ, Zhuhai 519082, Peoples R China
[4] Northeast Forestry Univ, Sch Forestry, Harbin 150040, Peoples R China
[5] Northeast Forestry Univ, Key Lab Sustainable Forest Ecosyst Management, Minist Educ, Harbin 150040, Peoples R China
[6] Heilongjiang Inst Technol, Sch Surveying & Mapping Engn, Harbin 150050, Peoples R China
[7] Heilongjiang Geomat Ctr Minist Nat Resources, Harbin 150081, Peoples R China
基金
中国国家自然科学基金;
关键词
Gaofen-7 (GF-7); stereophotogrammetry; canopy height; aboveground biomass; LIDAR; TOMOGRAPHY; EXTRACTION; ELEVATION; IMAGERY; MODELS;
D O I
10.3390/rs17010047
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Accurate estimates of forest aboveground biomass (AGB) are necessary for the accurate tracking of forest carbon stock. Gaofen-7 (GF-7) is the first civilian sub-meter three-dimensional (3D) mapping satellite from China. It is equipped with a laser altimeter system and a dual-line array stereoscopic mapping camera, which enables it to synchronously generate full-waveform LiDAR data and stereoscopic images. The bulk of existing research has examined how accurate GF-7 is for topographic measurements of bare land or canopy height. The measurement of forest aboveground biomass has not received as much attention as it deserves. This study aimed to assess the GF-7 stereo imaging capability, displayed as topographic features for aboveground biomass estimation in forests. The aboveground biomass model was constructed using the random forest machine learning technique, which was accomplished by combining the use of in situ field measurements, pairs of GF-7 stereo images, and the corresponding generated canopy height model (CHM). Findings showed that the biomass estimation model had an accuracy of R2 = 0.76, RMSE = 7.94 t/ha, which was better than the inclusion of forest canopy height (R2 = 0.30, RMSE = 21.02 t/ha). These results show that GF-7 has considerable application potential in gathering large-scale high-precision forest aboveground biomass using a restricted amount of field data.
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页数:18
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