文献类型：期刊文献

英文题名：Forest Height Extraction Using GF-7 Very High-Resolution Stereoscopic Imagery and Google Earth Multi-Temporal Historical Imagery

作者：Ni, Wenjian[1,2] Li, Zijia[1,2] Wang, Qiang[3] Zhang, Zhiyu[1] Liu, Qingwang[4] Pang, Yong[4] He, Yating[5] Li, Zengyuan[4] Sun, Guoqing[6]

第一作者：Ni, Wenjian

通信作者：Ni, WJ[1];Ni, WJ[2]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Heilongjiang Inst Technol, Dept Surveying Engn, Harbin 150040, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[5]Chinese Acad Forestry, Res Inst Forest Policy & Informat, Beijing 100091, Peoples R China;[6]Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

年份：2024

卷号：4

外文期刊名：JOURNAL OF REMOTE SENSING

收录：EI(收录号：20243416915605);Scopus(收录号：2-s2.0-85201496880);WOS:【ESCI(收录号:WOS:001284462800001)】；

基金：Funding: This study was sponsored by the National Natural Science Foundation of China (Grant Nos. 42022009 and 42090013) and the National Key Research and Development Program of China (2020YFE0200800) .

语种：英文

外文关键词：Photomapping - Stereo vision - Tropics

摘要：With the advent of very high-resolution (VHR) imaging satellites, it is possible to measure the heights of forest stands or even individual trees more accurately. However, the accurate geometric processing of VHR images depends on ground control points (GCPs). Collecting GCPs through fieldwork is timeconsuming and labor-intensive, which presents great challenges for regional applications in remote or mountainous regions, particularly for international applications. This study proposes a promising approach that leverages GF-7 VHR stereoscopic images and Google Earth's multi-temporal historical imagery to accurately extract forest heights without the need for fieldworks. Firstly, an algorithm is proposed to collect GCPs using Multi-temporal Averaging of historical imagery provided by Google Earth (GE), known as MAGE. Digital surface model (DSM) is then derived using GF-7 stereoscopic imagery and MAGE GCPs in Switzerland. Forest heights are finally extracted by subtracting ground surface elevations from GF-7 DSM. Results show that absolute coordinate errors of MAGE GCPs are less than 2.0 m. The root mean square error (RMSE) of forest heights extracted from GF-7 DSM, derived using the original geolocation model, is 12.3 m, and the determination coefficient (R2) R 2 ) of linear estimation model is 0.72. When the geolocation model is optimized using MAGE GCPs, the RMSE is reduced to 1.5 m and the R 2 increases to 0.95. These results not only demonstrate the effectiveness of MAGE GCPs but, more importantly, also reveal the significance of precise geometric processing of VHR stereoscopic imagery in forest height estimations.