文献类型：期刊文献

英文题名：Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China

作者：Li, Guiying[1,2] Xie, Zhuli[3,4] Jiang, Xiandie[3,4] Lu, Dengsheng[1,2] Chen, Erxue[5]

第一作者：Li, Guiying

通信作者：Lu, DS[1];Lu, DS[2]

机构：[1]Fujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350007, Fujian, Peoples R China;[2]Fujian Normal Univ, Sch Geog Sci, Fuzhou 350007, Fujian, Peoples R China;[3]Zhejiang A&F Univ, State Key Lab Subtrop Silviculture, Hangzhou 311300, Zhejiang, Peoples R China;[4]Zhejiang A&F Univ, Sch Environm & Resource Sci, Hangzhou 311300, Zhejiang, Peoples R China;[5]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2019

卷号：11

期号：19

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20194207559283);Scopus(收录号：2-s2.0-85073452385);WOS:【SCI-EXPANDED(收录号:WOS:000496827100142)】；

基金：This study was financially supported by the National Natural Science Foundation of China (grant #41571411) and by the National Key R&D Program of China project "Research of Key Technologies for Monitoring Forest Plantation Resources" (2017YFD0600900).

语种：英文

外文关键词：aboveground biomass estimation; data saturation; ZY-3 data; canopy height; larch plantations

摘要：Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.