文献类型：期刊文献

英文题名：Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling

作者：Li, Wang[1,2] Niu, Zheng[1] Liang, Xinlian[3] Li, Zengyuan[4] Huang, Ni[1] Gao, Shuai[1] Wang, Cheng[5] Muhammad, Shakir[1,2]

第一作者：Li, Wang

通信作者：Li, W[1]

机构：[1]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Finnish Geodet Inst, Dept Remote Sensing & Photogrammetry, FI-02431 Masala, Finland;[4]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[5]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Lab Digital Earth Sci, Beijing 100094, Peoples R China

年份：2015

卷号：41

起止页码：88-98

外文期刊名：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

收录：;Scopus(收录号：2-s2.0-84943639292);WOS:【SCI-EXPANDED(收录号:WOS:000356996500008)】；

基金：This work was funded by China's Special Funds for Major State Basic Research Project (2013CB733405), project supported by the National Natural Science Foundation of China (41201345), and the National High Technology Research and Development Program of China (863 Program) (2014AA06A511, 2012AA12A304). We thank all the people working in the projects especially for their efforts in the field work.

语种：英文

外文关键词：Geostatistical modeling; LiDAR; SPOT-6; Canopy cover; Above-ground biomass

摘要：Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics. (C) 2015 Elsevier B.V. All rights reserved.