文献类型：期刊文献

英文题名：Reprint of: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area

作者：Tian, Xin[1,2] Su, Zhongbo[2] Chen, Erxue[1] Li, Zengyuan[1] van der Tol, Christiaan[2] Guo, Jianping[3] He, Qisheng[1]

第一作者：田昕;Tian, Xin

通信作者：Li, ZY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Univ Twente, Fac Geoinformat Sci & Earth Observat, NL-7500 AA Enschede, Netherlands;[3]Chinese Acad Meteorol Sci, Ctr Atmosphere Watch & Serv, Beijing 100081, Peoples R China

年份：2012

卷号：17

起止页码：102-110

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

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000305265200012)】；

基金：This study is financially supported by the Active Remote Sensing Model and Forest Structure Information Extraction (grant 2007CB714404) project under the framework of National Basic Research Program of China (973 Program) and the Study on the Technologies of Estimating Forest Above Ground Biomass (grant IFRIT200902) project. The ALOS PALSAR data were provided by JAXA through the ALOS PI project (ID_315). Some of the remote sensing data are provided by the Dragon II program "Key Eco-Hydrological parameters retrieval and land data assimilation system development in a typical inland river basin of China's arid region" (grant number: 5322). The other data used in the paper are obtained from the Watershed Allied Telemetry Experimental Research (WATER), which is jointly supported by the Chinese State Key Basic Research Project (grant 2007CB714400) and the Chinese Academy of Sciences Action Plan for West Development Program (grant KZCX2-XB2-09). We thank the joint experimental team for providing all the supports for carrying out the campaign. Sincerely, we are grateful to the anonymous reviewers for the valuable comments which improved this study.

语种：英文

外文关键词：k-NN method; Regression method; Above-ground biomass; Configuration

摘要：Remote sensing is a valuable tool for estimating forest biomass in remote areas. This study explores retrieval of forest above-ground biomass (AGB) over a cold and arid region in Northwest China, using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a SPOT-5 HRG image, multi-temporal dual-polarization ALOS PALSAR and airborne LiDAR data. The non-parametric method was applied in 300 different configurations, varying both the mathematical formulation and the data input (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by Leave One Out (LOO) cross-validation against ground measurements. For the parametric method (the multivariate linear regression), the same remote sensing data were used, but in one additional configuration the airborne LiDAR data were used for stepwise multiple regression. The result of the best performing non-parametric configuration was satisfactory (R = 0.69 and RMSE = 20.7 tons/ha). The results for the parametric method were notoriously inaccurate, except for the case where airborne LiDAR data were included. The regression method with airborne low density LiDAR point cloud data was the best of all tested methods (R = 0.84 and RMSE = 15.2 tons/ha). A cross comparison of the two best results showed that the non-parametric method performs nearly as well as the parametric method with LiDAR data, except for some areas where forests have a very heterogeneous structure. It is concluded that the non-parametric method with SPOT data is able to map forest AGB operatively over the cold and arid region as an alternative to the more expensive airborne LiDAR data. (C) 2011 Elsevier B.V. All rights reserved.