文献类型：期刊文献

英文题名：Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

作者：Cartus, Oliver[1] Santoro, Maurizio[2] Schmullius, Christiane[1] Li, Zengyuan[3]

第一作者：Cartus, Oliver

通信作者：Cartus, O[1]

机构：[1]Univ Jena, Dept Earth Observat, D-07743 Jena, Germany;[2]Gamma Remote Sensing, CH-3073 Gumlingen, Switzerland;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 1000091, Peoples R China

年份：2011

卷号：115

期号：3

起止页码：931-943

外文期刊名：REMOTE SENSING OF ENVIRONMENT

收录：;EI(收录号：20110313601794);Scopus(收录号：2-s2.0-78651428989);WOS:【SCI-EXPANDED(收录号:WOS:000287067400012)】；

基金：This study has been carried out in the frame of the Young Scientist Program of the European Space Agency (ESA) and Chinese Ministry of Science and Technology (MOST) Dragon Project. Ground and satellite data were available from the EC SIBERIA (ENV4-CT98-0743) and SIBERIA-II (EVG1-CT-2001-00048) projects as well as the Dragon project (C1P.2583). MODIS VCF was downloaded from the Global Land Cover Facility. Weather data were made available by the German Weather Service, DWD.

语种：英文

外文关键词：ERS; Coherence; Boreal forest; Stem volume; MODIS VCF; China; Siberia

摘要：ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multiseasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (similar to 1.5 million km(2)) to map four stem volume classes (0-20, 20-50, 50-80, and >80 m(3)/ha). (C) 2010 Elsevier Inc. All rights reserved.