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Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations
Huang, HB (Huang, Huabing)1; Liu, CX (Liu, Caixia)1; Wang, XY (Wang, Xiaoyi)2
Source PublicationREMOTE SENSING
2019
Volume11Issue:15Pages:1740
DOI10.3390/rs11151740
Abstract

Monitoring forest height is crucial to determine the structure and biodiversity of forest ecosystems. However, detailed spatial patterns of forest height from 30 m resolution remotely sensed data are currently unavailable. In this study, we present a new method for mapping forest height by combining spaceborne Light Detection and Ranging (LiDAR) with imagery from multiple remote sensing sources, including the Landsat 5 Thematic Mapper (TM), the Phased Array L-band Synthetic Aperture Radars (PALSAR), and topographic data. The nationwide forest heights agree well with results obtained from 525 independent forest height field measurements, yielding correlation coefficient, root mean square error (RMSE), and mean absolute error (MAE) values of 0.92, 4.31 m, and 3.87 m, respectively. Forest heights derived from remotely sensed data range from 1.41 m to 38.94 m, with an average forest height of 16.08 +/- 3.34 m. Mean forest heights differ only slightly among different forest types. In natural forests, conifer forests have the greatest mean forest heights, whereas in plantations, bamboo forests have the greatest mean forest heights. Important predictors for modeling forest height using the random forest regression tree method include slope, surface reflectance of red bands and HV backscatter. The uncertainty caused by the uneven distribution of Geoscience Laser Altimeter System (GLAS) footprints is estimated to be 0.64 m. After integrating PALSAR data into the model, the uncertainty associated with forest height estimation was reduced by 4.58%. Our finer-resolution forest height could serve as a benchmark to estimate forest carbon storage and would greatly contribute to better understanding the roles of ecological engineering projects in China.

Subject AreaRemote Sensing
WOS IDWOS:000482442800004
Language英语
Indexed BySCI
KeywordStock Volume Estimation Canopy Height Aboveground Biomass Cover Radar Lidar Classification Backscatter Coherence Srtm
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
Cooperation Status国内
Department高寒生态重点实验室
URL查看原文
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/9203
Collection图书馆
Corresponding AuthorHuang, HB (Huang, Huabing)
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;
2.Chinese Acad Sci, Inst Tibet Plateau Res, Key Lab Alpine Ecol & Biodivers, Beijing 100101, Peoples R China.
Recommended Citation
GB/T 7714
Huang, HB ,Liu, CX ,Wang, XY . Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations[J]. REMOTE SENSING,2019,11(15):1740.
APA Huang, HB ,Liu, CX ,&Wang, XY .(2019).Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations.REMOTE SENSING,11(15),1740.
MLA Huang, HB ,et al."Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations".REMOTE SENSING 11.15(2019):1740.
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