气候变化领域集成服务门户(CCPortal): Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations

CCPortal

DOI	10.3390/rs11151740
	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, Huabing; Liu, Caixia; Wang, Xiaoyi
通讯作者	Huang, HB (通讯作者)
发表日期	2019
EISSN	2072-4292
卷号	11 期号:15
英文摘要	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.
关键词	STOCK VOLUME ESTIMATION CANOPY HEIGHT ABOVEGROUND BIOMASS COVER RADAR LIDAR CLASSIFICATION BACKSCATTER COHERENCE SRTM
英文关键词	forest height; PALSAR imagery; ICESat; GLAS; plantations
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000482442800004
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259395
推荐引用方式 GB/T 7714	Huang, Huabing,Liu, Caixia,Wang, Xiaoyi. 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]. 中国科学院青藏高原研究所,2019,11(15).
APA	Huang, Huabing,Liu, Caixia,&Wang, Xiaoyi.(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).
MLA	Huang, Huabing,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).