globalchange  > 气候减缓与适应
DOI: 10.1016/j.rse.2018.11.017
WOS记录号: WOS:000456640700018
论文题名:
Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China
作者: Huang, Huabing1; Liu, Caixia1; Wang, Xiaoyi1; Zhou, Xiaolu2; Gong, Peng3
通讯作者: Huang, Huabing
刊名: REMOTE SENSING OF ENVIRONMENT
ISSN: 0034-4257
EISSN: 1879-0704
出版年: 2019
卷: 221, 页码:225-234
语种: 英语
英文关键词: Forest aboveground biomass ; Carbon storage ; PALSAR imagery ; ICESat/GLAS
WOS关键词: CARBON STOCKS ; LIDAR ; MAP ; BACKSCATTER ; ICESAT/GLAS ; INVENTORY ; TEXTURE ; IMAGERY ; VOLUME ; PROPAGATION
WOS学科分类: Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向: Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
英文摘要:

Quantification of forest aboveground biomass density (AGB) is useful in forest carbon cycle studies, biodiversity protection and climate-change mitigation actions. However, a finer resolution and spatially continuous forest AGB map is inaccessible at national level in China. In this study, we developed forest type- and ecozone-specific allometric models based on 1607 field plots. The allometric models were applied to Geoscience Laser Altimeter System (GLAS) data to calculate AGB at the footprint level. We then mapped a 30 m resolution national forest AGB by relating the GLAS footprint AGB to various variables derived from Landsat images and Phased Array L-band Synthetic Aperture Radar (PALSAR) data. We estimated the average forest AGB to be 69.88 Mg/ha with a standard deviation of 35.38 Mg/ha and the total AGB carbon stock to be 5.44 PgC in China. Our AGB estimates corresponded reasonably well with AGB inventories from the top ten provinces in the forested area, and the coefficient of determination and root mean square error were 0.73 and 20.65 Mg/ha, respectively. We found that the main uncertainties for AGB estimation could be attributed to errors in allometric models and in height measurements by the GLAS. We also found that Landsat-derived variables outperform PALSAR-derived variables and that the textural features of PALSAR better support forest AGB estimates than backscattered intensity.


Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/129314
Appears in Collections:气候减缓与适应

Files in This Item:

There are no files associated with this item.


作者单位: 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Univ Quebec, Dept Biol Sci, Ecol Modeling & Carbon Sci, Montreal, PQ H3C 3P8, Canada
3.Tsinghua Univ, Dept Earth Syst Sci, Key Lab Earth Syst Modeling, Minist Educ, Beijing 100084, Peoples R China

Recommended Citation:
Huang, Huabing,Liu, Caixia,Wang, Xiaoyi,et al. Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China[J]. REMOTE SENSING OF ENVIRONMENT,2019-01-01,221:225-234
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Huang, Huabing]'s Articles
[Liu, Caixia]'s Articles
[Wang, Xiaoyi]'s Articles
百度学术
Similar articles in Baidu Scholar
[Huang, Huabing]'s Articles
[Liu, Caixia]'s Articles
[Wang, Xiaoyi]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Huang, Huabing]‘s Articles
[Liu, Caixia]‘s Articles
[Wang, Xiaoyi]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.