Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

doi:10.3390/rs12101690

	Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data
	Hu, Tianyu 1; Zhang, YingYing ; Su, Yanjun 1; Zheng, Yi 2; Lin, Guanghui 2; Guo, Qinghua1
	2020
发表期刊	REMOTE SENSING
卷号	12 期号:10
摘要	Mangrove forest ecosystems are distributed at the land-sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R-2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.
关键词	mangrove LiDAR random forest GLAS aboveground biomass
学科领域	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.3390/rs12101690
收录类别	SCI
语种	英语
WOS关键词	LIDAR ; CARBON ; BIODIVERSITY ; ECOSYSTEMS ; PREDICTION ; ALLOMETRY ; DYNAMICS ; AIRBORNE ; HEIGHT ; FUTURE
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000543394800157
出版者	MDPI
文献子类	Article
出版地	BASEL
EISSN	2072-4292
资助机构	National Key R&D Program of China [2017YFC0503905]
作者邮箱	tianyuhu@ibcas.ac.cn ; zhangyysee@pku.edu.cn ; ysu@ibcas.ac.cn ; zhengyi15@tsinghua.org.cn ; lingh@mail.tsinghua.edu.cn ; qguo@ibcas.ac.cn
作品OA属性	gold
引用统计	被引频次：41[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/21703
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China
推荐引用方式 GB/T 7714	Hu, Tianyu,Zhang, YingYing,Su, Yanjun,et al. Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data[J]. REMOTE SENSING,2020,12(10).
APA	Hu, Tianyu,Zhang, YingYing,Su, Yanjun,Zheng, Yi,Lin, Guanghui,&Guo, Qinghua.(2020).Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data.REMOTE SENSING,12(10).
MLA	Hu, Tianyu,et al."Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data".REMOTE SENSING 12.10(2020).

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