Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data
文献类型：期刊论文
| 作者 | Hu, Tianyu2; Zhang, YingYing; Su, Yanjun2; Zheng, Yi3; Lin, Guanghui3; Guo, Qinghua2
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020 |
| 卷号 | 12期号:10 |
| 关键词 | mangrove LiDAR random forest GLAS aboveground biomass |
| DOI | 10.3390/rs12101690 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| 学科主题 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | BASEL |
| 电子版国际标准刊号 | 2072-4292 |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000543394800157 |
| 出版者 | MDPI |
| 资助机构 | National Key R&D Program of China [2017YFC0503905] |
| 源URL | [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.Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, 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). |
入库方式： OAI收割
来源：植物研究所
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