Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China
文献类型：期刊论文
| 作者 | Hu, Luojia1; Xu, Nan2; Liang, Jian; Li, Zhichao; Chen, Luzhen; Zhao, Feng |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-10-01 |
| 卷号 | 12期号:19页码:22 |
| 关键词 | mangroves China small patches Sentinel-1 Sentinel-2 |
| DOI | 10.3390/rs12193120 |
| 通讯作者 | Hu, Luojia(huluojia@qxslab.cn) |
| 英文摘要 | A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest. |
| WOS关键词 | DIFFERENCE WATER INDEX ; COASTAL HABITATS ; CARBON STOCKS ; LAND-COVER ; CLASSIFICATION ; AQUACULTURE ; VEGETATION ; EMISSIONS ; DEFORESTATION ; FEATURES |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000586408500001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156527]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hu, Luojia |
| 作者单位 | 1.China Acad Space Technol, Qian Xuesen Lab Space Technol, Beijing 100094, Peoples R China 2.Nanjing Normal Univ, Coll Marine Sci & Engn, Nanjing 210023, Peoples R China 3.China Acad Space Technol, Inst Spacecraft Syst Engn, Beijing 100094, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 5.Xiamen Univ, Coll Environm & Ecol, Key Lab, Minist Educ Coastal & Wetland Ecosyst, Xiamen 361102, Peoples R China 6.East China Normal Univ, Inst Ecochongming, State Key Lab Estuarine & Coastal Res, Shanghai 200241, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Luojia,Xu, Nan,Liang, Jian,et al. Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China[J]. REMOTE SENSING,2020,12(19):22. |
| APA | Hu, Luojia,Xu, Nan,Liang, Jian,Li, Zhichao,Chen, Luzhen,&Zhao, Feng.(2020).Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China.REMOTE SENSING,12(19),22. |
| MLA | Hu, Luojia,et al."Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China".REMOTE SENSING 12.19(2020):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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