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
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/19/3120 |
| _version_ | 1797552808903311360 |
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| author | Luojia Hu Nan Xu Jian Liang Zhichao Li Luzhen Chen Feng Zhao |
| author_facet | Luojia Hu Nan Xu Jian Liang Zhichao Li Luzhen Chen Feng Zhao |
| author_sort | Luojia Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:06:20Z |
| format | Article |
| id | doaj.art-18727483bf6e4d97ae5801c3192c256f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:06:20Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-18727483bf6e4d97ae5801c3192c256f2023-11-20T14:48:39ZengMDPI AGRemote Sensing2072-42922020-09-011219312010.3390/rs12193120Advancing 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 ChinaLuojia Hu0Nan Xu1Jian Liang2Zhichao Li3Luzhen Chen4Feng Zhao5Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, ChinaCollege of Marine Science and Engineering, Nanjing Normal University, Nanjing 210023, ChinaBeijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, ChinaState Key Laboratory of Estuarine and Coastal Research, Institute of Eco-Chongming, East China Normal University, Shanghai 200241, ChinaA 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.https://www.mdpi.com/2072-4292/12/19/3120mangrovesChinasmall patchesSentinel-1Sentinel-2 |
| spellingShingle | Luojia Hu Nan Xu Jian Liang Zhichao Li Luzhen Chen Feng Zhao 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 mangroves China small patches Sentinel-1 Sentinel-2 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | mangroves China small patches Sentinel-1 Sentinel-2 |
| url | https://www.mdpi.com/2072-4292/12/19/3120 |
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