Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine
Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/9/1489 |
| _version_ | 1797488224782778368 |
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| author | Ziyu Wang Kai Liu Jingjing Cao Liheng Peng Xin Wen |
| author_facet | Ziyu Wang Kai Liu Jingjing Cao Liheng Peng Xin Wen |
| author_sort | Ziyu Wang |
| collection | DOAJ |
| description | Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper, we used Landsat satellite series data combined with the normalized difference vegetation index (NDVI) and adaptive threshold partitioning (OTSU method) to monitor mangrove dynamics in coastal China from 1986 to 2021 based on Google Earth Engine (GEE), with three main results. (1) Based on the massive data and efficient computational capability of the GEE platform, we achieved large-scale interannual mangrove distribution extraction. The overall classification accuracy for 2019 exceeded 0.93, and the mangrove distribution extraction effect was good. (2) The total mangrove area and the mean patch size in China showed overall increasing trends, and Guangdong and Guangxi were the top two provinces in China in terms of the largest mangrove area. (3) Except for Dongzhaigang National Nature Reserve, the mangrove areas in other national mangrove reserves mainly showed increasing trends, confirming the effectiveness of the reserves. Data on the spatial structure and area trends of mangroves in China can provide scientific references for mangrove conservation and development, and serve in the further restoration of mangrove ecosystems. |
| first_indexed | 2024-03-09T23:59:59Z |
| format | Article |
| id | doaj.art-0e4e092d36864510be6e40dee704cbbe |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T23:59:59Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-0e4e092d36864510be6e40dee704cbbe2023-11-23T16:18:21ZengMDPI AGForests1999-49072022-09-01139148910.3390/f13091489Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth EngineZiyu Wang0Kai Liu1Jingjing Cao2Liheng Peng3Xin Wen4Guangdong Key Laboratory for Urbanization and GeoSimulation, Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, ChinaGuangdong Key Laboratory for Urbanization and GeoSimulation, Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, ChinaGuangdong Key Laboratory for Urbanization and GeoSimulation, Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, ChinaGuangdong Research Institute of Water Resources and Hydropower, Guangzhou 510635, ChinaGuangdong Key Laboratory for Urbanization and GeoSimulation, Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, ChinaMangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper, we used Landsat satellite series data combined with the normalized difference vegetation index (NDVI) and adaptive threshold partitioning (OTSU method) to monitor mangrove dynamics in coastal China from 1986 to 2021 based on Google Earth Engine (GEE), with three main results. (1) Based on the massive data and efficient computational capability of the GEE platform, we achieved large-scale interannual mangrove distribution extraction. The overall classification accuracy for 2019 exceeded 0.93, and the mangrove distribution extraction effect was good. (2) The total mangrove area and the mean patch size in China showed overall increasing trends, and Guangdong and Guangxi were the top two provinces in China in terms of the largest mangrove area. (3) Except for Dongzhaigang National Nature Reserve, the mangrove areas in other national mangrove reserves mainly showed increasing trends, confirming the effectiveness of the reserves. Data on the spatial structure and area trends of mangroves in China can provide scientific references for mangrove conservation and development, and serve in the further restoration of mangrove ecosystems.https://www.mdpi.com/1999-4907/13/9/1489remote sensingmangrovemonitoringGoogle Earth Engine (GEE)China |
| spellingShingle | Ziyu Wang Kai Liu Jingjing Cao Liheng Peng Xin Wen Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine Forests remote sensing mangrove monitoring Google Earth Engine (GEE) China |
| title | Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine |
| title_full | Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine |
| title_fullStr | Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine |
| title_full_unstemmed | Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine |
| title_short | Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine |
| title_sort | annual change analysis of mangrove forests in china during 1986 2021 based on google earth engine |
| topic | remote sensing mangrove monitoring Google Earth Engine (GEE) China |
| url | https://www.mdpi.com/1999-4907/13/9/1489 |
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