Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data
Although significant progress has been made in the remote sensing extraction of mangroves, research at the species level remains relatively limited. <i>Kandelia obovata</i> is a dominant mangrove species and is frequently used in ecological restoration projects in China. However, owing t...
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MDPI AG
2025-03-01
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| author | Chen Lin Jiali Zheng Luojia Hu Luzhen Chen |
| author_facet | Chen Lin Jiali Zheng Luojia Hu Luzhen Chen |
| author_sort | Chen Lin |
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| description | Although significant progress has been made in the remote sensing extraction of mangroves, research at the species level remains relatively limited. <i>Kandelia obovata</i> is a dominant mangrove species and is frequently used in ecological restoration projects in China. However, owing to the fragmented distribution of <i>K. obovata</i> within mixed mangrove communities and the significant spectral and textural similarities among mangrove species, accurately extracting large-scale <i>K. obovata</i>-based remote sensing data remains a challenging task. In this study, we conducted extensive field surveys and developed a comprehensive sampling database covering <i>K. obovata</i> and other mangrove species across mangrove-distributing areas in China. We identified the optimal bands for extracting <i>K. obovata</i> by utilizing time-series remote sensing data from Sentinel-1 and Sentinel-2, along with the Google Earth Engine (GEE), and proposed a method for extracting <i>K. obovata</i> communities. The main conclusions are as follows: (1) The spectral-temporal variability characteristics of the blue and red-edge bands play a crucial role in the identification of <i>K. obovata</i> communities. The 90th percentile metric of the blue wavelength band ranks first in importance, while the 75th percentile metric of the blue wavelength band ranks second; (2) This method of remote sensing extraction using spectral-temporal variability metrics with time-series optical and radar remote sensing data offers significant advantages in identifying the <i>K. obovata</i> species, achieving a producer’s accuracy of up to 94.6%; (3) In 2018, the total area of pure <i>K. obovata</i> communities in China was 4825.97 ha; (4) In the southern provinces of China, Guangdong Province has the largest <i>K. obovata</i> community area, while Macau has the smallest. This research contributes to the understanding of mangrove ecosystems and provides a methodological framework for monitoring <i>K. obovata</i> and other coastal vegetation using advanced remote sensing technologies. |
| format | Article |
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| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Remote Sensing |
| spelling | doaj-art-17b8e9132c0440dd8ff12a0c91d9a85f2025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-03-0117589810.3390/rs17050898Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series DataChen Lin0Jiali Zheng1Luojia Hu2Luzhen Chen3State Key Laboratory of Marine Environmental Science, Key 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 Marine Environmental Science, Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, ChinaQian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, ChinaState Key Laboratory of Marine Environmental Science, Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, ChinaAlthough significant progress has been made in the remote sensing extraction of mangroves, research at the species level remains relatively limited. <i>Kandelia obovata</i> is a dominant mangrove species and is frequently used in ecological restoration projects in China. However, owing to the fragmented distribution of <i>K. obovata</i> within mixed mangrove communities and the significant spectral and textural similarities among mangrove species, accurately extracting large-scale <i>K. obovata</i>-based remote sensing data remains a challenging task. In this study, we conducted extensive field surveys and developed a comprehensive sampling database covering <i>K. obovata</i> and other mangrove species across mangrove-distributing areas in China. We identified the optimal bands for extracting <i>K. obovata</i> by utilizing time-series remote sensing data from Sentinel-1 and Sentinel-2, along with the Google Earth Engine (GEE), and proposed a method for extracting <i>K. obovata</i> communities. The main conclusions are as follows: (1) The spectral-temporal variability characteristics of the blue and red-edge bands play a crucial role in the identification of <i>K. obovata</i> communities. The 90th percentile metric of the blue wavelength band ranks first in importance, while the 75th percentile metric of the blue wavelength band ranks second; (2) This method of remote sensing extraction using spectral-temporal variability metrics with time-series optical and radar remote sensing data offers significant advantages in identifying the <i>K. obovata</i> species, achieving a producer’s accuracy of up to 94.6%; (3) In 2018, the total area of pure <i>K. obovata</i> communities in China was 4825.97 ha; (4) In the southern provinces of China, Guangdong Province has the largest <i>K. obovata</i> community area, while Macau has the smallest. This research contributes to the understanding of mangrove ecosystems and provides a methodological framework for monitoring <i>K. obovata</i> and other coastal vegetation using advanced remote sensing technologies.https://www.mdpi.com/2072-4292/17/5/898<i>Kandelia obovata</i>Sentinel-1Sentinel-2Google Earth Engineremote sensingmangrove forests |
| spellingShingle | Chen Lin Jiali Zheng Luojia Hu Luzhen Chen Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data Remote Sensing <i>Kandelia obovata</i> Sentinel-1 Sentinel-2 Google Earth Engine remote sensing mangrove forests |
| title | Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data |
| title_full | Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data |
| title_fullStr | Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data |
| title_full_unstemmed | Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data |
| title_short | Extraction of Mangrove Community of <i>Kandelia obovata</i> in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data |
| title_sort | extraction of mangrove community of i kandelia obovata i in china based on google earth engine and dense sentinel 1 2 time series data |
| topic | <i>Kandelia obovata</i> Sentinel-1 Sentinel-2 Google Earth Engine remote sensing mangrove forests |
| url | https://www.mdpi.com/2072-4292/17/5/898 |
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