Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images
Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/10/2373 |
| _version_ | 1797495999948652544 |
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| author | Yiqiong Li Junwu Bai Li Zhang Zhaohui Yang |
| author_facet | Yiqiong Li Junwu Bai Li Zhang Zhaohui Yang |
| author_sort | Yiqiong Li |
| collection | DOAJ |
| description | Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea. |
| first_indexed | 2024-03-10T01:57:28Z |
| format | Article |
| id | doaj.art-fe8a076dc0dd43988715f9f6f8db47c5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:57:28Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fe8a076dc0dd43988715f9f6f8db47c52023-11-23T12:55:08ZengMDPI AGRemote Sensing2072-42922022-05-011410237310.3390/rs14102373Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite ImagesYiqiong Li0Junwu Bai1Li Zhang2Zhaohui Yang3School of Geographic Sciences & Surveying and Mapping Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Geographic Sciences & Surveying and Mapping Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Geographic Sciences & Surveying and Mapping Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSeagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea.https://www.mdpi.com/2072-4292/14/10/2373seagrass monitoring and mappingremote sensingGF2 and Sentinel-2 satellite dataMLC and OBIA |
| spellingShingle | Yiqiong Li Junwu Bai Li Zhang Zhaohui Yang Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images Remote Sensing seagrass monitoring and mapping remote sensing GF2 and Sentinel-2 satellite data MLC and OBIA |
| title | Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images |
| title_full | Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images |
| title_fullStr | Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images |
| title_full_unstemmed | Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images |
| title_short | Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images |
| title_sort | mapping and spatial variation of seagrasses in xincun hainan province china based on satellite images |
| topic | seagrass monitoring and mapping remote sensing GF2 and Sentinel-2 satellite data MLC and OBIA |
| url | https://www.mdpi.com/2072-4292/14/10/2373 |
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