Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China
Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study wa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/10/12/2020 |
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| author | Chunyan Lu Jinfu Liu Mingming Jia Mingyue Liu Weidong Man Weiwei Fu Lianxiu Zhong Xiaoqing Lin Ying Su Yibin Gao |
| author_facet | Chunyan Lu Jinfu Liu Mingming Jia Mingyue Liu Weidong Man Weiwei Fu Lianxiu Zhong Xiaoqing Lin Ying Su Yibin Gao |
| author_sort | Chunyan Lu |
| collection | DOAJ |
| description | Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and <i>Spartina</i> using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km<sup>2</sup>, which was mostly converted from mudflats and <i>Spartina</i>. Environmental threats including climate change and sea-level rise, aquaculture development and <i>Spartina</i> invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and <i>Spartina</i> control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests. |
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| language | English |
| last_indexed | 2024-12-22T06:43:02Z |
| publishDate | 2018-12-01 |
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| series | Remote Sensing |
| spelling | doaj.art-af1457afb5ba41539946c71fd5f62b172022-12-21T18:35:23ZengMDPI AGRemote Sensing2072-42922018-12-011012202010.3390/rs10122020rs10122020Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, ChinaChunyan Lu0Jinfu Liu1Mingming Jia2Mingyue Liu3Weidong Man4Weiwei Fu5Lianxiu Zhong6Xiaoqing Lin7Ying Su8Yibin Gao9College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaKey Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaMangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and <i>Spartina</i> using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km<sup>2</sup>, which was mostly converted from mudflats and <i>Spartina</i>. Environmental threats including climate change and sea-level rise, aquaculture development and <i>Spartina</i> invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and <i>Spartina</i> control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests.https://www.mdpi.com/2072-4292/10/12/2020mangrove forestsobject-oriented classificationoptimal segmentation scale modelmulti-seasonal imageQuanzhou Bayremote sensing dynamic monitoring |
| spellingShingle | Chunyan Lu Jinfu Liu Mingming Jia Mingyue Liu Weidong Man Weiwei Fu Lianxiu Zhong Xiaoqing Lin Ying Su Yibin Gao Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China Remote Sensing mangrove forests object-oriented classification optimal segmentation scale model multi-seasonal image Quanzhou Bay remote sensing dynamic monitoring |
| title | Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China |
| title_full | Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China |
| title_fullStr | Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China |
| title_full_unstemmed | Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China |
| title_short | Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China |
| title_sort | dynamic analysis of mangrove forests based on an optimal segmentation scale model and multi seasonal images in quanzhou bay china |
| topic | mangrove forests object-oriented classification optimal segmentation scale model multi-seasonal image Quanzhou Bay remote sensing dynamic monitoring |
| url | https://www.mdpi.com/2072-4292/10/12/2020 |
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