Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform
A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth E...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/18/4664 |
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| author | Thuy Thi Phuong Vu Tien Dat Pham Neil Saintilan Andrew Skidmore Hung Viet Luu Quang Hien Vu Nga Nhu Le Huu Quang Nguyen Bunkei Matsushita |
| author_facet | Thuy Thi Phuong Vu Tien Dat Pham Neil Saintilan Andrew Skidmore Hung Viet Luu Quang Hien Vu Nga Nhu Le Huu Quang Nguyen Bunkei Matsushita |
| author_sort | Thuy Thi Phuong Vu |
| collection | DOAJ |
| description | A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam. |
| first_indexed | 2024-03-09T22:36:53Z |
| format | Article |
| id | doaj.art-9e603452b5fd47098ec38302187e9130 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:36:53Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9e603452b5fd47098ec38302187e91302023-11-23T18:46:29ZengMDPI AGRemote Sensing2072-42922022-09-011418466410.3390/rs14184664Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine PlatformThuy Thi Phuong Vu0Tien Dat Pham1Neil Saintilan2Andrew Skidmore3Hung Viet Luu4Quang Hien Vu5Nga Nhu Le6Huu Quang Nguyen7Bunkei Matsushita8Forest Inventory and Planning Institute (FIPI), Ministry of Agricultural and Rural Development (MARD), Vinh Quynh, Thanh Tri, Hanoi 100000, VietnamSchool of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, AustraliaSchool of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, AustraliaSchool of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, AustraliaCentre of Multidisciplinary Integrated Technologies for Field Monitoring (FIMO), The University of Engineering and Technology, Vietnam National University (VNU), 144 Xuan Thuy, Cau Giay, Hanoi 100000, VietnamForest Inventory and Planning Institute (FIPI), Ministry of Agricultural and Rural Development (MARD), Vinh Quynh, Thanh Tri, Hanoi 100000, VietnamDepartment of Marine Mechanics and Environment, Institute of Mechanics, Vietnam Academy of Science and Technology (VAST), 264 Doi Can Street, Ba Dinh District, Hanoi 100000, VietnamDepartment of Policy and Planning Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8573, Ibaraki Prefecture, JapanFaculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8572, Ibaraki Prefecture, JapanA pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam.https://www.mdpi.com/2072-4292/14/18/4664mangroveremote sensingLandsatGoogle Earth EngineRed River DeltaVietnam |
| spellingShingle | Thuy Thi Phuong Vu Tien Dat Pham Neil Saintilan Andrew Skidmore Hung Viet Luu Quang Hien Vu Nga Nhu Le Huu Quang Nguyen Bunkei Matsushita Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform Remote Sensing mangrove remote sensing Landsat Google Earth Engine Red River Delta Vietnam |
| title | Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform |
| title_full | Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform |
| title_fullStr | Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform |
| title_full_unstemmed | Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform |
| title_short | Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform |
| title_sort | mapping multi decadal mangrove extent in the northern coast of vietnam using landsat time series data on google earth engine platform |
| topic | mangrove remote sensing Landsat Google Earth Engine Red River Delta Vietnam |
| url | https://www.mdpi.com/2072-4292/14/18/4664 |
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