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Technical Note

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

1
Department of Environmental Biology, Institute of Environmental Sciences, Leiden University, P.O. Box 9518, 2300 RA Leiden, The Netherlands
2
Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Ho Chi Minh City 700000, Vietnam
3
Institute for Circular Economy Development, Vietnam National University, Ho Chi Minh City 700000, Vietnam
4
Center of Water Management and Climate Change, Institute for Environment and Resources, Vietnam National University, Ho Chi Minh City 700000, Vietnam
5
Biogeography & Macroecology Lab, Department Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam (UvA), 1090 GE Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3729; https://doi.org/10.3390/rs12223729
Received: 19 September 2020 / Revised: 2 November 2020 / Accepted: 6 November 2020 / Published: 13 November 2020
(This article belongs to the Special Issue Remote Sensing in Mangroves)
Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring. View Full-Text
Keywords: data fusion; forest monitoring; Google Earth Engine; Landsat; mangrove forests; multi-temporal analysis; remote sensing; satellite earth observation; time series analysis; Vietnam data fusion; forest monitoring; Google Earth Engine; Landsat; mangrove forests; multi-temporal analysis; remote sensing; satellite earth observation; time series analysis; Vietnam
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MDPI and ACS Style

Hauser, L.T.; An Binh, N.; Viet Hoa, P.; Hong Quan, N.; Timmermans, J. Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization. Remote Sens. 2020, 12, 3729. https://doi.org/10.3390/rs12223729

AMA Style

Hauser LT, An Binh N, Viet Hoa P, Hong Quan N, Timmermans J. Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization. Remote Sensing. 2020; 12(22):3729. https://doi.org/10.3390/rs12223729

Chicago/Turabian Style

Hauser, Leon T., Nguyen An Binh, Pham Viet Hoa, Nguyen Hong Quan, and Joris Timmermans. 2020. "Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization" Remote Sensing 12, no. 22: 3729. https://doi.org/10.3390/rs12223729

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