Land cover mapping of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery

Ngo, Khanh Duc, Lechner, Alex M. and Vu, Tuong Thuy (2020) Land cover mapping of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery. Remote Sensing Applications: Society and Environment, 17 . p. 100272. ISSN 2352-9385

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Synthetic aperture radar (SAR) has great potential for land cover mapping, especially in tropical regions, where frequent cloud cover obstructs optical remote sensing. The aim of this study is to demonstrate the utility of Sentinel-1A SAR imagery for characterising land use and land cover (LULC) in Bac Lieu, a rapidly developing province of the Mekong Delta, Vietnam, to support natural resource management for land use planning and monitoring. Twenty-one SAR images acquired in 2016 were classified in a four-step process. Firstly, the SAR images were pre-processed to produce Grey Level Co-occurrence Matrix (GLCM) texture images. Then, to reduce the effects of rainfall variation confounding the classification, the images were divided into two categories: dry season (Jan–April) and wet season (May–December) and three input image sets were produced: 1) a single-date composite image (two VV-VH bands), 2) a multi-temporal composite image (eight bands for dry season and sixteen bands for wet season) and 3) a multi-temporal and textural composite image (sixteen bands for dry season and thirty two bands for wet season). We then applied Support Vector Machines (SVM) and Random Forest (RF) classifiers to characterise urban, forest, aquaculture, and rice paddy field for the three input image sets. We tested a combination of input images and classification algorithms and found that no matter the classification algorithms used, multi-temporal images had a higher overall classification accuracy than single-date images and that differences between classification algorithms were minimal. The most accurate classification resulted from a combination of the SVM classifier and multi-temporal and textural input image sets from the dry and wet seasons; with an overall accuracy of 94.81% and Kappa coefficient of 0.92. Our land cover mapping showed that Bac Lieu province is dominated by shrimp farming and rice paddy fields, with natural coastal forested ecosystems only made up a small proportion of the province. The results demonstrated the potential use of SAR as an up-to-date complementary data source of land cover information for local authorities, to support their land use master plan and to monitor illegal land use changes. Long-term monitoring of land use in Bac Lieu province, as well as in the Mekong Delta, will be crucial for decision makers, to ensure sustainability and food security in this region.

Keywords:Mekong delta land cover mapping, Multi-temporal Sentinel-1A SAR, Support vector machines, Random forest
Subjects:F Physical Sciences > F810 Environmental Geography
L Social studies > L700 Human and Social Geography
L Social studies > L727 Agricultural Geography
Divisions:College of Science > School of Geography
ID Code:41991
Deposited On:26 Aug 2020 09:14

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