Classifying Mangroves in Vietnam using Radar and Optical Satellite Remote Sensing

Abstract

Mangroves are forest ecosystems growing in (sub)tropical saline coastal environments. With their unique root structure they serve as important natural coastal protection and provide habitats with excellent conditions for cultivating fish, shrimp and crab species. Despite all benefits mangrove forests are disappearing at alarming rates around the world but especially in Asia such as the Mekong Delta coast. Therefore, this research focusses on the Ca Mau Province in Vietnam. The Ca Mau province is the southernmost province of Vietnam with mangroves present along the coastlines, the Mui Ca Mau National Park and in mixed mangrove aquaculture farms. Remote sensing has been widely proven to be essential in mapping mangrove ecosystems. Previous research used either expensive optical and radar data sources or free but lower resolution systems. This study is the first that uses the new Copernicus Sentinel-1 radar and Sentinel-2 multispectral satellite missions that provide free available data with high spatial (10-20 meter) and temporal (10-12 days) resolution. Since optical data is prone to cloud effects and radar data is hard to interpret, both data sets are combined to investigate improvements for classifying mangroves. The data is processed in the new online Google Earth Engine platform providing a powerful tool for big data applications such as land cover classification. Optical data is found to separate mangroves by their spectral reflectance mainly in the near-infrared wavelength domain. The dominant mangrove species in the Ca Mau province, Rhizophora Apiculata and Avicennia Alba, are found to be separable from comparing unsupervised clustering results with ground truth locations. The C-band radar signal is dominated by volume scattering, indicating the density of the canopy. Especially VV-polarization has good correlation with canopy parameters. To improve information from the radar signal a temporal analysis is executed. Seasonal variations are quantified and show an increase according to the spatial succession of mangroves. Pioneer species, such as Avicennia genus, show less seasonal variations than mature species, such as Rhizophora genus. With the previous information five classes are defined: urban area, water and three mangrove classes: Rhizophora Apiculata species in extensive shrimps, Rhizophora Apiculata species in natural environment and Avicennia Alba species. A classification method is set-up in the Google Earth Engine with a Random Forest classifier using the satellite data inputs and ground truth training input of the five classes. A combination of the optical data with the temporal information of the radar data is found to be the best data input for separating those five classes. Classification results are obtained for discriminating mangrove types up to an overall accuracy of 87\%. The classification gets less reliable when mangrove species are mixed or at locations where the ground truth training input was scarce. With the resulting yearly land cover maps land cover changes can be detected. Comparing the land cover map of 2017 with a mangrove cover product of 2000 shows a regression along the southern coastline. No significant changes inside the shrimp farms are found between 2016 and 2017 but with the future availability of a long time series of Sentinel-1 and 2 data those can be detected with the method that is resulted from this study.