Abstract

Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.

Highlights

  • Mangrove forests are swampy, woody plant communities that are distributed in the intertidal region between sea and land in tropical and subtropical coastlines

  • The results indicate that the combination of Synthetic Aperture Radar (SAR) and optical data can increase the separability between features, and improve the classification accuracy to some extent

  • We used optical remote sensing data from Landsat 8 and full-polarization C-band SAR data from Radarsat-2 to map the extent of mangrove forests in the Hainan Dongzhaigang National Nature Reserve (HDNNR) by applying an support vector machine (SVM) classifier with high dimensionality and the capability of solving non-linear problems

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Summary

Introduction

Mangrove forests are swampy, woody plant communities that are distributed in the intertidal region between sea and land in tropical and subtropical coastlines. They have special sea-land characteristics and provide a “natural barrier” to the coast [1,2,3]. Mangrove forests have enormous ecological, economic, and social value [4,5,6,7,8,9] They play an irreplaceable role in maintaining biodiversity, protecting the coastal environment, strengthening dykes, providing shelter from wind, protecting banks and inducing siltation, purifying the coastal water environment, and protecting farmland and villages from natural disasters, such as hurricanes and tsunamis. Sea-level rise could be the biggest threat to mangrove ecosystems [15,16]

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