Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

Qing Xia,Cheng-Zhi Qin,Chong Huang,Fen-Zhen Su,He Li

doi:10.3390/rs10091343

Abstract

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

Highlights

Mangrove forests are widely distributed along the coastal wetlands of tropical and subtropical regions in the world and play a key role in linking terrestrial and marine systems through inter-tidal zones [1,2,3,4]
From the classification results of SVMhh and SVMhh + submerged mangrove recognition index (SMRI), it can be found that the submerged mangrove forests were effectively distinguished and classified as mangrove forests by the proposed method when using high-tide images (i.e., SVMhh + SMRI) (Figure 9a)
Without the use of SMRI, the classification result from SVMh shows that mangrove forests above the water were classified as mangroves; the submerged mangrove forests were not distinguished by the SVMh

Summary

Introduction

Mangrove forests are widely distributed along the coastal wetlands of tropical and subtropical regions in the world and play a key role in linking terrestrial and marine systems through inter-tidal zones [1,2,3,4]. Image classification, which is often used with medium-resolution imagery (e.g., Landsat TM imagery, which has a typical resolution of 30 m) to distinguish mangrove forests, has limited accuracy. This is mainly because mangrove forests are normally distributed along shorelines and in elongated or fragmented patches, especially in subtropical regions such as China’s coast, which are often narrower or smaller than the pixel size of medium-resolution imagery

Methods

Results

Conclusion