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Open AccessArticle

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

by Qing Xia 1, Cheng-Zhi Qin 1,2,3,*, He Li 1, Chong Huang 1 and Fen-Zhen Su 1
State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; E-mails: [email protected] (Q.X.)
University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, Nanjing 210097, China
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1343;
Received: 26 June 2018 / Revised: 9 August 2018 / Accepted: 16 August 2018 / Published: 23 August 2018
(This article belongs to the Special Issue Remote Sensing of Mangroves)
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. View Full-Text
Keywords: mangrove forest mapping; high-resolution satellite imagery; tide; SVM classifier; spectral signature mangrove forest mapping; high-resolution satellite imagery; tide; SVM classifier; spectral signature
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MDPI and ACS Style

Xia, Q.; Qin, C.-Z.; Li, H.; Huang, C.; Su, F.-Z. Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery. Remote Sens. 2018, 10, 1343.

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