Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery
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 p...
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doaj-263ab1c60b2d402cb6e70f2617ec5f6e2020-11-25T01:15:18ZengMDPI AGRemote Sensing2072-42922018-08-01109134310.3390/rs10091343rs10091343Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite ImageryQing Xia0Cheng-Zhi Qin1He Li2Chong Huang3Fen-Zhen Su4State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; E-mails: <email>xiaq@lreis.ac.cn</email> (Q.X.)State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; E-mails: <email>xiaq@lreis.ac.cn</email> (Q.X.)State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; E-mails: <email>xiaq@lreis.ac.cn</email> (Q.X.)State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; E-mails: <email>xiaq@lreis.ac.cn</email> (Q.X.)State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; E-mails: <email>xiaq@lreis.ac.cn</email> (Q.X.)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.http://www.mdpi.com/2072-4292/10/9/1343mangrove forest mappinghigh-resolution satellite imagerytideSVM classifierspectral signature |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qing Xia Cheng-Zhi Qin He Li Chong Huang Fen-Zhen Su |
spellingShingle |
Qing Xia Cheng-Zhi Qin He Li Chong Huang Fen-Zhen Su Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery Remote Sensing mangrove forest mapping high-resolution satellite imagery tide SVM classifier spectral signature |
author_facet |
Qing Xia Cheng-Zhi Qin He Li Chong Huang Fen-Zhen Su |
author_sort |
Qing Xia |
title |
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery |
title_short |
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery |
title_full |
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery |
title_fullStr |
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery |
title_full_unstemmed |
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery |
title_sort |
mapping mangrove forests based on multi-tidal high-resolution satellite imagery |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-08-01 |
description |
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. |
topic |
mangrove forest mapping high-resolution satellite imagery tide SVM classifier spectral signature |
url |
http://www.mdpi.com/2072-4292/10/9/1343 |
work_keys_str_mv |
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1725154079725846528 |