Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and free...

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Main Authors: Dezhi Wang, Bo Wan, Penghua Qiu, Yanjun Su, Qinghua Guo, Run Wang, Fei Sun, Xincai Wu
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/9/1468
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author Dezhi Wang
Bo Wan
Penghua Qiu
Yanjun Su
Qinghua Guo
Run Wang
Fei Sun
Xincai Wu
author_facet Dezhi Wang
Bo Wan
Penghua Qiu
Yanjun Su
Qinghua Guo
Run Wang
Fei Sun
Xincai Wu
author_sort Dezhi Wang
collection DOAJ
description Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.
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spelling doaj.art-6642cef67cb541a4b071a5a39b0755a82022-12-21T20:01:09ZengMDPI AGRemote Sensing2072-42922018-09-01109146810.3390/rs10091468rs10091468Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and SpeciesDezhi Wang0Bo Wan1Penghua Qiu2Yanjun Su3Qinghua Guo4Run Wang5Fei Sun6Xincai Wu7Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaFaculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaCollege of Geography and Environmental Science, Hainan Normal University, Haikou 571158, ChinaState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, ChinaState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, ChinaFaculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaFaculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaFaculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaMapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.http://www.mdpi.com/2072-4292/10/9/1468mangrovesspeciesSentinel-2Landsat 8Pléiades-1random forest
spellingShingle Dezhi Wang
Bo Wan
Penghua Qiu
Yanjun Su
Qinghua Guo
Run Wang
Fei Sun
Xincai Wu
Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
Remote Sensing
mangroves
species
Sentinel-2
Landsat 8
Pléiades-1
random forest
title Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
title_full Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
title_fullStr Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
title_full_unstemmed Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
title_short Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
title_sort evaluating the performance of sentinel 2 landsat 8 and pleiades 1 in mapping mangrove extent and species
topic mangroves
species
Sentinel-2
Landsat 8
Pléiades-1
random forest
url http://www.mdpi.com/2072-4292/10/9/1468
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