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Remote Sens. 2018, 10(9), 1468; https://doi.org/10.3390/rs10091468

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

1,2
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1,2
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3,* , 4
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4,5
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1,2
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1,2
and
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1
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
2
National Engineering Research Center of Geographic Information System, Wuhan 430074, China
3
College of Geography and Environmental Science, Hainan Normal University, Haikou 571158, China
4
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 16 July 2018 / Revised: 9 September 2018 / Accepted: 10 September 2018 / Published: 14 September 2018
(This article belongs to the Collection Sentinel-2: Science and Applications)
Full-Text   |   PDF [5307 KB, uploaded 14 September 2018]   |  

Abstract

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. View Full-Text
Keywords: mangroves; species; Sentinel-2; Landsat 8; Pléiades-1; random forest mangroves; species; Sentinel-2; Landsat 8; Pléiades-1; random forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, D.; Wan, B.; Qiu, P.; Su, Y.; Guo, Q.; Wang, R.; Sun, F.; Wu, X. Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species. Remote Sens. 2018, 10, 1468.

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