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Remote Sens. 2017, 9(8), 838; doi:10.3390/rs9080838

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

1
National Institute for Space Research (INPE), Remote Sensing Department (DSR), 12227-010 São José dos Campos-SP, Brazil
2
Santa Catarina State University (UDESC), Department of Forest Engineering (DEF), 88520-000 Lages-SC, Brazil
*
Author to whom correspondence should be addressed.
Received: 7 June 2017 / Revised: 3 August 2017 / Accepted: 10 August 2017 / Published: 13 August 2017
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Abstract

Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification. View Full-Text
Keywords: textural features; vegetation indices; multitemporal information; random forest; support vector machine textural features; vegetation indices; multitemporal information; random forest; support vector machine
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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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MDPI and ACS Style

Sothe, C.; Almeida, C.M.; Liesenberg, V.; Schimalski, M.B. Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil. Remote Sens. 2017, 9, 838.

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