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Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso

1
Physical Geography Department, University of Göttingen, Goldschmidtstr. 5, 37077 Göttingen, Germany
2
Landesamt für Vermessung und Geoinformation Schleswig-Holstein, Dezernat 22, Mercatorstraße 1, 24106 Kiel, Germany
3
Forest Engineering Department, Santa Catarina State University, Av. Luiz de Camões 2090, 88.520-000 Lages, Santa Catarina, Brazil
4
Cartography, GIS & Remote Sensing Department, University of Göttingen, Goldschmidtstr. 5, 37077 Göttingen, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1161; https://doi.org/10.3390/rs11101161
Received: 18 April 2019 / Revised: 8 May 2019 / Accepted: 10 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)
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Abstract

Mapping vegetation types through remote sensing images has proved to be effective, especially in large biomes, such as the Brazilian Cerrado, which plays an important role in the context of management and conservation at the agricultural frontier of the Amazon. We tested several combinations of optical and radar images to identify the four dominant vegetation types that are prevalent in the Cerrado area (i.e., cerrado denso, cerradão, gallery forest, and secondary forest). We extracted features from both sources of data such as intensity, grey level co-occurrence matrix, coherence, and polarimetric decompositions using Sentinel 2A, Sentinel 1A, ALOS-PALSAR 2 dual/full polarimetric, and TanDEM-X images during the dry and rainy season of 2017. In order to normalize the analysis of these features, we used principal component analysis and subsequently applied the Random Forest algorithm to evaluate the classification of vegetation types. During the dry season, the overall accuracy ranged from 48 to 83%, and during the dry and rainy seasons it ranged from 41 up to 82%. The classification using Sentinel 2A images during the dry season resulted in the highest overall accuracy and kappa values, followed by the classification that used images from all sensors during the dry and rainy season. Optical images during the dry season were sufficient to map the different types of vegetation in our study area. View Full-Text
Keywords: Cerrado; Amazon; vegetation type; optical; sar; synergism; mapping Cerrado; Amazon; vegetation type; optical; sar; synergism; mapping
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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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de Souza Mendes, F.; Baron, D.; Gerold, G.; Liesenberg, V.; Erasmi, S. Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso. Remote Sens. 2019, 11, 1161.

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