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Open AccessArticle

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

1
Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Camões 2090, Lages SC 88520-000, Brazil
2
Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Camões 2090, Lages SC 88520-000, Brazil
3
Amazon Regional Center, Brazilian National Institute for Space Research (INPE), Avenida Perimetral 2651, Belem PA 66077-830, Brazil
4
Graduate Program in Geodetic Sciences, Federal University of Paraná (UFPR), Avenida Coronel Francisco Heráclito dos Santos 210, Curitiba PR 81531-990, Brazil
5
Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 229; https://doi.org/10.3390/rs13020229
Received: 1 December 2020 / Revised: 2 January 2021 / Accepted: 5 January 2021 / Published: 11 January 2021
The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR. View Full-Text
Keywords: SAR mapping; data fusion; polarimetric attributes; mapping purpose; supervised classification; classification accuracy; Coxilha Rica; Southern Brazil SAR mapping; data fusion; polarimetric attributes; mapping purpose; supervised classification; classification accuracy; Coxilha Rica; Southern Brazil
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MDPI and ACS Style

Costa, J.d.S.; Liesenberg, V.; Schimalski, M.B.; Sousa, R.V.d.; Biffi, L.J.; Gomes, A.R.; Neto, S.L.R.; Mitishita, E.; Bispo, P.d.C. Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau. Remote Sens. 2021, 13, 229. https://doi.org/10.3390/rs13020229

AMA Style

Costa JdS, Liesenberg V, Schimalski MB, Sousa RVd, Biffi LJ, Gomes AR, Neto SLR, Mitishita E, Bispo PdC. Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau. Remote Sensing. 2021; 13(2):229. https://doi.org/10.3390/rs13020229

Chicago/Turabian Style

Costa, Jessica d.S.; Liesenberg, Veraldo; Schimalski, Marcos B.; Sousa, Raquel V.d.; Biffi, Leonardo J.; Gomes, Alessandra R.; Neto, Sílvio L.R.; Mitishita, Edson; Bispo, Polyanna d.C. 2021. "Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau" Remote Sens. 13, no. 2: 229. https://doi.org/10.3390/rs13020229

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