Abstract
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.
Highlights
Optical orbital sensor systems, such as those in the Landsat series, are generally used in mapping land use and land cover (LULC)
Similar features from either synthetic aperture radar (SAR) or optical datasets were reported in the studies of Pal and Foody [68], Rabe et al [69], Waske et al [70], and van According to the results presented, the exclusive use of different polarimetric attributes from ALOS/PALSAR-2 data have potential use in mapping specific land cover types of
Research involving the use of SAR data combined with Light Detection and Ranging (LiDAR) data can assist in discriminating land cover classes, such as LiDAR data provided by the GEDI (Global Ecosystems Dynamics Investigation Lidar) instrument, launched in December 2018
Summary
Optical orbital sensor systems, such as those in the Landsat series, are generally used in mapping land use and land cover (LULC). The combined use of both optical and SAR data can result in improvements in classifying the land cover in complex environments [2,3,19,20] Such studies were carried out by, Pavanelli et al [3], Souza. Mendes et al [4], Pereira et al [20], and Liesenberg et al [21] All these initiatives were developed to analyze the ability to integrate SAR data with optical data in the classification of the land use and land cover classes in the Amazon biome (Brazil). Potential at and combined with optical in data from the SENTINEL-2A sensor for mapping purposes at higher southern latitudes in Southern Brazil
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