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

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.

Application of Hydro-Based Morphological Models for Environmental Assessment of Watersheds

“…We used fieldwork measurements and high spatial images from different data sources such as ArcGIS and Google Earth Pro. Similar procedures were previously adopted in another regional research [54,55].…”

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

“…Moreover, Sentinel-2 MSI (Multispectral Instrument) multispectral data used as the focused remote sensing data source in recent years are characterized by fine spatial resolution and the newly added red edge bands. The data have the possibility of providing detailed geometry and texture image features and new spectral information for wetlands [30]. Thus, the applicability of Sentinel-2 data on wetlands information extraction is necessarily examined.…”

“…Compared to the optical sensors, radar, as an active remote sensor, is capable of strong penetration that might still obtain clear images of the ground surface in cloudy and light rain weather. SAR sensors acquire microwave scattering information that is obviously different from the visible-infrared spectral information [30]. Some studies have shown that short-wavelength (e.g., C-band) radar has advantages in detecting herbaceous wetlands, such as bog, fen, and marshes, whereas the long-wavelength radar (e.g., L-band and P-band) is helpful for identifying tree-covered wetlands, such as swamps [10,21,31].…”

“…Therefore, it is necessary to search some suitable decomposition methods and extract and integrate multiple polarimetric features to improve the ability to determine the heterogeneity of the objects. Several studies have shown that both optical and radar remote sensing are able to be satisfactorily used for wetlands identification and mapping [20,30,31,[38][39][40]. Due to the different remote sensing modes of optical and radar sensors, the image characteristics have obvious discrimination.…”

Removal of Ionospheric Effects from Sigma Naught Images of the ALOS/PALSAR-2 Satellite

“…The greater penetration of microwaves allows us to understand the different forest strata that make up the forest cover at different stages of ecological succession. This makes it possible to improve the level of thematic characterization of the landscape, enabling the discrimination of different land use types [12][13][14][15][16][17][18].…”