Palsar-2/Alos-2 and Oli/Landsat-8 Data Integration for Land Use and Land Cover Mapping in Northern Brazilian Amazon

Abstract

In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.

Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery

“…With the fused approach, some familiar misclassifications for SAR (classes with similar surface backscatter patterns, i.e., roads, runways, still water or lawns) and optical (classes with similar spectral reflectance) data could be reduced. Some classes are difficult to detect using a spectral response from optical data or backscatter from the SAR instrument, but this might be easily distinguished by their combined use [26,104,105]. Although F1 and FoM metrics are more robust than UA and PA [75,106], UA values, as a measure of the reliability of the map, were visualized ( Figure 4) for each study area.…”

Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

“…Ullmann et al [90] examined the scattering characteristics from polarimetric data obtained at X-, C-, and L-band in a tundra-dominant ecosystem and found that L-band data were more appropriate to differentiate classes with low levels of vegetation cover. Pavanelli et al [39] integrated data from Landsat-8/OLI and ALOS/PALSAR-2 satellites and classified them in the RF algorithm, to map the LULC classes in a fragment located in the northern part of the Brazilian Amazon. They found that ALOS/PALSAR-2 data's most important contribution was the possibility to discriminate classes with low levels of biomass (grasslands and wooded savanna).…”

“…The results suggest a greater contribution of horizontally distributed components, such as fallen stems and branches in areas severely affected by fire. Recent studies focusing on multi-sensor analysis for LULC mapping and change detection in tropical regions [39,40] have used dual-polarimetric ALOS/PALSAR-2 images integrated with Landsat multispectral images. Pavanelli et al [39] highlighted the SAR potential to discriminate savannah-like vegetation in the Brazilian Amazon, with an improvement in OA of 6% in relation to the classification results obtained only from optical data.…”

“…Recent studies focusing on multi-sensor analysis for LULC mapping and change detection in tropical regions [39,40] have used dual-polarimetric ALOS/PALSAR-2 images integrated with Landsat multispectral images. Pavanelli et al [39] highlighted the SAR potential to discriminate savannah-like vegetation in the Brazilian Amazon, with an improvement in OA of 6% in relation to the classification results obtained only from optical data. De Alban et al [40], using a similar dataset, assessed the tropical landscapes of southern Myanmar using Random Forest (RF) classifier.…”

Analysis of the Target Decomposition Technique Attributes and Polarimetric Ratios to Discriminate Land Use and Land Cover Classes of the Tapajós Region

“…However, such actions need to take into consideration the inter and intra-pixel complexity in tropical landscapes, the loss of variables in the classifier models by attribute selection or by radiometric transformations intrinsic to the fusion of multi-source or multi-data. Therefore, scientific effort has been directed to the use of classifiers such as Random Forest that has been applied for both optical-based reflectance measures and SAR polarimetric-based texture metrics (Hagensieker et al 2018;Pavanelli et al 2018) to increase the accuracy level on LULC class stratification.…”