Integrated Use of Synthetic Aperture Radar and Optical Data in Mapping Native Vegetation: A Study in a Transitional Brazilian Cerrado–Atlantic Forest Interface

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-07, Vol.16 (14), p.2559
• Main Authors: Santos, Allita R., Barbosa, Mariana A. G. A., Anjinho, Phelipe S., Parizotto, Denise, Mauad, Frederico F.
• Format: Article
• Language: English
• Subjects: Algorithms; Atmospheric conditions; Backscattering; backscattering coefficients; Biodiversity; Classification; Creeks & streams; Deep learning; Ecological research; Environmental impact; Environmental protection; Forest management; Forestry; Interferometry; Land use; Machine learning; Mapping; multisensor optimizing; Natural resources; Near infrared radiation; Radar; Radar data; Remote sensing; River ecology; Sampling; Short wave radiation; Spatial data; Sustainability; Synthetic aperture radar; Tropical environment; Tropical environments; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16142559

This study develops a structure for mapping native vegetation in a transition area between the Brazilian Cerrado and the Atlantic Forest from integrated spatial information of Sentinel-1 and Sentinel-2 satellites. Most studies use integrated data to improve classification accuracy in adverse atmospheric conditions, in which optical data have many errors. However, this method can also improve classifications carried out in landscapes with favorable atmospheric conditions. The use of Sentinel-1 and Sentinel-2 data can increase the accuracy of mapping algorithms and facilitate visual interpretation during sampling by providing more parameters that can be explored to differentiate land use classes with complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near-infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings: SAR and optical data in separately settings (C1 and C2, respectively) and in an integrated setting (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlands in C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the integrated use of SAR and optical data can improve LULC mapping in tropical regions where occurs biomes interface, as in the transitional Brazilian Cerrado and Atlantic Forest.