Identificação de Infraestruturas de Exploração Florestal em Uma Área de Manejo Sustentável por Meio de Imagens OLI/Landsat 8 e MSI/Sentinel-2
Abstract
Forest infrastructures are essential for logging activities, but they also generate environmental impacts. This study aimed to analyze the feasibility of identifying logging infrastructure in a Sustainable Forest Management (SFM) area using remote sensing data and geoprocessing techniques. Ten techniques were applied, including five Vegetation Index (NDVI, TDVI, TVI, RATIO and SAVI), two Linear Spectral Mixture Model components (soil and vegetation fraction) and two Tasseled Cap Transformation (Wetness and Greeness) components, on Landsat 8 and Sentinel-2 images, to detect forest infrastructures built in a SFM area in the Saracá-Taquera National Forest (FLONA), Pará. The Wetness Tasseled Cap Transformation applied to the Sentinel-2 image had the great results for the kappa index and overall accuracy, a value of 0.93 and 83.60 respectively. In relation photointerpretation, the best performing techniques were from the Tasseled Cap Transformation Wetness and NDVI applied to the Sentinel-2 image, TVI in the Landsat 8 image and the Linear Spectral Mixture Model Soil Fraction. Based on these results, it was possible to successfully identify forest infrastructures such as primary and secondary roads using these techniques applied to medium resolution images.
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