Evaluation of polarimetric data and texture attributes in SAR images to discriminate secondary forest in an area of amazon rainforest

• Autor(a) principal: Kiyohara, Bárbara Hass
• Data de Publicação: 2023
• Outros Autores: Sano, Edson Eyji
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Ciência Florestal (Online)
• Texto Completo: https://periodicos.ufsm.br/cienciaflorestal/article/view/71235
• topic: Amazon; Secondary vegetation; Remote sensing; Amazônia; Vegetação secundária; Sensoriamento remoto

This study aims to evaluate the ability of Sentinel-1 polarimetric and backscatter attributes in relation to COSMO-SkyMed (CSM) texture and backscatter features to discriminate secondary vegetation areas in an Amazon Forest domain area, located in Mato Grosso state. In this study, we used polarizations VV and VH from Sentinel-1 Synthetic Aperture Radar (SAR) image and HH from CSM SAR image, both in Single Look Complex format. In the Sentinel-1 image, a covariance matrix was generated and the H-Alpha target decomposition theorem was applied, allowing to obtain the attributes Entropy and Angle alpha. In the CSM image obtained the Gray-Level Co-Occurrence Matrix (GLCM) texture attributes: dissimilarity, contrast, homogeneity and second moment. The Support Vector Machine (SVM) algorithm was used for the classification. The Sentinel-1 polarimetric attributes result, with a Kappa index of 0.70 and an overall accuracy of 79.58%, performed better than those derived from CSM, with a Kappa index of 0.56 and overall accuracy 63.67%. However, the Sentinel-1 and CSM attributes did not present satisfactory results to discriminate the different stages of secondary forest.