O objetivo do presente estudo foi avaliar a capacidade de atributos polarimétricos e de retroespalhamento do Sentinel-1 em relação às feições de textura e de retroespalhamento do COSMO-SkyMed (CSM), em discriminar diferentes estágios de floresta secundária em uma área de domínio de Floresta Amazônica, no estado do Mato Grosso. Neste estudo, utilizou-se uma imagem de Radar de Abertura Sintética (SAR) do Sentinel-1 nas polarizações VV e VH e uma imagem SAR do CSM na polarização HH, ambas no formato Single Look Complex. Na imagem Sentinel-1 foi gerada a matriz de covariância e aplicado o teorema de decomposição de alvos H-Alpha, para obtenção dos atributos Entropia e Ângulo alfa. Na imagem CSM, foram obtidos os atributos de textura a partir da matriz de co-ocorrência de níveis de cinza (GLCM): dissimilaridade, contraste, homogeneidade e segundo momento. Para a classificação, foi utilizado o algoritmo Máquina de Vetores de Suporte (SVM). A classificação derivada dos atributos polarimétricos do Sentinel-1, com índice Kappa de 0,70 e exatidão global de 79,58%, apresentou desempenho superior àquela derivada do CSM, com índice Kappa de 0,56 e exatidão global de 63,67%. Entretanto, tanto os atributos derivados do Sentinel-1 como do CSM não apresentaram resultados satisfatórios para discriminar os diferentes estágios de floresta secundária.
The re-suppression of secondary vegetation (SV) in the Brazilian Amazon for agriculture or land speculation occurs mostly in the rainy season. The use of optical images to monitor such re-suppression during the rainy season is limited because of the persistent cloud cover. This study aimed to evaluate the potential of C- and L-band SAR data acquired in the rainy season to discriminate SV in an area of new hotspot of deforestation in the municipality of Colniza, northwestern of Mato Grosso State, Brazil. This is the first time that the potential of dual-frequency SAR data was analyzed to discriminate SV, with an emphasis on data acquired during the rainy season. The L-band ALOS/PALSAR-2 and the C-band Sentinel-1 data acquired in March 2018 were processed to obtain backscattering coefficients and nine textural attributes were derived from the gray level co-occurrence matrix method (GLCM). Then, we classified the images based on the non-parametric Random Forest (RF) and Support Vector Machine (SVM) algorithms. The use of SAR textural attributes improved the discrimination capability of different LULC classes found in the study area. The results showed the best performance of ALOS/PALSAR-2 data classified by the RF algorithm to discriminate the following representative land use and land cover classes of the study area: primary forest, secondary forest, shrubby pasture, clean pasture, and bare soil, with an overall accuracy and Kappa coefficient of 84% and 0.78, respectively. The RF outperformed the SVM classifier to discriminate these five LULC classes in 14% of overall accuracy for both ALOS-2 and Sentinel-1 data sets. This study also showed that the textural attributes derived from the GLCM method are highly sensitive to the moving window size to be applied to the GLCM method. The results of this study can assist the future development of an operation system based on dual-frequency SAR data to monitor re-suppression of SV in the Brazilian Amazon or in other tropical rainforests.
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