Abstract One challenge in the study of optical remotely sensed time series in the Amazon is the constant cloud cover. The present study evaluates different compositing techniques using regular and non-regular intervals to obtain cloud-free images over large areas. The study area was the municipality of Capixaba, State of Acre, belonging to the Amazon region. The tests considered four compositing algorithms (maximum, minimum, mean, and median) for daily MODIS sensor data (b1 and b2, 250m). The compositing technique from regular intervals adopted the following periods: 8, 16, 24, 32, 40, and 48 days. The irregular interval composite images adopted different composition intervals for dry seasons (April to September) and rainy (October to March). The cloud mask and viewing angle constraint allowed to obtain information without atmospheric interference and closest to the nadir view. The composite images using regular intervals did not allow for overcoming the high frequency of cloud cover in the region. The composite images from non-regular intervals presented a higher percentage of cloud-free pixels. The mean and median methods provided a better visual appearance of the images, corroborating with the homogeneity test. Therefore, composite images from non-regular intervals may be an appropriate alternative in places with constant cloud coverage.

Abstract This research aimed to analyze MODIS-NDVI time series classifications, with three different algorithms, seeking to identify the ideal amount of images for studies in environments with high cloudiness rates. The spatial cut used for the study was the municipality of Capixaba, located in the state of Acre in the Amazon region. For each NDVI image, a cloud mask was constructed. This mask allowed to organize the temporal cube by cloud coverage quantity. Thus, the impact of eliminating high cloud images for the series classification was tested. At each cut, the temporal cube was redone, evaluating results for a new set of bands. For the accuracy analysis, the Kappa coefficient was adopted. A total of 84 classifications were made. Three classification algorithms (Minimum Distance, Spectral Angle Mapper and Spectral Correlation Mapper) and 4 different interactions between classes and samples were tested. Over the period analyzed, approximately 80% of images showed cloud cover above 90%. The tests showed that the removal of the images with cloud increased the quality of the classification, and the best results were found in small cubes (10 to 35 images) and with low cloud cover (0 to ~ 6%). The Minimum Distance algorithm presented the lowest coefficient of variation, showing a lower sensitivity to the presence of clouds.

Resumo Este trabalho teve como objetivo analisar classificações de séries temporais MODIS-NDVI, com 3 diferentes algoritmos, buscando identificar a quantidade ideal de imagens para estudos em ambientes com altas taxas de nebulosidade. O recorte espacial utilizado para o estudo foi o município de Capixaba, localizado no Estado do Acre na região amazônica. Para cada imagem NDVI, construiu-se uma máscara de nuvem. Esta máscara permitiu organizar o cubo temporal por quantitativo de cobertura de nuvem. Assim, testou-se o impacto da eliminação das imagens com alta nebulosidade para a classificação da série. A cada corte, o cubo temporal foi refeito, avaliando resultados para um novo conjunto de bandas. Para a análise de acurácia, adotou-se o coeficiente Kappa. Ao todo foram feitas 84 classificações. Foram testados 3 algoritmos de classificação (Mínima Distância, Spectral Angle Maper e Spectral Correlation Mapper) e 4 diferentes interações entre classes e amostras. Observou-se que, ao longo do período analisado, aproximadamente 80% das imagens apresentaram cobertura de nuvens acima de 90%. Os testes mostraram que a retirada das imagens com nuvem, aumentou a qualidade da classificação, sendo que os melhores resultados foram encontrados em cubos pequenos (10 a 35 imagens) e com baixa cobertura de nuvem (0 a ~6 %). O algoritmo Mínima Distância apresentou o menor coeficiente de variação dentre os resultados encontrados, mostrando menor sensibilidade à presença de nuvens.