MODerate resolution Imaging Spectroradiometer (MODIS) data are largely used in multitemporal analysis of various Earth-related phenomena, such as vegetation phenology, land use/land cover change, deforestation monitoring, and time series analysis. In general, the MODIS products used to undertake multitemporal analysis are composite mosaics of the best pixels over a certain period of time. However, it is common to find bad pixels in the composition that affect the time series analysis. We present a filtering methodology that considers the pixel position (location in space) and time (position in the temporal data series) to define a new value for the bad pixel. This methodology, called Window Regression (WR), estimates the value of the point of interest, based on the regression analysis of the data selected by a spatial-temporal window. The spatial window is represented by eight pixels neighboring the pixel under evaluation, and the temporal window selects a set of dates close to the date of interest (either earlier or later). Intensities of noises were simulated over time and space, using the MOD13Q1 product. The method presented and other techniques (4253H twice, Mean Value Iteration (MVI) and Savitzky–Golay) were evaluated using the Mean Absolute Percentage Error (MAPE) and Akaike Information Criteria (AIC). The tests revealed the consistently superior performance of the Window Regression approach to estimate new Normalized Difference Vegetation Index (NDVI) values irrespective of the intensity of the noise simulated.

The last few decades have been marked by important changes in the Brazilian agriculture, especially with respect to crop-management practices. This study aimed to analyze the dynamics of cropping systems in a typical agricultural river basin in the state of Mato Grosso. Landsat satellite images and time series of Moderate Resolution Imaging Spectroradiometer vegetation index profiles were analyzed from 2000 to 2010. First, we assessed the horizontal expansion that occurred in the agricultural areas. Subsequently, using the product MOD13Q1, some metrics were established to identify the vertical intensification of soil use (single- or double-cropping systems). Results showed stagnation in the expansion of new deforested areas for agriculture in the 2003/2004 growing season, with simultaneous vertical intensification of agriculture. The adoption of the double-cropping system (e.g., soybean/corn and soybean/cotton) expanded by 266% during the studied period and reached 56% of the croplands in the 2009/2010 growing season.

Currently, during the operation in shallow waters, scanning systems, such as multibeam systems, are capable of collecting thousands of points in a short time, promoting a greater coverage of the submerged bottom, with consequent increase in the detection capacity of objects. Although there has been an improvement in the accuracy of the depths collected, traditional processing, that is, manual, is still required. However, mainly due to the increased mass of data collected, manual processing has become extremely time-consuming and subjective, especially in the detection and elimination of spikes. Several algorithms are used to perform this task, but most of them are based on statistical assumptions hardly met and/or verified, such as spatial independence and normality. In this sense, the goal of this study is to present the SODA (Spatial Outlier Detection Algorithm) methodology, a new method for detection of spikes designed to treat bathymetric data collected through swath bathymetry systems. From computational simulation, promising results were obtained. SODA, in some cases, was capable to identify even 90% of spikes inserted on simulation, showing that the methodology is efficient and substantial to the bathymetric data treatment.

The amount of data produced in an echosounder has increased dramatically with scanning systems such as multibeam echo sounders and interferometric sonars, providing a considerable improvement in the submerged terrain representation, especially when it comes to detecting hazardous objects to the navigator. However, the available processing algorithms did not come along with this evolution; manual processing or, at least, constant intervention is usually necessary, which makes this task arduous with a high degree of subjectivity. In addition, statistical inconsistencies do not appear to be uncommon in most of the algorithms and filters available. Thus, SODA (Spatial Outliers Detection Algorithm) was recently presented, being a methodology directed, at first, to echosounder data treatment. Considering this, this article aims to evaluate the SODA efficiency for real data treatment from a multibeam sonar. SODA, in some cases, was capable to identify up to 74% of spikes with the δ-Method, which means that this technique detected 28 out of the 38 confirmed spikes in the data set, reassuming the methodology strength regarding the search for spikes in echosounder data.

Normalized Difference Vegetation Index is a vegetation index widely applied in research. However, noise induced by cloud contamination and atmospheric variability affect the data quality. We propose the reconstruction of time series of MODIS NDVI data based on a quality assessment of the science data sets and on a spatial-temporal analysis of the low quality pixels. The MOD13Q1 product was analyzed over a period of one year. The first task was to identify the pixels with the lowest guarantee of quality. The next step was to recalculate the NDVI values based on spatial and temporal correlations. The results indicate that the spatial-temporal information, combined with pixel quality assessment, is a promising method for reconstructing high-quality MODIS NDVI time series.

A presente pesquisa teve por objetivo avaliar a potencialidade de dados multitemporais Landsat para classificação de cana-de-açúcar e de soja, conjuntamente, quando realizada via Análise de Imagens Orientada a Objetos (OBIA/ Random Forest). Foi utilizado um segmentador multi-resolução (SM) para gerar os polígonos (objetos). Um conjunto de 500 segmentações foi criado pela variação dos parâmetros Fe (fator de escala), Fm (forma) e Cp (compacidade), e avaliado pelo índice de Avaliação da Segmentação (IAVAS). Da segmentação que obteve menor IAVAS, foram extraídos os atributos espectrais das médias e desvios-padrão das bandas TM/Landsat-5 [setembro (S) e outubro (O) do ano 2000] e ETM+/Landsat-7 [fevereiro (F) e março (M) do ano 2001] dos objetos, e seus NDVIs. Estes atributos foram inseridos no algoritmo Random Forest (RF) e as exatidões das classificações foram testadas quanto ao uso dos seguintes conjuntos de datas: SOFM; SFM; OFM; SOF; FM; OF; SF; e F. O IAVAS definiu Fe (35), Fm (30) e Cp (50) como melhores parâmetros de segmentação. As melhores exatidões de classificação Random Forest situaram-se em torno de 86%. Duas datas produziram melhor resultado que apenas uma, entretanto, o uso de mais de duas não produziu melhora significativa na exatidão final da classificação.