The northeast region of Brazil (NEB) suffers with the worst drought during 2012-2016 that has greatly affected water availability in general, in particular the hydropower reservoirs. We have analyzed a large dataset of satellite measurements and images to understand the variability of precipitation, land surface temperature (LST) and their association with the Normalized Difference Vegetation Index (NDVI), indicator of water and vegetation stress. The drought conditions during 2012-2016 show association of poor rainfall in the year 2012, an increase of LST 7ºC above the average, reduction in NDVI upto 30% and a sharp decrease upto 28% in Relative Humidity (RH). The largest reservoir of the NEB, Sobradinho, shows decline in surface water upto about 50% which is clearly evident from the Normalized Difference Water Index (NDWI) for the period 2015-2016 compared to the year 2011.

Impact craters help scientists to understand the geological history of planetary bodies. The aim of this paper is to improve the existing methodology for impact craters detection in images of planetary surfaces using a new approach based on morphological image processing (MIP). The improved methodology uses MIP followed by template matching based on fast Fourier transform (FFT). In this phase, a probability volume is generated based on the correlation between templates and images. The analysis of this probability volume allows the detection of different size of impact craters. We have applied the improved methodology to detect impact craters in a set of images from Thermal Emission Imaging System onboard the 2001 Mars Odyssey Space probe. The improved methodology has achieved a crater detection rate of 92.23% which can be considered robust, since results were obtained based on geomorphological features, different illumination conditions and low spatial resolution. The achieved results proved the viability of using MIP and template matching by FFT, to detect impact craters from planetary surfaces.

ABSTRACT:While high-resolution remote sensing images have increased application possibilities for urban studies, the large number of shadow areas has created challenges to processing and extracting information from these images. Furthermore, shadows can reduce or omit information from the surface as well as degrading the visual quality of images. The pixels of shadows tend to have lower radiance response within the spectrum and are often confused with low reflectance targets. In this work, a shadow detection method was proposed using a morphological operator for dark pattern identification combined with spectral indices. The aims are to avoid misclassification in shadow identification through properties provided by them on color models and, therefore, to improve shadow detection accuracy. Experimental results were tested applying the panchromatic and multispectral band of WorldView-2 image from São Paulo city in Brazil, which is a complex urban environment composed by high objects like tall buildings causing large shadow areas. Black top-hat with area injunction was applied in PAN image and shadow identification performance has improved with index as Normalized Difference Vegetation Index (NDVI) and Normalized Saturation-Value Difference Index (NSDVI) ratio from HSV color space obtained from pansharpened multispectral WV-2 image. An increase in distinction between shadows and others objects was observed, which was tested for the completeness, correctness and quality measures computed, using a created manual shadow mask as reference. Therefore, this method can contribute to overcoming difficulties faced by other techniques that need shadow detection as a first necessary preprocessing step, like object recognition, image matching, 3D reconstruction, etc.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

On September 28, 2016, an intense dust storm impacted the city of Bakersfield and surrounding areas in California. The dust event coincided with smoke aerosols from the forest fire located in the northwest of Bakersfield. In California, forest fires are frequent during summer and fall seasons. The forest fire smoke plumes were subjected to large dispersion and appeared widespread. In this study, we present a detailed analysis of satellite and surface observations indicating pronounced changes in air quality, aerosol characteristics, trace gases, along the prevailing meteorological conditions over Bakersfield associated with the dust event and its interactions with the forest fire smoke. Back trajectory simulations clearly show inflow of the dust airmass from the Mojave Desert located east of Bakersfield, in contrast to the forward trajectories originating from the forest fire event located in the northwestern region, suggesting possibility of mixing of smoke and dust in the Bakersfield area. In addition, low and strong wavelength dependence of aerosol single scattering albedo also supports the observations of strong aerosol mixing of dust and smoke.

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods.

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