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The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874. [ABSTRACT FROM AUTHOR]

On average, 70% of the world's freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for this irrigation. The proposed approach involves grouping the detected flooded areas using k-means clustering with the electricity customers' geographical coordinates, provided by the Power Distribution Company. The methodology was evaluated in a dataset of satellite images from southern Brazil, and the results demonstrate the potential of using U-Net models to identify rice fields. Furthermore, comparing the estimated electrical energy consumption required for irrigation in each cluster with the billed energy values provides valuable insights into the sustainable management of rice production systems and the electricity grid, helping to identify non-technical losses and improve irrigation efficiency. [ABSTRACT FROM AUTHOR]

The porous systems of carbonate reservoirs show a great diversity of pore types and high heterogeneity that can reflect the interplay of several elements including physical, chemical, and biological depositional processes, intense diagenesis, fracturing, and hydrothermal alterations. All these factors combine in the Aptian Barra Velha Formation carbonates from Santos Basin, SE offshore Brazil, which comprises the most prolific hydrocarbon reservoirs from Brazil. The geological complexity of this formation has represented a major challenge for oil forecast, recovery and production. The absence of outcrops, proper analogs and the limited access to core samples of this deep formation adds to the challenges involved in the geological and petrophysical characterization of such reservoirs. In this context, X-ray computed tomography (CT) can become a key technique applied for non-destructive quantitative analyses and 3D visualization of different rock structures. The present article reports the results obtained from X-ray CT images applying digital rock analysis to characterize the complex pore networks and mineral phases of the Barra Velha Formation reservoir. Multi-resolution CT-scan petrophysics was combined with detailed petrography and petrology, aiming to understand the geological factors controlling porosity distribution. Mineral segmentation was split into three mineral phases: carbonate, undefined (microporosity and diagenetic material) and dense minerals. The high percentage of undefined phase was commonly associated with intense dolomitic cementation and related to low permeabilities. Shrubstone and Intraclastic rudstone lithologies showed the best reservoir properties, mainly when the dominant growth framework and interparticle porosity, was associated with dissolution, enhancing the pore connectivity. Pore network analysis revealed the tendency of larger pores to be associated with higher coordination numbers, presenting greater pore connectivity. This study aims to contribute to future work on Brazilian pre-salt carbonates' pore type and multi-scale characterization, providing new insights into the characteristics and factors related to the samples' connectivity. Article highlights: The use of X-ray CT image processing approach allowed non-destructively to analyze rock samples, and to obtain detailed information about their internal structure. Image analysis using X-ray CT has enabled a more detailed characterization of lacustrine pre-salt carbonate rock samples by segmenting the mineral phases and calculating porosity. The DRA approach was proposed to obtain a digital rock model based on different pore-scale information, including medial CT and high-resolution CT scans. [ABSTRACT FROM AUTHOR]

The differentiation between the main crop and weeds is an important step for selective spraying systems to avoid agrochemical waste and reduce economic and environmental impacts. In this sense, this study aims to classify and map the area occupied by weeds, determine the percentage of area occupied, and indicate treatment control strategies to be adopted in the field. This study was conducted by using a yellow Bourbon cultivar (IAC J10) with 1 year of implementation on a commercial coffee plantation located at Minas Gerais, Brazil. The aerial images were obtained by a remotely piloted aircraft (RPA) with an embedded multispectral sensor. Image processing was performed using PIX4D, and data analysis was performed using R and QGIS. The random forest (RF) and support vector machine (SVM) algorithms were used for the classification of the regions of interest: coffee, weed, brachiaria, and exposed soil. The differentiation between the study classes was possible due to the spectral differences between the targets, with better classification performance using the RF algorithm. The savings gained by only treating areas with the presence of weeds compared with treating the total study area are approximately 92.68%. [ABSTRACT FROM AUTHOR]

We applied a robust framework for agricultural drought identification in the State of Espírito Santo, Brazil, by employing the Vegetation Condition Index (VCI) based on data obtained through the Enhanced Vegetation Index (EVI). By doing so, we analyzed the interrelationships between the VCI and anomalies in the Land Surface Temperature (LST), along with connections between the VCI and data considering water deficits in vulnerable areas. When it came to image processing, we focused on the use of analytics and GIS algorithms, while the Scott–Knott method elucidated the statistical analyses. Consequently, we identified drought areas followed by periods susceptible to their occurrence, indicating 2016 as the driest year. The North macroregion presented the lowest average values regarding VCI values in the most vulnerable periods, followed by the Central one. We also call attention to the highest LST averages observed in 2015 and 2016, as strong El Niño events marked the same timeframe periods. The methodological approach was efficient for the identification, analysis, and characterization of agricultural drought occurrences, enabling mitigation actions, as well as the management of the exploitation and protection of water resources. Moreover, further research should be conducted by incorporating other indices to enhance the understanding of agricultural drought and its effects on vegetation. [ABSTRACT FROM AUTHOR]

The aimed of the study was to identify the characteristics of fish farms and the progress of the enterprises that practice the activity during the time span of 13 years (2008 to 2021) in Alto Paraíso municipality (RO, Western Amazon), aiming to know the general aspects and verify the number of enterprises, spatialization in the municipality and size. The study was carried out using remote sensing tools, analyzing the occupation in the municipality with fish farming, through the processing of images from the SPOT-5 satellite (for 2008) and CBERS-4A satellite (for 2021), made available by SEDAM and INPE Agencies, respectively. Image processing was performed using the GIS QGis 3.20.2 (Odense) and allowed the creation of image maps of fish farming in the study area. In addition, the vectorization of fish farms was carried out, where the water surface area was analyzed for the composition of heat maps that allowed a geographic analysis of the behavior of patterns. The results obtained shown that in the municipality there are small, medium and large fish farms, the evolution of fish farms in the municipality was in the order of 200% for the number of enterprises and 874% for the amount of water surface. [ABSTRACT FROM AUTHOR]

Coriander seeds have high socio-economic value in several regions of Brazil, especially in the North and Northeast. Seed maturation determined by color influences the seed quality. With this, digital image processing has become an important tool for separating seeds by color since this classification is usually performed by humans and is highly susceptible to error. The study established parameters for separating coriander seeds by red green and blue (RGB) image analysis, seeking a better selection of coriander seeds according to their color, and evaluating the physiological quality by the germination test. Separation was carried out from two coriander seed lots to obtain samples of 20 g each in three shades: yellowish, gray, and mixed. Images were acquired by the HP C4480 Scanner and processed in the MATLAB software; then, a histogram was constructed for each color analyzed in each sample by the RGB system. ANOVA tested the averages of the scales to ratify the difference in the components' distributions. The germination test was performed to confirm the results of seed separation using image analysis. The best selection of coriander seeds was achieved by the blue scale, and the germination test indicated that yellow seeds have a higher physiological quality than brownish/greyish seeds. [ABSTRACT FROM AUTHOR]

Copyright of Revista Caatinga is the property of Revista Caatinga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas' rotation, especially the eucalypt's short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations' characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales. [ABSTRACT FROM AUTHOR]

Introduction: Image quality and acquisition protocol adherence assessment is a neglected area in teledermatology. We examine if it is feasible to use deep learning methods to automate the assessment of the adherence of examinations to image acquisition protocols. In this study, we focused on the quality criteria of two image acquisition protocols: (1) approximation image and (2) panoramic image, as these are present in all teledermatology examination protocols currently used by the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC). Methods: We use a data set of 36,102 teledermatological examinations performed at the STT/SC during 2021. As our validation process, we adopted standard machine learning metrics and an inter-rater agreement (IRA) study with 11 dermatologists. For the approximation image protocol, we used the Mask-Region based Convolutional Neural Network (RCNN) Object Detection Deep Learning (DL) architecture to identify the presence of a lesion identification tag and a ruler used to provide a frame reference of the lesion. For the panoramic image protocol, we used DensePose, a pose estimation DL, architecture to assess the presence of a whole patient body and its orientation. A combination of the two approaches was additionally validated through an IRA study between specialists. Results: Mask-RCNN achieved a score of 96% mean average precision (mAP), while DensePose presented 75% mAP. IRA achieved a level of agreement of 96.68% with the Krippendorff alpha score. Conclusions: Our results show the feasibility of using deep learning to automate the image quality and protocol adherence assessment in teledermatology, before the specialist's manual analysis of the examination.

The identification of micropore systems in carbonate rocks is an important task of image processing because of the high impact these systems cause on fluid flow. Currently, one of the main tools used to characterize rock samples is computed tomography (CT). Such micro information poses the challenge associated with the limitation of the CT's resolution. Therefore, we propose an alternative method of inserting the micropore features of μ CT to lower resolution images, but with higher coverage. We can perform this by a novel application of Style Transfer that can insert the heterogeneity pattern of high-resolution (HR) images (CT of 7 and 40 μm resolution) into low-resolution (LR) images (CT of 90 μm resolution), downscaling the image through a super-resolution method. This technique uses the power of VGG19, a convolutional neural network that won the ImageNet Large-Scale Visual Recognition Challenge in 2014, as a texture extractor. We applied this novel technique to condensed shell rocks, called coquinas, from the Itapema Formation, Santos Basin offshore Brazil. The porosity of the LR image, with initial average value of 11%, resulted in an average porosity of 12% (40 μm res.) and 13% (7 μm res.) after downscaling. This is closer to the porosity range of the coquina (13% to 32%, with a mean of 21%) and an increase in the porosity of 6% and 19% in average, respectively. Despite this, the connectivity of the original LR CT was of 3% on average and, in the simulated HR CT, the connectivity was of 5% (40 μm res.) and 6% (7 μm res.). In addition, in such examples, this method inserted connectivity in directions that were null in low-resolution images before the style transfer. Hence, the results demonstrated that the Style Transfer offers an alternative for downscaling CT images by inserting the texture from high-resolution images. [ABSTRACT FROM AUTHOR]

Since the approval of the Brazilian Federal Technical Assistance Law, whose objective is to guarantee adequate technical assistance to the residents of poor areas to improve their homes, several municipalities have implemented programs, not always successful, to improve housing in slums. Aiming to subsidize these programs implementation workflow, the present paper analyses use of drones as a tool to gather information about the physical characteristics slum households in Salvador de Bahia city, Brazil. As an experiment we flight over a single census sector within the Alto das Pombas slum, and after image processing we extracted and organized the collected data, extracting the possible information that could be applied to identify and quantify the most precarious houses that could be prioritized from a health improvement perspective. We conclude that many of the necessary data needed, on the urbanistic scale in Brazilian slums, can benefit from drone photogrammetry at low cost and fast execution. [ABSTRACT FROM AUTHOR]

Gully erosion represents the most severe soil loss, with far-reaching consequences beyond the immediate site. Assessing the stability of gullies is particularly challenging in tropical regions with sandy soils and limited accurate data. Nonetheless, initiating gully inventories is a crucial first step in guiding public policies and conservation projects. In this study, we focus on the Upper Taquari River Basin (UTRB) situated on the fringes of the Brazilian Pantanal, where extensive erosion occurs in the upper regions and flooding occurs in the plains. We present the first qualitative and quantitative analysis of gullies in this region. Considering the historical context of the UTRB, it has long suffered from land mismanagement, particularly in livestock activities. Our objective was to evaluate the correspondence between gullies and land use classes in the MapBiomas Project, Brazil's most reliable non-governmental land use map, and the Rural Environmental Registry (CAR), the official information shared between landowners and public authorities. Thirteen remote-sensed indicators encompassing vegetation, water, soil, and terrain indices were assessed for 2022. Gullies were digitized through visual interpretation of a high-resolution Maxar Vivid Basic 2017 image. The classification was performed using the Random Forest (RF) algorithm, wherein pixels were classified into three classes: active, intermediate, and stable, based on the degree of vegetation cover and bare soil. The agreement of the gullies with the features of MapBiomas and CAR was also examined. The results revealed an overall accuracy of 96% and a Kappa index of 93% for the pixel classification. In the study area, 2960 gullies were digitized, with 60% classified as active features and only 2% as stable. Furthermore, the MapBiomas algorithm misclassified many pixels with active gullies as pasture. Conversely, the CAR data failed to identify gullies as areas demanding restoration. To address these issues, we recommend revising both land use maps to accurately represent the presence of erosions and improve decision-making that favors efficient conservation efforts of the region. As a further result of our actions, the method described here may prove valuable in formulating restoration plans for other tropical savanna regions. [Display omitted] • Visual interpretation and machine learning provide accurate gully mapping. • The percentage of vegetation cover is a viable metric for evaluating pixels. • Active pixels were misinterpreted in CAR and MapBiomas maps. • Include erosion class into land use platforms would help guide for prompt recovery. • Our method serves as previous inventory at severely degraded basins at low cost. [ABSTRACT FROM AUTHOR]

Background: The San Francisco Valley region from Brazil is known worldwide for its fruit production and exportation, especially grapes and wines. The grapes have high quality not only due to the excellent morphological characteristics, but also to the pleasant taste of their fruits. Such features are obtained because of the climatic conditions present in the region. In addition to the favorable climate for grape cultivation, harvesting at the right time interferes with fruit properties. Results: This work aims to define grape maturation stage of Syrah and Cabernet Sauvignon cultivars with the aid of deep‐learning models. The idea of working with these algorithms came from the fact that the techniques commonly used to find the ideal harvesting point are invasive, expensive, and take a long time to get their results. In this work, convolutional neural networks were used in an image classification system, in which grape images were acquired, preprocessed, and classified based on their maturation stage. Images were acquired with varying illuminants that were considered as parameters of the classification models, as well as the different post‐harvesting weeks. The best models achieved maturation classification accuracy of 93.41% and 72.66% for Syrah and Cabernet Sauvignon respectively. Conclusions: It was possible to correctly classify wine grapes using computational intelligent algorithms with high accuracy, regarding the harvesting time, corroborating chemometric results. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Leaf area is an important growth variable in agricultural crops and the leaf is the main variable of interest in the tobacco industry. So, the aim of this scientific research was to estimate the Burley tobacco leaf area by linear dimensions of the leaves and to determine which mathematical model is more adequate for this purpose. Two experiments were carried out with Burley tobacco, cultivar DBH 2252, in 2016/2017 and 2018/2019 agricultural years, respectively, in the municipalities of Itaqui and Vanini - RS - Brazil. In 600 leaves were measured length (L), width (W), length×width product (LW), length/width ratio (L/W) and determined the real leaf area (LA). Four hundred and fifty leaves were separated to generate models of the leaf area as a function of linear dimension and the other 150 leaves were used for model's validation. The power model LA = 0.5037LW1.04435 (R² = 0.9960) is the most adequate for Burley tobacco 'DBH 2252' leaf area estimation. Alternatively, the models LA=2.0369W1.8619 (R²=0.9796) and LA=0.1222L2.2771 (R²=0.9738) based on width and length, respectively, can be used when only one leaf dimension is measured. [ABSTRACT FROM AUTHOR]

The advance of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has enabled the collection of data for reliably extracting vegetation indices and biophysical parameters derived from the Structure from Motion (SfM) algorithm. This work aimed to evaluate the accuracy of the photogrammetry technique using an SfM point cloud for the estimation of the height (h) and crown diameter (d) of coffee trees from aerial images obtained by UAV with an RGB (Red, Green, Blue) camera and compared the results with data measured in situ for 12 months. The experiment was carried out in a coffee plantation, Lavras, Minas Gerais, Brazil. A rotary-wing UAV was used in autonomous flight mode and coupled to a conventional camera, flying at a height of 30 m with an image overlap of 80% and a speed of 3 m/s. The images were processed using PhotoScan software, and the analyses were performed in Qgis. A correlation of 87% was obtained between the h values in the field and h values obtained by the UAV, and there was a 95% correlation between the d values obtained in the field and the values obtained by the UAV. It was possible to obtain significant estimates of the attributes, such as the h and d of coffee trees, using UAV–SfM images acquired with an RGB digital camera. [ABSTRACT FROM AUTHOR]

Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform. [ABSTRACT FROM AUTHOR]

The objective of the study is to analyze the development of research in Education in Brazil. What theoretical / methodological problems limit the work of researchers?. This work is the result of reflections and studies carried out in the Graduate Program in Education at the Federal University of Triângulo Mineiro (UFTM). It seeks to characterize, by means of bibliographic survey, the current scenario of qualitative research, to present the contemporary educational context in the country, as well as its obstacles. The transition from spontaneous to rational knowledge is explained, showing the differences between scientific method in Natural and Human Sciences. A historical picture of the maturation process of research in education is outlined, the methodological issues, challenges and relevance of research for the advancement of knowledge in the area. The idea is defended that adequate qualitative research can point out ways to improve Brazilian education. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The knowledge about fire danger and propagation is essential for environmental monitoring. This enables the determination of areas susceptible of fire occurrence and can allow the establishment of fire prevention methods. The aim of this study was to set up a methodology to determine the likelihood of fire occurrence with the aid of an index called Fire Danger Index (IPI). The study area included a power line in the State of Tocantins, Brazil. The methodology included the use of dynamic data such as NDVI (Normalized Difference Vegetation Index) obtained from TERRA MODIS images (MOD13 product) and meteorological data such as temperature, rain and air humidity gathered from automatic weather stations. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The world production of meats is mainly concentrated in the countries with a hot climate. Among these countries, Brazil, considered "world's breadbasket", is located in the intertropical zone, with hot climates and presents lower thermal amplitude. Brazilian poultry is a leader in the production and export of broilers. Brazil has the second largest cattle herd in the world, has the largest commercial herd besides being the largest exporter of beef. Brazilian swine breeding, among the most advanced production chains in the world, occupy the position of fourth largest producer of pork in the world. Projections for Brazil's meat production sector indicate strong growth in the coming years, with countries in hot climates that will continue to sustain future growth in world meat production, ensuring food security in many countries. One of the factors responsible for the success or failure of animal production is the environment, defined by the sum of all physical and biological factors that affect animals. In these regions of hot climates, climatic factors are among the main limiting factors to the development of animal production, which may compromise animal welfare and productivity indices. Behavioral information can aid in the analysis of problems arising from environmental conditions unfavorable to animals, helping both decision making and the use of different environmental conditioning systems. The introduction of technification in the animal behavior evaluation processes, through the use of video cameras and image processing programs, allowed a better interpretation of the behavioral responses, quickly, accurately and non-invasively, gradually being used with greater frequency in the animal production sectors. The study of animal behavior assumes an important role in animal production, since, to rationalize the breeding methods, management, feeding and facilities techniques have been developed that interfere with animal behavior. In this context, the use of digital image analysis techniques, where the animals themselves are used as biosensors in response to environmental conditions, contributes to the analysis of animal behavior, and consequently also to the assessment of the internal environment of the production in hot weather. [ABSTRACT FROM AUTHOR]

Urban forests offer city residents a better quality of life. They also act as barrier and filter to pollutants resulting from human activities. Mapping the distribution of forests in urban areas is therefore important for good urban planning. Currently, high resolution images are considered useful tools to quantify forested urban areas for large scale urban development. The objective of this work was to evaluate multi-temporal urban forest area changes over different land-use classes, using available high-resolution images obtained from different satellites. The municipality of Araucaria (Paraná State) was chosen as study area because it is a large industrial zone of southern Brazil. The effects on the forests within this municipality caused by the increase in human activities between 2005 and 2012 were determined using high resolution images (5 m). We recorded a reduction of 22.8% in the forests surrounding urban areas of the municipality, as a result of deforestation of 791 ha and plantation of only 251 ha. The utility, commercial and residential zones which are more crowded the areas of highest population density were those which showed the greatest loss of tree cover. Object-based classification accuracy using images from different satellites was sufficient to quantify the evolution of tree-cover over the studied period. [ABSTRACT FROM AUTHOR]

The expansion of the productive activities in plateau sections of the Upper Paraguai River Basin (BAP) can potentially impact the abiotic and biotic systems of the Pantanal. The objective of this study is to investigate the dynamics of sugarcane plantation in the Bugres river basin, Mato Grosso state. The methodological procedures followed are based on the generation of data from satellite images and processing in a GIS. In 1990 sugarcane occupied, in the area under study, 137,44 Km2 increasing to 981.11 Km2 with an expansion of 613.84%, preferentially in North-Northeast direction. Sugarcane occupied areas of Sub-montane Semi-Deciduous Seasonal Forests, Alluvial Forests and Livestock farms, not advancing over Urban Areas and Areas of Permanent Protection, the so-called “"APP’'s"”. The declivity is an impeditive factor to increase sugar cane plantations in BAP, wherein it was established in sectors of flat to light undulated relief. As far as soils are concerned, plantations predominate on Oxisols. It was verified that the undulated relief is correlated with the proximal variables: roads, drainage and Indian lands. It was concluded that the good physical soil conditions and flat to light undulated terrain, favored the sugarcane plantation which, under such conditions, presents no restrictions to mechanization. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze the spatial and temporal distribution of burned areas in Rondônia State, Brazil during the years 2000 to 2011 and evaluate the burned area maps. A Linear Spectral Mixture Model (LSMM) was applied to MODIS surface reflectance images to originate the burned areas maps, which were validated with TM/Landsat 5 and ETM+/Landsat 7 images and field data acquired in August 2013. The validation presented a correlation ranging from 67% to 96% with an average value of 86%. The lower correlation values are related to the distinct spatial resolutions of the MODIS and TM/ETM+ sensors because small burn scars are not detected in MODIS images and higher spatial correlations are related to the presence of large fires, which are better identified in MODIS, increasing the accuracy of the mapping methodology. In addition, the 12-year burned area maps of Rondônia indicate that fires, as a general pattern, occur in areas that have already been converted to some land use, such as vegetal extraction, large animal livestock areas or diversified permanent crops. Furthermore, during the analyzed period, land use conversion associated with climatic events significantly influenced the occurrence of fire in Rondônia and amplified its impacts. [ABSTRACT FROM AUTHOR]

The optimization of gait performance is an important goal in the rehabilitation of children with cerebral palsy (CP) who present a prognosis associated with locomotion. Gait analysis using videos captured by digital cameras requires validation. Objective: To evaluate the validity of a method that involves the analysis of videos captured using a digital camera for quantifying the temporal parameters of gait in toddlers with normal motor development and children with CP. Method: Eleven toddlers with normal motor development and eight children with spastic hemiplegia who were able to walk without assistive devices were asked to walk through a space contained in the visual field of two instruments: a digital camera and a three-dimensional motion analysis system, Qualisys Pro-Reflex. The duration of the stance and swing phases of gait and of the entire gait cycle were calculated by analyzing videos captured by a digital camera and compared to those obtained by Qualisys Pro-Reflex, which is considered a highly accurate system. Results: The Intraclass Correlation Coefficient (ICC) demonstrated excellent agreement (ICC>0.90) between the two procedures for all measurements, except for the swing phase of the normal toddlers (ICC=0.35). The standard error of measurement was less than 0.02 seconds for all measures. Conclusions: The results reveal similarities between the two instruments, suggesting that digital cameras can be valid instruments for quantifying two temporal parameters of gait. This congruence is of clinical and scientific relevance and validates the use of digital cameras as a resource for helping the assessment and documentation of the therapeutic effects of interventions targeted at the gait of children with CP. [ABSTRACT FROM AUTHOR]

Abstract: Estimation of large area forest attributes, such as area of forest cover, from remote sensing-based maps is challenging because of image processing, logistical, and data acquisition constraints. In addition, techniques for estimating and compensating for misclassification and estimating uncertainty are often unfamiliar. Forest area for the state of Santa Catarina in southern Brazil was estimated from each of four satellite image-based land cover maps, and an independent estimate was obtained using observations of forest/non-forest for more than 1000 points assessed as part of the Santa Catarina Forest and Floristic Inventory. The latter data were also used as an accuracy assessment sample for evaluating the four maps. The map analyses consisted of identifying classification errors, constructing error matrices, calculating associated accuracy measures, estimating bias, and constructing 95% confidence intervals for proportion forest estimates using a model-assisted regression estimator. Overall accuracies for the maps ranged from 0.876 to 0.929. The standard errors of the estimates were all smaller than the standard error of the simple random sampling estimate by factors ranging from approximately1.23 to approximately 1.69. The model-assisted regression estimator lends itself to easy implementation for adjusting for estimated classification bias and for constructing confidence intervals. [Copyright &y& Elsevier]

The Brazilian Amazonian rain forests are under imminent threat of serious degradation and ultimately deforestation. Human activities such as selective logging are an important cause. Selectively logged locations are difficult to detect from medium-resolution Landsat images, due to their relatively small sizes and subtle spatial patterns. Spectral linear unmixing provides an effective tool for the purpose. The orientation of geometrically corrected images, however, artificially introduces zero-reflectance background pixels. These change the variance–covariance structure of the image bands and hinder the identification of pure endmembers. In this study, we compare image cropping and image rotation as two alternative approaches. Selectively logged forests were detected in northern Rondônia state, north-western Mato Grosso state and south-eastern Amazonas state in Brazil by applying spectral unmixing. The study shows that image rotation is a better approach as it preserves the image extent and thus provides information on forest degradation over a wider region. Spatial statistical analysis of the detected locations shows strong clustering within the study area. We conclude that the endmembers used in this study represent basic components of a degraded forest environment. As spectral unmixing of remote-sensing images avoids collection of field data, it may broadly be applied towards other Amazonian regions as well. [ABSTRACT FROM AUTHOR]

Abstract: The large incidence of lung cancer in Brazil and around the world, in addition to its difficult diagnosis, especially in the initial stages, has been driving efforts to develop tools that support image-based diagnosis. The main objective is to avoid invasive procedures, which usually pose risks to patients. This work uses Getis spatial autocorrelation statistics, Getis∗, plus its accumulated forms to verify patterns occurring in geographic areas, aiming to indicate the nature of the lung nodule (benign or malignant). Nodule analysis is performed on its volume in a directional way, checking whether there are distances inside the nodule with large intensity variability of the voxels, for malignant and benign nodules. The classification is done by selecting the best four features from the 2400 generated features, for each of the Getis estimates. The Lung Image Database Consortium (LIDC) is used to verify the efficacy of the measures in the diagnosis. Results have shown that all of the Getis estimates succeeded in the discrimination of nodules in LIDC, with accuracy higher than 80% and confirmed by three different classifiers. [Copyright &y& Elsevier]

Copyright of Estúdio (1647-6158) is the property of Revista Estudio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Estúdio (1647-6158) is the property of Revista Estudio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Three-dimensional (3D) digital preservation aims at generating 3D models of objects that have cultural or scientific value. It allows realistic visualization of objects through virtual museums or scientific applications, and the restoration of the preserved object in case of natural wear or accidents. This work contributes to this research area by presenting a method to improve color texture quality of 3D models obtained from color and depth images of a laser scanner. Although this device offers precise depth information, the resulting color information is still poor and limits the generation of realistic textures. Our approach is to capture photographs of the object with a high-resolution digital camera and use them to generate a new color texture for the 3D model. Our work proposes a practical technique, easy to replicate, to generate high quality textures for 3D models from photographs. The method is composed by three main steps: (1) calibration of the image acquisition devices; (2) data acquisition; and (3) texture generation. In this paper, we detail our color texture generation method and apply it on the digital preservation of many artworks made by native Brazilians (indians) from the Wauja and Karaja communities. These indigenous communities are acknowledged as great ceramic artists, each bearing their own main themes, using a very rich symbolism in their paintings. Their artworks represent important aspects of the native South American culture and their digital preservation is motivated by three main reasons: (1) their fragility; (2) the paintings loose their original appearance with time; and (3) the possibility of extinction of these communities. We present several results of preserved artworks with enhanced quality realistic texture maps. Also, we present a methodology to analyze the quality and accuracy of texture maps. The resulting 3D models can be visualized through a tool we developed to support the virtual exhibit of 3D preserved heritage. [Copyright &y& Elsevier]

Abstract: This paper describes an electronic imaging system for producing real time neutron radiography from a low power research reactor, which will allow inspections of samples with high efficiency, in terms of measuring time and result analysis. This system has been implanted because of its potential use in various scientific and industrial areas where neutron radiography with photographic film could not be applied. This real time system is installed in neutron radiography facility of Argonauta nuclear research reactor, at the Instituto de Engenharia Nuclear of the Comissão Nacional de Energia Nuclear, in Brazil. It is adequate to perform real time neutron radiography of static and dynamic events of samples. [Copyright &y& Elsevier]

Several areas of land degradation are present in the northeastern part of Brazil, resulting primarily from old practices of vegetation clearing and burning for subsistence farming and grazing. However, no reliable information is currently available on the extent of these areas and whether they are expanding over time. Taking the Gilbues region as an example, this study shows the potential for obtaining this information using multitemporal dual-season Landsat Thematic Mapper (TM) imagery. We used digital image processing, which involved the enhancement of soil-fraction images by segmentation/region classification and Normalized Difference Index (NDI) techniques. The results show highly degraded soils covering an almost continuous area of approximately 600 km2. The data also show that, over the past two decades, land degradation has been concentrated mainly along and in the heads of drainage systems, a pattern that indicates removal of riparian forests, thereby compounding the serious problem of sediment loading in the streams in the area. We show that satellite remotely sensed data can provide valuable information in support of environmental policies for northeast Brazil. [ABSTRACT FROM AUTHOR]

The purpose of this study was to verify the inter-rater agreement level as a means of obtaining an efficiency measure of a standard mastication evaluation through video recordings. The studied population included oral breathing children and teenagers with maxillary atresia. The chewing aspects studied were mode of chewing and preferential chewing side. A white tag was placed on half the subjects´ chins while the recordings were made. Two expert evaluators analyzed 54 video recordings at regular viewing speed. The lead author analyzed the same video recordings both at reduced speed and at reduced speed linked through graphical computing techniques. The analysis was conducted on chewing cycles with the viewing angle of the frontal plane. Findings indicated that when comparing the data for the three manners of watching the video recordings, the agreement level was higher for videos with the tag on the chin watched at reduced speed. It was also determined that alternating and bilateral mastication modes were prevalent (64.7%) in this sample. [ABSTRACT FROM AUTHOR]

Significance: Rehabilitation through facial prostheses' main goal is to aid individual's social reintegration as well as improving their quality of life. However, this treatment is not yet widely available in Brazil due to the lack of specialized clinics and the cost associated with the high number of necessary medical appointments until the final result. One of the steps in the process consists of measuring skin color, which is observer-dependent and may suffer from the effect of metamerism. Aim: The methodology of our work aims to obtain a standard between different devices and greater fidelity to the color seen in person in order to reduce face-to-face iterations, reduce costs, and ensure better final results. Approach: A physical device and a computer program were improved from previous projects. The changes included implementing the Thin-Plate Spline 3D algorithm for color calibration, in addition to an optional non-uniform illumination correction in the process. We also aim to improve the project's accessibility using a colorimeter. The methodology and the algorithms were both compared to readings from direct skin measurements as well as color references. Results: After processing, the ΔEab* metric between images from the same segments is taken with different cameras and conditions of illumination decreased from 18.81 ± 4.85 to 4.85 ± 1.72. In addition, when the images were compared to colorimetric readings of the skin, the difference went from 14.93 ± 4.11 to 5.85 ± 1.61. It was also observed that using a less expensive device did not impact the readings. The project is open source and available at Github. Conclusions: The results demonstrate the possibility of applying the methodology to assist in the manufacturing of facial prostheses to decrease the total number of consultations, in addition to providing greater reliability of the final result. [ABSTRACT FROM AUTHOR]

• Study of two iron ore deposits in Brazil using reflectance spectroscopy and Sentinel-2 image processing. • Samples of hematitite, itabirites, quartzites, cangas and breccias were collected for petrographic, geochemical and reflectance spectroscopy analysis. • The classifications of the spectra performed on the studied targets (Caetité and Piatã) allowed to differentiate types of spectra associated with hematite and goethite. Reflectance Spectroscopy showed great potential for the mapping of iron rich domains. • Using Sentinel-2 image processing is a good tool for searching large areas over a high temporal and spatial range. The large iron ore reserves of Brazil are already known, but there are still few studies regarding mineral prospection in smaller deposits and/or areas with lower contents. Exploration techniques that cover large areas at low costs, such as remote sensing, are important for setting targets for mineral exploration. This paper proposes to use of Sentinel-2 images and reflectance spectroscopy to evaluate the use of these techniques for iron prospecting, using two deposits in the state of Bahia, Brazil, as case studies. To validate image processing results, samples were collected in these areas for analysis of optical petrography, x-ray fluorescence and reflectance spectroscopy. Images were processed at a spatial resolution of 20 and 60 m with Mixture Tuned Matched Filtering and Spectral Angle Mapper. Corresponding spectral types were determined based on the analyses of mineralogy variation and composition of the spectral mixture. It was also possible to establish a correlation of spectral types with iron content. The results achieved showed that Sentinel-2 data can be used for spectral mapping of different lithotypes composed of iron oxides, with Reflectance Spectroscopy as an important tool for mapping, and that the use of images in spatial resolution 20 m are sufficient to discriminate different types of ore. Image processing has shown that Sentinel-2 sensor data can support iron ore mineral research and the discovery of new mineralized areas. [ABSTRACT FROM AUTHOR]

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets. [ABSTRACT FROM AUTHOR]

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was "weed control + plant seedlings" (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations. [ABSTRACT FROM AUTHOR]

In Brazil the use of drones has become popular in commercial and scientific sectors. In geosciences, however, this tool is yet little applied, being restricted to the initiative of some research institutes. One example is the brazilian Instituto de Pesquisas Tecnológicas of the State of Sao Paulo (IPT) that has been using small multi-rotor UAVs for mapping and monitoring of geological-geomorphological surface processes and for natural disaster and risk management.Drones are an important tool for monitoring the evolution of geological-geomorphological process, allowing quick response, damage assessment and stability analysis, in places with limited access or that pose danger to the authorities. A working routine was created by the Institute, based on versatile tools, with low cost and easy-to-use. Popular commercial low-cost UAVs were applied, as well as free applications and softwares for flight planning and image processing, using mainly cloud services, allowing agility in data processing and in the obtention of its main products (orthomosaics and tridimensional models).We use the DJI UAVs Phantom 3 and DJI Inspire 1, with conventional RGB cameras. For flight planning and execution, we use DJI Go application. As a protocol, we make a preliminary reconnaissance flight on the field above the area of interest to recognize its main characteristics, especially regarding topographic differences and vegetation heights, anticipating possible collisions and distortions on the products obtained. Images are then processed in a cloud web service (Precision Mapper), through which the orthomosaics and digital elevation models are obtained.This routine allows the analysis and monitoring of geological-geomorphological processes, such as landslides, debris-flows and gullies evolution, allowing estimates of the volume of mobilized material, number of endangered houses and risk analysis associated with anthropic activities, such as mining, pipelines and landfills.Up to now the main results obtained through this routine consist in the monitoring of two major landslides and the evolution of a gully, located in the state of São Paulo. Regarding the landslide cases, based on the orthomosaic and the DEMs, we obtained the scar area and perimeter, extension of damages, volume of the mobilized material and its possible evolution. As for the gully monitoring, we obtained the volume of mobilized material and identified the morphologies associated with the process evolution for posterior analysis and monitoring.The use of drones in emergency and geomorphological disaster management has so far been very satisfactory, allowing the support of field teams, agility on data acquisition, damage analysis, evaluation of process evolution and the monitoring of potential instability conditions, allowing more fast and assertive decision making.However, there are limitations and challenges that need to be overcome, such as the presence of dense vegetation, of special importance in the humid tropics, pointing out the need of replacing conventional RGB cameras for sensors that penetrate the vegetation, like LiDAR. Also, the limited availability of free data and the cost of sensors in Brazil are other important limitations to the diffusion of this technology, even though its adoption is inevitable considering the various advantages that it presents. [ABSTRACT FROM AUTHOR]

Information on the 6th international symposium on Mobile Mapping Technology (MMT) held at Presidente Prudente in Sao Paulo, Brazil on July 21-24, 2009 is presented. Topics include alternative sensor and data integration algorithms, direct georeferencing, and image sequence processing. The symposium, which was attended by 144 participants from 19 countries including China, Brazil, and France, featured several professors including João Silva, Ian Dowman, and Dorota Brzezinska.