Abstract: Tomatoes are widely cultivated, both by family farmers and corporate producers. During the tomato growth cycle, several diseases can affect the plant. The identification of these diseases through short-range images is significant, and computer vision techniques are commonly used to identify diseases in plant leaves. In this paper, a hybrid model that combines a convolutional neural network (CNN) and a Random Forest (RF) decision tree is used for foliar spot detection in tomato leaves. High-level features learned and extracted from CNN are used as input for the RF classifier. To evaluate the proposed model’s performance for plant disease identification, a case study of 2480 low-cost digital RGB images collected in actual field conditions, under different intensities of light exposure, were used, including healthy tomato leaves and leaves with visible symptoms of powdery mildew fungus, which attacks the tomato leaf. The results were compared with six conventional machine learning classifiers: Logistic Regression (LR), Linear Discriminant Analysis (LDA), K- Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The results show that the proposed model outperformed conventional classifiers, reaching an accuracy of 98%. The results highlight the importance of fusing models to improve the detection plant´s diseases.

Abstract: The use of a transformation method for the passage from one geodetic system to another requires the use of some common points as support points, these points are used in the determination of the transformation parameters. Generally, the choice of the support points is effected manually by choosing the best distribution of these points in the transformation area. In the present study, we present a methodology of selection of these points where an algorithm takes into account the computation of the transformation parameters with all combinations between the common points and the best result will be adopted. An application of this methodology are carried out in North-East of Algeria to determine the best set of the 09 transformation parameters between the WGS84 system and the National North Sahara system using 10 common points (05 support and 05 control). This methodology is efficient in the case where the common points are near one another.

Abstract: Income variables from the Brazilian population census (IBGE) are often used as proxies for the population’s socioeconomic level in spatial analyses of urban segregation, inequality and social exclusion. However, income variables are dependent on reference values (minimum wage) that change over time, which can be challenging for multitemporal analysis. This paper discusses this issue and proposes a methodology to adjust income data that allows a meaningful comparison between the datasets of two Census periods. The methodology was applied to five medium-sized cities of the state of São Paulo by adjusting income data from Census 2000 and 2010 according to the period’s inflation rates. The analysis shows that the methodology mitigates the comparability issues. Results better reflect the changes in population composition and in residential patterns of different income groups that took place over the 2000s in Brazil in medium-sized cities.

Abstract: The mapping of rainfall is fundamental in the hydrological modeling process. In this sense, the importance of knowing the geographic and seasonal dynamics of average estimates of rainfall and associated uncertainties is evident. Thus, the present study aimed to predict the spatial and seasonal distribution of rainfall, with the estimation of related uncertainties, in the state of Rio Grande do Sul (RS). Average rainfall varies over the months of the year. In January, February, June, July, August, and September it rains more north and northeast. In March, April, May, October, November, and December it rains more northwest and north. In general, it rains a lot in October and little rain in August. From a geographical point of view, it is possible to highlight that greater volumes of rain occur in the northern part of the state of RS. The uncertainties associated with rainfall estimates show divergent temporal dynamics, with the greatest uncertainties tending to occur in January, February, September, and October and that the smallest uncertainties are observed in June, July, and August.

Abstract: The potential of intrinsic parameters to estimate geospatial data quality on Voluntary Geographic Information (VGI) platforms is a recurrent theme in Cartography. The spatial-temporal distribution in these platforms is very heterogeneous, depending on several factors such as input availability, number, and motivation of volunteers, especially in developing countries. The most recent approaches have been aiming to detail temporal patterns as an additional measure of quality in VGI. This research proposes a methodology to identify and analyze the behavior of the contribution parameters over time (2007-2022) of the OSM platform and differentiates the influences that affect its growth. Part of the Metropolitan region of Curitiba was the study area, subdivided into 1 x 1 km cells. The cumulative growth of contributions was calculated and later adjusted using a Logistic Regression. The obtained parameters made it possible to identify abruptly growing cells caused by external data import, mass contributions, or collective mapping activities. In addition, heterogeneity in the growth of the data available in OSM over time was evident. Furthermore, the proposed methodology promoted the investigation of a new indicator of intrinsic quality based on modelling the spatiotemporal evolution of OSM feature insertions.

Abstract: Due to the absence of studies related to the digital processing of synthetic aperture radar (SAR) images in the semi-arid region of Brazil, the aim of this study was to test different filters for reducing speckle noise in SAR images, serving as a reference for choosing the most suitable filter for different studies in this vegetation. The filters: Gamma Map, Lee Sigma, Median, Frost and Refined Lee in different window sizes were tested on the VV, VH and VH/VV polarizations in the Sentinel-1A images, verifying the responses under the influence of the dry and post-rainy period in the Caatinga vegetation. In the state of Sergipe, Brazil, 30 samples of Caatinga fragments obtained from Sentinel-1A images for the dry and post-rainy season were selected. For all images evaluated, the values of the averages of the equivalent number of looks (ENL) were compared by the Tukey test at 5% significance. The Gamma filter showed the highest amount of means (22) with the highest ENL values, followed by Median (5). The generation of ENL results and their comparison, considering all variables used, was essential to serve as a basis for choosing the filtering method in studies that use data from Sentinel-1A in the Caatinga region.

Abstract: Vegetation Indices (VIs) provide spatial information on the vegetation state, which has been associated with landslide propensity. To evaluate how VIs information indicate the landslide propensity, the current study analyzed nine different IVs to identify the categories of vegetation states in the hydrographic basin of Pedra Branca before and after landslide event. The different VIs were obtained using Sentinel-2A (2016) and Sentinel-2B (2018) images. All VIs were tested by cross-table analysis regard to the ability to identify the calculated area for landslide scars, and the VIs were also compared to the NDVI reference by error matrix for the analysis of the accuracy in identifying the vegetation state before the landslide occurrence. The areas with landslide scars totalized 86700m² in 2018 image and NDVI matched ~57% of the No Vegetation category. Before the landslide event, almost all VIs indicated a loss of vegetation vigor (with exception of RENDVI and ARVI) in 2016 image. In addition, the indices (exceptionality MSI) also presented high rates of match to the analysis of NDVI in discerning both Intermediate and Vigorous Vegetation states. However, the areas presenting a healthy vegetation state are reduced, which therefore might be indicating the propensity to landslide event before their occurrences.

Abstract: Multibeam Echosounders (MBES) are hydrographic tools used primarily to survey the seafloor bathymetry and backscatter. Modern MBES systems are not limited to the seafloor, as they can also map water column profiles, which holds important biological, thermal and chemical information of oceans and shores. Unfortunately, this feature is normally disregarded during routine surveys operations, as it generates a very large amount of data, requiring data compression for possible use in future analysis. For the compression, we propose to map the water column data into images and to compress each of them using image compressors. We devised two methods: a lossless coder based on linear predictors, and a lossy coder based on thresholding followed by lossless coding. Both methods seem to better suit the echosounder image data than traditional image coders. We tested our methods in sequences that capture different water column activities in the Bay of Brest, France. Results indicate our method outperforms other standard image compression methods, ranging from 4 to 70% average gains in compression ratio in lossless coding, and equivalent results in lossy coding. Compression-induced distortion was measured as traditional mean squared error and as analysis-parameter estimation errors.

Abstract: This work considers the terrain slope factor as well as census dataset with variables related to socioeconomic and demographic characteristics, sanitation, water supply, garbage collection, and electricity in identifying centralities, or new urban centers in the municipality of Barra do Piraí, located in the State of Rio de Janeiro, Brazil, as a case study. The morphological approach was used with a Principal Components analysis and spatial analysis involving Global Moran Index, Local Indicator of Spatial Association - LISA, and Kernel Density Estimator. Among the variables considered in the study, results indicated that the slope was a preponderant factor in identifying the centralities in the study area and that it limits the urban expansion both in the municipality and in some existing districts.

Abstract: Many efforts have been made to understand climatic and hydrological variables’ variability, magnitude, and standards. In this sense, spatial data has been a fundamentally important tool in supporting the development of agriculture and environmental management research. We start this review by giving a brief overview of the use of satellites in Brazilian agriculture. Besides that, we present a couple of examples of satellite applications in managing water resources in agriculture. The second part of this review illustrates a detailed scenario concerning the orbital sensors available for water applications in agriculture. Finally, we provide a synthesis of the future of satellites in agriculture in terms of nanosatellites, artificial intelligence, and onboard processing. Abstract variables variability magnitude standards sense research that Finally nanosatellites intelligence processing