The aim of this study was to quantify the spatio-temporal changes in land use/ cover (LULC), as well as analyze landscape patterns over a 20-year period (1995 - 2015) in the Catolé watershed, northern Minas Gerais State, using landscape metrics. The LULC maps were obtained using Landsat 5 and 8 data (Processing level 1) through supervised classification using the maximum likelihood classifier. Seven thematic classes were identified: dense vegetation, sparse vegetation, riparian vegetation, cropland, planted forest, bare soil, and water. From the LULC maps, classes related to the natural landscape (dense, sparse, and riparian vegetation) were grouped into forest patches, which was then ordered by size: very small (< 5 ha); small (5 - 10 ha); medium (10 - 100 ha); large (100 ha); and a general class (no distinction of patch size). Then, metrics of area, size and density, edge, shape, proximity and core area were calculated. The dense vegetation portion of the study area decreased considerably within a given time, while the portion of cropland and bare soil increased. Overall, in the Catolé river basin, the total area of natural vegetation decreased by 3,273 hectares (4.62%). Landscape metrics analysis exhibited a reduction in the number of very small patches, although the study area was still considered as fragmented. Moreover, a maximum edge distance of 50 m is suggested for conducting studies involving core area metrics in the Catolé watershed, as values above this distance would eliminate the very small patches.
Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.
The improper disposal of industrial waste and exploitation of natural resources has resulted in the scarcity of river sand and environmental degradation, such as river erosions and pollution. This study aimed to assess the durability of mixed mortar lining walls and ceilings, containing 0 (default), 10 and 20% of dregsgrits compounds-waste of the pulp industry-in substitution with river sand. This was done with tests that simulated both natural and artificial conditions: Direct solar incidence (testing ultraviolet radiation), attack by spraying solution (salt spray test), natural warming of the walls and ceilings incidence by indirect solar (thermal degradation) and residential fires (thermogravimetric test), in compliance with both national and/or international standards. The grout containing dregs-grits compounds showed similarity to standard (0%) for testing thermal degradability, thermogravimetric and ultraviolet radiation, but shows significantly less durability when exposed to salty environments.
The delimitation of management classes is critical for successful precision agriculture. This process involves choosing the variables to use and analyzing the spatial variability of the variables. Thus, the objective of this study was to analyze the correlation between management class maps generated from orbital images and yield maps. A 95-hectare area of rainfed grain was evaluated. Yield maps were obtained for the 2015/2016 and 2016/2017 soybean crops. Orbital images were used from two dates for each crop to generate vegetation index maps. The spatial correlation between the vegetation indices and the yield maps was obtained using a bivariate Moran index. The delineated management classes were compared using the Kappa index. This study demonstrated that the Kappa values resulting from the comparison between the management class maps generated from the soybean yield and the vegetation index ranged from 5% to 67% depending on the number of delineated classes. The highest Kappa values were observed when the area was delineated into three classes.
O presente trabalho teve como objetivo mapear a fragilidade potencial e emergente na Bacia do Rio Peruaçu, com base na metodologia proposta por Ross (1994). A Bacia do Rio Peruaçu está localizada na Região Norte de Minas Gerais, cobrindo uma área de 1.552,3 km². A partir da sobreposição de mapas de diferentes atributos, tais como, declividade, erosividade da chuva, tipo de solo e uso e cobertura do solo, foi possível mapear a fragilidade ambiental. A fragilidade foi classificada em cinco diferentes níveis: muito fraca, fraca, média, forte e muito forte. Como resultado, para a fragilidade potencial predominou a classe de alta fragilidade com 56,88% (882,9 km²) da área. Enquanto que, na análise da fragilidade emergente, observou-se a predominância das classes de Alta e média fragilidade, com 37,22% (577,71 km²) e 36,83% (571,73 km²), respectivamente. Os resultados obtidos evidenciam a necessidade de recuperação das áreas degradadas e a implantação de técnicas de conservação do solo nas áreas de maior fragilidade.
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