01. Universidade Federal Rural de Pernambuco - UFRPE (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/1
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4 resultados
Resultados da Pesquisa
Item Desertificação - Uso de visualização geométrica para análise de risco(2022-06-06) Paiva Neto, José Augusto de; Bocanegra, Silvana; Albuquerque, Jones Oliveira de; http://lattes.cnpq.br/1220553574304474; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/0458859679894808The process of misuse of land, lack of rain and several other factors can lead the region to become a desert. Once a region becomes deserted it may be impossible to reverse this condition.Frequently analyzing the risk of desertification can facilitate public policies to mitigate these effects.This paper aims to bring a proposal for risk analysis of regions that are at the nucleus of desertification in the northeast using vegetation data. This paper presented a high risk of desertification in 3 municipalities in northeastern Brazil.Item Análise temporal do uso e cobertura da terra do município de Macaparana - Pernambuco(2020-11-03) Moura, Lucas Araujo; Duarte, Simone Mirtes Araújo; Moreira, Giselle Lemos; http://lattes.cnpq.br/6171199372079024; http://lattes.cnpq.br/5876968040869585; http://lattes.cnpq.br/2567696308015910The geotechnologies linked to remote sensing are essential tools to understand the use and occupation of a territory, in addition to effectively and economically assisting in the monitoring of natural resources. Through data from different years, it is possible to create a study of the main factors of degradation of natural resources. Thus, the present work aimed at elaborating a temporal analysis of the municipality of Macaparana - PE for the years 2007 and 2018. All the geoprocessing activities for the supervised classification and vegetation index generation were computed through the software Qgis version 2.18.10 and 3.10.9, the supervised classification was performed through the Semi Automatic Classification Plugin (SCP), where several samples were selected in the bands compiling for the due years, and through the MaxVer, the classes of exposed soil/urban area, agricultural culture, forests and water resources were computed. For the accuracy of the data, the kappa index was performed. The kappa index for the years 2007 and 2018 was 0.49 and 0.79, showing that it is a good mapping. And through the maps generated and the quantified classes, where the exposed soil of the area increased by 48%, the vegetation had a decrease of 35%, the agricultural crop had its area reduced by 10% and the water resources increased about 303%. There was a big change in the results of water resources due to the amount of clouds in the image of 2018, which hindered the classification, but when going to the field it was possible to observe, lack of vegetation in the area around the water resources, which may occur a process of silting up the rivers. It was possible to establish a relationship between the exposed soil and the agricultural crop, the lack of effective management in search of greater productivity instead of cutting more areas to plant, justified with the data of the vegetation area, which decreased to give space to new agricultural crops. And relating the forest area obtained through the supervised classification, with the area computed from IVDN, showed homogeneity in the results, varying less than 5% for the two years. Therefore, it is possible to conclude that anthropic activities, without any management plan for planting and harvesting, are having direct effects on the reduction of vegetation in the area, requiring better control in their production and alternatives that do not require burning cane for cutting, where it has direct impacts on the soil.Item Uso de machine learning e sensoriamento remoto para a identificação da floresta tropical sazonalmente seca no Parque Nacional do Catimbau(2021-01-20) Monteiro Junior, José Jorge; Alba, Elisiane; El-Deir, Soraya Giovanetti; http://lattes.cnpq.br/3202139188457904; http://lattes.cnpq.br/1465154212352591; http://lattes.cnpq.br/0911037640720248The classification of seasonally dry tropical forests is one of the biggest challenges of environmental analysis by remote sensing, considering the forest physiognomic characteristics that are remotely similar to the characteristics of the exposed soil, generating sample errors in forest monitoring studies. The objective of this work was to use machine learning to understand the dynamics of land use and land cover in Catimbau National Park during periods of greater precipitation (wet) and less precipitation (dry) from LANDSAT imagery. The methodological treatment took place from the obtaining of LANDSAT data in 2019 for the wet period and dry period, the raw data were pre-processed in geographic information systems to (1st) select bands; (2nd) delimit the study area; (3rd) perform the atmospheric correction, and (4th) join the satellite bands (band set). A shapefile was created to train the machine learning algorithms containing samples of the classes found in the study area, these being the tree-shrub and shrub-herbaceous phytophysiognomies, anthropized areas, exposed soil, and other areas (i.e. clouds, water bodies, highways). In the R application, algorithms were used both for supervised classification (based on cross-validation, k-fold method, and Friedman and Nemenyi test) and for data spatialization using the aforementioned algorithms. With the described methods, it was possible to observe that the NDVI values promoted the idea that the shrub-herbaceous phytophysiognomy shows reflectance similar to the exposed soil in some areas in the dry period. In the wet period, the kNN algorithm performed better in-class differentiation and vegetation identification (Kappa = 0.9887). In the dry period, the kNN, SVM, and ANN algorithms did not show statistically significant differences regarding their performance, which are considered good classifiers for the period (Kappa = 0.9965; 0.9973; 0.9962, respectively). Therefore, the present study brought innovation in the use of Artificial Intelligence techniques to solve problems in the monitoring, management, and administration of seasonally dry tropical forests with remote data. Being an alternative method to identify, quickly and economically, changes in the forest structure.Item Inteligência artificial na classificação de uso e cobertura da terra no semiárido de Pernambuco(2020-11-03) Almeida, Gabriela Costa de; Silva, Emanuel Araújo; Moreira, Giselle Lemos; http://lattes.cnpq.br/6171199372079024; http://lattes.cnpq.br/2765651276275384The Brazilian Tropical Dry Forest, known as Caatinga, is located in Brazil's northeastern region and has severe climatic characteristics, with dry weather and poorly distributed rainfall. Those climatic characteristics make Remote sensing analysis difficult due to its large vegetation differences between the dry and rainy periods. In order to help the remote sensing analysis in this biome, this work aims to test different Artificial Intelligence algorithms through supervised classification and to identify land use and land cover patterns in the city of Petrolina, in Pernambuco. Three algorithms were tested: Random Forest, Artificial Neural Networks, and K-Nearest Neighbors using QGIS and RStudio software based on Landsat 8 images from the dry period. Twenty samples from the classes were selected: Water, Agriculture, Urban Area, Forest, and Exposed Soil, and these samples served as a basis for training the algorithms for the classification of images. Occupancy data and precision quality assessment were obtained using Mapping Accuracy and Kappa Index, respectively: 0.9878706 and 0.9653555 for Random Forest; 0.9199973 and 0.9454833 for Artificial Neural Networks, 0.9873741 and 0.9598640 for K-Nearest Neighbors, all being considered excellent. These values were higher than those found in the most commonly used algorithms, as in the Maximum Likelihood algorithm. It was observed that the use of artificial intelligence algorithms could generate better results in the classification of land use in semiarid regions.