Engenharia Florestal (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/15
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APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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Resultados da Pesquisa
Item A experiência agroecológica do Sítio do Futuro: um estudo de caso como modelo de desenvolvimento sustentável(2022-05-27) Silva, Luana Cristine Ferreira da; Oliveira, Maria do Socorro de Lima; http://lattes.cnpq.br/1088572350623888; http://lattes.cnpq.br/6404147255589837The rural environment formed by farmers, agroecological experimenters is a space rich in experiences, of ancestral knowledge that is passed between generations constituting itself as a territory of resistance and confrontations to the packages provided by the green revolution that in a way caused and causes social, environmental and economic problems. From this perspective, systematizing and describing agroecological experiences is a way of confronting this model of conventional agriculture. The present work is configured as a case study that seeks to portray the profound reality, seeking to reveal the multiple dimensions present in the agroecological experience of Seu Barrim and Dona Marilene. In the methodology, an analysis will be carried out in the agroecosystem with the purpose of shedding light on the agrobiodiversity present in productive backyards, in agricultural, forestry and cultural assets. As well as conversation circles and an analysis of some qualitative variables of the LUME method such as: Autonomy and gender equity. The data obtained in this research demonstrate the importance and contribution of the experience of Seu Barrim and Dona Marilene in the preservation and maintenance of agrobiodiversity, in the sovereignty and food security of the family, helping to combat the processes of climate change, semi-arid desertification, loss of biodiversity and the degradation of ecosystems, as well as their social contribution.Item Influência de granitos anorogênicos (Tipo A) na geoquímica de elementos terras raras em solo no semiárido brasileiro(2023-02-24) Santana, Laura Mariana Nascimento de; Silva, Ygor Jacques Agra Bezerra da; Nascimento, Rennan Cabral; http://lattes.cnpq.br/5916790861002578; http://lattes.cnpq.br/0904824873761236; http://lattes.cnpq.br/5352138228552126The global demands for rare earth elements (REEs) are continuously increasing and, consequently, the accumulation of these elements in the soil and the environment has caused adverse effects on human and environmental health, causing growing concern not only in the scientific community, but throughout society. The geochemistry of REEs in soils remains poorly understood, particularly in semi-arid environments. Studies on the dynamics of REEs in different geological, pedological and climatic contexts are necessary to understand the different biogeochemical behaviors of these elements. However, there are still no studies on the effect of type A granites on the physical, chemical and mineralogical attributes of soils located in different environmental conditions. Thus, the present work aimed to evaluate the influence of the mineralogy of anorogenic granites (A Type) on the mineralogy and geochemistry of rare earth elements in the Brazilian semi-arid region. The soil profile was selected based on the geological map of Pernambuco. A Type granite was analyzed using a petrographic microscope, scanning electron microscopy and energy-dispersive X-ray spectroscopy. Soil morphology was described based on the Soil Description and Collection Manual in the Field and the soil profile classification was according to the Brazilian Soil Classification System. REEs readings were made by optical emission spectrometry. An X-ray diffractometer was used to identify the minerals in the soil fractions. The soil profile derived from type A granite in the semi-arid region was classified as CAMBISSOLO HÁPLICO Ta Eutrófico Típico, defined by the presence of an incipient B diagnostic horizon (Bi), being deep, not stony, not gravelly and not rocky. Based on the fertilizer recommendation manual for the state of Pernambuco, the natural fertility of the soil profile originating from type A granite was considered moderate. A Type granite showed a high proportion of mafic and accessory minerals, mainly biotite, hornblende, allanite, garnet, opaque minerals and apatite. Bastnasite and monazite weathering were the main sources of REEs in the soil. The average total concentration of REEs in the soil profile originating from A type granite was very high (394.2 mg kg-1), being higher than the average of soils derived from other granite types (I and S Types), as well as from average of the soils of Europe, China, Japan, Sweden and the reference soils of Brazil. Soil derived from A type granite showed REE enrichment, with slight fractionation between LREEs and HREEs. The LaN/YbN ratio slightly higher than one also confirmed the slight fractionation between LREEs/HREEs. There was no fractionation between LREEs (LaN/SmN ratio <1). However, there was a slight fractionation among the HREEs (GdN/YbN ratio > 1) due to these metals being more mobile than the LREEs. The positive cerium (Ce) anomaly is explained by the oxidizing condition of the profile, in which Ce3+ transforms into Ce4+, presenting low solubility and a greater tendency to enrichment. The positive europium (Eu) anomaly is explained by the substitution of Eu by strontium (Sr2+) in plagioclase. Therefore, the positive Eu anomaly in the soil reflects the same geochemical signature in its parent material. The mineralogical composition of A type granite, as well as the mineralogy of the soil, had a strong influence on the geochemistry of REEs in the soil. This study provides evidence of the influence of A type granite on the geochemistry of REEs in soil located in the Brazilian semi-arid region.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.Item Dinâmica espacial do cenário florestal em paisagens do bioma Caatinga no município de Araripina - PE(2022-05-27) Andrade, Adrielle; Silva, Emanuel Araújo; Melo, Lorena de Moura; http://lattes.cnpq.br/1486808425687522; http://lattes.cnpq.br/2765651276275384; http://lattes.cnpq.br/8750022516521279