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
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14 resultados
Resultados da Pesquisa
Item Análise das características dos índices de vegetação em um plantio de Eucalyptus spp. utilizando imagens do Sentinel-2A(2023-09-14) Silva, Adailton Domingos Salustiano da; Silva, Emanuel Araújo; Sá, Vânia Aparecida de; http://lattes.cnpq.br/5807408661337266; http://lattes.cnpq.br/2765651276275384; http://lattes.cnpq.br/7511858370212406Item Influência das áreas verdes urbanas sobre a temperatura de superfície utilizando sensoriamento remoto(2023-07-07) Siqueira, Ítalo Fernandes Pessôa; Alba, Elisiane; Oliveira, Géssyca Fernanda de Sena; http://lattes.cnpq.br/8717407990656771; http://lattes.cnpq.br/1465154212352591; http://lattes.cnpq.br/6866008330390945Item Avaliação da contaminação por metais pesados em solos urbanos da Região Metropolitana do Recife: bioacessibilidade e correlação com NDVI(2024-03-07) Mello, Lucas José Souza de; Biondi, Caroline Miranda; Lins, Simone Aparecida da Silva; http://lattes.cnpq.br/7329862411748916; http://lattes.cnpq.br/8326756664758702; http://lattes.cnpq.br/8741487779369891Urban pollution by heavy metals is a subject of great socio-environmental relevance due to its potential deleterious effects on human and ecological health. Remote sensing, particularly the use of the Normalized Difference Vegetation Index (NDVI), emerges as a promising tool to assess vegetation health and potential impacts of soil contamination. In this regard, the present study aimed to evaluate the total contents and bioaccessibility of heavy metals in urban soil of the Recife Metropolitan Region and its chemical characteristics, obtain the NDVI of the sampled areas, and correlate it with the total metal contents analyzed. The research was conducted in the Recife Metropolitan Region (RMR), where samples of surface soil were collected in distinct urban areas and their points were georeferenced. The samples were analyzed for pH values, Organic Carbon, Soil Cation Exchange Capacity (CTC), and total heavy metal contents using Energy Dispersive X-ray Fluorescence (pXRF). Metal contents were compared with Quality Reference Values, and in vitro assays were performed to determine metal bioaccessibility in the soil. For NDVI estimation, images obtained from the CBERS-4A satellite with 8m spatial resolution were used, and buffers with radii of 55, 110, and 220m were applied to demarcate the area to be analyzed based on the sampled points. The results revealed high levels of heavy metals in urban areas of the RMR, exceeding the Quality Reference Values for the state of Pernambuco, yet the metals exhibit low bioaccessibility. Regarding the size of the analyzed area, there was no significant difference in relation to the values obtained. Additionally, a negative correlation was observed between most soil metal contents and NDVI, meaning that as NDVI increases, metal contents decrease.Item Dinâmica da cobertura da terra (2016-2023): um estudo no Parque Natural Municipal Mata do Frio e seu entorno, Paulista - PE(2023-09-18) Lima, Richely da Silva; Lima Neto, Everaldo Marques de; Silva, Emanuel Araújo; http://lattes.cnpq.br/2765651276275384; http://lattes.cnpq.br/6791561445213969; http://lattes.cnpq.br/5078677187654553The Conservation Units (UC) in Brazil are used as tools for increasing environmental preservation and ecosystem protection. However, without proper monitoring and management, they become targets for degradation and environmental crimes, particularly those units located in urban areas, which are subject to anthropogenic pressure. Taking into consideration the reported cases of deforestation within the Municipal Natural Park Mate do Frio in Paulista - PE, this study aimed to analyze the land use and land cover of this conservation unit using remote sensing techniques, specifically the Normalized Difference Vegetation Index (NDVI), to assess changes over a 7-year interval based on satellite images from the Planet Satellite. To achieve this, study area cutouts from the years 2016 and 2023 were utilized, and a 1 km buffer was generated to assess the influence area. The NDVI calculation was applied to the cutout images. and the classes were reclassified based on their values into water, exposed soil, anthropized areas, low vegetation. and dense vegetation. Additionally, images from the studied years were correlated to identify changes in land cover. The study revealed an increase in dense vegetation within the UC, rising from 26.72% to 65.81%, along with a reduction in anthropized areas from 3.33% to 1.89% of the total area. Conversion of anthropogenic areas into low and dense vegetation was observed, accounting for 4.74% and 0.28%, respectively. Despite these positive findings, deforestation of 1.17 ha (5.70% of vegetation area) was noted. Concerning the surrounding area, there was an increase in anthropized areas from 33.17% to 47.12% due to urban expansion, with part of this anthropized area resulting from the deforestation of 67.41 ha of low vegetation. To validate the accuracy of the obtained data, the kappa index was used, showing values above 80% (very good) for the 2016 images and above 90% (excellent) for the 2023 images. The study results indicated that the environmental degradations in the specific UC were not significant but were noticeable, highlighting the need for increased monitoring, environmental education practices with the community, implementation of the unit's management plan, and the delineation of its buffer zone, given the anthropogenic pressure in the surrounding area of the Park.Item Dinâmica do risco de incêndios sob efeito do El Niño em paisagem do bioma Caatinga em Petrolina - PE(2023-02-17) Feitosa, Márcio Faustino; Silva, Emanuel Araújo; Souza, Ioneide Alves de; http://lattes.cnpq.br/0383867840261318; http://lattes.cnpq.br/2765651276275384; http://lattes.cnpq.br/7669915736150355Remote sensing techniques have been used since the 1960s to work on a particular object or specific area. Over time, technologies have gained improvements and new software and high resolution satellites have emerged. The Landsat-8 satellite can capture scenes up to 705 km away from earth, with a percentage of 10% of clouds, these scenes can be processed and studied for a certain purpose, among these was the dynamics of fire risk under the effect of El Niño in landscape of the Caatinga Biome. Therefore, it is intended to study the vulnerability of Caatinga and the use of monitoring technologies. The objective of this work is to evaluate the influence of El Niño on the dynamics of fire risk under the landscape of the Caatinga biome in Petrolina-PE, monitoring the risk of fire in relation to the severity of El Niño. Images of the Lansat-8 satellite in the municipality of Petrolina-PE were obtained from the USGS website. The following criteria were adopted for image selection: Data from 2015 to 2020, counting from August 1st to December 31st, at times when there are few precipitations, few clouds and high temperatures due to the warmer months. The tool in data processing was Qgis software, a free license software, indicated for those seeking high quality in academic work in the area of Remote Sensing. In the interval of these years, six fire risk maps were obtained. Togenerate the forest fire risk maps and adopted the AHP methodology, widely used by several authors. Where we obtained eight variables: hypsometric map, land use and occupation, slope orientation, slope, road system, precipitation, surface temperature and vegetation index of the normalized difference. According to the results, a temporal analysis of fire risks was obtained, proving that the years 2015 to 2018 had a continuous increase, and in 2019 and 2020 there was a fall in fire risk. Between these last years there was an El Niño and a La Niña and 2020 was the year that the pandemic occurred, that is, there were few transport flows on the highways, causing low risk of fires.Item Histórico das mudanças nas classes de uso e cobertura do solo nas fazendas da Eucatex no estado de São Paulo(2022-09-27) Silva, Yasmim Victória de Araújo e; Berger, Rute; Marques, Luísa Pereira; http://lattes.cnpq.br/1603075418219366; http://lattes.cnpq.br/5395827385005105; http://lattes.cnpq.br/0643141145421813The consumption of forest-based products has increased in recent decades and forestry has been considered a strategic segment to collaborate and encourage the increase in the production of wood products. Until 2019, forest plantations represented about 9.8 million hectares in Brazil. The first land use and land cover classification system with remote sensing data aimed to identify the different categories of land classes. Land cover changes can be related to conversions, which are the complete replacement of one type of cover with another. The objective of this work was to measure the conversion of areas in forest management farms planted with Eucalyptus sp. of Eucatex Florestal, which are the scope of forest certification (FSC-FM), following the natural forest in an interval of up to 27 years (1994 – 2021). The study areas correspond to 51 Eucatex Florestal farms in the regions of Botucatu, Sorocaba and Bauru, in the state of São Paulo, distributed in 18 municipalities. In this study, Eucalyptus is not planted in conjunction with natural forest, but at the stands for commercial purposes, and areas with native species are separated by setbacks or trails. Imagery from satellite Landsat 5, 7, and 8 was used, depending on availability for the requested date. The images were downloaded from the Earth Explorer website and the maximum likelihood method was applied. Of the 51 farms analyzed, 43 had an increase in their natural forest areas and only eight had some type of vegetation loss. The Morrinhos Radar farm showed the greatest increase in the “natural forest” class since 1994, totaling 455.13 ha converted. The Santa Filomena farm had the greatest area loss, with 39.82 ha of its natural forest areas lost, and the other seven farms lost less than 10 ha. There was a increase in the natural forest cover spontaneously, without the application of forest restoration techniques, only with the isolation of the areas. The farms lost more area of natural forest before being acquired by the company, whereas after the implantation of the Eucalyptus stands, there was an increase in the natural areas. The culture of Eucalyptus sp. did not interfere with the regeneration of natural forest areas.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.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