TCC - Engenharia Florestal (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/436
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Resultados da Pesquisa
Item Crescimento e qualidade de mudas de Piptadenia stipulacea (Benth.) Ducke, em diferentes recipientes e doses de fertilizante misto(2024-09-12) Souza, Thallyta Valentin dos Santos de; Freitas, Eliane Cristina Sampaio de; http://lattes.cnpq.br/7525975084334972; http://lattes.cnpq.br/1375547405957419In 2022, Brazil suffered a neduction of 2.05 million hectares of native forest, the Caatinga biome, in tum, lost the equivalent of 140.637 hectares. This highlights the urgency of actions to curb environmental degradation and promote conservation. It is vital to invest in research on native forest species to fill technological gaps, meet the demand for seedlings and drive sustainable development. Piptadenia stipulacea (Benth.) Ducke, a species native to Brazil, is a recommended option for recoverins degraded areas in the Caatinga. Therefore, the objective o£ this work is to evaluate the influence of the volumetric capacity of containers for the production of seedlings such as tubes and polyethylene bags, combined with different doses of NPK, on the growth and quality of seedlings of Piptadenia stipulacea (Benth.) Ducke. The study was carried out in the forest nursery of the Forestry Science Department of the Federal Rural University of Pernambuco (UFRPE), from November 2022 to March 2023. Three containers were tested for the production of seedlings: a 120 cm3 tube, a 280 cm3 and plastic bag of 3449 cm3 (20 cm x 30 cm) and four doses of NPK (4-14-8): 0; 2.0; 4.0; 6.0 Kg/m3. The analyzes of height and diameter of the stem were monitored monthly and a destructive analysis was performed at the end of the 120 days. The results indicated that the reduction in container volume caused a decrease in dry mass, diameter and height. Increasing NPK doses had a significant effect on recipient height and stem diameter. For the robustness index, the dose of 2 kg/m3 obtained the best performance. In view of the results, the use of polyethylene bags with the addition of 2 kg/m3 of NPK is recommended for the production of jurema branca seedlings.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 Uso de geotecnologias no diagnóstico da mata ciliar do Reservatório Engenheiro Francisco Sabóia, Ibimirim - PE(2023-04-13) Silva, Jaimeson Jardel França da; Duarte, Simone Mirtes Araújo; Vasconcelos, Géssica dos Santos; http://lattes.cnpq.br/0802316667174979; http://lattes.cnpq.br/5876968040869585; http://lattes.cnpq.br/9465682002571649With the development of cities, disorderly occupations have grown exponentially, impacting many vegetation formations, especially riparian forests, even though they are still protected by law for being in Permanent Preservation Areas (PPAs) and presenting great importance in maintaining the quality and stability of water bodies, such as in artificial water reservoirs, which provide society with regulation and water supply, especially in regions with water scarcity, such as the Caatinga. Therefore, the present study aims to diagnose the situation of riparian forests in the PPA strip around the Engineer Francisco Sabóia Reservoir, located in the municipality of Ibimirim, in the state of Pernambuco, seeking to understand and describe the degrees of degradation and conservation, verifying their adequacy in relation to current environmental laws, in a GIS environment. Through documentary research, the absence of licensing was verified, and consequently, the corresponding PPA strip was not defined. Therefore, for the study, a measurement of 100m was considered according to CONAMA Resolution 302/2002. High-resolution images from the CEBERS 4A satellite were used. The data were manipulated in QGIS software to create thematic maps of land use and occupation, through supervised classification of five classes: water, consolidated vegetation, shrub vegetation, agriculture, exposed soil, the NDVI map to quantify the percentage of preserved and degraded riparian forest, and the slope map to understand the altimetric configuration of the region. As results, the land use and occupation map showed the presence of agricultural activity with approximately 22%, as well as approximately 9% of exposed soil, 9% of consolidated vegetation, 25% of shrub vegetation and water 35%, evidencing non-compliance with the legislation. In addition, the NDVI calculation clearly indicated the fragility of vegetation throughout the extension of the PPA, with few fragments of vegetation cover and a lot of area with dead or water-stressed vegetation. It is concluded that this diagnosis has results capable of guiding effective management with planning based on territorial planning and restoration actions of the reservoir's PPAs, providing reflections on the environmental situation of an area with such environmental, economic, and social importance for a severely degraded biome.Item Levantamento da fauna apícola em monocultivo de sabiá (Mimosa caesalpiniifolia) em área de Caatinga no Agreste pernambucano(2021) Silva, Isabela Nascimento; Gonçalves, Maria da Penha Moreira; http://lattes.cnpq.br/0539509819672370; http://lattes.cnpq.br/6582276513482324Due to the semi-arid climate characteristics, the Caatinga biodiversity presents a rich diversity of plant and animal life. Within this wealth, native bees stand out, which play an important role in the balance of forest ecosystems. Thus, the objective of the present work was to carry out a survey of the apicultural fauna in areas of sable cultivation and native vegetation of Caatinga in the rural region of Pernambuco. The research was carried out at the experimental station of the Instituto Agronomic° de Pernambuco - IPA, municipality of Caruaru, Pernambuco. The study was carried out during the months of December 2020 and January 2021 in two areas of Caatinga equidistant 30 m from each other, one planted with thrush (Mimosa caesalpiniifolia) and the other with native vegetation. In each area, two 10 m x 10 m transects were drawn for the installation of traps. Two models of traps were installed, one with a bottle using scent essences (vanilla and methyl salicylate) and another with yellow Pantraps, in which the attractiveness is based on color. 29 individuals of bees distributed in three genera were catalogued. Of these, two were identified at the genus level (Trigona sp., Bombus sp.) and one at the species level (Apis melifera scutellata). Pantraps did not attract any insect considered to be a bee, regardless of the area or period of collection. A higher frequency of bees was observed in the dry period compared to the rainy period. In the sabia area there was greater visitation by bees (82% of the individuals) and greater preference for visitation after using the methyl salicylate essence (79% of the individuals), however the vanilla essence was efficient in attracting them. It is concluded that the method of collecting bees using scent traps in the Caatinga environment of the Pernambuco agreste proved to be efficient when using vanilla and methyl salicylate essences, the latter being the most effective in attracting these insects. The modifications that occurred in the monoculture of Sabia in the Caatinga environment did not negatively influence the diversity of bees, when compared to the area of native forest in the region, with similar diversity being observed in both areas.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 Análise da distribuição espacial do índice de umidade do solo em regiões semiáridas a partir de dados de sensoriamento remoto(2019-11-26) Santos, Jadiene Moura dos; Silva, Emanuel Araújo; Oliveira, Cinthia Pereira de; http://lattes.cnpq.br/8148643000907549; http://lattes.cnpq.br/2765651276275384; http://lattes.cnpq.br/5414923091157764Soil moisture represents a fraction of water that is at a surface level of the earth where there is interaction with the atmosphere through evapotranspiration. It is a fundamental variable in the functioning of several processes that act in the terrestrial system, besides characterizing the desertification of semiarid and arid regions. This course conclusion work aimed to evaluate the spatial distribution of the soil moisture index in a dry tropical forest area, in the city of Floresta/PE, through orbital images. The methodology was applied for four distinct dates (11/21/15, 11/23/16, 12/12/17 and 11/13/18) and data processing to obtain the Normalized Difference Vegetation Index (NDVI), Surface Temperature (Ts) and Soil Moisture Index (IUS) were performed using Qgis software. In addition, the time series of the annual precipitation of the municipality of Floresta / PE were classified into dry, normal or rainy years, using the quantile method and the monthly precipitation analysis in relation to the climatological normals from 2015 to 2018, using the data obtained from the APAC website. Finally, the point cloud distribution was distributed between IUSxNDVI and IUSxTs. The results showed that the classification from 1999 to 2018 eight years behaved as normal and six years as dry and rainy, where the values for dry years ranged from 149.50 to 349.20mm, the normal ones from 392.70 to 538.1mm and the rainy ones from 559.10 to 750.60mm. For the years 2015, 2016, 2017 and 2018, the accumulated monthly precipitation values were 223.00mm, 395.10mm, 399.20mm and 653.50mm, respectively. The NDVI values in exposed soil plus thin vegetation ranged from 0.124 to 0.323, in arboreal vegetation between 0.351 to 0.649 and in surrounding water bodies -0.072. At surface temperature, minimum values of 23.80 ° C and maximum values of 44.93 ° C were found. For the soil moisture index, 0.240 were found in exposed soil and thin vegetation, 0.417 to 0.746 in tree vegetation and 0.821 in water. In the distribution of the IUS point cloud with NDVI and Ts biophysical parameters, it was observed that 2015 and 2016 had no pixels in the negative NDVI region and Ts pixels were above 40 ° C. On the other hand, in 2017 and 2018 due to the presence of water there was a reduction in Ts, where most pixels were below 40 ° C. The Quantis method allowed to identify in an interval of twenty years an irregular pattern between years classified as dry, normal and rainy. The monthly precipitation of the four showed great variability in relation to the climatological normal of the municipality. The NDVI allowed to identify the presence of water bodies, exposed soil with herbaceous and arboreal vegetation in the farm Itapemirim / PE. The achievement of low surface temperature values on the imaged dates is associated with high NDVI and soil moisture values. IUS values were lower in exposed soil and more expressive in water and under tree vegetation, due to the rapid response of dry tropical forests during the rainy season during the imaging date. Point cloud distribution showed increasing behavior for IUSxNDVI and inverse for IUSxTs.