Engenharia Florestal (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/15
Siglas das Coleções:
APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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Resultados da Pesquisa
Item Abordagem comparativa entre a aplicação da metodologia KATAM e inventário tradicional em plantios de Khaya senegalensis (Desr.) A. Juss(2023-09-15) Silva, Kamilo Alaboodi da; Silva, Emanuel Araújo; Hakamada, Rodrigo Eiji; http://lattes.cnpq.br/4186459700983170; http://lattes.cnpq.br/2765651276275384; http://lattes.cnpq.br/5612600854790108The forest inventory helps forest managers taking decisions. Installing, measuring and managing a network of inventory plots is a costly and time-consuming activity. The remote sensing techniques are increasingly gaining ground in the forestry sector because they have the potential to reduce costs without incurring any loss of precision, but they are not widely used due to their high cost. In this context, the Swedish company Katam Technologies has developed a solution for acquiring and analyzing forest data: KATAM Forest, which works using the KASLAM algorithm, which has not yet been widely used and tested in national forests. The goal of this study was to compare, in terms of accuracy and operational performance, the application of KASLAM artificial intelligence through the KATAM Forest application in forest inventory activities in Khaya senegalensis (Desr.) A. Juss plantations (5 years old), located in the state of Pernambuco, with the sampling techniques of a traditional forest inventory. Diameter at breast height (DBH) data was collected within 9 plots, as well as videos with artificial intelligence, recorded within the coordinates of the sampling units. Descriptive statistics were performed on the DBH data by plot, followed by the parametric Shapiro-Wilk normality test, where, if the null hypothesis was rejected, a non-parametric Mann-Whitney U test was required to understand the difference in averages. Operational performance was assessed using the time data obtained during the inventory process within the plots in both approaches. The DBH variable in the two inventory methodologies does not have a clear distribution concentrated close to the mean. The non-parametric test resulted in the averages obtained for DBH not showing statistical differences between the methodologies at the 5% significance level. The operational performance of the Katam methodology was half of the traditional inventory. The Katam technologies are very promising in terms of reducing time and costs in forest inventory operations. Therefore, further studies are recommended so that the subject can be disseminated in a practical way.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.