The spatial distributions of four plant species native to Asia, yet considered invasive in southwestern Virginia, were studied in order to produce predictive habitat maps. The study took place in the mountains to the north of Blacksburg, VA, on National Forest lands. A random GPS survey of each of the four species, Microstegium vimineum, Lonicera japonica, Rosa multiflora and Elaeagnus umbellate, was used in combination with a series of Geographic Information System (GIS) layers representing environmental variables (Elevation, Aspect, Roads, Trails, Streams, & Normalized Difference Moisture Index) to produce logistic regression models. After field- validating the models, the models were ranked according to usefulness, with the E. umbellate model proving most accurate. It is hoped that such GIS models will allow forest managers to more productively search for invasive species within their jurisdiction, by indicating sites more likely to provide habitat suitable to the invasive species described by the model. A non-GIS search for correlations between the study species' presence and field-collected discrete environmental variables was also included. Both Disturbance and Canopy Cover were considered for their effect upon Microstegium vimineum, Lonicera japonica, Rosa multiflora and Elaeagnus umbellate presence. Using Pearson's Correlation with the Canopy Cover data, and Chi-squared Correlation with the Disturbance data, only R. multiflora and E. umbellate showed significant correlation to decreasing canopy cover. / Master of Science

The aim of the thesis was understanding the energy fluxes in different types of land cover. The area of interest is located in Novohradské hory in the basins of Váčkový and Pasecký potok. On a relatively small area there are five different types of land cover to be found (field, forest, permanent grassland, wetland and built-up area). For each of the types of land cover three properties of the surface were calculated: amount of vegetation on the surface, wetness of the surface and surface temperature. Moreover, soil heat flux, latent heat flux and sensible heat flux were established therefore surface thermal balance was evaluated. Results were statistically processed with boxplot diagrams as an outcome. The results support the hypothesis of vegetation having a positive effect on microclimate conditions. Especially the permanent cultures (wetlands, permanent grassland, forest) demonstrated much larger microclimatic stability and balance as well as capability of keeping moist. Thanks to insufficient intensity of vaporization (due to lack of water), built-up areas showed higher surface temperatures and sensible heat flux at most of the cases. Vegetation is capable of holding water and redistributing it in an environment via evapotranspiration. Thus, solar energy is stored inside of the water vapour which condensates on cool objects releasing the energy spent during the vaporization process. Water moves in the landscape through the local-scale water cycle stabilizing the microclimate.

Orientador: Jurandir Zullo Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-05T08:07:53Z (GMT). No. of bitstreams: 1 Antunes_JoaoFranciscoGoncalves_M.pdf: 7524504 bytes, checksum: e36a3c933615dc4ef031bf119f6c09ff (MD5) Previous issue date: 2005 / Resumo: A estimativa precisa com antecedência à época da colheita de áreas plantadas com culturas agrícolas, como a soja, é de fundamental importância para a economia brasileira. A previsão do escoamento e comercialização da produção agrícola é estratégica para o Brasil, pois estão diretamente relacionados com o planejamento, custos e preço. Com o recente avanço tecnológico na obtenção de dados por sensoriamento remoto orbital é possível melhorar a previsão de safras, diminuindo cada vez mais o nível de subjetividade. Embora designadas para fins meteorológicos, as imagens AVHRR-NOAA de elevada repetitividade temporal, têm sido utilizadas para o monitoramento agrícola. Porém, a sua baixa resolução espacial faz com que possa ocorrer a mistura espectral das classes de cobertura do solo dentro de um mesmo pixel e isso pode acarretar problemas de imprecisão na estimativa de área plantada de uma cultura agrícola. O objetivo principal do trabalho foi desenvolver uma metodologia de classificação automática com a aplicação de lógica fuzzy para o reconhecimento de padrões em imagens AVHRR-NOAA, utilizando índices de vegetação para estimar a área plantada de soja no nível sub-pixel. Para oito municípios produtores de soja da região oeste do Estado do Paraná, foi possível obter a estimativa de área no final de janeiro de 2004, com antecedência em relação à época da colheita, ao contrário dos levantamentos oficiais que se estendem até o final da safra, além de utilizarem dados subjetivos vindos do campo. As estimativas de área de soja baseadas em classificação fuzzy mostraram-se altamente correlacionadas com as estimativas de área de referência obtidas a partir da máscara de soja e por expansão direta, sendo um indicativo de boa precisão. E também apresentaram alta correlação, balizadas com as estimativas oficiais da SEAB/DERAL e do IBGE. Em ambas comparações, o nível de erro relativo geral foi aceitável. O sistema desenvolvido para processamento e geração de produtos das imagens AVHRR-NOAA mostrou-se uma ferramenta fundamental de infra-estrutura, por aliar automação e precisão a metodologia do trabalho / Abstract: An early accurate estimation of agricultural crop areas, such as soybean, is fundamental for the Brazilian economy. The draining forecast and the estimation of agricultural production commercialization are strategic to Brazil, since they are directly related to planning, costs and price. Recent technological progress of data acquisition from orbital remote sensing makes possible to improve harvest forecast, reducing more and more the level of subjectivity. Although designed for meteorological aims, the AVHRR-NOAA images of high temporal resolution, have been used for the crop monitoring. However, its low spatial resolution might cause the spectral mixture of the different land cover classes within the same pixel and it can lead to accuracy problems on crop area estimation. The main objective of the work was to develop an automatic classification methodology with the application of fuzzy logic for pattern recognition in AVHRR-NOAA images, using vegetation indices to estimate the soybean crop areas at sub-pixel level. For eight soybean producer counties in the West region of the Paraná State, it was possible to obtain the crop area estimation at the end of january 2004, prior to the harvest period, on the contrary of the official surveys that extend until the end of the harvest, besides using subjective data collected on the field. The soybean crop area estimation based on fuzzy classification showed to be highly correlated with the reference area estimation obtained from the soybean mask and by direct expansion, being an indicative of good accuracy. And also presented high correlation, marked out with the official estimations from SEAB/DERAL and IBGE. In both comparisons, the level of general relative error was acceptable. The system developed for processing and products generation of AVHRR-NOAA images had proved to be a fundamental infrastructure tool, due to its capacity to combine automation and accuracy to the work methodology / Mestrado / Planejamento e Desenvolvimento Rural Sustentável / Mestre em Engenharia Agrícola

The urban heat island (UHI) is a phenomenon of noticeably higher temperatures in the cities as compared to their respective surrounding areas. This thesis aims at characterizing the influence of city expansion to the urban heat island phenomenon. The study is carried out in a city of Caceres in the Spanish province of the same name. A model input data is obtained with Landsat multispectral images. The analysis of satellite images shows that functional vegetation cover and water surfaces help in mitigating urban heat island effect. However, the Caceres city expansion does not influence the urban heat island intensity. A possible explanation for it is as the city expanded the ratio of vegetation to dry land remains constant in time.