The discontinuities play an important role both in design and development stages of many geotechnical engineering projects. Because of that considerable time and capital should be spent to determine discontinuity sets by conventional methods. This thesis present the results of the studies associated with the application of the Remote Sensing (RS) and the development of a methodology in accurately and automatically detecting the discontinuity sets. For detection of the discontinuities, automatic lineament analysis is performed by using high resolution satellite imagery for identification of rock discontinuities. The study area is selected as an Andesite quarry area in G&ouml / lbaSi, Ankara, Turkey. For the high resolution data 8-bit Ikonos Precision Plus with 1 meter resolution orthorectified image is used. The automatic lineament extraction process is carried out with LINE module of PCI Geomatica v8.2. In order to determine the most accurate parameters of LINE, an accuracy assessment is carried out. To be the reference of the output, manual lineament extraction with directional filtering in four principal directions (N-S, E-W, NE-SW, NW-SE) is found to be the most suitable method. For the comparison of automatic lineament extraction and manual lineament extraction processes, LINECOMP program is coded in java environment. With the written code, a location and length based accuracy assessment is carried out. After the accuracy assesssment, final parameters of automatically extracted lineaments for rock discontinuity mapping for the study area are determined. Besides these, field studies carried out in the study area are also taken into consideration.

Karst rocky desertification is a progressive process of land degradation in karst regions in which soil is severely, or completely, eroded. This process may be caused by natural factors, such as geological structure, and population pressure leading to poor ecosystem health and lagging economic development. Karst rocky desertification is therefore a significant obstacle to sustainable development in southwest China. We applied a radial basis function network model to assess the risk of karst rocky desertification in northwest Guangxi, a typical karst region located in southwest China. Factors known to influence karst rocky desertification were evaluated using remote sensing and geographic information systems techniques to classify the 23 counties in the study area from low to extreme risk of karst rocky desertification. Counties with extreme or strong karst rocky desertification risk (43.48%, nearly half of the study area) were clustered in the north, central and southeast portions of the study area. Counties with low karst rocky desertification (30.43%) were located in the west, northeast and southwest of the study area. The spatial distribution of karst rocky desertification was moderately correlated to population density.

Made available in DSpace on 2017-07-21T18:15:28Z (GMT). No. of bitstreams: 1 Carla Eva Prichoa.pdf: 3497532 bytes, checksum: 20bce1ab550371bbeacb23eeb6b5bcf5 (MD5) Previous issue date: 2012-07-06 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Landscapes have structural and functional uniqueness, especially when are inserted in spaces whose dynamics are substantially different uses. To analyze a landscape, it is necessary understanding that it is the result of multiple relationship between society and nature, in which both are considered a set of elements that interact with each other. Currently, there are many methods for analysis and characterization of landscapes, however, at a massive rate, the techniques of GIS (Geographic Information Systems) and RS (Remote Sensing) have impacts on the production and availability of cartographic products, minimizing costs and optimizing validation field. One technique associated with SR and GIS data is the Multivariate Statistical Analysis of Principal Components, in this project used for dimensionality reduction and Clustering for the analysis of homogeneity / heterogeneity of the landscape. In this context, the research was conducted using qualitative classification, directed the characterization and recognition of physical standards as well as the occupation of two landscape units belonging to Pitangui River Basin, which includes the cities of Castro , Carambeí and Ponta Grossa, located in east-central state of Paraná. The study involved images of Landsat 5 TM satellite, which provide overall vision, particular units and values of reflectance targets contained therein. Data regarding the spectral bands and relief were integrated and spatially related to GIS, extracting variables of topography and hydrography in order to conduct a visual reconnaissance and topographic prior units. Prior to Multivariate Analysis (Principal Component and Cluster Analysis) images, spectral and representatives of relief were targeted by Object-Oriented Analysis which generated regions described by their spectral properties, space and texture through new image as containing minimum element regions and even a relational database containing all descriptors spectral, spatial and textural. After Object-Oriented Analysis, it was performed the Principal Component Analysis which listed the descriptors coming from the relational database of Object-Oriented Analysis. The res outcomes were highest correlations decreasing dimensionality, 39 to 17 descriptors or variables, and concurrently applying the technique of Cluster Analysis, in order to find characteristics of homogeneity / heterogeneity present in the units. In cluster analysis of the first and second landscape units were always generated three distinct subgroups of initial variables, both with the Landsat image (5R4G3B) as with the Landsat image (5R4G3B) associated with the relief (DTM). It was observed that the combination of spectral data with altimetry (DTM) data possible groupings emphasizing the physical characteristics and use and occupancy of the units, note where the distribution of the variables consistently, aggregating the regions of higher reflectance spectral band red and middle infrared. The near infrared reflected more occupations anthropogenic aim in urban area in southwest in the second unit. However the middle-infrared reflected the land use units from agriculture and exposed soil the both units. The near infrared band reflected the vegetation present in the units, especially in this case the first unit of study. The insertion of relief variable (DMT) increased groups that caused greaters merging in the regions, highlighting portions in both units, such as flat areas at high altitudes and valleys where the rivers run Pitangui and Jotuba. / Cada paisagem apresenta sua singularidade estrutural e funcional, principalmente quando está inserida em espaços cujas dinâmicas de usos apresentam diferenças significativas. Para analisar uma paisagem, faz-se necessário compreender que ela é resultante da relação múltipla entre sociedade e natureza, nas quais ambas são consideradas um conjunto de elementos que interagem entre si. Atualmente, muitos são os métodos para análise e caracterização das paisagens; porém, de maneira maciça, as técnicas de SIG (Sistemas de Informação Geográfica) e SR (Sensoriamento Remoto) apresentam impactos na produção e disponibilização de produtos cartográficos, minimizando custos e otimizando a validação a campo. Outra técnica utilizada associada a dados de SR e SIG é a análise estatística multivariada de Componentes Principais, aqui utilizada para a redução de dimensionalidade e a de agrupamento para a análise da homogeneidade/heterogeneidade da paisagem. Neste contexto, esta pesquisa foi desenvolvida utilizando classificação qualitativa, ou seja, visual, voltada a caracterização e ao reconhecimento de padrões físicos, bem como de ocupação de duas unidades de paisagem pertencentes à Bacia Hidrográfica do Rio Pitangui, a qual abrange os municípios de Castro, Carambeí e Ponta Grossa, localizados a centro-leste do Estado do Paraná. O estudo envolveu a utilização de imagem de satélite Landsat 5 TM, a qual forneceu visão global, particular das unidades e os valores de refletância dos alvos nelas contidos. Os dados referentes as bandas espectrais e do relevo foram integrados, relacionados e espacializados em ambiente SIG, extraindo variáveis de relevo e de hidrografia a fim de se realizar um reconhecimento visual e topográfico prévio das unidades. Anteriormente a Análise Multivariada (Componentes Principais e Análise de Agrupamento) as imagens, espectrais e representantes do relevo, foram segmentadas por meio da Análise Orientada a Objetos a qual gerou regiões descritas por suas propriedades espectrais, espaciais e de textura, mediante nova imagem contendo como elemento mínimo as regiões e ainda um banco de dados relacional contendo todos os descritores espectrais, espaciais e de textura. Após a Análise Orientada a Objetos utilizou-se a Análise por Componentes Principais a qual elencou os descritores advindos do banco de dados relacional da Análise Orientada a Objetos, com os maiores coeficientes de correlação diminuindo a dimensionalidade, de 39 para 17 descritores ou variáveis, e concomitantemente aplicando-se a técnica de Análise de Agrupamento, com o intuito de encontrar características de homogeneidade/heterogeneidade presentes nas unidades. Na análise de agrupamento da primeira e segunda unidades de paisagem sempre foram gerados três subgrupos iniciais distintos de variáveis, tanto com a imagem Landsat (5R4G3B) quanto com a imagem Landsat (5R4G3B) associada ao relevo (MDT). Constatouse que, a associação de dados espectrais com dados altimétricos (MDT) possibilitaram agrupamentos salientando as características físicas e de uso e ocupação das unidades, onde nota-se a distribuição das variáveis de forma coerente agregando as regiões de maior refletância dos canais espectrais vermelho, infravermelho médio e infravermelho próximo. O canal do vermelho refletiu mais ocupações antrópicas, sobretudo percebidas na porção urbana à sudeste da segunda unidade. Já o infravermelho médio refletiu os usos pela agricultura e solos expostos de ambas as unidades. A banda do infravermelho próximo refletiu a vegetação presente nas unidades, destacando-se, neste caso a primeira unidade de estudo. A inserção da variável do relevo (MDT) aumentou os agrupamentos ocasionando maior fusão nas regiões, salientando porções de destaque nas unidades, como os aplainamentos em altitudes elevadas, bem como vales onde percorrem os rios Pitangui e Jotuba.