O presente trabalho propõe uma metodologia para a elaboração de modelos digitais de elevação e ortofotos, em média e pequena escala, de maneira automática, utilizando correlação de imagens digitais, a partir de fotografias aéreas métricas. Aborda conceitos básicos de Fotogrametria e correlação de imagens digitais, e lança o conceito da janela epipolar como ferramenta auxiliar na busca e correlação. Estas janelas permitem obter a máxima correlação simultânea entre duas ou mais imagens, como também a varredura em qualquer direção. Isso torna bastante robusto o método de elaboração dos produtos propostos. Esta metodologia foi implementada em um caso prático onde se utilizou uma cobertura aerofotogramétrica em escala 1:25.000. Os produtos foram validados, a partir de elementos de referência retirados de outra cobertura em escala 1:5.000. As precisões atenderam os requisitos do PEC Classe A para a escala 1:2.500 em planimetia, e intervalos de curvas de nível de 5 metros de eqüidistância, em altimetria. / This work presents a methodology for the production of digital elevation model and orthophotos on medium and small scales, with an automatic manner, using the concepts of digital image correlation. It presents basic fundamentals of photogrammetry and digital image correlation, and launches the concept of epipolar window like an auxiliary search and correlation tool. These windows allow the simultaneous multiple image correlation, in different direction scanning. This makes quite robust the production method of the proposed products. This methodology was implemented in a practical case, where 1:25.000 scale photogrammetric images were used. The products were validated using a 1:5.000 scale coverage as reference. The accuracy and precision achieved are into the Brazilian Cartography Standards as \"Classe A\" for the 1:2.500 scale, in planimmetry, and 5 meter contour lines interval in altimetry.

Studying past landscapes existing before the modern era (pre-1945) carries great difficulty. Historical maps can offer insight to researchers, but the two-dimensional cartographic features on these maps remain largely inaccessible for geospatial analyses. This study investigates the idea of unlocking the data within historical maps to be utilized by Geographic Information Systems (GIS). To realize this goal, the cartographic features must be extracted and converted into digital vector (line) and raster (grid) data. For the purposes of this study, we focus on the extraction of elevation contour lines in United States Geological Survey (USGS) historical topographic maps. These lines are converted into Digital Elevation Models (DEMs), thus creating historically accurate digital landscapes. To ensure a high-quality result, the topographically derived DEMs (TOPO-DEMs) are compared to modern satellite-derived DEMs. The implications of this study can be directly applied to historical, archeological, and environmental research.

The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility. / Master of Science

In recent years there has been large increase in the amount of digital mapping data of landscapes and urban environments available through satellite imaging. This digital information can be used to develop wind flow simulators over large cities or regions for various purposes such as pollutant transport control, weather forecasts, cartography and other topographical analysis. It can also be used by architects for city planning or by game programmers for virtual reality and similar applications. But this data is massive and contains a lot of redundant information such as trees, cars, bushes, etc. For many applications, it is beneficial to reduce these huge amounts of data through elimination of unwanted information and provide a good approximate model of the original dataset. The resultant dataset can then be utilized to generate surface grids suitable for CFD purposes or can be used directly for real-time rendering or other graphics applications. Digital Elevation Model, DEM, is the most basic data type in which this digital data is available. It consists of a sampled array of elevations for ground positions that are regularly spaced in a Cartesian coordinate system. The purpose of this research is to construct and test a simple and economical prototype which caters to image procesing and data reduction of DEM images through noise elimination and compact representations of complex objects in the dataset. The model is aimed at providing a synergy between resultant image quality and its size through the generation of various levels of detail. An alternate approach using the concepts of standard deviation helps in achieving the desired goal and the results obtained by testing the model on Salt Lake City dataset verify the claims. Thus, this thesis is aimed at DEM image processing to provide a simple and compact representation of complex objects encountered in large scale urban environment datasets and reduce the size of the dataset to accommodate efficient storage, computation, fast transmission across networks and interactive visualization.

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Topographic analysis using digital elevation models (DEMs) has become commonplace in soil and hydrologic modeling and analysis and there has been considerable assessment of the effects of grid resolution on topographic metrics using DEMs of 10 m resolution or coarser. However, examining fine-scale (i.e., 1-10 m) soil and hydrological variability of headwater catchments may require higher-resolution data that has only recently become available, and both DEM accuracy and the effects of different high-resolution DEMs on topographic metrics are relatively unknown. This study has two principle research components. First, an error analysis of two high-resolution DEMs derived from light detection and ranging (LiDAR) data covering the same headwater catchment was conducted to assess the applicability of such DEMs for modeling fine-scale environmental phenomena. Second, one LiDAR-derived DEM was selected for computing topographic metrics to predict fine-scale functional soil units termed hydropedological units (HPUs). HPU development is related to topographic and surface/subsurface heterogeneity resulting in distinct hydrologic flowpaths leading to variation of soil morphological expression. Although the two LiDAR datasets differed with respect to data collection methods and nominal post-spacing of ground returns, DEMs interpolated from each LiDAR dataset exhibited similar error. Grid resolution affected DEM-delineated catchment boundaries and the value of computed topographic metrics. The best topographic metrics for predicting HPUs were the topographic wetness index, bedrock-weighted upslope accumulated area, and Euclidean distance from bedrock. Predicting the spatial distribution of HPUs may provide a more comprehensive understanding of hydrological and biogeochemical functionality of headwater systems. / Master of Science

Mountain glaciers are expected to be the greatest contributor to sea level rise over the next century. Glaciers provide a good indicator of global climate and how to monitor their change is an increasingly important issue for climate science and for sea level rise forecasts. However, there has been little direct measurement of glacier volume change in New Zealand. This study explores the use of remotely sensed data for measuring glacier volume change from 1965 to 2006. Digital photogrammetric methods were used to extract topographic data of the Tasman Glacier from aerial photography and ASTER imagery for the years 1965, 1986, 2002 and 2006. SRTM C band data from 2000 were also analysed. Data were compared to an existing digital elvation model produced from the New Zealand Digital Topographic Database to test for their reliability. Using regression analysis, the data were filtered and points representing rock were used to correct points on the glacier ice for vertical bias. The quality of the data extracted from the aerial photography was good on rock and debris covered ice, but poor on snow. The data extracted from ASTER was much more reliable on snow in the upper glacier than the aerial photography, but was very poor in the lower debris covered region of the glacier. While the quality of the SRTM data is very high, there is a second order distortion present in the data that is evident over elevation differences. However, the overall mean difference of the SRTM rock from TOPODATA is close to zero. An overall trend could be seen in the data between dates. However, the 2006 ASTER data proved unreliable on the debris covered section of the glacier. Total volume change is therefore calculated for the period between 1965 and 2002. The data show a loss of 3:4km³ or 0:092km³ per year, an estimated 6% of the total ice in New Zealand. This is compared to estimates using the annual end of summer snowline survey between 1977 and 2005 of 1:78 km³, or 0:064km³ per year. The spatial resolution of ASTER makes high temporal resolution monitoring of volume change unlikely for the New Zealand glaciers. The infrequency of aerial photography, the high cost and vast time involved in extracting good quality elevation data from aerial photography makes it impractical for monitoring glacier volume change remotely. However, SRTM and other radar sensors may provide a better solution, as the data do not rely heavily on user processing.

As part of a collaborative project at a stream segment reach on Proctor Creek in Atlanta, GA, UAV-based SfM photogrammetry was tested as a method for collecting fluvial topographic data relative to traditional USGS total station surveying methods. According to the USGS method, 11 transects were surveyed, and imagery was collected via a UAV to create a SfM DEM. The resulting DEM was incomplete but showed promise for the SfM method. Two additional stream segments were chosen in the Atlanta area, the first along SFPC in DHCL and the second along NFPC near Buford Hwy. For each site 11 transects were surveyed along with submerged GCPs so that the SfM DEMs could be compared to the surveyed data. The BW and BD values were collected from the TS transects and the DEM transects and compared according to the percent difference between the two. For SFPC, the average percent difference values for BW and BD were, respectively, 15.9 and 26.0 with standard deviations of 15.7 and 18.0. For NFPC, the BW and BD average percent difference values were 6.8 and 7.5 with standard deviations of 3.9 and 5.9. The GCPs were also compared for each site using linear regressions. There was no strong correlation for SFPC (R2 = 0.31 and p-value > 0.05), but there was a strong relationship indicated for NFPC (R2 = 0.78 and p-value < 0.05). While the results of this study are variable, the results do indicate promise for future work on this emerging method.

Nesse estudo foram mapeadas e mensuradas as variações de área, elevação mínima e orientação das geleiras da Colômbia e da Venezuela (trópicos internos), entre os anos 1985-2015. Para o mapeamento das áreas das geleiras foram utilizadas como base imagens Landsat, sensores TM, ETM+ e OLI. Às imagens selecionadas foi aplicado o Normalized Difference Snow Index (NDSI), no qual são utilizadas duas bandas em que o alvo apresenta comportamento espectral oposto ou com características bem distintas: bandas 2 e 5 dos sensores TM e ETM+ e bandas 3 e 6 do sensor OLI. Os dados de elevação e orientação das massas de gelo foram obtidos a partir do Modelo Digital de Elevação SRTM (Shuttle Radar Topography Mission – v03). Em 1985, a soma das áreas das sete geleiras estudadas correspondia a 92,84 km², enquanto no último ano estudado (2015/2016) esse valor passou para 36,97 km². A redução de área ocorreu em todas as geleiras analisadas, com taxas de retração anual variando entre 2,49% a.a. e 8,46% a.a. Houve retração das áreas de gelo localizadas em todos os pontos cardeais considerados, bem como, elevação da altitude nas frentes de geleiras. Além da perda de área ocorrida nas menores altitudes, onde a taxa de ablação é mais elevada, também se observou retração em alguns topos, evidenciado pela ocorrência de altitudes menores nos anos finais do estudo, em comparação com os anos iniciais. Como parte das geleiras colombianas está localizada sobre vulcões ativos, essas áreas sofrem influência tanto de fatores externos, quanto de fatores internos, podendo ocorrer perdas de massa acentuadas causadas por erupção e/ou terremoto. / In this study, glaciers located in Colombia and Venezuela (inner tropics) were mapped between 1985-2015. The area of these glaciers was measured and the variations that occurred in each glacier were compared to identify whether the glacier was growing or shrinking. The minimum elevation of the glaciers fronts and the aspect of the glaciers were analyzed. The glaciers areas ware obtained by the use of Landsat images, TM, ETM+ and OLI sensors. The Normalized Difference Snow Index (NDSI) was applied to the selected images, in which two bands were used, where the ice mass has opposite (or very different) spectral behavior: bands 2 and 5 from sensors TM and ETM+, and bands 3 and 6 from sensors OLI. The elevation and the aspect data of the glaciers were obtained from SRTM (Shuttle Radar Topography Mission – v03) Digital Elevation Model. In 1985/1986, the sum of the areas of the seven studied glaciers corresponded to 92.84 km², while in the last year analyzed (2015/2016), this value shrank to 36.97 km². The area shrinkage occurred in all the glaciers that were mapped, with annual decline rates ranging from 2.49%/year to 8.46%/year. It is also possible to observe a decrease of the ice covered in all aspects considered, as well as an elevation in all glaciers fronts. In addition to the area loss occurred at lower altitudes, where the ablation rate is higher than in higher altitudes, shrinkage in some mountain tops was also present, which is evidenced by the occurrence of lower maximum elevations in the final years of the study, when compared with the initial years. Considering that part of the Colombian’s glaciers are located on active volcanoes, these areas are influenced by external and internal factors, and the occurrence of volcanic eruption and/or earthquake can cause sharp mass losses.

Eventos de inundação são fenômenos geralmente associados a eventos de chuvas intensas. Nesses eventos a cobertura de nuvens, normalmente, prejudica o mapeamento com uso de imagens ópticas. Assim, este trabalho tem como objetivo avaliar os resultados de mapeamento de áreas de inundação utilizando imagens SAR e SRTM. Para aplicação dos métodos foram analisadas as áreas de inundação nas cidades de Santa Fe e Parana, na Argentina. Embora a maior inundação registrada tenha sido no ano de 2003, registros de inundação são frequentemente observados nas províncias de Santa Fé e Entre Ríos. Foi utilizado imagens do satélite Sentinel-1, equipado com sensor C-SAR com dupla polarização (VV/VH). As imagens obtidas são do tipo Interferométrico (IW) Ground Range Detected (GRDH) com resolução espacial de 10 m. Foram utilizadas imagens em períodos com e sem eventos de inundação entre 2016 e 2017, calibradas e coregistradas. Sobre as imagens foram aplicadas técnicas de limiarização e de análise temporal para mapear a mancha de inundação. Também foi elaborado mapa a partir do Modelo Digital de Elevação (MDE) utilizando como referência estações de medição de nível da água dos rios. A validação de todos os métodos foi totalmente remota, baseando-se em um mapeamento da inundação de abril de 2003 na cidade de Santa Fe. Além disso, imagens publicadas de eventos de inundação complementaram a validação e foi possível comparar os resultados com uma imagem óptica Landsat – 8 com resolução de 15 m do dia 22 de fevereiro de 2016, quando o nível do rio Paraná estava acima do nível de alerta Os resultados dos três mapeamentos foram somados para formar uma única imagem com a mancha de inundação em comum. Entre as melhores acurácias, o método de análise do MDE atingiu o melhor resultado, 82% da área de inundação, no entanto, considerando os três métodos, a acurácia atinge mais de 91% de precisão. A técnica de limiarização foi mais eficiente em áreas sem alvos verticais, como áreas urbanas por exemplo. O MDE foi eficiente para simular a inundação em todos os alvos, no entanto em modelos de elevação com melhor resolução, o resultado final do mapeamento será mais preciso. A análise temporal mostrou ser uma técnica promissora para mapeamentos de inundação, no entanto um mapa detalhado de uso de solo é fundamental para aprimorar o resultado desta análise. Todos os processos foram feitos remotamente, possibilitando o desenvolvimento no futuro de um sistema automático para detecção de evento de inundação que pode ser aplicado em áreas com características similares. / Flood events usually go hand in hand with intensive rainfall during which clouds compromise any mapping attempts with optical imagery. Thus, this thesis aims at evaluate the results of mapping flood areas using SAR and SRTM images. For this purpose, flood areas in the cities Santa Fe and Parana in Argentina were analyzed. While the worst flood was registered in 2003, flood events frequently occur in both provinces Santa Fé and Entre Ríos. The employed Sentinel-1 satellite carrying a C-SAR sensor with dual polarization (VV/VH) provided interferometric (IW) Ground Range Detected (GRDH) imagery with a spatial resolution of 10 meters. Images from periods with and without flood events between 2016 and 2017 were calibrated and co-registered. Subsequently on the images were applied threshold and time analysis techniques, as well as a Digital Elevation Model (DEM) analysis with data from stations which measure the rivers’ water levels. The validation of all methods was totally remote, based on a flood mapping of April 2003 in the city of Santa Fe. In addition, published images of flood events complemented the validation and it was possible to compare the results with an optical image Landsat - 8 with 15 m resolution of February 22, 2016, when the level of the Paraná River was above the alert level The three maps were summed to form a single image with the flood spot in common. Among the best accuracy, the MDE analysis method achieved the best result, 82% of the flood area, however, considering all three methods, the accuracy reaches more than 91% accuracy. The thresholding technique was more efficient in areas with no vertical targets, such as urban areas. The DEM was efficient to simulate flooding on all targets, however using elevation models with better resolution, the final result of the mapping will be more accurate. The temporal analysis showed to be a promising technique for flood mapping, however a detailed map of land use is fundamental to improve the results of this analysis. All processes were done remotely, allowing the future development of an automatic flood event detection system that can be applied in areas with similar characteristics.