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.

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

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Naiqian Zhang / Soil surface roughness is a major factor influencing soil erosion by wind and water. Studying surface roughness requires accurate Digital Elevation Model (DEM) data. A vehicle-based laser measurement system was developed to generate high-resolution DEM data. The system consisted of five units: a laser line scanner to measure the surface elevation, a gyroscope sensor to monitor the attitude of the vehicle, a real-time kinematic GPS to provide the geographic positioning, a frame-rail mechanism to support the sensors, and a data-acquisition and control unit. A user interface program was developed to control the laser system and to collect the sensors data through a field laptop. Laboratory experiments were conducted to evaluate the performance of the laser sensor on different type of targets. The results indicated that the laser measurement on a white paper had the least variability than that on other targets. The laser distance measurement was calibrated using the data acquired on the white paper. Static accuracy tests of the gyroscope sensor on a platform that allowed two-axis rotations showed that angle measurement errors observed in combined pitch/roll rotations were larger than those in single rotations. Within ±30° of single rotations, the measurement errors for pitch and roll angles were within 0.8° and 0.4°, respectively. A model to study the effect of attitude measurement error on elevation measurement was also developed. DEM models were created by interpolating the raw laser data using a two-dimensional, three-nearest neighbor, distance-weighted algorithm. The DEM models can be used to identify shapes of different objects. The accuracy of the laser system in elevation measurement was evaluated by comparing the DEM data generated by the laser system for an unknown surface with that generated by a more accurate laser system for the same surface. Within four replications, the highest correlation coefficient between the measured and reference DEMs was 0.9371. The correlation coefficients among the four replications were greater than 0.948. After a median threshold filter and a median filter were applied to the raw laser data before and after the interpolation, respectively, the correlation coefficient between the measured and reference DEMs was improved to 0.954. Correlation coefficients of greater than 0.988 were achieved among the four replications. Grayscale images, which were created from the intensity data provided by the laser scanner, showed the potential to identify crop residues on soil surfaces. Results of an ambient light test indicated that neither sunlight nor fluorescent light affected the elevation measurement of the laser system. A rail vibration test showed that the linear rail slightly titled towards the laser scanner, which caused small variations in the pitch angle. A preliminary test on a bare soil surface was conducted to evaluate the capability of the laser system in measuring the DEM of geo-referenced surfaces. A cross-validation algorithm was developed to remove outliers. The results indicated that the system was capable of providing geo-referenced DEM data.

The Darwin-Hatherton glacial system is an outlet glacial system in the Transantarctic Mountains, Antarctica, which drains ice from the East Antarctic Ice Sheet into the Ross Ice Shelf. This research provides remotely sensed data that can be used in modeling research for the Darwin-Hatherton glacial system, which in turn can be used in mass balance research for the West Antarctic Ice Sheet. Two improved digital elevation models (DEM) are produced to cover the lower Darwin Glacier and to cover the upper Darwin and Hatherton Glaciers. The new improved DEMs are generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data, with a resolution of 45 m. To produce the two final DEMs, multiple DEMs are firstly adjusted to remove systematic errors and are then stacked and averaged to increase the accuracy and produce the final two DEMs. For the lower Darwin Glacier, 5 DEMs were averaged and in the upper Darwin and Hatherton Glaciers, 6 DEMs were averaged. The accuracy is quantified by a remaining error of + 9 m for the lower Darwin Glacier DEM and + 37 m for the upper Darwin and Hatherton Glaciers DEM. This is a significant improvement from the existing 200 m resolution Radarsat Antarctic mapping project (RAMPv2) DEM which has a remaining error of + 138 m over the lower Darwin Glacier and + 152 m over the upper Darwin and Hatherton Glaciers. The accuracy is assessed by comparing the ASTER and RAMPv2 DEMs to highly accurate ice, cloud and land elevation satellite (ICESat) laser altimetry data. A 15 m resolution, true colour, orthorectified image is provided for the entire Darwin-Hatherton glacial system from ASTER satellite imagery. The DEMs used to orthorectify the ASTER satellite imagery are the two new 45 m resolution ASTER DEMs. Lastly feature tracking was explored as a method for measuring surface ice velocity. This research shows that feature tracking is unsuitable for the Darwin-Hatherton glacial system if using 15 m resolution satellite imagery over a 1 to 4 year time period.

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.