There is overwhelming scientific evidence that shows the temperature of the Earth's atmosphere is rising at an unprecedented rate. This is attributed to increased levels of greenhouse gas emissions, a large proportion of which originates from anthropogenic combustion of carbon-based fossil fuels for energy. There is therefore a strong argument for the increased role of less environmentally damaging, low carbon energy sources including renewable energy technologies. Run-of-river hydropower is one such renewable energy option, considered more environmentally benign than traditional hydropower which requires the construction of large dams to create a reservoir. The aim of this study was to develop a model to search for, and map, economically viable run-of-river hydropower resource that can function on any global catchment of any size. Development and testing of the model was conducted on China's 2 million km2 Yangtze River drainage basin, the third longest river in the world and a rich landscape for hydropower. A gridded, distributed hydrological model was developed integrating high-resolution meteorological datasets and a digital elevation model (DEM). Using the model, the surface hydrology of the Yangtze catchment was simulated at a timestep of 6 minutes to obtain the mean daily surface runoff for every day from the beginning of 1979 to the end of 2007. Observed river flow data from sub-catchments of the Yangtze were used to calibrate the model by differential optimisation, an evolutionary computation technique. Validation was carried out on a 1.6 million km2 sub-catchment resulting in a mean objective function of 0.95 (where a perfect fit would be 1.0) across 8 objective functions commonly used in hydrology. Catchment wide mean daily runoff data was used to develop flow duration curves across the catchment river network. Virtual power stations were constructed at each river cell, iteratively testing differing scheme configurations, and costed using the RETScreen methodology. A best performing hydropower network was determined by a conflict algorithm, designed to prioritise high profit schemes and to remove lower performing and conflicting schemes. This resulted in a potential run-of-river installed capacity across the Yangtze catchment of 103GW (at 10% discount rate), generating 394TWh per annum. This model would be a valuable tool in finding optimal locations for future hydropower resource.

Analysis of digital elevation models (DEMs) by means of geomorphometry provides means of recognising fractures and characterising the morphotectonics of an area in a quantitative way. The objective of the thesis is to develop numerical methods and a consistent GIS methodology for tectonic geomorphology and apply it to test sites. Based on the study of landforms related to faults, geomorphological characteristics are translated into mathematical and numerical algorithms. The methodology is based on general geomorphometry. In this study, the basic geometric attributes (elevation, slope, aspect and curvatures) are complemented with the automatic extraction of ridge and valley lines and surface specific points. Evan’s univariate and bivariate methodology of general geomorphometry is extended with texture (spatial) analysis methods such as trend, autocorrelation, spectral, wavelet and network analysis. Digital terrain modelling is carried out by means of (1) general geomorphometry, (2) digital drainage network analysis, (3) digital image processing, (4) lineament extraction and analysis, (5) spatial and statistical analysis and (6) DEM specific digital methods such as shaded relief models, digital cross-sections and 3D surface modelling. Geological data of various sources and scales are integrated in a GIS database. Interpretation of multi-source information confirmed the findings of digital morphotectonic investigation. A simple shear model with principal displacement zone in the NE-SW direction can explain most of the morphotectonic features associated with structures identified by geological and digital morphotectonic investigations in the Kali Basin. Comparison of the results of the DTA with the known geology from NW Greece indicated that the major faults correspond to clear lineaments. Thus, DTA of an area in the proposed way forms a useful tool to identify major and minor structures covering large areas. In this thesis, numerical methods for drainage network extraction and aspect analysis have been developed and applied to tectonic geomorphology.

The Prairie Pothole Region (PPR) of North American contains millions of wetlands in shallow depressions that provide important hydrological and ecological functions. To assess and model these functions it is important to have accurate methods to quantify wetland water volume storage. Hayashi and van der Kamp (2000) developed equations suitable for calculating water volume in natural, regularly shaped wetlands when two coefficients are known. This thesis tested the robustness of their full and simplified volume (V) area (A) depth (h) methods to accurately estimate volume for the range of wetland shapes occurring across the PPR. Further, a digital elevation model (DEM) derived from light detection and ranging (LiDAR) data was used to extract the necessary data for applying the simplified V-A-h method at a broad spatial scale. Detailed topographic data were collected for 27 wetlands in the Smith Creek Research Basin and St. Denis National Wildlife Area, Saskatchewan that ranged in surface area shape. The full V-A-h method was found to accurately estimate volume (errors <5%) across wetlands of various shapes and is therefore suitable for calculating water storage in the variety of wetland shapes found in the PPR. Analysis of the simplified V-A-h method showed that the depression (p) and size (s) coefficients are sensitive to the timing of area and depth measurements and the accuracy of area measurements. Surface area and depth should be measured concurrently at two points in time to achieve volume errors <10%. For most wetlands this means measuring area and depth in spring when water levels are approximately 70% of hmax, and also in late summer prior to water depths dropping below 0.1 m. The wetted perimeter of the deepest water level must also be measured accurately to have volume errors less than 10%. Applying the simplified V-A-h method to a LiDAR DEM required GIS analysis to extract elevation contours that represent potential water surfaces. From these data the total wetland depth and s coefficient were estimated. Volume estimates through this LiDAR V-A-h method outperformed estimates from two volume-area equations commonly used in the PPR. Furthermore, the process to extract the wetland coefficients from the LiDAR DEM was automated such that storage could be estimated for the entire St. Denis National Wildlife Area. Applying the simplified V-A-h method according to the guidelines and data sources recommended here will allow for more accurate, time-effective water storage estimates at multiple spatial scales, thereby facilitating evaluation and modelling of hydrological and ecological functions.

The Prairie Pothole Region (PPR) of North American contains millions of wetlands in shallow depressions that provide important hydrological and ecological functions. To assess and model these functions it is important to have accurate methods to quantify wetland water volume storage. Hayashi and van der Kamp (2000) developed equations suitable for calculating water volume in natural, regularly shaped wetlands when two coefficients are known. This thesis tested the robustness of their full and simplified volume (V) area (A) depth (h) methods to accurately estimate volume for the range of wetland shapes occurring across the PPR. Further, a digital elevation model (DEM) derived from light detection and ranging (LiDAR) data was used to extract the necessary data for applying the simplified V-A-h method at a broad spatial scale. Detailed topographic data were collected for 27 wetlands in the Smith Creek Research Basin and St. Denis National Wildlife Area, Saskatchewan that ranged in surface area shape. The full V-A-h method was found to accurately estimate volume (errors <5%) across wetlands of various shapes and is therefore suitable for calculating water storage in the variety of wetland shapes found in the PPR. Analysis of the simplified V-A-h method showed that the depression (p) and size (s) coefficients are sensitive to the timing of area and depth measurements and the accuracy of area measurements. Surface area and depth should be measured concurrently at two points in time to achieve volume errors <10%. For most wetlands this means measuring area and depth in spring when water levels are approximately 70% of hmax, and also in late summer prior to water depths dropping below 0.1 m. The wetted perimeter of the deepest water level must also be measured accurately to have volume errors less than 10%. Applying the simplified V-A-h method to a LiDAR DEM required GIS analysis to extract elevation contours that represent potential water surfaces. From these data the total wetland depth and s coefficient were estimated. Volume estimates through this LiDAR V-A-h method outperformed estimates from two volume-area equations commonly used in the PPR. Furthermore, the process to extract the wetland coefficients from the LiDAR DEM was automated such that storage could be estimated for the entire St. Denis National Wildlife Area. Applying the simplified V-A-h method according to the guidelines and data sources recommended here will allow for more accurate, time-effective water storage estimates at multiple spatial scales, thereby facilitating evaluation and modelling of hydrological and ecological functions.

Aogu Farm was built on reclaimed land. Because of its rich ecological resources, Aogu was defined as a "Major Wildlife Habitat" in Chiayi County by the Forest Service and is also expected to reduce carbon in the plan for the flatland forest. Power pumps are currently used to irrigate and cultivate the area. However, after becoming the Forest Recreation Area in the future, Aogu Farm has to reduce the influence of human beings. However, if the Taisugar Company doesn¡¦t support the plan of Forest Service or abandons farming and stops the pumping power, the Aogu Wetlands will suffer the crisis of coastal inundation. Hydrological models have their own characteristics. For example, inundation models for regional drainage using one-dimensional channel flow, two-dimensional overland flow, and runoff in the mountains all can be assessed to solve the inundation problems in the coastal lowlands. Nevertheless, the Aogu Wetlands, the Case Study area, has both a dry season and a wet season even in the lowland. When rainstorms occur, the area is unable to discharge the water by gravity but can depend only on pumps to discharge the water into the sea. Therefore, based on the need to manage water, hydrological surveys must be conducted to assess the hydrological impact of continuous rainfall on the Aogu Wetlands and to provide reference information to assist in the management. In the thesis, I use both the Geographic Information System (GIS) and the Storm Water Management Model (SWMM) as analysis tools. Moreover, the different regions are further divided into watershed and the route of drainage, establishing two kinds of models of watershed hydrology for precipitation simulations. Finally, to compare these two methods, the Arc Hydro and SWMM models are used in watershed analysis.

Visibility analysis is an important GIS tool that is used in a diverse array of disciplines ranging from earth sciences to telecommunications. Multi-visibility, as a cumulative type of visibility, combines many point-to-point results into a multi-value array. Points, lines, or areas can be used as sources or targets / and the combined values can be calculated in both ways. Through multi-visibility, a special 2.5D visibility value surface can be constructed over a digital elevation model. The effectiveness of multi-visibility can be increased with weighted target zones. Other types of weighting criteria can be defined, such as distance and angle. Open source GIS tools offer a limited amount of support for that type of multivisibility analysis. In this study, a weighted multi-visibility methodology has been developed which accepts a path as the source. The path can have a specific direction to account for moving subjects that have a specific view angle based on their direction. A software tool has been developed to apply the methodology in a practical and automated way. The tool was written in Python programming language and can be run as a plugin to the open source Quantum GIS software. The proposed weighted multi-analysis methodology and its software tool can be used to assess the quality of visibility through the generation of value surfaces and calculation of a combined quantitative visibility value for the full path.

There is an increasing need for three-dimensional description for various applications such as the development of catchment areas, forest fire control and restoration. Three-dimensional information plays an indispensable role; therefore acquisition of the digital elevation models (DEMs) is the first step in these applications. LiDAR is a recent development in remote sensing with great potential for providing high resolution and accurate three-dimensional point clouds for describing terrain surface. The acquired LiDAR data represents the surface where the laser pulse is reflected from the height of the terrain and object above ground. These objects should be removed to derive the DEMs. Many LiDAR data-filtering studies are based on surface, block, and slope algorithms. These methods have been developed to filter out most features above the terrain; however, in certain situations they have proved unsatisfactory. The different algorithm based on different point of view to describe the terrain surface. The appropriate adoption of the advantages from these algorithms will develop a more complete way to derive DEMs. Knowledge-based system is developed to solve some specific problems according to the given appropriate domain knowledge. Huang (2007) proposed a Knowledge-based classification system in urban feature classification using LiDAR data and high resolution aerial imagery with 93% classification accuracy. This research proposed a knowledge-based LiDAR filtering (KBLF) as a follow-up study of Huang¡¦s study. KBLF integrates various knowledge rules derived from experts in the area of ground feature extraction using LiDAR data to increase the capability of describing terrain and ground feature classification. The filtering capability of KBLF is enhanced as expected to get better quality of referenced ground points to recover terrain height and DEMs using Inverse Distance Weighting (IDW) and Nearest Neighbor (NN) methods.

Closed depressions, low elevation features in a landscape with no outlet point, play an important role in both surface and subsurface hydrology. These depressions, which are common in hummocky morainal landscapes, pool incoming surface flow, promoting infiltration and facilitating leaching of surface pollutants into vital groundwater resources. Due to the cost of ground based identification in large areas and difficulties with the identification of irregular depressions, remote identification using digital elevation models (DEMs) stands as a practical and effective tool for the mapping of these closed depressions. A modified stochastic depression identification algorithm was used in this study to characterize depressions in the Waterloo and Paris-Galt-Guelph moraines of Southwestern Ontario. The simulation output was a map of depressions in the study area. Depressions were corroborated using GRCA Wetlands data, Google Street View imagery, SWOOP 2006 orthophotos and field validation. Depression corroboration showed that the algorithm was able to accurately identify the location of closed depressions containing wetlands and closed depressions that are dry (largely due to wetland draining) in the hummocky topography of the study site. This research has implications for depression mapping in the field of digital terrain analysis as it enables the identification of real depressions in large study areas with a moderate resolution DEM. Providing a means of efficiently mapping closed depressions is important because of the role closed depressions play in the recharge of important groundwater stores. / Natural Resources Canada

In this study, DEMs and orthoimages were generated from ASTER imagery and their accuracies were assessed. The study site covers an area of approximately 60 x 60 km and encloses the city of Ankara. First, DEMs were generated from stereo ASTER images. In order to find the best GCP combination, different number of GCPs (8, 16, 24, and 32) was used. The accuracies of the generated DEMs were then assessed based on the check points (CP), slopes and land cover types. It was found that 16 GCPs were good compromise to produce the most accurate DEM. The post processing and blunder removal increased the overall accuracy up to 38%. It was also found that there is a strong linear relationship between the accuracies of DEMs and the slopes of the terrain. The accuracies computed for water, urban, forest, mountainous, and other areas were found to be 5.01 m, 8.03 m, 12.69 m, 17.14 m, and 10.21 m, respectively. The overall accuracy was computed as 10.92 m. The orthorectification of the ASTER image was carried out using 12 different mathematical models. Based on the results, the models First Order 2D Polynomial, Direct Linear Transformation and First Order Polynomial with Relief have produced the worst results. On the other hand, the model Second Order Rational Function appears to be the best model to orthorectify the ASTER images. However, the developed model Second Order Polynomial with Relief provides simplicity, consistency and requires less number of GCPs when compared to the model Second Order Rational Function.

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.