The germination and emergence of a seedling, or seedling recruitment remains an essential process in the establishment of a plant. This establishment can be limited by the availability of microsites within the soil profile, or the availability of seed within a given area. Three field experiments were initiated in Saskatchewan, Canada to examine the relative effect of seed and microsite limitations on weed seedling recruitment. The first experiment examined the effect of landscape position as well as nitrogen (N) rate and tillage system (zero tillage vs. conventional tillage) on weed seedling recruitment from an indigenous weed population. Survey results indicated habitat differentiation of the weed population with wild oat and cleavers preferentially recruiting in the lower landscape positions, Russian thistle and Kochia in the upper landscape positions, while green foxtail recruited in high levels on all landscape positions. This suggested that different weed species have different microsite requirements for weed seedling recruitment across contrasting landscape positions. The second field experiment examined the effect of landscape position and moisture availability on weed seedling recruitment from an artificial hand-seeded weed seedbank. This experiment indicated that seed limitation remained a very important factor, but even when irrigated, total seedling recruitment did not reach maximum recruitment, indicating water was not the only limiting resource for weed seedling recruitment. Microsite limitations were greatest on the upper slope position for all species with green foxtail having the greatest overall recruitment of the species across all landscapes and moisture regimes. The third experiment examined the effect of tillage system and density on weed seedling recruitment of wild oat, green foxtail, and wild mustard. Again, weed seedling recruitment remained a function of both microsite and seed limitations as absolute recruitment values increased for each density examined in this experiment. The agronomic significance of microsite limitation was negligible as high weed population numbers occurred for the highest weed seeding densities. Overall, microsite limitations remained negligible in these experiments for arable agriculture with the main influence on weed seedling recruitment most often being seed limitation in the natural seedbank.

The germination and emergence of a seedling, or seedling recruitment remains an essential process in the establishment of a plant. This establishment can be limited by the availability of microsites within the soil profile, or the availability of seed within a given area. Three field experiments were initiated in Saskatchewan, Canada to examine the relative effect of seed and microsite limitations on weed seedling recruitment. The first experiment examined the effect of landscape position as well as nitrogen (N) rate and tillage system (zero tillage vs. conventional tillage) on weed seedling recruitment from an indigenous weed population. Survey results indicated habitat differentiation of the weed population with wild oat and cleavers preferentially recruiting in the lower landscape positions, Russian thistle and Kochia in the upper landscape positions, while green foxtail recruited in high levels on all landscape positions. This suggested that different weed species have different microsite requirements for weed seedling recruitment across contrasting landscape positions. The second field experiment examined the effect of landscape position and moisture availability on weed seedling recruitment from an artificial hand-seeded weed seedbank. This experiment indicated that seed limitation remained a very important factor, but even when irrigated, total seedling recruitment did not reach maximum recruitment, indicating water was not the only limiting resource for weed seedling recruitment. Microsite limitations were greatest on the upper slope position for all species with green foxtail having the greatest overall recruitment of the species across all landscapes and moisture regimes. The third experiment examined the effect of tillage system and density on weed seedling recruitment of wild oat, green foxtail, and wild mustard. Again, weed seedling recruitment remained a function of both microsite and seed limitations as absolute recruitment values increased for each density examined in this experiment. The agronomic significance of microsite limitation was negligible as high weed population numbers occurred for the highest weed seeding densities. Overall, microsite limitations remained negligible in these experiments for arable agriculture with the main influence on weed seedling recruitment most often being seed limitation in the natural seedbank.

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.

<p>A laser radar can be used to generate 3D data about the terrain in a very high resolution. The development of new support technologies to analyze these data is critical to the effective and efficient use of these data in decision support systems, due to the large amounts of data that are generated. Adequate technology in this regard is currently not available and development of new methods and algorithms to this end are important goals of this work.</p><p>A semi-qualitative data structure for terrain surface modelling has been developed. A categorization and triangulation process has also been developed to substitute the high resolution 3D model for this data structure. The qualitative part of the structure can be used for detection and recognition of terrain features. The quantitative part of the structure is, together with the qualitative part, used for visualization of the terrain surface. Substituting the 3D model for the semi-qualitative structures means that a data reduction is performed.</p><p>A number of algorithms for detection and recognition of different terrain objects have been developed. The algorithms use the qualitative part of the previously developed semi-qualitative data structure as input. The taken approach is based on matching of symbols and syntactic pattern recognition. Results regarding the accuracy of the implemented algorithms for detection and recognition of terrain objects are visualized.</p><p>A further important goal has been to develop a methodology for determining driveability using 3D-data and other geographic data. These data must be fused with vehicle data to determine the properties of the terrain context of our operations with respect to driveability. This fusion process is therefore called context fusion. The recognized terrain objects are used together with map data in this method. The uncertainty associated with the imprecision of the data has been taken into account as well.</p> / Report code: LiU-Tek-Lic-2008:29.

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.

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.