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.

In order to understand the migration patterns of migrating birds, it is necessary to understand whenand where to they migrate. Many of these birds are very small and thus cannot carry heavy sensors;hence it is necessary to be able to perform positioning using a very small sensor. One way to do this isto use a light-intensity sensor. Since the sunrise and sunset times are known given time and position onthe earth, it is possible to determine the global position using light intensity. Light intensity increasesas the sun rises. Data sets from several calibration sensors, mainly from different locations in Sweden, have been examinedin different ways in order to get an understanding of the measurements and what affects them. Inorder to perform positioning, it is necessary to know the solar elevation angle, which can be computedif the time and position are known, as is the case for the calibration sensors. This has been utilized toidentify a mapping from measured light intensity to solar elevation angle, which is used to computepseudo-measurements for target tracking, described below. In this thesis, positioning is performed using methods from the field of target tracking. This is doneboth causally (filtering) and non-causally (smoothing). There are certain problems that arise; firstly,the measured light intensity can be attenuated due to weather conditions such as cloudiness, which ismodelled as a time-varying offset. Secondly, the sensor can be shadowed causing outliers in the data.Furthermore, birds are not always in a migratory state, they oftentimes stay in one place. The lattertwo phenomena are modelled using an Interacting Multiple Model (IMM) where they are representedas discrete states, corresponding to different models.

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.

This report compares surface elevation change and volume change esti- mates from three methods: repeat track (RT), crossover (CX), and overlapping footprints (OFP). These three methods use different approaches to group- ing elevation point measurements taken at different measurement epochs and estimating elevation change. Volume changes are calculated from elevation changes in the same manner for all three methods but differences in sampling resolution between the methods affect volume change estimates in different ways. The recently reprocessed Release 633 version of elevation measurements from the Geoscience Laser Altimeter System (GLAS), flown on the Ice, Cloud and land Elevation Satellite (ICESat), are used in this analysis. Both elevation changes and volume changes are compared for both the Greenland Ice Sheet (GrIS) and the Antarctic Ice Sheet (AIS). Additionally, uncertainties in the estimates for each method are quantified and compared. Results are separated by drainage systems and by above/below 2000 m surface elevation for the GrIS. For the AIS, results are aggregated to the East, West, and Penin- vi sula regions. Volume change estimates agree well for the three methods for the GrIS, with estimates of -227.75 ± 2.12 km³/yr, -249.30 ± 3.42 km³/yr, and -218.24 ± 7.39 km³/yr for the RT, CX, and OFP methods, respectively. These estimates are similar to those published from previous studies. For the AIS, however, larger discrepancies are found in the estimates. This stems primarily from a large discrepancy in the volume change estimate of the East AIS, where the RT, CX, and OFP methods estimate volume changes of 33.39 ± 1.42 km³/yr, 46.42 ± 5.46 km³/yr, and -2.72 ± 2.12 km³/yr, respectively. It's not entirely clear why this large discrepancy exists in this particular region, and elevation change estimates for a few particular drainage systems in this region are examined. Previously published volume changes for the AIS also show a large scatter and more work must be done to reconcile the various estimates. Finally, the volume change uncertainties reported do not completely account for the discrepancies in most regions. Additional analysis must be done to completely quantify all error sources. / text

Revised; Originally Published: 2003 / 7 pp.