Habitat loss and human-caused mortality are the most serious threats facing grizzly bear (<i>Ursus arctosi</i> L.) populations in Alberta, with conflicts between people and bears in agricultural areas being especially important. For this reason, information is needed about grizzly bears in agricultural areas. The objectives of this research were to find the best possible classification approach for determining multiple classes of agricultural and herbaceous land cover for the purpose of grizzly bear habitat mapping, and to determine what, if any, spatial and compositional components of the landscape affected the bears in these agricultural areas. Spectral and environmental data for five different land-cover types of interest were acquired in late July, 2007, from Landsat Thematic Mapper satellite imagery and field data collection in two study areas in Alberta. Three different classification methods were analyzed, the best method being the Supervised Sequential Masking (SSM) technique, which gave an overall accuracy of 88% and a Kappa Index of Agreement (KIA) of 83%. The SSM classification was then expanded to cover 6 more Landsat scenes, and combined with bear GPS location data. Analysis of this data revealed that bears in agricultural areas were found in grasses / forage crops 77% of the time, with small grains and bare soil / fallow fields making up the rest of the visited land-cover. Locational data for 8 bears were examined in an area southwest of Calgary, Alberta. The 4494 km2 study area was divided into 107 sub-landscapes of 42 km2. Five-meter spatial resolution IRS panchromatic imagery was used to classify the area and derive compositional and configurational metrics for each sub-landscape. It was found that the amount of agricultural land did not explain grizzly bear use; however, secondary effects of agriculture on landscape configuration did. High patch density and variation in distances between neighboring similar patch types were seen as the most significant metrics in the abundance models; higher variation in patch shape, greater contiguity between patches, and lower average distances between neighboring similar patches were the most consistently significant predictors in the bear presence / absence models. Grizzly bears appeared to prefer areas that were structurally correlated to natural areas, and avoided areas that were structurally correlated to agricultural areas. Grizzly bear presence could be predicted in a particular sub-landscape with 87% accuracy using a logistic regression model. Between 30% and 35% of the grizzlies‟ landscape scale habitat selection was explained.

This thesis discusses the field testing of a newly-developed product, a geosynthetic capillary break (GCB). The GCB was developed for use in engineered soil covers when a cover incorporating a capillary break effect would be desirable, but the coarse-grained material (gravel or sand) is unavailable or uneconomical. Engineered soil covers aim to reduce the amount of acid generated from sulphide bearing waste by limiting the ingress of water and/or oxygen. The GCB is a geosynthetic system that is composed of a finely ground rock flour sandwiched between two nonwoven geotextiles and manufactured as a composite layer by needle punching in a process similar to the used for GCL (geosynthetic clay liner). The goal of the GCB is to recreate the capillary break that is achieved with soil layers using a geosynthetic product that is only a few centimetres thick and that can be rolled up and for transportation, The GCB concept has been demonstrated in a previous study (Park, 2005) based on laboratory column studies and computer modelling. The goal of this project was to determine the effectiveness of the GCB when applied at field scale. Four 25 square test plots were constructed at the tailings management area (TMA) of the HudBay Minerals Inc.(HudBay) mine site located near Flin Flon, MB. One plot contained 1 m of cover soil over top of the GCB (Plot A), one contained only 1 m of cover soil (Plot B), one contained 0.3 m of cover soil over top the GCB (Plot C), and one consisted of a conventional capillary break system with 1 m of cover soil over lying 0.2 m of sand. All of the plots, along with a control plot with no cover, were instrumented with water content sensors and gas sampling ports to monitor the movement of water and oxygen through the various covers. Matric suction sensors were also installed in Plots A and B to measure the water suction within the covers. A meteorological station was installed to gather climatic data which was used to develop a water balance for each of the plots. The plots were constructed and instrumented in the fall of 2005. Data was collected and analyzed until spring of 2007. Data from the water content sensors show that the GCB was effective in increasing the water content in the soil portion of the cover system. The suction sensors show that the suction across the GCB drops significantly (40 kPa versus less than 1 kPa) as compared to plots which contain no GCB. Data from the gas concentration sensors show that the plots containing capillary breaks reduce the oxygen flux into the tailings. The plots containing the GCB (Plots A and C) resulted in the lowest flux rates, followed by the sand capillary break (Plot D )and no capillary break (Plot B), respectively. This reduction in oxygen flux will reduce the amount of acid generated from waste, as oxygen is required for the creation of acid mine drainage. Overall the study demonstrated that at field scale that the GCB is effective in limiting the ingress of water and oxygen into the tailings under the observed conditions and the manufactured GCB is comparable to the performance of the previous hand constructed column tests.

The use of airborne LiDAR (Light Detection and Ranging) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns. The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus taeda, forests) of the southeastern United States. More specific objectives were to: (1) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests; (2) investigate whether a LiDAR and normalized difference vegetation index (NDVI) data fusion through linear regression improve estimates of these forest canopy characteristics; (3) generate maps of PCC and LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application, was used to locate individual trees to derive an estimate of plot-level PCC. A canopy height model (CHM) was created from the LiDAR dataset and used to determine tree heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers. Linear regression analysis resulted in models explaining 84% and 78% of the variability associated with PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point density must be 2.5 m. The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR-derived PCC map to lower resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not very accurate at local scales. The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management.

Freshwater inflows are a crucial component for maintaining estuarine health, function and productivity. Streamflows, the primary source of freshwater inflows, have been modified and altered from their natural flow by population growth and anthropogenic impacts on the contributing watersheds. The Guadalupe Estuary is a primary habitat for many endangered species. The Guadalupe River Watershed, which supplies 70% of freshwater inflows, experiences rapid urbanization and agricultural development. This study proposed to characterize the impact of land use/cover change in the Guadalupe River Watershed on freshwater inflows to the Guadalupe Estuary. Pre-whitening, Mann-Kendall and bootstrap techniques were used to test for significant trends on streamflow and precipitation. Analyses suggested more trends in annual and seasonal minimum and mean streamflow than would be expected to occur by chance in the periods of 1930-2005 and 1950-2005. No significant trends were found in the period of 1970-2005. Significant trends were more prominent in the upper watershed and decreased as analysis moved downstream in the period of 1950-2005. Trend tests on precipitation data in the period of 1950-2005 revealed more significant trends than would be expected by chance in mean annual and winter precipitation. Analyses of Landsat images of the watershed using an unsupervised classification method showed an increase in forest, urban and irrigated land by 13, 42 and 7%, respectively, from 1987 to 2002. Urbanized areas were mostly found in the middle part of watershed surrounding the I-35 corridor. More than 80% of irrigated lands are distributed over the San Marcos and Middle Guadalupe River Watersheds. Soil and Water Assessment Tool (SWAT) model was applied for the Guadalupe River Watershed. Calibration and validation using data recorded at USGS 08176500 indicated the model performed well to simulate streamflow. The coefficient of Nash- Sutcliffe, determination and percent bias were 0.83, 0.96 and 3.81, respectively, for calibration and 0.68, 0.75 and 29.38 for validation period. SWAT predicted a 2% decrease in annual freshwater inflow rates from the effect of land use/cover change from 1987 to 2002. Reservoirs increased freshwater inflows during low flow months and decreased the inflows during high flow months. Precipitation variability changed characteristics of monthly freshwater inflows.

In this thesis, we study two optimization problems which have a lot of important applications in diverse domains: Line Cover Problem (LCP) in Computational Geometry and Maximum Genus Embedding (MGE) in Topological Graph Theory. We study LCP whose decision version is known NP-Complete from the perspective of Parameterized Complexity, as well as classical techniques in Algorithm Design. In particular, we provide an exact algorithm in time O(n^3 2n) based on Dynamic Programming and initiate a dual problem of LCP in terms of Linear Programming Duality. We study the dual problem by applying approximation and kernelization, obtaining an approximation algorithm with ratio k - 1 and a kernel of size O(k^4). Then we survey related geometric properties on LCP. Finally we propose a Parameterized Algorithm to solve LCP with running time O*(k^k/1:35^k). We explore connections between the maximum genus of a graph and its cycle space consisting of fundamental cycles only. We revisit a known incorrect approach of finding a maximum genus embedding via computing a maximum pairing of intersected fundamental cycles with respect to an arbitrary spanning tree. We investigate the reason it failed and conclude it confused the concept of deficiency. Also, we characterize the upper-embeddablity of a graph in terms of maximum pairings of intersected fundamental cycles, i.e. a graph is upper-embeddable if and only if the number of maximum pairings of intersected fundamental cycles for any spanning tree is the same. Finally, we present a lower and an upper bound of the maximum number of vertex-disjoint cycles in a general graph, beta(G) - 2gammaM(G) and beta(G) - gammaM(G), only depending on maximum genus and cycle rank.

Problems of some regularities in strings, such as repetition, period, seed, square, etc., have been studied extensively recently. Many algorithms have been proposed to solve these problems in O(1) time complexity on an n imes n reconfigurable bus model, where $n$ is the length of the given string. In this paper, we concentrate to solve problems of another form of regularity, the string superprimitivity test problem and the LCS (longest common subsequence) problem in strings on the reconfigurable bus model. And we propose a O(log n) time parallel algorithm to solve the string superprimitivity test problem. We also review some algorithms for the LCS problem. Further research is also given in this paper.

Thesis (doctoral)--Oulun yliopisto, 2002. / Includes bibliographical references. Also available in electronic format.

Revised; Originally Published: 2002 / 6 pp.

Grazing and no-till management in organic systems have been recently proposed tools to improve nutrient cycling and sustainability. From 2008 to 2012 a series of field experiments were established to identify green manure species and green manure management options to maximize N benefit to following cash crops and explore the opportunities to reduce tillage during the green manure phase of an organic rotation. A total of four green manure systems (double-cropped green manures, relay-cropped green manures, full season green manures, and catch crops after grazed full season green manures), three green manure management options (soil incorporation, grazing and no-till), and 10 green manure species, as well as, three green manure mixtures were tested. Double-cropped pea (Pisum sativum cv. 40-10) and relay-cropped red clover (Trifolium pratense) produced around 900 kg ha-1 and 2000 kg ha-1 of biomass respectively. The greatest biomass producing full season green manures were hairy vetch (Vicia villosa L.), pea/oat (Avena sativa cv. Leggett/Pisum sativum cv. 40-10) and sweet clover (Mellilotus officinalis cv. Norgold). Pea/oat and hairy vetch were the most weed competitive species and on average contained less than 15% weed biomass. Among all the systems and managements tested, nitrogen availability was greatest when full season green manures were grazed. On average grazing increased soil NO3-N by 25% compared to soil incorporation using tillage. Among grazed species, pea/oat mix and hairy vetch green manures resulted in the greatest amount of soil available NO3-N. Catch crops after grazing green manures, regardless of the species, significantly reduced N leaching risk compared to no catch crop treatment, but also reduced wheat productivity the following year. Catch crop biomass productivity and N uptake, soil NO3-N, and wheat productivity were similar in direct seeded and conventionally seeded plots. Grazing may be an effective tool in reducing tillage in organic agriculture because of its ability to accelerate the N mineralization from catch crop biomass. This study was the first study to use grazing as a management tool for green manures in organic systems. Results provide strong evidence that green manures, especially when grazed, can be effective nitrogen suppliers in organic grain based rotations.

Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images. The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers. Evaluation was based on several criteria: classification accuracy, impact of different training sample size, and computational speed. LiDAR data and aerial imagery with 0.5-m resolution were used to classify four land categories in the study area located in the City of Niagara Falls (ON, Canada). The results clearly demonstrate that the use of RF improved the classification performance in terms of accuracy and speed. Support vector machines (SVM) based and RF based classifiers showed similar accuracies. However, RF based classifiers were much quicker than SVM based methods. Based on the results from this work, it can be concluded that the RF based method holds great potential for recent and future urban land cover classification problem with LiDAR data and aerial images.