Cover crops are typically sown between cash crops and can suppress weed emergence and growth. If cover crops are sown after cash crop harvest the system is left susceptible to weed emergence while they establish. Interseeding cover crops into a standing cash crop may limit this bare period by allowing cover crops to become established, go into dormancy, and then revive around cash crop senescence. Studies were conducted in Princeton and Lexington, KY, to determine (i) which corn pre-emergent herbicides and mixtures of herbicide active ingredients commonly used by Kentucky growers would impact interseeded cover crop density and biomass, (ii) which grass entries that are adapted to Kentucky would be best to interseed in corn, and (iii) if interseeded cover crops would suppress weeds similar to a cover crop planted after cash crop harvest. There were few reductions in interseeded cover crop density and biomass from the pre-emergent herbicides tested. Among the entries interseeded in four site-years, the tall fescue pre-cultivars generally performed the best but none were consistently able to survive the summer when interseeded into corn. Compared to a cereal rye cover crop seeded after corn harvest, interseeded cover crops produced less biomass and therefore suppressed fewer weeds.

Thesis (M.S.)--Northern Michigan University, 2007. / Bibliography: leaves 44-48.

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.

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.

Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour IObservation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program.

One of the major issues in the successful decommissioning of any waste disposal system is to mitigate the spread of contaminants into the surrounding environment. In many instances this is achieved by reducing amounts of net percolation and/or oxygen diffusion into the underlying waste. An engineered cover system incorporating a capillary break is a common solution to this problem. However, traditional soil capillary breaks can often be impractical for large facilities where desirable construction materials are not readily available. The primary objective of this research is to show the initial steps in the development of a new type of geosynthetic product, namely a geosynthetic capillary break (GCB). This new product, composed of a nonwoven geotextile coupled with a fine-grained rock flour, will function similar to, and has the possibility of replacing traditional, soil capillary breaks in many applications. The specific objectives of this research are to: i) determine the pertinent material parameters of the materials used to evaluate the GCB; ii) examine one-dimensional column testing of a typical engineered soil cover system incorporating the GCB; and iii) model the cover systems to better understand current performance and predict long-term performance of the GCB. The GCB was evaluated based on the objectives outlined above. The material characterization consisted of the selection of suitable materials for the GCB, as well as the determination of their unsaturated properties. The results indicate that a geotextile-rock flour combination will develop a capillary break within an engineered cover. The one-dimensional column tests evaluated four cover systems. Soil thicknesses of 30 and 60 cm were utilized, with one column of each cover thickness incorporating the GCB. The columns were tested under both high evaporative fluxes and high infiltration rates over the course of 111 days. The measured results show that there is less moisture movement in columns that incorporate the GCB. A coupled soil-atmospheric finite element model was then used to develop a predictive model for the cover systems. The model was calibrated to the measured results from the column testing to ensure consistency. The parameters obtained from this model were used to evaluate an engineered cover system incorporating the GCB for a minesite in Flin Flon, MB. The results from the predictive modeling show that moisture infiltration is reduced approximately 80% when comparing columns with the same cover thickness. Oxygen diffusion is also reduced by 20 to 25% with the inclusion of the GCB.

Pneumonia is the main killer of under-five children worldwide. The developing nations suffer the most. But within such countries, the spatial and temporal distribution of pneumonia cases is not uniform; yet little is known of the spatial and temporal distribution of pneumonia or the factors that might affect spatial and temporal variability. This dissertation explores the causes of spatial and temporal variation in under-five pneumonia morbidity in Tanzania. This study uses an ecological analysis to explore weather, land cover and politics as potential drivers of the observed differences in the distribution of pneumonia. A study is at an ecological level when it examines the population-level health aspects. That is, ecological analyses in health studies evaluate groups of people rather than individuals. The current study found that weather variables such as temperature and atmospheric pressure partially explained pneumonia variance. The strength of weather-pneumonia association varies over space and time in both seasonal elements (temporal factors) and broadly-defined climate zones (spatial factors). For example, the prevalence rate was higher in the regions with bimodal rainfall compared with the regions with unimodal rainfall, with a statistically difference 117.3 (95% confidence interval: 36.6 to 198.0) cases per 100,000. In addition, within the regions (mikoa) with unimodal rainfall regime, however, the rainy season (msimu) had lower rates of pneumonia compared to the dry season (kiangazi). Land use and land cover also were partial drivers of pneumonia. Some land cover types—particularly urban areas and croplands—were associated with high rates of childhood pneumonia. In addition, districts (wilaya) categorized as urban land cover had high rates of pneumonia compared to those categorized as only rural. To determine the associations between politics and pneumonia, this study compared the pneumonia cases in the administrative locations that received less central government funding with those locations that were financially rewarded for voting for the ruling party. The locations with lower funding generally had higher rates of childhood pneumonia. However, it is unclear whether these locations had higher rates of childhood pneumonia because of, or in addition, to their funding gaps. In sum, this dissertation evaluated population-level factors affecting distribution of childhood pneumonia. Like other similarly population-level studies, this dissertation provides an understanding of the coarse-scale dynamics related to childhood pneumonia. By so doing, it contributes to the pneumonia etiology scientific literature. That is, this dissertation contributes to the understanding of within-nation pneumonia distribution in developing nations. It is the first in Tanzania to evaluate the impact of weather, land cover and politics on childhood pneumonia. By evaluating the impact of weather and land cover, this dissertation also provides an example of non socio-economic factors affecting health inequalities. By analyzing a large landmass of two main climatic types, this dissertation also contributes appreciation of non-stationarity of temporal variations of childhood pneumonia, in addition to the commonly-evaluated spatial variations.

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.

One of the major issues in the successful decommissioning of any waste disposal system is to mitigate the spread of contaminants into the surrounding environment. In many instances this is achieved by reducing amounts of net percolation and/or oxygen diffusion into the underlying waste. An engineered cover system incorporating a capillary break is a common solution to this problem. However, traditional soil capillary breaks can often be impractical for large facilities where desirable construction materials are not readily available. The primary objective of this research is to show the initial steps in the development of a new type of geosynthetic product, namely a geosynthetic capillary break (GCB). This new product, composed of a nonwoven geotextile coupled with a fine-grained rock flour, will function similar to, and has the possibility of replacing traditional, soil capillary breaks in many applications. The specific objectives of this research are to: i) determine the pertinent material parameters of the materials used to evaluate the GCB; ii) examine one-dimensional column testing of a typical engineered soil cover system incorporating the GCB; and iii) model the cover systems to better understand current performance and predict long-term performance of the GCB. The GCB was evaluated based on the objectives outlined above. The material characterization consisted of the selection of suitable materials for the GCB, as well as the determination of their unsaturated properties. The results indicate that a geotextile-rock flour combination will develop a capillary break within an engineered cover. The one-dimensional column tests evaluated four cover systems. Soil thicknesses of 30 and 60 cm were utilized, with one column of each cover thickness incorporating the GCB. The columns were tested under both high evaporative fluxes and high infiltration rates over the course of 111 days. The measured results show that there is less moisture movement in columns that incorporate the GCB. A coupled soil-atmospheric finite element model was then used to develop a predictive model for the cover systems. The model was calibrated to the measured results from the column testing to ensure consistency. The parameters obtained from this model were used to evaluate an engineered cover system incorporating the GCB for a minesite in Flin Flon, MB. The results from the predictive modeling show that moisture infiltration is reduced approximately 80% when comparing columns with the same cover thickness. Oxygen diffusion is also reduced by 20 to 25% with the inclusion of the GCB.

Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour IObservation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program.