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.

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.

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.

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.

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.

Agriculture is currently the leading cause of stream impairment in the United States. As the population continues to grow as well as the demand for biofuels, more pressure is being exerted on land to produce greater quantities of food. To satisfy the need for increased production marginal forest and grasslands have been converted to agriculture, fertilizers and equipment have rapidly evolved, and land has been taken out of conservation programs. Unfortunately, water quality impairment often accompanies these efforts to increase crop production. To reduce the impacts of agriculture on water quality, best management practices (BMPs) have been developed and tested at the field scale, with fewer studies focusing on the effects of agricultural land cover and BMPs (e.g., riparian buffers) on water quality at the watershed scale. Thus, a study was designed to assess the effects of riparian buffers and agricultural land cover on water quality at the watershed scale. Within Richland and Silver Creek watersheds, tributaries of the Lower Kaskaskia River Watershed in Illinois, forty-three catchments ranging from 12 to 50 km2 were selected across an agricultural to urban land cover gradient. Between January 18, 2008 and August 3, 2009, grab samples were collected twice a month during the wet portion of the year and once a month during the dry portion of the year and analyzed for nutrients (ammonium, nitrate, and orthophosphate), bacteria (total coliform, fecal coliform, and E. coli), and total suspended solids (TSS). Correlation analyses were performed on the data to determine relationships between the water quality variables, whole-catchment land cover (agriculture, forest, and urban), and percent forest canopy cover within 50 m of the stream using two different stream layers (National Hydrologic Dataset (NHD), and Flow Accumulation Boundaries (FAB)). Also, riparian buffer characteristics were quantified in headwater streams to determine if they were more highly correlated with water quality variables than in larger order streams. The percent of agricultural land cover within a watershed was significantly correlated with TSS (r = 0.4556, p = 0.0021) and ammonium-N (r = 0.3043, p = 0.0473) during baseflow, and TSS (r = 0.2837, p = 0.0652), ammonium-N (r = 0.5306, p = 0.0003), nitrate-N (r = 0.2654, p = 0.0854), and orthophosphate (r = 0.3783, p = 0.0124) during stormflow. Total amount of enrolled Conservation Reserve Program (CRP) land within Richland Creek and Silver Creek watersheds were found not to be correlated with water quality. A possible reason for these results could be because only a very small percent of lands in Richland Creek and Silver Creek were enrolled in CRP. Whole-catchment land cover in most cases explained more variance than percent forest canopy cover within 50 m of streams for the water quality parameters analyzed. There were only slight differences between the two stream layers (NHD and FAB). However, the headwater streams of the FAB stream layer explained more variance in critical water quality parameters, ammonium-N (r = -0.5309, p = 0.002) during baseflow and ammonium-N (r = -0.6107 p <0.0001), and orthophosphate (r = -0.5273 p = 0.0003) during stormflow. Having an understanding of the impacts that riparian buffers and headwater streams have on water quality is key for watershed managers to focus restoration efforts in the most critical areas for maintaining stream quality.

In northeastern Mississippi, land cover types vary from agriculture, forests, urban surfaces, pasture, to bodies of water. Substantial evidence exists supporting the contribution of land cover and land cover discontinuities, or physiographic transition zones, to cloud formation on synoptically benign days in many areas across the globe. However, research is lacking on the specific type of land cover and/or land cover discontinuities that convection favors in the warm season. The objective of this study was to develop a synoptically benign convective cloud climatology for northeastern Mississippi and compare this climatology to land cover to determine whether a relationship between land cover type and convective cloud enhancement exists. The study shows a statistically significant clustered pattern occurring in the study area. In addition, enhanced convective events appear to favor land use regions of evergreen needleleaf forest; dryland, cropland, and pasture; and savanna. The study indicates that these three land cover types occur significantly more frequently for the enhancement points than in the study area. The findings support the existence of a significant relationship between land cover and convective enhancement in northeastern Mississippi and provide opportunities for additional future research on relationships between land cover and convection to improve forecast applications and our knowledge of mesoscale circulations.

Southern Chile is recognized by different international organizations such as UNESCO (United Nations Educational, Scientific and Cultural Organization), and Conservational International as an ecologically sensitive area. The country of Chile is home to one third of the earth’s remaining temperate forests. Monitoring and assessing land cover changes in these forests is important not only to international organizations but to the people of Chile. The lack of multi-temporal studies that evaluate changes in land cover biomass make this study an important one for increasing awareness of how the evolution of the landscape affects environmental planning and development of legal precedents aimed at protecting this rich ecological habitat. The results of this study revealed important differences between the growth and loss of biomass in the period between 1986 and 2003. In the study area, 40% of areas that traditionally supported row crops and pasture hay were replaced my forestry plantations and herbaceous successional vegetation. This replacement has impacted the Chilean people and their agricultural way of life. The results obtained depict the usefulness of Change Vector Analysis (CVA) as a technique to analyze changes in biomass over time.

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