Sanitation marketing is an emerging approach of strengthening the local private sector to implement scalable and sustainable improved sanitation coverage in developing countries, specifically among the poor. It encourages the enhancement of sanitation market supply and demand by developing distribution infrastructure and stimulating consumer interest. Unlike interventions that provide hardware subsidies to initiate sanitation demand, financial support for sanitation marketing is used exclusively for the research and development of the market; this encourages the private sector to become independent and self-sufficient. Qualitative data suggests that while sanitation marketing projects have been successful at implementing replicable and sustainable sanitation coverage, they are not effective in close proximity to other programs that provide hardware subsidies. The aim of this study is to determine how hardware subsidies impacted iDE’s (formerly International Development Enterprise) Cambodia Sanitation Marketing Scale-Up (SMSU) project using quantitative data collected between 2010 and 2014, and to develop an approach that best illustrates this relationship. Using their project database of 48,844 transactions in 9 provinces, QGIS 2.8.1 and MS Excel were used to determine the correlations between the NGO (subsidized) and customer sales. QGIS maps and time-lapse animations were effective in spatially juxtaposing the quantity and location of both NGO and customer sales, and MS Excel charts quantified the relationship as a function of time, identifying opposing correlational patterns. Within the Cambodia SMSU project, the provision of hardware subsidies (represented by NGO sales) resulted in the attrition of the sanitation marketplace (represented by customer sales) when the NGO sales landed between 71 and 889 in a single month, averaging 400 NGO sales in a month. Overall, 14 districts showed decreased customer sales in the presence of subsidies, and 36 districts showed increased customer sales in the presence of subsidies. Within this study, any district with over 395 sales in one month showed a decline in customer sales. There were 106 months within this project that the NGO and customer sales had a positive correlation and 110 months showing a negative correlation.

Planning and regulatory environmental agency San Francisco Bay Conservation and Development Commission (BCDC) plays an important role in the permitting of development around the San Francisco Bay. As the agency works to add an environmental justice amendment to its primary policy document, this research explores the S.F. Bay Area’s history of approved development project proposal permits, and the associated patterns of land use and environmental justice implications in order to support the proposed change in permitting policy. By classifying all major permits found within BCDC’s internal permit database into groups based on the type of land use associated with the permit project, i.e. Industrial, Flood Control, Ports, etc., it was possible to create maps showing the geographic distribution of each group of permits. To analyze potential environmental justice implications of the patterns of geographic distribution of development permits, each group of permit types was layered on top of spatial data representing areas around the SF Bay that have been identified as highly socially vulnerable. Based on the findings of this project, it appears that highly socially vulnerable communities around the San Francisco Bay bear a disproportionate amount of land-use related environmental burdens. Furthermore, it is crucial to recognize the limitations of geospatial analysis tools in conveying the magnitude of disproportionate environmental and community health impacts of land use on socially vulnerable communities in the San Francisco Bay Area.

Due to increasing water treatment costs and conservation needs, traditional water loss analysis and acoustic leak detection methods are becoming heavily scrutinized by water utilities. This study explores water loss in Johnson City, Tennessee and how geospatial data analysis techniques improve water loss mitigation. This project uses sample water system pressure data and ordinary kriging spatial interpolation methods to identify leakage areas for further investigation. Analysis of existing geographic information system (GIS) water utility datasets with interpolated hydraulic grade values at sample water pressure points produce manageable survey areas that pinpoint areas with possible water leakage. Field detection methods, including ground-penetrating radar (GPR) and traditional acoustic methods, are employed to verify leakage predictions. Ten leakage areas are identified and verified using traditional acoustic detection methods, work order research, and GPR. The resulting data show that spatial analysis coupled with geospatial analysis of field pressure information improves water loss mitigation.

The Florida Sandhill Crane (Grus canadensis pratensis) is a state threatened endemic subspecies of the Sandhill Crane (Nesbitt & Tacha, 1997). With a population that was estimated at a maximum of 5,000 individuals in 2003 (Nesbitt & Hatchitt, 2008), it is imperative to identify potentially viable habitats, as Florida is rapidly developing. This research develops a Habitat Suitability Index model to determine unsuitable to optimally suitable habitat locations throughout west-central Florida. To do so, six suitability variables based on the crane’s life history were evaluated: Potential nesting area, immediate nesting area, wetland coverage, foraging area, brooding area, and road proximity. The results were compiled into a map, which showcased a gradient of habitat suitability within the Southwest Florida Water Management District boundary. Validation of this model included assessing the 2013-2017 stop data obtained from the North American Breeding Bird Survey for two routes in the project area. However, this data proved to be insufficient and unreliable, resulting in insignificance. The intention of this research was to prioritize those areas that are of optimal suitability to assist on conservation management of this threatened species. However, it highlighted the necessity for updated research, data, and population information for the Florida Sandhill Crane.

Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing has assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of heath for trees for many years. This study provides spatial details regarding the general palm decline in Florida by taking advantage of the new developments in deep learning constructs coupled with high resolution WorldView-2 multispectral/temporal satellite imagery and LiDAR point cloud data. A novel approach using TensorFlow deep learning classification, multiband spatial statistics and indices, data reduction, and step-wise refinement masking yielded a significant improvement over Random Forest classification in a comparison analysis. The results from the TensorFlow deep learning were then used to develop an Empirical Bayesian Kriging continuous raster as an informative map regarding palm decline zones using Normalized Difference Vegetation Index Change. The significance from this research showed a large portion of the study area exhibiting palm decline and provides a new methodology for deploying TensorFlow learning for multispectral satellite imagery.

No description available.

The Ebola outbreak in West Africa in 2014 to 2016 had more than 28,000 suspected, probable, and confirmed cases. It was the largest Ebola outbreak in history. Of the 28,000 cases in the three Ebola-affected countries, Liberia had 10,000 cases with almost 5,000 deaths. The Ebola Virus Disease (EVD) entered Liberia along the border of Guinea and moved to the capital city of Monrovia where the virus spread. Ebola Treatment Units (ETUs) were constructed throughout the response in locations where there were available facilities versus distance to care challenges. This study examined the association of distance from villages to ETUs and mortality. Using Geographic Information System (GIS) and statistics framed within the Social Ecological Model and the GIS Framework, this study geolocated the Ebola cases by village, mapped the travel routes and calculated the distance to the ETU. A logistic regression was then used to determine if there was an association between distance and mortality, with and without controlling for age and gender, and, to calculate the odds ratio. A logistic regression model showed there is an association between distance and mortality and that Ebola patients living within 12 kilometers of the ETU were 1.8 times less at risk of mortality (OR = 1.778, 95% CI [1.171 - 2.7]) than those living more than 12 kilometers. In addition, males had a 1.4 times lower risk of death due to EVD. This understanding can inform future outbreak responses and placement of treatment units. In addition, this information can lead to social change with respect to individual understanding of access to care, community expectations, and national health care planning.

Whether a species is rare and requires protection or is overabundant and needs control, an accurate estimate of population size is essential for the development of conservation plans and management goals. Wildlife censuses in remote locations or over extensive areas are logistically difficult, frequently biased, and time consuming. My dissertation examined various techniques to determine the probability of detecting animals using remotely sensed imagery. We investigated four procedures that integrated unsupervised classification, texture characteristics, spectral enhancements, and image differencing to identify and count animals in remotely sensed imagery. The semi-automated processes had relatively high errors of over-counting (i.e., greater than 60%) in contrast to low (i.e. less than 19%) under-counting errors. The single-day image differencing had over-counting errors of 53% while the manual interpretation had over-counting errors of 19%. The probability of detection indicates the ability of a process or analyst to detect animals in an image or during an aerial wildlife survey and can adjust total counts to estimate the size of a population. The probabilities of detecting an animal in remotely sensed imagery with semi-automated techniques, single-day image differencing, or manual interpretation were high (e.g. ≥ 80%). Single-day image differencing resulted in the highest probability of detection suggesting this method could provide a new technique for managers to estimate animal populations, especially in open, grassland habitats. Remotely sensed imagery can be successfully used to identify and count animals in isolated or remote areas and improve management decisions. Sightability models, used to estimate population abundances, are derived from count data and the probability of detecting an animal during a census. Global positioning systems (GPS) radio-collared bison in the Henry Mountains of south-central Utah provided a unique opportunity to examine remotely sensed physiographic and survey characteristics for known occurrences of double-counted and missed animals. Bison status (detected, missed, or double-counted) was determined by intersecting helicopter survey paths with bison travel paths during annual helicopter surveys. The probability of detecting GPS-collared bison during the survey ranged from 91% in 2011 to 88% in 2012.

I used Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery to answer two ecological questions. In the first project, I investigated the interactions between bark beetle-caused tree mortality and fire occurrence in western U.S. forests. I used remotely sensed fire data detected by MODIS satellite and bark beetle-caused tree mortality data. I tested the hypothesis that there is an increased probability of fire incidence in bark beetle-damaged forests compared to healthy forests using conditional probability modeling across the national forests of the western U.S. regardless of forest type. My results did not show a consistent pattern (increase or decrease of conditional probability of fire occurrence, &#;CP) across all lag time periods considered. However, when &#;CP is averaged across the 5-year study period (2001-2005) fire probability increased at 2-year (16%) and 3-year (9%) lags with 0, 1, 4, and 5-year lags showing no positive effect of bark beetle activity on fire probability. Further, when I analyzed fire-bark beetle-caused tree mortality separately for persistent fires (fires that lasted for at least two 8-day composite periods per season) and transient fires (fires that lasted for only one 8-day composite period per season), the &#;CP increased in all lag periods except the 5-year lag for persistent fires. In the second stage of this project, I used a non-parametric modeling approach to test how important bark beetle-caused tree mortality is in influencing fire occurrence relative to other climate and topography-derived variables in spruce-fir, Douglas-fir, lodgepole, and ponderosa pine forests. My results showed that climate and topography-derived predictors were consistently selected as important predictors of fire occurrence while bark beetle-caused tree mortality showing the least importance. In the second project, I predicted the invasive potential of a Puerto Rican frog species in Hawaii using the following MODIS products: land surface temperature; normalized difference vegetation index and enhanced vegetation index; and leaf area index/fraction of photosynthetically active radiation absorbed by plant canopies. My predicted maps showed that the invasive frog species in Hawaii is likely to expand its current habitat. My results also showed that MODIS-derived biophysical variables are able to characterize the suitable habitats of the invasive frog species.

This thesis investigates the application of GIST features [13] to the problem of object detection in images. Object detection refers to locating instances of a given object category in an image. It is contrasted with object recognition, which simply decides whether an image contains an object, regardless of the object's location in the image. In much of computer vision literature, object detection uses a "sliding window" approach to finding objects in an image. This requires moving various sizes of windows across an image and running a trained classifier on the visual features of each window. This brute force method can be time consuming. I investigate whether global, easily computed GIST features can be used to classify the size and location of objects in the image to help reduce the number of windows searched before the object is found. Using K–means clustering and Support Vector Machines to classify GIST feature vectors, I find that object size and vertical location can be classified with 73–80% accuracy. These classifications can be used to constrain the search location and window sizes explored by object detection methods.