This study used two modelling approaches to predict future urban landscape for the Baltimore-Washington metropolitan areas. In the first approach, we implemented traditional SLEUTH urban simulation model by using publicly available and locally-developed land cover and transportation data. Historical land cover data from 1996, 2001, 2006, and 2011 were used to calibrate SLEUTH model and predict urban growth from 2011 to 2070. SLEUTH model achieved 94.9% of overall accuracy for a validation year of 2014. For the second modelling approach, we predicted future county-level population (e.g., 2050) using historical population data and time-series forecasting. We then used future population projection of 2050, aided by strong population-imperviousness statistical relationship (R2, 0.78-0.86), to predict total impervious surface area for each county. These population-predicted total impervious surface areas were compared to SLEUTH model output, at the county-aggregated spatial scale. For most counties, SLEUTH generated substantially higher number of impervious pixels. An annual urban growth rate of 6.24% for SLEUTH model was much higher than the population-based approach (1.33%), suggesting a large discrepancy between these two modelling approaches. The SLEUTH simulation model, although achieved high accuracy for 2014 validation, may have over-predicted urban growth for our study area. For population-predicted impervious surface area, we further developed a lookup table approach to integrate SLEUTH out and generated spatially explicit urban map for 2050. This lookup table approach has high potential to integrate population-predicted and SLEUTH-predicted urban landscape, especially when future population can be predicted with reasonable accuracy. / Master of Science

Anticipating future trends in land development and climate change is a constant challenge for engineers and planners who wish to effectively compensate for the resulting changes in stormwater runoff that will inevitably follow. This study is a regional attempt at predicting how predicted changes in land cover will affect runoff characteristics in a number of watersheds throughout the Delmarva Peninsula when compared to the current state. To predict changes in land cover and the associated land use, the SLEUTH model coupled with PED utilized a number of different inputs including population growth trends, existing geography, current land planning policies as well as different growth factors to predict where urban growth is most likely to occur. The model creates maps which show the approximate location of predicted growth for the year 2030. Using SLEUTH output, the magnitude of changes that can occur in runoff quality and quantity due to land cover changes were estimated in each of the seventeen representative watersheds that were chosen within the Delmarva Peninsula. Changes in water quality were calculated based on nutrient loading rates for sediment, phosphorus, and nitrogen. These nutrient loading rates correspond to different land uses within different county segments in the peninsula. The expected changes in water quantity were quantified using the United States Department of Agriculture's Natural Resources Conservation Services' TR-20 which estimated the peak flows for each watershed based on watershed's size, land cover, soils, and slope. Evaluating the magnitude of these potential changes in the Delmarva Peninsula provides an important look into the effects of increased urban development on the predominantly agrarian land mass, the majority of which drains to the Chesapeake Bay. / Master of Science

An urban tree canopy assessment (UTCA) is a new technology that can inform management decisions to optimize the economic, social and environmental benefits provided by urban forests. A UTCA uses remote sensing to create a comprehensive spatial snapshot of a locality's land cover, classified at a very fine scale (1 meter or less). Over the past decade, UTCAs have been conducted for numerous localities in the Chesapeake Bay watershed (CBW) as part of a strategy to enhance urban tree canopy (UTC) and reduce stormwater runoff that pollutes the Chesapeake Bay. Our research examined how local governments employ these UTCAs and identified barriers to and drivers of UTCA use for urban forest planning and management. We conducted a web-based survey of all localities in the CBW with populations over 2,500 for which a UTCA existed as of May 2013. We found that 33% of respondents reported being unaware that a UTCA existed for their locality. Even so, survey results showed that localities aware of their UTCA were using it to establish UTC goals, create and implement strategies to achieve those goals, and monitoring progress towards UTC goals. Survey localities were segmented based on how they reported using their UTCA to provide insight on possible outreach and technical assistance strategies that might improve future UTCA use. Overall, we found that larger localities with more developed urban forestry programs use their UTCA more frequently. However, we found several exceptions, suggesting that UTCAs could be an important catalyst for expanding municipal urban forestry programs. / Master of Science

Lyme disease is a common tick borne disease in the US. Lyme disease emerged from the Northeast and in the past decade, Virginia has been witnessing a rapidly increasing trend in incidence. This thesis uses land cover projection data as a basis to look at the potential future trend of Lyme disease incidence in Virginia for the IPCC (Intergovernmental Panel on Climate change) scenarios of A1B and A2, which indicate a global and regional focus respectively. This study is a continuation of previous work done by an NSF funded research team at Virginia Tech, in exploring the variables affecting Lyme disease in Virginia. A Poisson point process is implemented in this thesis with land cover parameters (implemented land, water bodies, and edge metrics) and demographic parameters (population percentage and per capita income) as the spatial covariates. Lyme disease incidence data obtained from the Virginia Department of Health was used for model validation. The overall model was implemented using Python and its associated libraries while ArcGIS software was used for preliminary covariate analysis and data visualization. This thesis generates risk maps for A1B and A2 scenarios for each decade from 2010 through 2060. Spatial occurrence of disease incidence has been generated by the Poisson point process and the risk level of each county in Virginia has been calculated based on the incidence count predicted for it. Population and area at risk under each scenario for each decade was calculated. Results show that in A1B scenario 22.1% and 42.9% of the total population of Virginia are under high risk and in the A2 scenario, 21% and 33% of the total population of Virginia are under high risk of Lyme disease in 2010 and 2060 respectively. In terms of the area, A1B scenario has 28% under high risk in 2010 and 66% of the total area under high risk in 2060, while A2 scenarGIS, Lyme disease, Land cover projections, IPCC scenariosio has 22.4% under high risk of Lyme disease in 2010 62.7% of the total area in Virginia is under high risk in 2060. / Master of Science

The forests of the eastern United States reflect complex and highly dynamic patterns of change. This thesis seeks to explore the highly variable nature of these changes and to develop techniques that will enable researchers to examine their temporal and spatial patterns. The objectives of this research are to: 1) determine whether the forest change dynamics in the eastern US differ across levels of the urban hierarchy; 2) identify and explore key micropolitan areas that deviate from anticipated trends in forest change; and 3) develop and apply techniques for Big Data exploration of Landsat satellite images for forest cover analysis over large regions. Results demonstrate that forest change at the micropolitan level of urbanization differs from rural and metropolitan forest dynamics. The work highlights the dynamic nature of forest change within the Piedmont Atlantic megaregion, largely attributed to the forestry industry. This is by far the most dominant change phenomenon in the region but is not necessarily indicative of permanent forest change. A longer temporal analysis may be required to separate the contribution of the forest industry from permanent forest conversion in the region. Techniques utilized in this work suggest that emerging tools that provide supercomputing/parallel processing capabilities for the analysis of big satellite data open the door for researchers to better address different landscape signals and to investigate large regions at a high temporal and spatial resolution. The opportunity now exists to conduct initial assessments regarding spatio-temporal land cover trends in the southeast in a manner previously not possible. / Master of Science

Lyme disease is the most significant vector-borne disease in the United States. Its southward advance over the last several decades has been quantified, and previous research has examined the potential role of climate change on the disease's expansion, but no research has considered the role of future land cover patterns upon its distribution. This research examines Lyme disease risk in the southeastern United States based on estimated land cover projections under four different Intergovernmental Panel on Climate Change Special Report Emissions Scenarios (IPCC-SRES) A1B, A2, B1, and B2. Results are aggregated to census tracts which are the basic unit of analysis for this study. This study applied previously established relationships between Lyme disease and land cover in Virginia to the projected land cover layers under each scenario. The study area, the southeastern United States, was defined from Level III Ecoregions that are present in Virginia and extend throughout the Southeast. Projected land cover data for each scenario were obtained from the USGS. The projected land cover datasets are compatible with the National Land Cover Dataset (NLCD) categories and had seventeen land cover categories. The raster datasets were reclassified to four broad land cover types: Water, Developed, Forest, and Herbaceous areas and the relationship between certain landscape configurations were analyzed using FRAGSTATS 4.2. Significant variables established in previous research were used to develop a spatial Poisson regression model to project Lyme disease incidence for each decade to the year 2100. Results indicated that potential land cover suitability for Lyme disease transmission will increase under two scenarios (A1B and A2) while potential land cover suitability for Lyme disease transmission was predicted to decrease under the other two scenarios (B1 and B2). Total area under the highest category of potential land cover suitability Lyme disease transmission was calculated for each year under each scenario. The A2 scenario experiences the most rapid acceleration of potential land cover suitability for Lyme disease transmission, with an average increase of 16,163.95 km² per decade, while the A1B scenario was projected to show an average increase of 3,458.47 km² per decade. Conversely, the B1 scenario showed an average decrease of 595.7 km² per decade and the B2 scenario showed the largest decrease of potential land cover suitability for Lyme disease transmission with an average decrease of 2,006.83 km² per decade. This study examined the potential spatial distribution of potential land cover suitability for Lyme disease transmission in the southeastern United States under four different future land cover scenarios. The results indicate geographic regions of the study area that are at greatest risk of potential land cover suitability for Lyme disease transmission under four different predictive scenarios developed by the IPCC. The A1B and A2 land cover projections are predicted to have an overall increase in areas where the Lyme disease transmission cycle will be enhanced by 2100 and the scenarios have a primary focus on economic development. Economic concerns outweigh environmental concerns for the A1B scenario, in addition to a high standard of living. The A2 scenario describes rapid population growth which results in high rates of land cover conversion to developed land; in addition, this scenario describes a reduction of environmental protection. The B1 and B2 land cover projections are predicted to have an overall decrease in areas of high Lyme disease transmission by 2100 and these scenarios have a central focus on environmental sustainability. The B1 scenario is characterized by a high environmental awareness which results in lower demand for forest products. A common theme for the B1 scenario is restoration and forest protection. Finally, the B2 scenario is described as improving local and regional environmental value which results in a high demand for biofuels and repossession of degraded lands, and an overall increase of forest cover. This study was the first to predict potential land cover suitability for Lyme disease risk and geographic distribution using projected land cover in the southeastern United States, and the results of this research can aid in the reduction of Lyme disease as it continues to expand in the south. / Master of Science

Knowledge of land cover is important in a variety of natural resources applications. This knowledge becomes more powerful within the spatial analysis capabilities of a geographic information system (GIS). This thesis presents a digital land cover map of Virginia, produced through interpretation of 14 Landsat Thematic Mapper (TM) scenes, circa 1991-1993. The land cover map, which has a 30m pixel size, was produced entirely with personal computers. Hypercluster aggregation, an unsupervised classification method, was used when hazy and mountainous conditions were not present. A haze correction procedure by Lavreau (1991) was used, followed by a supervised classification on coastal areas. An enhanced supervised classification, focusing on topographic shading, was performed in the mountains. Color infrared photographs, digital maplets, expert knowledge, and other maps were used as training data. Aerial videography transects were flown to acquire reference data. Due to the spatial inaccuracies inherent in the videography reference data, only homogeneous land cover areas were used in the accuracy assessment. The results of the overall accuracy for each scene determined the ordering of scenes within the statewide land cover mosaic (i.e., scenes with higher accuracy had a higher proportion of area represented). An accuracy assessment was then performed on the statewide land cover mosaic. An overall accuracy of 81.8% and a Kappa statistic of 0.81 resulted. A discussion of potential reasons for land cover class confusion and suggestions for classification improvements are presented. Overall deciduous forest was the most common land cover in Virginia. Herbaceous areas accounted for 20% of the land area, which was the second largest. Mixed forest and coastal wetlands were the cover types with the least area, each under 3%. / Master of Science

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies. Johannesburg, 2016. / The purpose of this research was to quantify the impact of the land reform programme on land use and land cover changes (LULCC) in Chipinge district situated in Manicaland Province of Zimbabwe. The Fast Track Land Reform Programme (FTLRP) of 2000 was selected as the major cause of LULCC in the district. This research addresses the problem of knowing and understanding if there was LULCC in the district before and after the enactment of the FTLRP in the year 2000. The research objectives of this study were as follows: to investigate the impact of the FTLRP of 2000 on land use and land cover in Chipinge district; to test the use of Landsat earth observation data in quantifying the changes on land use and cover from 1992 to 2014 in Chipinge district and to predict LULCCs in the year 2028 in Chipinge district. The methodology for detecting the impact of LULCC was based on the comparison of Landsat MSS, TM, ETM+ and OLI/ TIRS scene p168r74 images covering Chipinge district taken on diverse dates in five different years. In order to prepare the Landsat images for change detection analysis, a number of image processing operations were applied which include radiometric calibration and atmospheric correction. The images were classified using the Support Vector Machine (SVM) and evaluation was done through accuracy assessment using the confusion matrix. The prediction of LULCC in the year 2028 was modeled by the Markov Chain Analysis (MCA) and the Cellular Automata Markov Chain Analysis (CA MCA) so as to show land distribution in the future. The results show that agricultural farmland, estates and area covered by water bodies declined whilst there was an increase in built-up areas, forest land and bare land since the enactment of the FTLRP. The prediction results show that in the year 2028, there will be a decrease in the amount of land covered by water bodies, forest and agricultural farmland. There will be an increase in the amount of built-up in the year 2028 as a result of population growth. It is the recommended in this study that better remedies be put in place to increase forest cover and also the use of high resolution images in further studies. There should be exploration of the relationships between LULCC, socio-economic and demographic variables would develop more understanding of LULCC. The study also recommends the preparation of a proper land use plan to deal with a reduction in the growth of settlement which is vital in the planning and management of social and economic development programs. / LG2017

This doctoral thesis investigates the potential of classification methods based on spatial context to infer specific forms of land use from remote sensing data. The problem is that some types of land use are characterized by a complex configuration of land covers that traditional per-pixel based methods have problems classifying due to spectral heterogeneity. The problem of spectral heterogeneity is also present in classification of high resolution imagery. Two novel methods based on contextual information are evaluated, Spatial Relational Post-Classification (SRPC) and Window Independent Context Segmentation (WICS). The thesis includes six case studies in rural and urban areas focusing on the classification of: agricultural systems, urban characteristics, and dead wood areas. In the rural case studies specific types of agricultural systems associated with different household strategies are mapped by inferring the physical expression of land use using the SRPC method. The urban remote sensing studies demonstrate how the WICS method is able to extract information corresponding to different phases of development. Additionally, different urban classes are shown to correspond to different socioeconomic profiles, demonstrating how urban remote sensing can be used to make a connection between the physical environment and the social lives of residents. Finally, in one study the WICS method is used to successfully classify dead trees from high resolution imagery. Taken together these studies demonstrate how approaches based on spatial context can be used to extract information on land use in rural and urban environments where land use manifests itself in the form of complex spectral class and land cover patterns. The thesis, thus, contributes to the research field by showing that contextual methods can capture multifaceted patterns that can be linked to land use. This, in turn, enables an increased use of remote sensing data, particularly in the social sciences. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript.</p>

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. / Central to the study of animal ecology is the usage an animal makes of its environment. In arid and semi-arid environments worldwide, the availability of surface water largely determines the behaviour, distribution and abundance of animals. As a consequence, the distribution and quality of water are factors that influence carrying capacities of protected areas in environments such as the Kruger National Park, South Africa. Just as wildlife may select water sources according to water quality, they may also select drinking locations according to perceived predation risk. Predation risk can therefore strongly influence the patch use and resource selection of animals. Similarly, human traffic and activity in natural areas can also have an effect on the behaviour and resource use of resident wildlife. This research investigates mammal usage patterns at selected water sources within the Makuleke Wetland System in Kruger National Park to contribute towards management planning for this important Ramsar Wetland Site. The research examines daily and seasonal trends in usage as well as possible links to water quality, land cover and human disturbance. Camera traps were set up seasonally at perennial pans and rivers within the Makuleke Wetland system to determine mammal species’ usage patterns. Environmental characteristics associated with each water source, such as water quality, vegetation cover and extent of human activity were also determined. A cluster analysis and Canonical Correspondence Analysis (CCA) were run in order to determine how environmental variables may influence mammal species’ seasonal drinking site selection. Overall, mammals appear to be selecting for drinking sites with increased distances to ground cover where they are more likely to see predators in advance. Mammal species appear to be avoiding the Zimbabwean border as a result of human activity in the Zimbabwean side of the Great Limpopo Transfrontier Park. The perennial pans and rivers appear to be a significant water source during the dry months to large herbivore and large carnivore species, which display the greatest seasonal fluctuations. Elephants show the greatest demand for water during the dry season and access the perennial water sources throughout the day and night. The Makuleke wetland system, and in particular the perennial water sources, provide an important dry season refuge for the northern Kruger National Park’s and the Greater Limpopo Transfrontier Park’s elephant population. The Luvuvhu River and its associated pan (Nwambe), within its floodplain, are sources of water for the greatest diversity and richness of species when compared to the water sources associated with the Limpopo floodplain, within the Makuleke Wetland System. Information from this study may aid South African National Parks management with their “adaptive management” strategy for Kruger National Park, to manage the park in an ever changing environment. It is widely accepted that emphasis be placed on the major role river systems play in biodiversity, and hence their high priority in conservation.