This dissertation investigates selected empirical and theoretical aspects of land-use and land-cover change (LUCC) in exurban areas. Two challenges – observation and monitoring of LUCC, and spatially explicit modeling, are addressed using three main approaches – measuring, reviewing and agent-based modeling (ABM). All of these approaches focus on LUCC at the individual household level, investigating how micro-scale elements interact to influence macro-scale functional patterns—bottom-up analysis. First, the temporal change of the quantity and pattern of land-cover types within exurban residential parcels in three townships in the southeastern Michigan is examined using landscape metrics and local indicators of spatial association at the parcel and parcel-neighborhood level respectively. The results demonstrate that the number and area of exurban residential parcels increased steadily from 1960 to 2000, and different land-cover types have distinctive temporal changes over time. The results also indicate that there is a convergence process at the neighborhood level through which the quantity and pattern of land cover in parcels conform with the neighborhood appearance. Second, 51 urban residential choice models based on ABM are reviewed. The results divide these models into three categories (i.e. models based on classical theories, models focusing on different stages of urbanization process; and integrated ABM and microsimulation models). This review also compares the differences among these models in their representations of three essential features brought by the technique of ABM: agent heterogeneity, the land market and output measurement. Challenges in incorporating these features, such as the trade-off between the simplicity and abstraction of model and the complexity of urban residential system, interactions of multiple features and demands for data at individual level, are also discussed. Third, the effects of agent heterogeneity on spatial and socioeconomic outcomes under different levels of land-market representations are explored through three experiments using a stylized agent-based land-market model. The results reveal that budget heterogeneity has prominent effects on socioeconomic outcomes, while preference heterogeneity is highly pertinent to spatial outcomes. The relationship between agent heterogeneity and macro-measures becomes more complex as more land-market mechanisms are represented. The results also imply that land-market representation (e.g., competitive bidding) is indispensable to reproduce the results of classical urban land market models (e.g., monocentric city model) in a spatial ABM when agents are heterogeneous.

Movement of growth faults, a type of normal fault which is formed during sedimentation and is characterized by having greater vertical thickness on the downthrown fault side, on barrier islands contributes to wetland losses. The opening objective of this study was to quantify land cover change within a Matagorda, Texas wetland that results from sea level rise and elevation change over time due to coastal faulting. The closing objective of this study was to simulate land cover conversion as a function of relative sea level rise (RSLR) within the wetland and to compare and contrast the impact of specific rates of both fault-induced elevation change and predicted International Panel on Climate Change (IPCC) sea level rise projections. To accomplish these objectives a time series of aerial images was classified using automated unsupervised classification and hand digitization. After classification, total wetland losses on both the upthrown and downthrown sides of the fault were evaluated as a function of spatial distance from the fault plane. This classified product was draped over a digital elevation model (DEM) layer to evaluate elevations of land cover classes and model potential future outcomes based on RSLR. Classification results show that while wetland loss occurred on both sides of the fault, losses were far more extensive on the downthrown side. It was concluded that this vertical fault movement impacts wetland losses, especially on the downthrown side. Modeling results show that rapid water level rise can force wetland land cover class conversion regardless of whether this relative rise is caused by vertical fault displacement or eustatic sea level rise, resulting in the destruction of vital wetland areas. Current recorded conditions of sea level rise along the Texas Gulf Coast leave a grim outlook for regions similar to this study area.

Riparian ecosystems of the southwestern United States are highly valuable to both the ecological and human communities which surround them. Over the past century, they have been subject to shifting management practices to maximize human use, control, ecosystem service, and conservation. This creates a complex relationship between water policy, management, and the natural ecosystem necessitating research on spatial and temporal dynamics of riparian vegetation. The San Acacia Reach of the Middle Rio Grande, a 60 mile stretch from the San Acacia Diversion Dam to San Marcial, has experienced multiple management and river flow fluctuations over the past 80 years, resulting in threats to riparian and aquatic ecosystems. This research was completed through the use and analysis of multi-source remote sensing data, GIS, and a review of the on-the-ground management decisions to better understand how the location and composition of the riparian vegetation has been affected by these shifting practices. This research focused on four phases, each highlighting different management practices and river flow patterns during the last 80-years. Each of these periods provides a unique opportunity to observe a direct relationship between river management and riparian land cover response and change. Overall, management practices reduced surface river flows and limited overbank flooding and resulted in changes in the composition, density, and spatial patterns of the vegetation, including increased non-native vegetation growth. Restoration efforts over the past few decades have begun to reduce the presence of non-native species. Despite these changes, this ecosystem was shown to be extremely resilient in maintaining its function/service throughout the entire study time frame.

abstract: Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity. / Dissertation/Thesis / M.A. Geography 2010

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations.

abstract: Rapid urbanization and population growth occurring in the cities of South Western United States have led to significant modifications in its environment at local and regional scales. Both local and regional climate changes are expected to have massive impacts on the hydrology of Colorado River Basin (CRB), thereby accentuating the need of study of hydro-climatic impacts on water resource management in this region. This thesis is devoted to understanding the impact of land use and land cover (LULC) changes on the local and regional hydroclimate, with the goal to address urban planning issues and provide guidance for sustainable development. In this study, three densely populated urban areas, viz. Phoenix, Las Vegas and Denver in the CRB are selected to capture the various dimensions of the impacts of land use changes on the regional hydroclimate in the entire CRB. Weather Research and Forecast (WRF) model, incorporating the latest urban modeling system, is adopted for regional climate modeling. Two major types of urban LULC changes are studied in this Thesis: (1) incorporation of urban trees with their radiative cooling effect, tested in Phoenix metropolitan, and (2) projected urban expansion in 2100 obtained from Integrated Climate and Land Use Scenarios (ICLUS) developed by the US Environmental Protection Agency for all three cities. The results demonstrated prominent nocturnal cooling effect of due to radiative shading effect of the urban trees for Phoenix reducing urban surface and air temperature by about 2~9 °C and 1~5 °C respectively and increasing relative humidity by 10~20% during an mean diurnal cycle. The simulations of urban growth in CRB demonstratedii nocturnal warming of about 0.36 °C, 1.07 °C, and 0.94 °C 2m-air temperature and comparatively insignificant change in daytime temperature, with the thermal environment of Denver being the most sensitive the urban growth. The urban hydroclimatic study carried out in the thesis assists in identifying both context specific and generalizable relationships, patterns among the cities, and is expected to facilitate urban planning and management in local (cities) and regional scales. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2017

Riparian ecosystems are valuable to the ecological and human communities that depend on them. Over the past century, they have been subject to shifting management practices to maximize human use and ecosystem services, creating a complex relationship between water policy, management, and the natural ecosystem. This has necessitated research on the spatial and temporal dynamics of riparian vegetation change. The San Acacia Reach of the Middle Rio Grande has experienced multiple management and river flow fluctuations, resulting in threats to its riparian and aquatic ecosystems. This research uses remote sensing data, GIS, a review of management decisions, and an assessment of climate to both quantify how riparian vegetation has been altered over time and provide interpretations of the relationships between riparian change and shifting climate and management objectives. This research focused on four management phases from 1935 to 2014, each highlighting different management practices and climate-driven river patterns, providing unique opportunities to observe a direct relationship between river management, climate, and riparian response. Overall, we believe that management practices coupled with reduced surface river-flows with limited overbank flooding influenced the compositional and spatial patterns of vegetation, including possibly increasing non-native vegetation coverage. However, recent restoration efforts have begun to reduce non-native vegetation coverage.

Rapid urban growth and unprecedented rural to urban transition, along with a huge population growth are new phenomena for both high and low income countries, which started in the mid-20th century. However, urban growth rates and patterns are different in developed countries and developing ones. In less developed countries, urbanization and rural to urban transition usually takes place in an unmanaged way and they are associated with a series of socioeconomical and environmental issues and problems. Identification of the city growth trends in past decades can help urban planners and managers to minimize these negative impacts. In this research, urban growth in the city of Isfahan, Iran, is the subject of study. Isfahan the third largest city in Iran has experienced a huge urban growth and population boom during the last three decades. This transition led to the destruction of natural and agricultural lands and environmental pollutions. Historical and recent remotely sensed data, along with different remote sensing techniques and methods have been used by researchers for urban land use and land cover change detection. In this study three Landsat TM and ETM+ images of the study site, acquired in 1985, 2000 and 2009 are used. Before starting processing, radiometric normalization is done to minimize the atmospheric effects. Then, processing methods including principal component analysis (PCA), vegetation indices and supervised classification are implemented on the images. Accuracy assessment of the PCA method showed that the first PC was responsible for more than 81% of the total variance, and therefore used for analysis of PCA differencing. ΔPC1t1-t2 shows the amount of changes in land use and land cover during the period of study. In this study ten vegetation indices were selected to be applied to the 1985 image. Accuracy assessments showed that Transformed Differencing Vegetation Index (TDVI) is the most sensitive and accurate index for mapping vegetation in arid and semi-arid urban areas. Hence, TDVI was applied to the 2000 and 2009 images. ΔTDVIt1-t2 showed the changes in land use and land cover especially the land use transformation from vegetation cover into the urban class. Supervised classification is the last method applied to the images. Training sites were assigned for the selected classes and accuracy was monitored during the process of training site selection. The results of classification show the expansion of urban class and diminishment in natural and agricultural lands.

Hydrologic models were used to examine the effects of land cover change on the flow regime of a watershed located in North-Central Texas. Additionally, the effect of spatial resolution was examined by conducting the simulations using sub-watersheds of different sizes to account for the watershed. Using the Army Corps of Engineers, Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS), two different modeling methods were evaluated at the different sub-watershed resolutions for four rainfall events. Calibration results indicate using the smaller spatial resolutions improves the model results. Different scenarios for land cover change were evaluated for all resolutions using both models. As land cover change increased, the amount of flow from the watershed increased.

Invasive plant species threaten native plant communities and inhibit efforts to restore disturbed landscapes. Surface coal mines in the Appalachian Mountains are some of the most disturbed landscapes in North America. Moreover, there is not a comprehensive understanding of the land cover characteristics of post- mined lands in Appalachia. Better information on mined lands' vegetative cover and ecosystem recovery status is necessary for implementation of effective environmental management practices. The invasive autumn olive (Elaeagnus umbellata) is abundant on former coal surface mines, often outcompeting native trees due to its faster growth rate. The frequent revisit time and spatial and spectral resolution of Landsat satellites make Landsat imagery well suited for mapping and characterizing land cover and forest recovery on former coal surface mines. I performed a multitemporal classification using a random forest analysis to map autumn olive on former and current surface coal mines in southwest Virginia. Imagery from the Operational Land Imager on Landsat 8 were used as input data for the study. Calibration and validation data for use in model development were obtained using high-resolution aerial imagery. Results indicate that autumn olive cover is sufficiently dense to enable detection using Landsat imagery on approximately 12.6% of the current and former surface coal mines located in the study area that have been mined since the early 1980s. The classified map produced here had a user's and producer's accuracy of 85.3% and 78.6% respectively for the autumn olive coverage class. Overall accuracy in reference to an independent validation dataset was 96.8%. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in the Appalachian coalfields can be mapped using Landsat imagery. Additionally, autumn olive occurrence is a significant landscape feature on former surface coal mines in the Virginia coalfields. / Master of Science