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

Made available in DSpace on 2014-12-17T15:03:33Z (GMT). No. of bitstreams: 1 SalatielRV_DISSERT.pdf: 3344605 bytes, checksum: 083e5071106853d45a5c3fde7712fcac (MD5) Previous issue date: 2014-03-10 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Pitimbu River Watershed (PRW), belonging to Potiguar capital metropolitan area, State of Rio Grande do Norte, contributes, among other purposes, to human using and animal watering. This watershed is extremely important because, besides filling up with freshwater approximately 30% of the south part of Natal (South, East and West Zones), contributes to the river shore ecosystem equilibrium. Face to the current conjuncture, this study aims to evaluate the urban development dynamics in the PRW, applying Cellular Automata as a modeling instrument, and to simulate future urban scenarios, between 2014 and 2033, using the simulation program SLEUTH. In the calibration phase, urban spots for 1 984, 1992, 2004 and 2013 years were used, with resolution from 100 meters. After the simulation, it was found a predominance of organic growth, expanding the BHRP from existing urban centers. The spontaneous growth occurred through the fullest extent of the watershed, however the probability of effective growth should not exceed 21%. It was observed that, there was a 68% increase for the period between 2014 and 2033, corresponding to an expansion area of 1,778 ha. For 2033, the source of Pitimbu River area and the Jiqui Lake surroundings will increase more than 78%. Finally, it was seen an exogenous urban growth tendency in the watershed (outside-in). As a result of this growth, hydraulics resources will become scarcer / A Bacia Hidrogr?fica do Rio Pitimbu (BHRP), pertencente ? regi?o metropolitana da capital Potiguar, Estado do Rio Grande do Norte (RN), contribui, entre outros fins, para o consumo humano e dessedenta??o animal. Essa bacia ? de suma import?ncia, pois al?m de abastecer com ?gua doce aproximadamente 30% da popula??o da parte sul de Natal (zonas sul, leste e oeste), contribui para o equil?brio do ecossistema ao longo do rio. Diante da conjuntura atual, os objetivos deste estudo foram avaliar a din?mica do desenvolvimento urbano na BHRP, aplicando Aut?matos Celulares como instrumento de modelagem, e simular cen?rios urbanos futuros, entre 2014 e 2033, empregando o programa de simula??o SLEUTH. Na fase de calibra??o, foram utilizadas as manchas urbanas para os anos de 1984, 1992, 2004 e 2013, com resolu??o 100 metros. Ap?s a simula??o, verificou-se que houve uma predomin?ncia do crescimento org?nico, expandindo-se na BHRP, a partir de centros urbanos existentes. O crescimento espont?neo ocorreu por toda extens?o da Bacia, por?m a probabilidade de crescimento efetivo n?o deve ultrapassar 21%. Verificou-se um crescimento de 68% para o per?odo entre 2014 e 2033, correspondendo a uma ?rea de expans?o de 1.778 ha. Para o ano de 2033, a ?rea da nascente do rio Pitimbu e proximidades da lagoa do Jiqui ter?o a possibilidade efetiva de crescimento acima de 78%. Por fim, observou -se uma tend?ncia de crescimento urbano ex?geno (de fora para dentro) na Bacia. Em consequ?ncia desse crescimento, os recursos h?dricos tornar-se-?o cada vez mais escassos

The Houston-Galveston-Brazoria Consolidated Metropolitan Statistical Area (Houston CMSA) has experienced rapid population growth during the past decades and is the only major US metropolitan area with no zoning regulations. We use SLEUTH, a spatially explicit cellular automata model, to simulate future (2002-2030) urban growth in the Houston metropolitan area, one of the fastest growing metropolises in the United States during the past decades. The model is calibrated with historical data for the period 1974-2002 that are extracted from a time series of satellite images. The dataset consists of four historical urban extents (1974, 1984, 1992, 2002), two land use layers (1992, 2002), five transportation layers (1974, 1984, 1990, 2002, 2025), slope layer, hillshade layer, and excluded layer. Future growth patterns are predicted based on growth coefficients derived during the calibration phase. After calibrating the model successfully, the spatial pattern of urban growth of the Houston CMSA for the period from 2002 to 2030 is predicted. Within SLEUTH, growth in the Houston CMSA is predominately "organic" with most growth occurring along the urban/rural fringe. Projected increases in urban area from 2002 to 2030 parallel projected increases in population growth within the Houston CMSA. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the unmanaged growth with no restrictions. The second scenario is to project the moderate growth trend by taking into consideration environmental protection, specifically for agricultural areas, forests and wetlands. The last scenario is to simulate the managed growth with maximum environmental protection. Adjusting the level of protection for different land cover types was found to markedly affect the land use changes in the Houston CMSA. Without any protection on resource lands, Houston CMSA is estimated to lose 2,000 km2 of forest land by 2030, about 600 km2 of agricultural land, and approximately 400 km2 of wetland. Approximately half of all resource land could be saved by the third scenario, managed growth with maximum protection.

The Houston-Galveston-Brazoria Consolidated Metropolitan Statistical Area (Houston CMSA) has experienced rapid population growth during the past decades and is the only major US metropolitan area with no zoning regulations. We use SLEUTH, a spatially explicit cellular automata model, to simulate future (2002-2030) urban growth in the Houston metropolitan area, one of the fastest growing metropolises in the United States during the past decades. The model is calibrated with historical data for the period 1974-2002 that are extracted from a time series of satellite images. The dataset consists of four historical urban extents (1974, 1984, 1992, 2002), two land use layers (1992, 2002), five transportation layers (1974, 1984, 1990, 2002, 2025), slope layer, hillshade layer, and excluded layer. Future growth patterns are predicted based on growth coefficients derived during the calibration phase. After calibrating the model successfully, the spatial pattern of urban growth of the Houston CMSA for the period from 2002 to 2030 is predicted. Within SLEUTH, growth in the Houston CMSA is predominately "organic" with most growth occurring along the urban/rural fringe. Projected increases in urban area from 2002 to 2030 parallel projected increases in population growth within the Houston CMSA. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the unmanaged growth with no restrictions. The second scenario is to project the moderate growth trend by taking into consideration environmental protection, specifically for agricultural areas, forests and wetlands. The last scenario is to simulate the managed growth with maximum environmental protection. Adjusting the level of protection for different land cover types was found to markedly affect the land use changes in the Houston CMSA. Without any protection on resource lands, Houston CMSA is estimated to lose 2,000 km2 of forest land by 2030, about 600 km2 of agricultural land, and approximately 400 km2 of wetland. Approximately half of all resource land could be saved by the third scenario, managed growth with maximum protection.

Thesis (MSc (Geography and Environmental Studies))--Stellenbosch University, 2008. / Accelerating urban growth places increasing pressure not only on the efficiency of infrastructure and service provision, but also on the natural environment. City managers are delegated the task of identifying problem areas that arise from this phenomenon and planning the strategies with which to alleviate them. It is with this in mind that the research investigates the implementation of an urban growth model, SLEUTH, as a support tool in the planning and decision making process. These investigations are carried out on historical urban data for the region falling under the control of the Cape Metropolitan Authority. The primary aim of the research was to simulate future urban expansion of Cape Town based on past growth patterns by making use of cellular automata methodology in the SLEUTH modeling platform. The following objectives were explored, namely to: a) determine the impact of urbanization on the study area, b) identify strategies for managing urban growth from literature, c) apply cellular automata as a modeling tool and decision-making aid, d) formulate an urban growth policy based on strategies from literature, and e) justify SLEUTH as the desired modeling framework from literature. An extensive data base for the study area was acquired from the product of a joint initiative between the private and public sector, called “Urban Monitoring”. The data base included: a) five historical urban extent images (1977, 1988, 1993, 1996 and 1998); b) an official urban buffer zone or ‘urban edge’, c) a Metropolitan Open Space System (MOSS) database, d) two road networks, and d) a Digital Elevation Model (DEM). Each dataset was converted to raster format in ArcEdit and finally .gif images were created of each data layer for compliance with SLEUTH requirements. SLEUTH processed this historic data to calibrate the growth variables for best fit of observed historic growth. An urban growth forecast was run based on the calibration parameters. Findings suggest SLEUTH can be applied successfully and produce realistic projection of urban expansion. A comparison between modelled and real urban area revealed 76% model accuracy. The research then attempts to mimic urban growth policy in the modeling environment, with mixed results.