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The SLEUTH model provides a framework for understanding land use evolution around urban areas. Calibration of SLEUTH's behavioral coefficients can be impacted by scale and nonlinear transitions due to the SLEUTH land use deltatron module's assumption of linear Markov change probabilities. This study attempted to establish what spatial resolution and temporal scale produces the most accurate forecasts given the linear change assumption. The impact of tiling the input data was also examined. To determine these, SLEUTH was calibrated at four spatial and three temporal scales for Ibadan, Nigeria using both untiled and tiled data. Calibration results were evaluated using accuracy metrics including Figure of Merit (FOM) and mean uncertainty. The best mix of calibration metrics (FOM 0.26) and mean uncertainty (11.64) was achieved at 30 m resolution and an intermediate temporal interval. Tiling input data led to overfitting, allowing good model fit within individual tiles but a reduction in trend recognition across land use types. Subsequently, a 2040 projection that is as accurate as possible, and scientifically justifiable given the available data, was produced. The findings provide a framework for understanding the effect of spatiotemporal scale on SLEUTH inputs that require tiling particularly for urban areas in the global south. [ABSTRACT FROM AUTHOR]

Scenario-based land-use planning depends on choosing from among a range of inputs to a calibrated model to explore the corresponding range of future outputs and impacts—but how are these inputs chosen? This case study examined how a landscape ecology approach could better shape scenario development for a SLEUTH (slope, land use, exclusion, urban, transportation, and hillshade) model application toward achieving possibly more ecologically sustainable scenarios and, therefore, future landscapes. Our methodology relied on two main steps––developing landscape-ecology-led growth scenarios and using SLEUTH to model their impacts on urban growth and land-use change. As a case study, we applied the model to Sariyer, Istanbul, for the year 2045, where transportation infrastructure changes portend significant implications for the future landscape. Even though there are many works on urban growth in the international literature, there has been only limited investigation of the effects of urban transportation systems in developing nations and their implications for urban landscapes. This study's novelty lies in including a landscape ecology approach in the scenario development to enable better integration with modeling and the use of landscape metrics to modify the scenarios and interpret the model outcomes. Our research question is: can a joint approach of scenario development using landscape ecology principles (associated with landscape metrics) and SLEUTH modeling accurately forecast the impacts of landscape ecology and ecosystem services on human and urban systems? We conclude that a landscape ecology approach does help illuminate future urban-growth behavior led by transportation system development. Landscape ecology can be a medium through which the SLEUTH model can articulate more-comprehensive ecological planning related to the implications of different growth scenarios. [ABSTRACT FROM AUTHOR]

Many countries have been constructing modern ground transportation projects. This raises questions about the impacts of such projects on development of impervious surfaces, yet there have been few attempts to systematically analyze these impacts. This paper attempts to narrow this information gap using the Hangzhou Bay Bridge project, China, as an exploratory case study. Using remotely sensed data, we developed a framework based on statistical techniques, wavelet multi-resolution analysis and Theil-Sen slope analysis to measure the changes in impervious surfaces. The derived changes were then linked to the bridge project with respect to socio-economic factors and land use development activities. The findings highlight that the analytical framework could reliably quantify the area, pattern and form of new urban area and urban intensification. Change detection analysis showed that urban area, GDP and the length of highways increased moderately in the pre-Hangzhou Bay Bridge period (1995–2002) while all of these variables increased more substantially during (2002–2009) and after (2009–2013) the bridge construction. The results indicate that the development of impervious surfaces due to new urban area came at the expense of permeable surfaces in the urban fringe and within rural regions, while urban intensification occurred mainly in the form of the redevelopment of older structures to modern high-rise buildings within existing urban regions. In the context of improved transportation infrastructure, our findings suggest that new urban area and urban intensification can be attributed to consecutive events which act like a chain reaction: construction of improved transportation projects, their impacts on land use development policies, effects of both systems on socio-economic variables, and finally all these changes influence new urban area and urban intensification. However, more research is needed to better understand this sequential process and to examine the broader applicability of the concept in other developing regions. [ABSTRACT FROM AUTHOR]

Abstract: This study aims to analyze the spatial patterns of urban growth in South Korea between 2000 and 2010. Fourteen suspected causative independent variables were selected and latent class regression (LCR) was used to analyze the relationship between dependent (urban growth) and independent (causative) variables. The goodness‐of‐fit was assessed by comparison to logistic regression (LR) analysis. The LR analysis produced consistent coefficients for each independent variable across the study area. In contrast, an LCR analysis, with a three‐class assumption, resulted in a different magnitude and directional effects of the coefficients for each class. The LCR analysis enabled the identification of both spatially homogeneous and heterogeneous areas. In addition, the LCR analysis performed better than the LR analysis with a lower Akaike information criterion and Bayesian information criterion value, and a higher receiver operating characteristic value. We conclude that LCR analysis should be used to establish causative “driving” factors for efficient urban growth planning and urban spatial policy. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa has the highest levels of fertility in the world, despite rapid urban growth in most nations of the region. While there are many reasons for the fact that fertility decline is slow in Africa, we hypothesize that the relationship between fertility and urbanization is obscured by the fact that urbanization takes place along a gradient. In the most urban places (e.g., neighborhoods of the largest cities), fertility is apt to be very low, but most urban residents are residing in places that are somewhere along a continuum from completely rural to the most urban possible. All previous attempts to define that urban gradient and relate it to fertility levels rely in one form or another on census data. Because sub-Saharan-African countries are among the least prolific in terms of census gathering, a measure that relies solely on satellite imagery to gage a place's position along the urban gradient could be extremely useful. This paper describes a methodology for doing this and then uses data from the West African country of Ghana to examine how spatial patterns of land cover are associated with fertility. Satellite imagery and landscape metrics are used to create an urban context definition based on landscape patterns using a gradient approach. Census data are used to model the association between urban context and fertility through ordinary least square regression and spatial autoregressive models. Results indicate that there are significant differences in fertility between different urban contexts. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the effect of a policy aimed at the removal of a greenbelt on future urban growth. The SLEUTH model was applied to the Seoul Metropolitan Area, South Korea, to predict urban growth under three different greenbelt removal scenarios. The accuracy of the model was verified using historical data with ROC and Kappa statistics of 82.6 and 76.3%, indicating reasonable accuracy. In the scenarios, suburban development grew in proportion to the degree of reduction of the greenbelt. In two of the scenarios, suburban cities in the inner part of the greenbelt were integrated into the metropolitan area. In scenario 3, a complete removal of the greenbelt resulted in the highest rate of projected urban development. The Seoul Metropolitan Area is under continuous developmental pressure, and the sacrifice of a certain amount of protected land to satisfy this demand may be inevitable. Accordingly, effective urban growth management is necessary to promote ecofriendly and sustainable development in formerly protected areas and to strengthen protection in the areas that will remain protected. The model outputs will be used by the government and policy makers to devise a more flexible and sustainable urban growth management policy. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the application of cellular automata in urban modeling to give insights into a wide variety of urban phenomena, using the most commonly used urban modeling approaches including: Markov Chain, SLEUTH, Dinamica EGO modelling with the Logistic Regression (LR), Regression Tree (RT) and Artificial Neural Networks (ANN). The effectiveness of these approaches in forecasting the urban growth was assessed in the example of Adana as a fast growing City in Turkey for the year 2023. Different models have their own merits and advantages, the empirical results and findings of various approaches provided a guide for urban sprawl modeling. The accuracy figures to assess the models were derived using Allocation and Disagreement maps together with Kappa statistics. Calibration data were from remotely sensed images recorded in 1967, 1977, 1987, 1998 and 2007. SLEUTH, Markov Chain and RT models resulted in overall Kappa accuracy measures of 75%, 72% and 71% respectively, measured over the past data using hindcasting. LR and ANN yielded the least accurate results with an overall Kappa accuracy of 66%. Different modeling approaches have their own merits. However, the SLEUTH model was the most accurate for handling the variability in the present urban development. [ABSTRACT FROM AUTHOR]

This paper aims to emphasize the importance of the calibration process in urban growth modeling studies. The application of cellular automata (CA) in urban modeling can give insights into a wide variety of urban phenomena. The SLEUTH model, being as a well-tested CA, was utilized. Calibration data for the model were acquired from different sources of remotely sensed data recorded in 1967, 1977, 1987, 1998 and 2007. In this context three different excluded maps representing different scenarios were utilized during the calibration process in order to analyze the effects of different policies on urban growth. Each calibration scenario yielded its own parameter values. Thirteen calibration metrics for each scenario were derived. Integrating different exclusion layers to the beginning of the calibration process has reduced the number of possible growth patterns. The overall growth characteristics of Adana were similar for all calibration results and defined as organic growth except for the fact that the spatial allocation and the amount of potential urban pixels were different. [ABSTRACT FROM AUTHOR]

What is the true value of simulation modelling to urban growth? This article assesses the validity of an integrated approach, based on the SLEUTH Model and land-use planning theory, as used to create an eight-year forecast in 1998. With actual data on the extent of urbanization in 2006 now available, the authors find that the 1998 forecasts were accurate. The case study is located in Macomer, an inland municipality of Sardinia, Italy, an island in the central Mediterranean Sea. Noting that data collection is an essential first step of planning, the authors assess Macomer's land-use history, geography, economy, and demographics as context for more integrated and holistic planning than has been undertaken in the region to date. The 1998 calibration and prediction of the Urban Growth Model, a component of SLEUTH, simulated Macomer's urban growth for the following eight years and has been reviewed and statistically validated. With detailed geographical results, the authors confirm that the 1998 simulation closely reflects real urban growth as of 2006. This finding is particularly notable because urban growth in Sardinia has been slow, and a higher level of accuracy in urban planning is necessary to achieve stronger predictive capability. [ABSTRACT FROM AUTHOR]

Upgrading the SLEUTH urban-growth and land-use-change model, realizing its full capability in modeling change simultaneously in land-use and land-cover types, and using it as a self-organizing dynamic land-use planning tool have been the three main objectives of this study. In doing so, SLEUTH was applied to design a better plan for future and assess two scenarios concerning land-use and land-cover changes in Gorgan Township of the Golestan Province of Iran. Four land-use and land-cover maps were derived from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus imagery through a hybrid method including unsupervised, supervised, and on-screen classifi cation for four years. To provide a more desirable forecast of future land-use and land-cover changes, SLEUTH's exclusion layer was combined with an urbanizationsuitability layer from a multicriteria evaluation (MCE) using fi fteen map layers that most infl uence land suitability for urban development. The layers used in the MCE process were related to landform, vegetation, soil and geology, and surrogate socioeconomic factors; hence, they portrayed the desirability of the urban growth. SLEUTH was used for forecasting with both this new and a standard exclusion layer. Using the new layer, the fragmentation of the future land-use pattern was controlled and urban development along roads was restrained, thereby safeguarding the remaining urban green space and remnant rural vegetation patches. The results were also compared with a separate site selection process for future urban development showing the desirability of MCE-guided SLEUTH modeling over original SLEUTH and the standalone urban MCE in terms of landform, surrogate socioeconomic factors, and landscape metrics such as patch size, shape, and proximity and fractal dimension. As SLEUTH derives change rules simultaneously for diff erent land-use and land-cover types in a self-modifying self-organizing manner, we showed the approach can be regarded as a tool for dynamic land-use planning. [ABSTRACT FROM AUTHOR]

Abstract: China''s “building a new countryside” strategy for coordinating urban and rural development and gearing up national economic growth brings challenges to the country''s farmland protection. The objective of this study is to evaluate potential impacts of implementing the strategy on farmland and to provide scientific guidelines and decision support for decision makers in northeast China. We analyzed three “building a new countryside” implementation scenarios (Historical Trend, Intensive Development, and Extensive Development) using the SLEUTH urban growth and land cover change model in combination with remote sensing and GIS analysis. The results indicated that farmland loss was inevitable, but revealed large differences in landscape patterns and the amount of farmland loss among the three BNC implementation scenarios. The Extensive Development scenario showed the largest increase in urban and rural residential land, the highest level of landscape fragmentation, and the largest loss of farmland. Farmland loss under the Intensive Development scenario is higher than that under the Historical Trend scenario; however, urban and rural sprawl and the fragmentation of landscape under the Intensive Development scenario were lower than those under the Historical Trend scenario. Consequently, the Intensive Development scenario was recommended for actual “building a new countryside” implementation in the study area. Potential rural sprawl under the Intensive Development scenario was also discussed, which provided useful information for guiding scientific-based decision support and policy making. While most studies of sprawl prediction involve urban centers only, our study presents a case of predicting urban and rural sprawl simultaneously. [Copyright &y& Elsevier]

A critical challenge of global change is managing the uncontrolled spread of cities into their surrounding rural and other land. The phenomenon of urban 'sprawl' is well known, but it remains controversial because there are no universal definitions about its etiology, nor of the causes and variables related to it. The goal of this study is to depict the temporal trend of sprawl, so as to identify a 'sprawl signature' and its evolution for the Italian Province of Pordenone focusing exclusively on spatial dispersion features. Data were compiled from multitemporal remote sensing and used to delimit urban expansion over time. We aim to describe the spatiotemporal patterns associated with urban sprawl using the perspective of the cyclical urban growth theory and focusing on measures that can detect the degree of spatial dispersion during time related to sprawl both in past and projected urban forms. Exactly how the spatiotemporal patterns of urban growth are identified is crucial for urban planners, as knowledge of them allows more efficient calibration of policies to control land-use change in order to satisfy specific needs of the population and prevent the risks and costs related to sprawl. [ABSTRACT FROM AUTHOR]

Urban growth models have been used for decades to forecast urban development in metropolitan areas. Since the l990s cellular automata, with simple computational rules and an explicitly spatial architecture, have been heavily utilized in this endeavor. One such cellular-automata-based model, SLEUTH, has been successfully applied around the world to better understand and forecast not only urban growth but also other forms of land-use and land-cover change, but like other models must be fed important information about which particular lands in the modeled area are available for development. Some of these lands are in categories for the purpose of excluding urban growth that are difficult to quantify since their function is dictated by policy. One such category includes voluntary differential assessment programs, whereby farmers agree not to develop their lands in exchange for significant tax breaks. Since they are voluntary, today's excluded lands may be available for development at some point in the future. Mapping the shifting mosaic of parcels that are enrolled in such programs allows this information to be used in modeling and forecasting. In this study, we added information about California's Williamson Act into SLEUTH's excluded layer for Tulare County. Assumptions about the voluntary differential assessments were used to create a sophisticated excluded layer that was fed into SLEUTH's urban growth forecasting routine. The results demonstrate not only a successful execution of this method but also yielded high goodness-of-fit metrics for both the calibration of enrollment termination as well as the urban growth modeling itself. [ABSTRACT FROM AUTHOR]

Fire is an important natural disturbance in the Mediterranean-climate coastal shrublands of southern California. However, anthropogenic ignitions have increased fire frequency to the point that it threatens the persistence of some shrub species and favors the expansion of exotic annual grasses. Because human settlement is a primary driver of increased ignitions, we integrated a landscape model of disturbance and succession (LANDIS) with an urban growth model (UGM) to simulate the combined effects of urban development and high fire frequency on the distribution of coastal shrublands. We tested whether urban development would contribute to an expansion of the wildland-urban interface (WUI) and/or change in average fire return intervals and compared the relative impacts of direct habitat loss and altered fire regimes on functional vegetation types. We also evaluated two methods of integrating the simulation models. The development pattern predicted by the UGM was predominantly aggregated, which minimized the expansion of the WUI and increase in fire frequency, suggesting that fire risk may be higher at intermediate levels of urbanization due to the spatial arrangement of ignition sources and fuel. The comparison of model coupling methods illustrated how cumulative effects of repeated fires may occur gradually as urban development expands across the landscape. Coastal sage scrub species and resprouting chaparral were more susceptible to direct habitat loss, but increased fire frequency was more of a concern to obligate seeder species that germinate from a persistent seed bank. Simulating different scenarios of fire frequency and urban growth within one modeling framework can help managers locate areas of highest risk and determine which vegetation types are most vulnerable to direct habitat loss, altered fire regimes, or both. [ABSTRACT FROM AUTHOR]

Abstract: Land use change is one of the most important anthropogenic factors affecting terrestrial ecosystems, causing habitat loss, fragmentation, and interactions with other components of global change, such as biological invasions of non-native species. In southern California, population growth and economic expansion are the primary drivers of land use change, and the population is expected to double in 40 years. Although directly adjacent to the region''s largest metropolitan area, the Santa Monica Mountains National Recreation Area (SMMNRA) remains mostly undeveloped, with 50% of the area protected as parkland. In this study, a cellular automaton (CA) model was calibrated using historical growth patterns in the region, and used to forecast three scenarios of urban growth in the SMMNRA from 2000 to 2050, with development prohibited on slopes greater than 25%, 30%, and 60% slope. Habitat pattern and extent under these scenarios was assessed using several landscape metrics, then compared to results from a GIS overlay model developed for the same region. The CA model predicted urbanization to increase from 11% of the landscape in 2000 to 26%, 35%, and 47% in 2050, respectively, for the three slope scenarios. In 2000, the majority of vegetation constituted one large, interconnected patch. With development prohibited beyond 25% and 30% slope, this patch will become, by 2050, increasingly perforated, but should stay relatively intact. However, if growth is permitted up to 60% slope, the patch breaks apart, resulting in a shift in spatial pattern dynamics on the landscape (as reflected by other landscape metrics). General growth patterns predicted by the GIS overlay model resembled those generated by the CA, but the CA model produced more patches and edge in the landscape. Because it is temporally explicit, the CA model was able to capture non-linear, emergent behavior and a phase transition in the type of growth occurring in the landscape that was not apparent in the GIS overlay predictions. [Copyright &y& Elsevier]

The last decade has seen a renaissance in spatial modeling. Increased computational power and the greater availability of spatial data have aided in the creation of new modeling techniques for studying and predicting the growth of cities and urban areas. Cellular automata is one modeling technique that has become widely used and cited in the literature; yet there are still some very basic questions that need to be answered with regards to the use of these models, specifically relating to the spatial resolution during calibration and how it can impact model forecasts. Using the SLEUTH urban growth model (), urban growth for San Joaquin County (CA) is projected using three different spatial grains, based on four calibration routines, and the spatial differences between the model outputs are examined. Model outputs show that calibration at finer scaled data results in different parameter sets, and forecasting of urban growth in areas that was not captured through the use of more coarse data. [ABSTRACT FROM AUTHOR]

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts. • Heterogeneous factors at different scales were integrated into SLEUTH. • Taking spatial heterogeneity into consideration can improve the performance of the SLEUTH model. • Topography, transportation and economic factors are the main driving forces of urban expansion in the Wuhan Metropolitan Area. • Future urban planning policy has the potential to promise urban development that is more sustainable. [ABSTRACT FROM AUTHOR]

Understanding the impacts of climate and land use changes driven by urbanization upon ecosystem services (ESs) has great importance in ES management and policy making. However, knowledge of their impacts on ESs correlation and bundles are still lacking. This study quantified five ESs in a spatially explicit manner for 1989–2017 in the City Belt along the Yellow River in Ningxia, China. Ten scenarios based on two Representative Concentration Pathway (RCP 2.6 and 8.5) and five urban expansion policies, were used to predict the potential change of ESs in 2017–2045. Temporal trade-off and synergy relations among ESs were analyzed using correlation analysis, while ES bundles were delineated and mapped through a self-organizing map. Results indicated that climate change has larger impacts on sand fixation and carbon sequestration, especially in mountain areas. In 2017–2045, RCP 8.5 would result in a larger increase in sand fixation (by 1.75 times to 2045) and a slight decrease in carbon sequestration (by 1.62%) than RCP 2.6. Land use change has a more substantial impact on the other three ESs than climate change, especially in the central plain. Specifically, cropland reclamation during 1989–2017 and urban expansion in 2017–2045 caused the most intense alterations in ESs. Reclamation can increase food production and nutrient retention, but reduce recreational opportunity, while urban expansion has the opposite impacts. Encouraging compact urban growth, creating riparian vegetation buffers and environment protection policies can effectively reduce the trade-offs and simultaneous losses among ESs. Specific recommendations are proposed for different sub-regions indicated by the ES bundle classification. Our results can provide references for urban planning to enhance ESs under future global warming. • We evaluated five ESs in historical (1989–2017) and future (2017–2045) periods. • Temporal correlations and bundles among ESs have been analyzed and classified for the two periods. • Impacts of different climate and land use change driven by urbanization on ESs was compared. • Climate change has larger impact on sand fixation and carbon sequestration than the other three ESs. • Compact urban growth, riparian vegetation buffer and ecological protection policies can reduce ES losses and trade-offs. [ABSTRACT FROM AUTHOR]

• The paper aimed to understand the process of urbanization in Kolkata Urban Agglomeration in India • Drivers of urbanization were modeled using locational and socio-demographic variables and spatially lagged regression model • Multiple scenarios were set up to test the need and best approach to integrate the drivers of urbanization in SLEUTH model • Results showed that pace and pattern of present and future urbanization is different from past. • Integrating urban driver information in the model results in more realistic predictions of future urbanization [ABSTRACT FROM AUTHOR]

Abstract The agent-based modelling (ABM) is commonly used to simulate urban land growth. A key challenge of ABM for the simulation of urban land-use dynamics in support of sustainable urban management is to understand and model how human individuals make and develop their location decisions that then shape urban land-use patterns. To investigate this issue, we focus on modelling the agent learning process in residential location decision-making process, to represent individuals' personal and interpersonal experience learning during their decision-making. We have constructed an extended reinforcement learning model to represent the human agents' learning when they make location decisions. Consequently, we propose and have developed a new agent-based procedure for residential land growth simulation that incorporates an agent learning model, an agent decision-making model, a land use conversion model, and the impacts of urban land zoning and the developers' desires. The proposed procedure was first tested by using hypothetical data. Then the model was used for a simulation of the urban residential land growth in the city of Nanjing, China. By validating the model against empirical data, the results showed that adding agent learning model contributed to the representation of the agent's adaptive location decision-making and the improvement of the model's simulation power to a certain extent. The agent-based procedure with the agent learning model embedded is applicable to studying the formulation of urban development policies and testing the responses of individuals to these policies. Highlights • We construct an agent learning model for urban growth simulation • We assign a computational equation to the payoff function and extend the to-be-reinforced strategy set of RL algorithm • We develop an agent-based procedure with an agent learning model embedded for geo-simulation • The procedure is applicable to studying the formulation of urban policies and testing the responses of individuals to them • Future research should consider embedding social network into learning model and multi-stages learning models [ABSTRACT FROM AUTHOR]

The paper explores a framework combining remote sensing and spatial metrics aimed at improving the analysis and modeling of urban growth and land use change. While remote sensing data have been used in urban modeling and analysis for some time, the proposed combination of remote sensing and spatial metrics for that purpose is quite novel. Starting with a review of recent developments in each of these fields, we show how the systematic, combined use of these tools can contribute an important new level of information to urban modeling and urban analysis in general. We claim that the proposed approach leads to an improved understanding and representation of urban dynamics and helps to develop alternative conceptions of urban spatial structure and change. The theoretical argument is then illustrated with actual examples from the urban area of Santa Barbara, California. Some questions for future research are finally put forward to help strengthen the potential of the proposed framework, especially regarding the further exploration of urban dynamics at different geographic scales. [Copyright &y& Elsevier]

Urban development is shown to induce predictable changes in satellite-based measures of radiant surface temperature and evapotranspiration fraction—as long as certain features of the development are known. Specifically, the vegetation changes that accompany the development and the initial climatic state of the land parcel must be noted. Techniques are also developed for quantifying the effects of urbanization on the surface hydrology at a watershed scale. Streamflow and precipitation data are related graphically in order to determine a watershed''s general ratio of stormwater runoff to rainfall, along with any changes in the ratio over time. Four distinct runoff responses, separated by season and antecedent moisture conditions, are distinguishable for a particular basin, with the response during the non-summer months under typical antecedent moisture conditions the most representative of and responsive to land-use patterns. This particular runoff response can be estimated from satellite-derived land cover patterns and certain physical attributes of a basin. These satellite-based microclimate and hydrologic analyses are coupled with an existing urban growth model (SLEUTH). The SLEUTH urban growth model simulates future development scenarios for a region of interest. The resulting changes in urban land use lead to the evolution of site-specific climate and hydrology based on the scheme that is presented in this paper. This study, as well as related tools and bodies of knowledge, can be used to broaden the scientific basis behind land-use management decisions. [Copyright &y& Elsevier]

This study explores the combined application of remote sensing, spatial metrics and spatial modeling to the analysis and modeling of urban growth in Santa Barbara, California. The investigation is based on a 72-year time series data set compiled from interpreted historical aerial photography and from IKONOS satellite imagery. Spatial metrics were used both specifically to assess the impact of urban development in four administrative districts, and generally to analyze the spatial and temporal dynamics of urban growth. The metrics quantify the temporal and spatial properties of urban development, and show definitively the impacts of growth constraints imposed on expansion by topography and by local planning efforts. The SLEUTH urban growth and land use change model was calibrated using the multi-temporal data sets for the entire study region. The calibrated model allowed us to fill gaps in the discontinuous historical time series of urban spatial extent, since maps and images were available only for selected years between 1930 and 2001. The model also allowed a spatial forecast of urban growth to the year 2030. The spatial metrics provided a detailed description of the accuracy of the model''s historical simulations that applied also to forecasts of future development. The results illustrate the utility of modeling in explaining the amount and spatial pattern of urban growth. Even using modeling, however, the forecasting of urban development remains problematic and could benefit from further research on spatial metrics and their incorporation into the model calibration process. The combined approach using remote sensing, spatial metrics and urban modeling is powerful, and may prove a productive new direction for the improved understanding, representation and modeling of the spatiotemporal forms due to the process of urbanization. [Copyright &y& Elsevier]

Urban growth is a complex spatio-temporal phenomenon that includes built-up activities taking place both horizontally and vertically. The built-up land density in a city is a function of land desirability and suitability of a location in terms of the quality of the available public services, access to infrastructure, neighborhood type, vibrancy of socio-economic and cultural characteristics. The simulation of built-up land density may help planning and development authorities in making better land developmental decisions, making appropriate provisions for services, long term land use planning, and allocation of natural resources. In the recent past, various efforts have been made to assess built-up density in terms of the densification of built-up activities at the city scale in terms of floor space indices, building density, residential density (number of housing units per hectare) and urban volume. However, only limited research was reported on the simulation and prediction of spatially distributed built-up land density. In the present study, an algorithm is developed to simulate the spatial distribution of built-up land density as a function of a set of selected urbanization explanatory variables. Its application has been demonstrated using a newly developed version of the SLEUTH model (SLEUTH-Density) to simulating the built-up land density for Ajmer city in India. Development of SLEUTH-Density included designing a density algorithm, writing the programming code, integrating the code with the existing SLEUTH model, and testing the algorithm. The model results were validated indirectly using few built-up land density indices and directly through field verification, which were found to be in good agreement with the simulated built-up land density from SLEUTH-Density • Development of an algorithm and SLEUTH-Density version of SLEUTH for the estimation of spatial built-up land density. • The model application has been demonstrated for simulating the built-up land density for Ajmer city in India. • The model may be applied to other geographical locations and helpful in making land development decisions by the developmental agencies. [ABSTRACT FROM AUTHOR]

This study aims to support sustainable urban and environmental planning by using urban growth simulation models, in which environmental quality is employed as one of the inputs. We proposed an extended SLEUTH urban growth model (UGM) for the regions threatened by environmental quality degradation caused by uncontrolled urban expansion. In this model, habitat quality is assessed by the InVEST model and is used to represent environmental quality, which is utilized in urban growth simulation. The habitat quality map is used to replace the slope layer as input for the SLEUTH model's urban growth simulation for cities where relatively flat topography makes this layer of minimal explanatory value. The extended SLEUTH UGM was calibrated using data for Changzhou city, China in 1990, 2000, 2010, and 2014. The best value of the Optimal SLEUTH Metric (OSM) was calculated for both the standard SLEUTH UGM and the extended SLEUTH UGM independently. The OSM value for the latter model was much higher than that of the former model, which indicated that the extended model provided a better explanation of urban growth in the study area. The calibrated extended SLEUTH UGM was applied to predict growth in Changzhou city from 2014 to 2030. The result showed that the urban area is expected to expand about 626 km 2 by 2030. Comparison with the prediction result by using standard SLEUTH UGM showed that the area with high habitat quality could be reserved and the urban expansion could be limited by using our model. The findings demonstrate that the extended SLEUTH UGM could be a valuable tool for sustainable urban and environmental planning and management in developing regions where environmental protection should be considered as one of the major land-use objectives in their rapid urbanization process. [ABSTRACT FROM AUTHOR]

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM +) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 m uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient. [ABSTRACT FROM AUTHOR]