Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China

Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria

Cellular automata (CA) models like SLEUTH (an acronym for slope, land use, excluded area, urban extent, transport-network and hill shade) have predominantly been developed and applied in developed countries. Modeling can serve as a tool to guide policy measures in facing urbanization challenges. However, developing cities have peculiar differences (heterogeneity, poor planning, and low infrastructure) thus the existing modeling approaches may not be able to apprehend heterogeneous urban growth. This research will use selected cities with similar spatial extents as controls but disparate urban extents, and growth indices to analyze the performance of SLEUTH simulations. Presumably, a comparison of the model simulations of the cities would display some significant differences, due to these variations and the scale of observation that has to be used for the model simulations. The results for the successfully calibrated cities (Kano/Funtua couple: 0.48/0.02. Katsina/Kaduna: 0.48/0.83 respectively) showed that in each city couple, the more expansive city with the most compact urban settlement pattern had a higher prediction accuracy, also predicted images of the cities showed underestimation of the urban areas over the years with the exception of Katsina city. The study further showed the model's effectiveness in modeling cities in developing countries, such as Nigeria. It is recommended that the type of urban growth experienced by cities be taken into consideration when implementing SLEUTH. Limitations of the study are centered on the inherent limitations of the model, the possibility of the occurrence of errors in data preparation, the scale and urban settlement type, which play an important role in the success of the calibration. Future research could be focused on adding other relevant inputs to the model and creating a metric that ascertains the best satellite image resolutions for a particular study area's growth coefficient values.

Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas

Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could reveal the land use change trend in the cities of the regions. Taking the urban areas of Wuhan, a typical rapid urbanizing region in China, as the study area, this study built a Markov chain–artificial neural network (ANN)–cellular automaton (CA) coupled model. The model used land use classification spatial data with a spatial resolution of 5 m in 2010 and 2020, obtained by remote sensing image interpretation, and data on natural and socio-economic driving forces for land use change simulation. Using the coupled model, the land use patterns of Wuhan urban areas in 2020 were simulated, which were validated in comparison with the actual land use data in 2020. Finally, the model was used to simulate the land uses in the study area in 2030. The model validation indicates that the land use change simulation has a high accuracy of 90.7% and a high kappa coefficient of 0.87. The simulated land uses of the urban areas of Wuhan show that artificial surfaces will continue to expand, with an area increase of approximately 7% from 2020 to 2030. Moreover, the area of urban green spaces will also increase by approximately 7%, while that of water bodies, grassland, cropland, and forests will decrease by 12.6%, 13.6%, 34.9%, and 1.3%, respectively, from 2020 to 2030. This study provides a method of simulating the land use changes in the cities of rapid urbanizing regions and helps to reveal the patterns and driving mechanisms of land use change in Wuhan urban areas.

A GIS-Cellular Automata-Based Model for Coupling Urban Sprawl and Flood Susceptibility Assessment

In Urban Planning (UP), it is necessary to take under serious consideration the inhibitors of the spread of a settlement in a specific direction. This means that all those parameters for which serious problems may arise in the future should be considered. Among these parameters are geo-hazards, such as floods, landslides, mud movement, etc. This study deals with UP taking into account the possibility of widespread flooding in settlement expansion areas. There is a large flooding history in Greece, which is accompanied by a significant number of disasters in different types of land use/land cover, with a large financial cost of compensation and/or rehabilitation. The study area is the drainage basin of Erasinos River in the Attica Region, where many and frequent flood events have been recorded. The main goal of this study is to determine the flood susceptibility of the study area, taking into account possible factors that are decisive in flood occurrence. Furthermore, the flood susceptibility is also determined, taking into account the scenarios of precipitation and the urban sprawl scenario in the area of reference. The study of flood events uses the Analytic Hierarchy Process (AHP) model and the urban sprawl model SLEUTH, which calibrates historical urban growth, using open and cost-free data and software. Eventually, flood susceptibility maps were overlaid with future urban areas to find the vulnerable areas. Following, three scenarios of flood susceptibility with the corresponding susceptibility maps and vulnerability maps, which measure the flood susceptibility of the current and future urban space of the study area, are presented. The results have shown significant peaks in the moderate class of flood susceptibility, while, in the third scenario, high values of flood susceptibility seem to appear. The proposed methodology and specifically the output maps can serve as a decision support tool to assist urban planners and hazard managers in making informed decisions towards sustainable urban planning.

Modeling urban encroachment on ecological land using cellular automata and cross-entropy optimization rules

Rapid urban expansion often leads to substantial encroachment on ecological lands and destruction of natural environments. We developed a new cellular automata model (named CA_CEO) that uses cross-entropy optimization (CEO) to reproduce and project urban expansion into coastal areas and to assess urban encroachment on ecological lands. The CEO algorithm automatically searches for the near-optimal CA parameters and is capable of objectively parameterizing CA models to predict multi-objective scenarios. We calibrated CA_CEO by simulating urban expansion at Wenzhou from 1995 to 2005, validated the model from 2005 to 2015 using real data, and then predicted urban expansion for 2025 and 2035. End-state overall accuracies were 93.8% for 2005 and 94.4% for 2015, while figure-of-merit metrics were 27.9% for 2005 and 19.1% for 2015. We predicted four different scenarios to year 2025 and 2035: (1) a business-as-usual (BAU)-scenario using benchmark settings; (2) a District-scenario based on a district-oriented urban development strategy; (3) a Road-scenario based on a road network-oriented urban development strategy; and (4) a Coast-scenario based on a coast-oriented urban development strategy. Each scenario predicts a substantially different pattern of urban encroachment on ecological land and significant loss of farmland, forest, wetland and grassland. These scenarios should be useful in adjusting urban development strategies at Wenzhou and elsewhere.

A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models

Due to the increase in future uncertainty caused by rapid environmental, societal, and technological change, exploring multiple scenarios has become increasingly important in urban planning. Land Change Modeling (LCM) enables planners to have the ability to mold uncertain future land changes into more determined conditions via scenarios. This paper reviews the literature on urban LCM and identifies driving factors, scenario themes/types, and topics. The results show that: (1) in total, 113 driving factors have been used in previous LCM studies including natural, built environment, and socio-economic factors, and this number ranges from three to twenty-one variables per model; (2) typical scenario themes include “environmental protection” and “compact development”; and (3) LCM topics are primarily growth prediction and prediction tools, and the rest are growth-related impact studies. The nature and number of driving factors vary across models and sites, and drivers are heavily determined by both urban context and theoretical framework.

Necessity of a Multifaceted Approach in Analyzing Growth of Impervious Surfaces

While substantial efforts have been devoted to the remote sensing of impervious surfaces, few studies have developed frameworks to connect impervious surfaces’ growth with spatial planning decisions. To this end, this paper develops a multifaceted approach with three components: Visualization, numerical analysis, and simulation at the sub-pixel level. First, the growth of impervious surfaces was visualized through write function memory (WFM) insertion for the period of 1974–2009 of Cixi County in Zhejiang Province, China. Second, anomaly detection, statistical analysis, and landscape metrics were used to quantify changes in impervious surfaces over time. Finally, a slope, land use, exclusion, urban extent, transportation, and hill shade (SLEUTH) cellular automata model was employed to simulate the impervious surface growth until 2015 under four specific spatial decision scenarios: Current trends, environmental protection growth, business growth, and Chinese policy for protecting rural regions. The results show that Cixi County experienced compact growth due to expansion and internal intensification. Interestingly, the SLEUTH reveals that the projected space of impervious surfaces’ growth was consistent with reality in 2015. The framework established in this study holds considerable potential for improving our understanding of the interaction between impervious surfaces’ growth and planning aspects.

Integrating Data-Driven and Participatory Modeling to Simulate Future Urban Growth Scenarios: Findings from Monastir, Tunisia

Current rapid urbanization trends in developing countries present considerable challenges to local governments, potentially hindering efforts towards sustainable urban development. To effectively anticipate the challenges posed by urbanization, participatory modeling techniques can help to stimulate future-oriented decision-making by exploring alternative development scenarios. With the example of the coastal city of Monastir, we present the results of an integrated urban growth analysis that combines the SLEUTH (slope, land use, exclusion, urban extent, transportation, and hill shade) cellular automata model with qualitative inputs from relevant local stakeholders to simulate urban growth until 2030. While historical time-series of Landsat data fed a business-as-usual prediction, the quantification of narrative storylines derived from participatory scenario workshops enabled the creation of four additional urban growth scenarios. Results show that the growth of the city will occur at different rates under all scenarios. Both the “business-as-usual” (BaU) prediction and the four scenarios revealed that urban expansion is expected to further encroach on agricultural land by 2030. The various scenarios suggest that Monastir will expand between 127–149 hectares. The information provided here goes beyond simply projecting past trends, giving decision-makers the necessary support for both understanding possible future urban expansion pathways and proactively managing the future growth of the city.

The Impacts of the Expansion of Urban Impervious Surfaces on Urban Heat Islands in a Coastal City in China

The effect of the expansion of urban impervious surfaces on surface urban heat islands (UHIs) has attracted research attention due to its relevance for studies of local climatic change and habitat comfort. In this study, using five satellite images of Xiamen city, Southeast China (four images from the Landsat 5 Thematic Mapper (TM) and one from the Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS)) acquired in summer between 1989 and 2016, together with spatial statistical methods, the changes in impervious surface area (ISA) were investigated, the spatiotemporal variation of the intensity of urban heat islands (UHIs) was explored, and the relationships between land surface temperature (LST) and the percentage of impervious surface area (ISA%), the normalized difference vegetation index (NDVI), and fractional vegetation coverage (Fv) were investigated. The results showed the following: (1) According to the biophysical composition index (BCI) combined with an ISA post-processing method, Xiamen has witnessed a substantial increase in ISA, showing a 6.1-fold increase from 1989 to 2016. The direction of ISA expansion was consistent throughout the study period in each of the five districts of Xiamen; (2) a bay-like UHI form is observed in the study area, which is remarkably distinct from the central-radial UHI form observed in previous studies of other cities; (3) the extent of UHIs in Xiamen greatly increased between 1989 and 2016, experiencing a 4.7-fold increase in UHI areas during this time. However, during the same period, the urban heat island ratio index (URI)—that is, the ratio of UHI area to ISA—decreased slightly. The UHI area decreased in some urban parts of Xiamen due to a significant increase in vegetation coverage, urban village redevelopment, and the construction of new parks; (4) sea ports and heavy industrial zones are the greatest contributor to surface UHI, followed by urban villages; and (5) LST is strongly positively correlated with ISA%. Each 10% increase in ISA was associated with an increase in summer LST of 0.41 to 0.91 K, which compares well with the results of related studies. This study presents valuable information for the development of regional urban planning strategies to mitigate the effects of UHIs during rapid urbanization.

Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei, East China

As uncontrolled urban growth has increasingly challenged the sustainable use of urban land, it is critically important to model urban growth from different perspectives. Using the SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, the historical data of Hefei in 2000, 2005, 2010, and 2015 were collected and input to simulate urban growth from 2015 to 2040. Three different urban growth scenarios were considered, namely a historical growth scenario, an urban planning growth scenario, and a land suitability growth scenario. Prediction results show that by 2040 urban built-up land would increase to 1434 km2 in the historical growth scenario, to 1190 km2 in the urban planning growth scenario, and to 1217 km2 in the land suitability growth scenario. We conclude that (1) exclusion layers without effective limits might result in unreasonable prediction of future built-up land; (2) based on the general land use map, the urban growth prediction took the governmental policies into account and could reveal the development hotspots in urban planning; and (3) the land suitability scenario prediction was the result of the trade-off between ecological land and built-up land as it used the MCR -based (minimum cumulative resistance model) land suitability assessment result. It would help to form a compact urban space and avoid excessive protection of farmland and ecological land. Findings derived from this study may provide urban planners with interesting insights on formulating urban planning strategies.

Modeling Intersecting Processes of Wetland Shrinkage and Urban Expansion by a Time-Varying Methodology

Continuous urban expansion worldwide has resulted in significant wetland degradation and loss. A limited number of studies have addressed the coupling of wetland and urban dynamics, but this relationship remains unclear. In this study, a time-varying methodology of predicting wetland distribution was developed to support decision-making. The novelty of the methodology is its ability to dynamically simulate wetland shrinkage together with urban expansion and reveal conflicts and potential tradeoffs under different scenarios. The developed methodology consists of three modules: a historical change detection of wetland and urban areas module, a spatial urban sprawl simulation and forecasting module that can accommodate different development priorities, and a wetland distribution module with time-varying logistic regression. The methodology was applied and tested in the Tonghu Wetland as a case study. The wetland and urban extents presented a spatially intersecting shift, where wetlands lost more than 40% of their area from 1977 to 2017, while urban areas expanded by 10-fold, threatening wetlands. The increase in the relative importance metric of the time-varying regression model indicated an enhanced influence of urban expansion on the wetland. An accuracy assessment validated a robust statistical result and a good visual fit between spatially distributed wetland occurrence probabilities and the actual distribution of wetland. Incorporating the new variable of urban expansion improved modeling performance and, particularly, realized a greater ability to predict potential wetland loss than provided by the traditional method. Future wetland loss probabilities were visualized under different scenarios. The historical trend scenario predicted continuously expanding urban growth and wetland shrinkage to 2030. However, a specific urban development strategy scenario was designed interactively to control the potential wetland loss. Consideration of such scenarios can facilitate identifying tradeoffs to support wetland conservation.

A Spatio-Temporal Autowave Model of Shanghai Territory Development

A spatio-temporal model of megacity development that treats the megacity as an active medium is presented. From our point of view, it is advisable to consider the process of urban ecosystem development from the standpoint of the theory of autowave self-organization in active media. According to this concept, the urban ecosystem is considered as interacting with each other’s natural and anthropogenic subsystems with significant heterogeneity of areas affected by human intervention and urban geobiocoenoses. The model is based on the general principles of active medium dynamics; therefore, it is universal for any object to be considered an active medium. The only difference when using the model to predict the development of urban ecosystems in countries with different socio-economic and political prerequisites is the variety of parameters included in the model, i.e., the activation parameter, the autowave process inhibitors, and the characteristic scales of the activator and inhibitor. The model was tested on the example of Moscow expansion in the period of 1952–1968 and showed good agreement with the map data. By means of the model, a prediction of Shanghai and surrounding territory development until 2030 was made.

Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston

Rising sea levels and coastal population growth will increase flood risk of more people and assets if land use changes are not planned adequately. This research examines the efficacy of flood protection systems and land use planning by comparing Amsterdam in the Netherlands (renown for resilience planning methods), with the city of Houston, Texas in the US (seeking ways of increasing resilience due to extreme recent flooding). It assesses flood risk of future urban growth in lieu of sea level rise using the Land Transformation Model, a Geographic Information Systems (GIS)-based Artificial Neural Network (ANN) land use prediction tool. Findings show that Houston has currently developed much more urban area within high-risk flood-prone zones compared to Amsterdam. When comparing predicted urban areas under risk, flood-prone future urban areas in Amsterdam are also relatively smaller than Houston. Finally, the increased floodplain when accounting for sea level rise will impact existing and future urban areas in Houston, but do not increase risk significantly in Amsterdam. The results suggest that the protective infrastructure used in the Netherlands has protected its future urban growth from sea level rise more adequately than has Houston.

Analysis of road development and associated agricultural land use change

Development of road network is one of the strongest drivers of habitat fragmentation. It interferes with ecological processes that are based on material and energy flows between landscape patches. Therefore, changes in temporal patterns of roads may be regarded as important landscape-level environmental indicators. The aim of this study is to analyze road development and associated agricultural land use change near the town of Erdemli located in the eastern Mediterranean coast of Turkey. The study area has witnessed an unprecedented development of agriculture since the 2000s. This process has resulted with the expansion of the road network. Associations between agricultural expansion and road development were investigated. High-resolution satellite images of 2004 and 2015 were used to analyze spatial and temporal dimensions of change. Satellite images were classified using a binary approach, in which land areas were labeled as either "agriculture" or "non-agriculture." Road networks were digitized manually. The study area was divided into 23 sublandscapes using a regular grid with 1-km cell spacing. Percentage of landscape (PL) for agriculture and road density (RD) metrics were calculated for the earlier (2004) and later (2015) years. Metric calculations were performed separately for each of the 23 sublandscapes in order to understand spatial diversity of agriculture and road density. Study results showed that both RD and PL exhibited similar increasing trends between 2004 and 2015.

Spatial distribution of soil cadmium and its influencing factors in peri-urban farmland: a case study in the Jingyang District, Sichuan, China

Semi-agricultural ecosystems in peri-urban areas are susceptible to contamination. The spatial distribution and influencing factors of such pollution are unclear and poorly constrained in many areas worldwide. Therefore, studying the problems of soil pollution in peri-urban areas is critical for environmental management and agricultural production. In this paper, with cadmium (Cd) as the target pollutant, the spatiotemporal variations of soil cadmium pollution and the relative importance of the affecting factors were analyzed at a peri-urban area from the Jingyang District, Sichuan, China. Statistical results showed that the farmland in the study area could be considered moderately soil Cd-polluted, under the dual influence of natural factors and human activity. In particular, the soil Cd concentration in Tianyuan and Bajiaojing exceeded 0.5 mg kg^-1, for intensive industrial enterprises are distributed in these areas. Correspondingly, the geoaccumulation index also showed that the contamination of Cd in this area was moderately polluted. Moreover, the ecological risk index was 80% in the study area, indicating that the soil Cd pollution potential risk was moderate to high. High geological background values (soil Cd = 0.29 mg kg^-1), river migration, industrial enterprises, and traffic significantly influenced soil Cd pollution, with natural geological factors playing greater roles. The significant horizontal-spatial effective distances away from Shiting River, Deyang-Aba Highway, and chemical plants were 200, 400, and 100 m, respectively. These results will be useful in guiding farmland cultivation and pollution remediation effectively in the peri-urban areas.