Urban Growth and Land Use Simulation Using SLEUTH Model for Adama City, Ethiopia

Author(s):  
Yanit Mekonnen ◽  
S. K. Ghosh
Keyword(s):  
Land Use ◽  
2020 ◽  
Author(s):  
Yanit Mekonnen ◽  
Abel Hailu

Abstract Urban growth / urban sprawl are the extension of a residential region into the surrounding area. The negative face of urban development is urban sprawl, criticizing the cause of environmental deterioration, growing inequality and diminishing the viability of aesthetic and urban areas. An effective and efficient planning of urban development and changes in land use and its effects on the environment needs, among other important details, details on development trends and patterns. Over the years, several models of urban growth have been developed and used to predict trends of growth. SLEUTH models are used to simulate and predict urban growth and land use transition for 2020-2050 in the City of Dilla (Ethiopia) in the analysis of Geographic Information System (GIS). The word SLEUTH was derived from the model's input image specifications: slope, land cover, exclusion, urban, transport, and Hill shade. Input data preparation used a cumulative time series dataset of 30 years, i.e. 1989, 1999, 2009 and 2019, such as historical topographical maps and satellite imagery. The SLEUTH model uses the parameters of the best fit growth rule by narrowing coefficients in the calibration mode and passing them down to forecast potential urban growth trends, creating different probability maps and LULC maps. The models generated future urban growth pattern predicted in the 31 years' from 2019, there will be nearly 41.14% urban rise in 2020, 52.95% in 2030, 59.91% in 2040 and 64.30% in 2050. In general, the extension of the urban growth trend introduces new spreading centers that are indicative of urban growth.


2016 ◽  
Author(s):  
Jie Song ◽  
Xinyu Fu ◽  
Yue Gu ◽  
Yujun Deng ◽  
Zhong-Ren Peng

Abstract. Coastal regions are under intense development because of their biodiversity and economic attractiveness. Meanwhile, these places are highly vulnerable to coastal hazards that are associated with sea level rise. Continuing urban development in these coastal areas that are prone to be flooded increasingly poses unnecessarily risk to their residents. While overwhelming efforts have been made to investigate coastal land use changes, few studies have simultaneously explored urban growth dynamics and its interaction with the coastal hazards. This paper applied the cellular automaton-based SLEUTH model to calibrate historical urban growth pattern from 1974 to 2013 in Bay County coastal areas, Florida. Three scenarios of urban growth – historical trend, compact development, and urban sprawl – up to 2080 were predicted by applying the calibrated SLEUTH model. To assess the effects of different policies, we developed three excluded layers – no regulations, flooding-risk mitigation, and conservational/agricultural land protection – and evaluated how different urban growth scenarios were oriented under these policies. Eventually, flooding maps were overlaid with future urban areas, and the exposure of different urban growth patterns to sea level rise induced flooding was examined. The findings suggest that if coastal cities expand in a compact manner, areas vulnerable to flooding will increase compared with historical trend and urban sprawl scenarios. With respect to policies, if no regulations are implemented, on average the flooded area in 2080 would be more than 25 times under flooding-risk mitigation. The joint model can serve as a decision support tool to assist city officials, urban planners, and hazard mitigation planners in making informed decisions. The visualization results can be also useful in public outreach regarding coastal communities' increasing risk to flooding enhanced by sea level rise.


2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.


2021 ◽  
Vol 13 (2) ◽  
pp. 748
Author(s):  
Iana Rufino ◽  
Slobodan Djordjević ◽  
Higor Costa de Brito ◽  
Priscila Barros Ramalho Alves

The northeastern Brazilian region has been vulnerable to hydrometeorological extremes, especially droughts, for centuries. A combination of natural climate variability (most of the area is semi-arid) and water governance problems increases extreme events’ impacts, especially in urban areas. Spatial analysis and visualisation of possible land-use change (LUC) zones and trends (urban growth vectors) can be useful for planning actions or decision-making policies for sustainable development. The Global Human Settlement Layer (GHSL) produces global spatial information, evidence-based analytics, and knowledge describing Earth’s human presence. In this work, the GHSL built-up grids for selected Brazilian cities were used to generate urban models using GIS (geographic information system) technologies and cellular automata for spatial pattern simulations of urban growth. In this work, six Brazilian cities were selected to generate urban models using GIS technologies and cellular automata for spatial pattern simulations of urban sprawl. The main goal was to provide predictive scenarios for water management (including simulations) and urban planning in a region highly susceptible to extreme hazards, such as floods and droughts. The northeastern Brazilian cities’ analysis raises more significant challenges because of the lack of land-use change field data. Findings and conclusions show the potential of dynamic modelling to predict scenarios and support water sensitive urban planning, increasing cities’ coping capacity for extreme hazards.


SAGE Open ◽  
2014 ◽  
Vol 4 (4) ◽  
pp. 215824401456119 ◽  
Author(s):  
Luca Salvati ◽  
Margherita Carlucci

2021 ◽  
Vol 10 (4) ◽  
pp. 212
Author(s):  
Rana N. Jawarneh

Urban expansion and loss of primarily agricultural land are two of the challenges facing Jordan. Located in the most productive agricultural area of Jordan, Greater Irbid Municipality (GIM) uncontrolled urban growth has posed a grand challenge in both sustaining its prime croplands and developing comprehensive planning strategies. This study investigated the loss of agricultural land for urban growth in GIM from 1972–2050 and denoted the negative consequences of the amalgamation process of 2001 on farmland loss. The aim is to unfold and track historical land use/cover changes and forecast these changes to the future using a modified SLEUTH-3r urban growth model. The accuracy of prediction results was assessed in three different sites between 2015 and 2020. In 43 years the built-up area increased from 29.2 km2 in 1972 to 71 km2 in 2015. By 2050, the built-up urban area would increase to 107 km2. The overall rate of increase, however, showed a decline across the study period, with the periods of 1990–2000 and 2000–2015 having the highest rate of built-up areas expansion at 68.6 and 41.4%, respectively. While the agricultural area increased from 178 km2 in 1972 to 207 km2 in 2000, it decreased to 195 km2 in 2015 and would continue to decrease to 188 km2 by 2050. The district-level analysis shows that from 2000–2015, the majority of districts exhibited an urban increase at twice the rate of 1990–2000. The results of the net change analysis of agriculture show that between 1990 and 2000, 9 districts exhibited a positive gain in agricultural land while the rest of the districts showed a negative loss of agricultural land. From 2000 to 2015, the four districts of Naser, Nozha, Rawdah, and Hashmyah completely lost their agricultural areas for urbanization. By 2050, Idoon and Boshra districts will likely lose more than half of their high-quality agricultural land. This study seeks to utilize a spatially explicit urban growth model to support sustainable planning policies for urban land use through forecasting. The implications from this study confirm the worldwide urbanization impacts on losing the most productive agricultural land in the outskirts and consequences on food production and food security. The study calls for urgent actions to adopt a compact growth policy with no new land added for development as what is available now exceeds what is needed by 2050 to accommodate urban growth in GIM.


1976 ◽  
Vol 102 (1) ◽  
pp. 81-94
Author(s):  
William R. Walker ◽  
William E. Cox
Keyword(s):  
Land Use ◽  

Author(s):  
Jiren Xu ◽  
Fabrice G. Renaud ◽  
Brian Barrett

AbstractA more holistic understanding of land use and land cover (LULC) will help minimise trade-offs and maximise synergies, and lead to improved future land use management strategies for the attainment of Sustainable Development Goals (SDGs). However, current assessments of future LULC changes rarely focus on the multiple demands for goods and services, which are related to the synergies and trade-offs between SDGs and their targets. In this study, the land system (combinations of land cover and land use intensity) evolution trajectories of the Luanhe River Basin (LRB), China, and major challenges that the LRB may face in 2030, were explored by applying the CLUMondo and InVEST models. The results indicate that the LRB is likely to experience agricultural intensification and urban growth under all four scenarios that were explored. The cropland intensity and the urban growth rate were much higher under the historical trend (Trend) scenario compared to those with more planning interventions (Expansion, Sustainability, and Conservation scenarios). Unless the forest area and biodiversity conservation targets are implemented (Conservation scenario), the forest areas are projected to decrease by 2030. The results indicate that water scarcity in the LRB is likely to increase under all scenarios, and the carbon storage will increase under the Conservation scenario but decrease under all other scenarios by 2030. Our methodological framework and findings can guide regional sustainable development in the LRB and other large river basins in China, and will be valuable for policy and planning purposes to the pursuance of SDGs at the sub-national scale.


2019 ◽  
Vol 8 (1) ◽  
pp. 87-91
Author(s):  
Bhanu Priya Chouhan ◽  
Monika Kannan

The world is undergoing the largest wave of urban growth in history. More than half of the world’s population now lives in towns and cities, and by 2030 this number will swell to about 5 billion. ‘Urbanization has the potential to usher in a new era of wellbeing, resource efficiency and economic growth. But due to increased population the pressure of demand also increases in urban areas’ (Drakakis-Smith, David, 1996). The loss of agricultural land to other land uses occasioned by urban growth is an issue of growing concern worldwide, particularly in the developing countries like India. This paper is an attempt to assess the impact of urbanization on land use and land cover patterns in Ajmer city. Recent trends indicate that the rural urban migration and religious significance of the place attracting thousands of tourists every year, have immensely contributed in the increasing population of city and is causing change in land use patterns. This accelerating urban sprawl has led to shrinking of the agricultural land and land holdings. Due to increased rate of urbanization, the agricultural areas have been transformed into residential and industrial areas (Retnaraj D,1994). There are several key factors which cause increase in population here such as Smart City Projects, potential for employment, higher education, more comfortable and quality housing, better health facilities, high living standard etc. Population pressure not only directly increases the demand for food, but also indirectly reduces its supply through building development, environmental degradation and marginalization of food production (Aldington T, 1997). Also, there are several issues which are associated with continuous increase in population i.e. land degradation, pollution, poverty, slums, unaffordable housing etc. Pollution, formulation of slums, transportation congestion, environmental hazards, land degradation and crime are some of the major impacts of urbanization on Ajmer city. This study involves mapping of land use patterns by analyzing data and satellite imagery taken at different time periods. The satellite images of year 2000 and 2017 are used. The change detection techniques are used with the help of Geographical Information System software like ERDAS and ArcGIS. The supervised classification of all the three satellite images is done by ERDAS software to demarcate and analyze land use change.


Sign in / Sign up

Export Citation Format

Share Document