Urban growth modeling using earth observation datasets, Cellular Automata-Markov Chain model and urban metrics to measure urban footprints

Kushwaha, Kamlesh; Singh, Madhulika; Singh, Sudhir Kumar; Patel, Adesh

doi:10.1016/j.rsase.2021.100479

Cited by 23 publications

(10 citation statements)

References 51 publications

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“…The Markov-CA model employed in this research was observed to be satisfactory and agrees with other studies (Hasan et al, 2020;Fitawok et al, 2020;Kushwaha et al, 2021). The Kappa value of 85% was achieved in accuracy assessment.…”

Section: Lulc Projection For the 2040supporting

confidence: 87%

Assessment of Past and Future Land Use/Land Cover Dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana

Koranteng¹,

Frimpong²,

Adu-Poku³

et al. 2023

GEP

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Ghana like all countries in Sub-Saharan region of Africa have long been undergoing intense land use land cover changes (LULCC) which have given rise to extensive forest loss (deforestation and degradation), loss of arable land and land degradation. This study assessed the past LULCC in the Atwima Nwabiagya which contains the Barekese and Owabi Headworks) and the old Kumasi Local Assemblies' areas in Ghana and projected the scenario in 2040 for business-as-usual (BAU). The synergies of satellite imagery of 1990, 2000, 2010 and 2020 were classified with an overall accuracy of 90%. Markov Cellular-Automata method was used to forecast the future LULC pattern after detecting main driving forces of LULCC. The findings showed an extensive increase in built up areas from 11% in 1990 to 39% in 2020 owing largely to 23% decrease in forest cover and 6% decrease in agricultural lands within the past 30 years . The projected LULC under the BAU scenario for 2040 showed built-up surge from 39% to 45% indicating additional forest loss from 43% in 2020 to 40% and decreasing agricultural land from 17% to 14%. The main driver for the LULCC is clearly anthropogenic driven as the human population in the study area keeps rising every censual year. This study exemplifies the fast-tracked forest loss, loss of arable land and challenges on ecosystem sustainability of the Barekese-Owabi-Kumasi landscape. The current and projected maps necessitate the apt implementation of suitable inter-

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Section: Lulc Projection For the 2040supporting

confidence: 87%

Assessment of Past and Future Land Use/Land Cover Dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana

Koranteng¹,

Frimpong²,

Adu-Poku³

et al. 2023

GEP

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“…The CA-Markov model has been commonly used in the last five years due to its simplicity and can be easily integrated with other models (Kushwaha et al, 2021). In addition, this model provides ease of use and simplicity of implementation and can add influencing variables to the simulation process.…”

Section: Introductionmentioning

confidence: 99%

Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City, Indonesia

Rakuasa

Pakniany

2022

For. Geo.

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The phenomenon of urban growth has become an important issue that affects the land use system and land cover in a region for several reasons, such as population growth and the economy. This phenomenon has also become one of the main environmental issues lately because it has devastated urban ecosystems. Ternate Tengah District has the highest population growth rate in Ternate City and has experienced extensive urban development due to several reasons, such as the pace of urbanization, economic growth, and population. Urbanization accelerates the demand to land for living. As a result, there will be gaps or disparities between land needs and available land, a decline in environmental carrying capacity, and potential environmental harm in the future. Spatial modeling of future land covers is needed to provide data on policy-making. GIS and remote sensing methods have been widely introduced, but the most effective one is CA-Markov. This model has been used in various areas worldwide, but its application to predicting land use change in the populous city of a small island under threat of volcanic hazards like Ternate is limited. This study aims to evaluate and forecast the land-use changes brought on by urbanization in Ternate City's Central Ternate District. We used a cellular automata-Markov chain to examine and forecast land cover changes in 2002, 2012, 2022, and 2032. The findings indicate that residential area development will increase along with population expansion and land demand. The results of this study can support the policy-making related to the future arrangement and utilization of space in The Central Ternate District.

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“…Among the dynamic modeling approaches, cellular automata (CA) has been one of the most widely used tools for urban growth modeling (Al‐Ahmadi et al, 2009; Batty et al, 1997; Charif et al, 2017; Dragicevic, 2004; Feng & Tong, 2019; Foroutan & Delavar, 2012; Gómez et al, 2020; Itami, 1994; Omrani et al, 2017; Shafizadeh Moghadam & Helbich, 2013; Yeh & Li, 2002). However, despite the obvious characteristics of CA being flexible and powerful for urban growth simulation, the calibration of CA remains the most significant challenge and a subject of considerable research (Aburas et al, 2017; Al‐Ahmadi et al, 2009; Barreira‐González et al, 2019; Dinda et al, 2021; Feng et al, 2011, 2016; Kushwaha et al, 2021; Li & Yeh, 2001; Liao et al, 2016; Rifat & Liu, 2022; Shafizadeh‐Moghadam, 2019; Wu, 2002; Zhou et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Urban growth modeling based on cellular automata with transition rules optimized using genetic fuzzy systems

Foroutan

Delavar

Pijanowski

et al. 2022

Transactions in GIS

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This article presents a new methodology for urban growth modeling by integrating cellular automata (CA) and genetic fuzzy algorithms. The urban growth phenomenon is a spatio‐temporal and continuous process that can be modeled by employing fuzzy logic as a framework for handling vagueness. Dependence upon an expert for assigning membership functions and rule weights has made the fuzzy logic approach rather subjective. Our proposed methodology overcomes this shortcoming by applying genetic algorithms to tune and optimize the fuzzy logic system and to make it expert independent. The optimization process encompasses a fuzzy membership function and weights for fuzzy rules. This model uses linguistic variables for defining CA transition rules and applies them to represent the non‐deterministic nature of urban growth. The proposed model is applied to simulate urban growth in the Tehran Metropolitan Area in Iran across time steps of 1988, 1999, and 2010 developed using Landsat TM and ETM+ images and a Digital Elevation Model. The first data pairs were employed for calibration of the model parameters, and the remainder of the data was used for validation of the model across time. The model was evaluated using a relative operating characteristic of 0.88 and a figure of merit statistic of 0.31, which quantifies the model goodness of fit.

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Urban growth modeling using earth observation datasets, Cellular Automata-Markov Chain model and urban metrics to measure urban footprints

Cited by 23 publications

References 51 publications

Assessment of Past and Future Land Use/Land Cover Dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana

Assessment of Past and Future Land Use/Land Cover Dynamics of the Old Kumasi Metropolitan Assembly and Atwima Nwabiagya Municipal Area, Ghana

Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City, Indonesia

Urban growth modeling based on cellular automata with transition rules optimized using genetic fuzzy systems

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