Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios

Chotchaiwong, Pakawan; Wijitkosum, Saowanee

doi:10.3390/land8090140

Cited by 27 publications

(7 citation statements)

References 53 publications

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“…Under the BDS, the amounts of future land use were estimated according to the land conversion probability of 2009-2019, which is calculated by the Markov chain [42]. According to Hu et al [43], Kindu et al [44], and Chotchaiwong and Wijitkosum [45], we defined the land conversion probability of the BDS as a benchmark, which could be adjusted according to the priority of protecting specific land type under the CPS and EPS (Table 1).…”

Section: Setting Scenarios and Estimating Land Demandmentioning

confidence: 99%

Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics

Chen

Yue

Xue

et al. 2021

Land

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There is growing concern about the consequences of future urban expansion on carbon storage as our planet experiences rapid urbanization. While an increasing body of literature was focused on quantifying the carbon storage impact of future urban expansion across the globe, rare attempts were made from the comparative perspective on the same scale, particularly in Central Asia. In this study, Central Asian capitals, namely Ashkhabad, Bishkek, Dushanbe, Nur Sultan, and Tashkent, were used as cases. According to the potential impacts of BRI (Belt and Road Initiative) on urban expansion, baseline development scenario (BDS), cropland protection scenario (CPS), and ecological protection scenario (EPS) were defined. We then simulated the carbon storage impacts of urban expansion from 2019 to 2029 by using Google Earth Engine, the Future Land Use Simulation model, and the Integrated Valuation of Environmental Services and Tradeoffs model. We further explored the drivers for carbon storage impacts of future urban expansion in five capitals. The results reveal that Nur Sultan will experience carbon storage growth from 2019 to 2029 under all scenarios, while Ashkhabad, Bishkek, Dushanbe, and Tashkent will show a decreasing tendency. EPS and CPS will preserve the most carbon storage for Nur Sultan and the other four cities, respectively. The negative impact of future urban expansion on carbon storage will be evident in Ashkhabad, Bishkek, Dushanbe, and Tashkent, which will be relatively inapparent in Nur Sultan. The potential drivers for carbon storage consequences of future urban expansion include agricultural development in Bishkek, Dushanbe, and Tashkent, desert city development in Ashkhabad, and prioritized development of the central city and green development in Nur Sultan. We suggest that future urban development strategies for five capitals should be on the basis of differentiated characteristics and drivers for the carbon storage impacts of future urban expansion.

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Section: Setting Scenarios and Estimating Land Demandmentioning

confidence: 99%

Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics

Chen

Yue

Xue

et al. 2021

Land

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“…CA Markov is another model for projecting future land cover. This model has also been used for projecting future state of various environmental phenomena such as urbanization, forest cover change and desertification (Aburas, Ho, Ramli, & Ash'aari, 2017;Adhikari & Southworth, 2012;Akin, Berberoglu, Erdogan, & Donmez, 2012;Alsharif & Pradhan, 2014;Alexakis et al, 2013;Aliani, Malmir, Sourodi, & Kafaky, 2019;Arsanjani, Helbich, Kainz, & Boloorani, 2013;Azizi, Malakmohamadi, & Jafari, 2016;Barros et al, 2018;Chotchaiwong & Wijitkosum, 2019;Durmusoglu & Tanriover, 2017;Fu, Wang, & Yang, 2018;Gai et al, 2018 As far as complementary analysis of landscapes is concerned, visibility of landscapes is also an important field of study. Visibility of landscapes is an asset that determines human preferences for some activities.…”

Section: Modelling Of Future Developmentmentioning

confidence: 99%

Quantitative Approach for Complementary Analysis of a Touristic Coastal Landscape: The Case of Erdemli (Mersin), Turkey.

Alphan¹

2020

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“…An open-space ratio is often used in the literature (e.g., urban geography [1], urban analysis [2], environmental studies [3], land use studies [4], public health studies [5], city planning [6], building studies [7], and urban morphology [8]) as a first basic metric to examine the distribution of open space in urbanized areas. Originally, the open-space ratio was defined in the 1960s (Felt [9]; Tough [10]) as the ratio of the area of open space (unbuilt area) to the area of its building site.…”

Section: Introductionmentioning

confidence: 99%

Functional Method for Analyzing Open-Space Ratios around Individual Buildings and Its Implementation with GIS

Okabe,

Okabe

2024

IJGI

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An open-space ratio is often used as a first basic metric to examine the distribution of open space in urbanized areas. Originally, the open-space ratio was defined as the ratio of the area of open space (unbuilt area) to the area of its building site. In recent years, residents have become more concerned with the open-space ratios in the broader neighborhoods of their individual buildings than with their own building sites. To address this concern, this paper proposes a method for dealing with the open-space ratio in the variable -meter buffer zone around each building, called the open-space ratio function, and implements it using standard GIS operators. The function and its implemented analytical tool can answer the following questions. First, this function shows how the ratio varies with respect to the bandwidth to discuss the modifiable area unit problem. Second, as the ratio changes, the function shows in which bandwidth zone the ratio is the highest, indicating the best open-space environment zone. Third, in the pairwise comparison for housing selection, the function shows in which bandwidth zone a specific house is better than another. Fourth, the function shows in which bandwidth zone the variance among all buildings in a region is the greatest. Fifth, in this zone, buildings are clustered in terms of open-space ratio. The resulting clusters are the most distinct. Sixth, to examine the open-space ratio around a clump of buildings (such as a housing complex), the function shows how to obtain clumps. Seventh, it is shown how the open-space function provides a wide range of applicability without changing the mathematical formulation. Finally, this paper shows how to implement the function in a simple computational method using operators and a processing modeler provided by the standard GIS without additional software.

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Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios

Cited by 27 publications

References 53 publications

Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics

Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics

Quantitative Approach for Complementary Analysis of a Touristic Coastal Landscape: The Case of Erdemli (Mersin), Turkey.

Functional Method for Analyzing Open-Space Ratios around Individual Buildings and Its Implementation with GIS

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