Deriving suitability factors for CA-Markov land use simulation model based on local historical data

Fu, Xin; Wang, Xinhao; Yang, Yu-Jun

doi:10.1016/j.jenvman.2017.10.012

Cited by 159 publications

(104 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, such models subsequently use these insights to predict the likely future trajectory of LUCs [26]. In many cases, a complementary analytical hierarchy process (AHP) is invoked to present a multi-criteria evaluation of the relative role of different factors to explain the expansion of a particular city [26][27][28]. Indeed, the application of such a model helps in revealing the trends, factors, and possible LUCs in a given area, acknowledging that these factors may be geographically specific.…”

mentioning

confidence: 99%

“…In practice, this is achieved by combining insight in processed high-resolution imageries, the potential factors influencing LUC, and a number of constraint variables in the urban expansion process. Several studies have applied this model in different geographical contexts [27,29,30]. For example, Mosammam [13] found a dramatic increase in the built-up area, mainly occurring along major roads and highways, resulting in an enormous decrease in agricultural land in Qom, Iran.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Fitawok

Derudder

Minale

et al. 2020

Land

View full text Add to dashboard Cite

The fast-paced urbanization of recent decades entails that many regions are facing seemingly uncontrolled land-use changes (LUCs) that go hand in hand with a range of environmental and socio-economic challenges. In this paper, we use an integrated cellular automata–Markov chain (CA–MC) model to analyze and predict the urban expansion of and its impact on LUC in the city of Bahir Dar, Ethiopia. To this end, the research marshals high-resolution Landsat images of 1991, 2002, 2011, and 2018. An analytical hierarchy process (AHP) method is then used to identify the biophysical and socioeconomic factors underlying the expansion in the research area. It is shown that, during the period of study, built-up areas are rapidly expanding in the face of an overall decline of the farmland and vegetation cover. Drawing on a model calibration for 2018, the research predicts the possible geographies of LUC in the Bahir Dar area for 2025, 2034, and 2045. It is predicted that the conversions of other land-use types into built-up areas will persist in the southern, southwestern, and northeastern areas of the sprawling city, which can mainly be traced back to the uneven geographies of road accessibility, proximity to the city center, and slope variables. We reflect on how our findings can be used to facilitate sustainable urban development and land-use policies in the Bahir Dar area.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Fitawok

Derudder

Minale

et al. 2020

Land

View full text Add to dashboard Cite

show abstract

“…The Gini index [38] is taken as the quality evaluation method of a transition rule in this paper. Thus, instead of measuring the nectar amount in original ABC algorithm, the fitness function is used for the classification, which is defined in Equation (8).…”

Section: Optimization Process Of Mhabc-ca Algorithmmentioning

confidence: 99%

“…Therefore, scientific studies on the development of land-use types and prediction of changes in land-use types can provide important help and support for the rational use of urban land resources, for the protection of the ecological environment, and for sustainable development [3][4][5]. Studies in this area include cellular automata (CA) models [6], Markov models [7], CA-Markov models [8], conversion of land use and its effects (CLUE) framework [9], and so on. Although many experts and scholars have done a lot of research works in the field of land use, there are still problems, such as the factors biased toward the ecological field, directly taking the conditional probability of images as the conversion rule, the lack of consideration of social factors, and so on.…”

Section: Introductionmentioning

confidence: 99%

Application of an Improved ABC Algorithm in Urban Land Use Prediction

Huo

Zhang

2018

Information

View full text Add to dashboard Cite

Scientifically and rationally analyzing the characteristics of land use evolution and exploring future trends in land use changes can provide the scientific reference basis for the rational development and utilization of regional land resources and sustainable economic development. In this paper, an improved hybrid artificial bee colony (ABC) algorithm based on the mutation of inferior solutions (MHABC) is introduced to combine with the cellular automata (CA) model to implement a new CA rule mining algorithm (MHABC-CA). To verify the capabilities of this algorithm, remote sensing data of three stages, 2005, 2010, and 2015, are adopted to dynamically simulate urban development of Dengzhou city in Henan province, China, using the MHABC-CA algorithm. The comprehensive validation and analysis of the simulation results are performed by two aspects of comparison, the visual features of urban land use types and the quantification analysis of simulation accuracy. Compared with a cellular automata model based on a particle swarm optimization (PSO-CA) algorithm, the experimental results demonstrate the effectiveness of the MHABC-CA algorithm in the prediction field of urban land use changes.

show abstract

“…The drawback of an empirical statistical method is that the spatial relationship between land use changes and driving factors is not considered. The modelling method aims to construct a complex model that is able to simulate the relationships among the structure, function, and dynamic changes across the entire land use system [38][39][40]. Researchers often use modeling methods to simulate land use changes; Identifying the key driving factors and understanding the mechanisms underlying land use changes are key to successful modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Rapid Socioeconomic Development on Cropping Intensity Dynamics in China during 2001–2016

Zhao

et al. 2019

IJGI

View full text Add to dashboard Cite

Changes in cropping intensity reflect not only changes in land use but also the transformation of land functions. Although both natural conditions and socioeconomic factors can influence the spatial distribution of the cropping intensity and its changes, socioeconomic developments related to human activities can exert great impacts on short term cropping intensity changes. The driving force of this change has a high level of uncertainty; and few researchers have implemented comprehensive studies on the underlying driving forces and mechanisms of these changes. This study produced cropping intensity maps in China from 2001 to 2016 using remote sensing data and analyzed the impacts of socioeconomic drivers on cropping intensity and its changes in nine major agricultural zones in China. We found that the average annual cropping intensity in all nine agricultural zones increased from 2001 to 2016 under rapid socioeconomic development, and the trends in the seven major agricultural zones were significantly increased (p < 0.05), based on a Mann–Kendall test, except for the Northeast China Plain (NE Plain) and Qinghai Tibet Plateau (QT Plateau). Based on the results from the Geo-Detector, a widely used geospatial analysis tool, the dominant factors that affected cropping intensity distribution were related to the arable land output in the plain regions and topography in the mountainous regions. The factors that affected cropping intensity changes were mainly related to the arable land area and crop yields in northern China, and regional economic developments, such as machinery power input and farmers’ income in southern China. These findings provide useful cropping intensity data and profound insights for policymaking on how to use cultivated land resources efficiently and sustainably.

show abstract

Deriving suitability factors for CA-Markov land use simulation model based on local historical data

Cited by 159 publications

References 33 publications

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Application of an Improved ABC Algorithm in Urban Land Use Prediction

Impacts of Rapid Socioeconomic Development on Cropping Intensity Dynamics in China during 2001–2016

Contact Info

Product

Resources

About