Assessment of Uncertainties in Modelling Land Use Change with an Integrated Cellular Automata–Markov Chain Model

2021 ◽  
Author(s):  
Santosh S. Palmate ◽  
Paul D. Wagner ◽  
Nicola Fohrer ◽  
Ashish Pandey
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Use Change ◽  
Markov Chain Model ◽  
Chain Model

scholarly journals Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Heliyon ◽  
2020 ◽  
Vol 6 (9) ◽  
pp. e05092
Author(s):  
Anne Gharaibeh ◽  
Abdulrazzaq Shaamala ◽  
Rasha Obeidat ◽  
Salman Al-Kofahi
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Use Change ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Change Modeling

scholarly journals Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

2020 ◽  
Vol 12 (24) ◽  
pp. 10452
Author(s):  
Auwalu Faisal Koko ◽  
Wu Yue ◽  
Ghali Abdullahi Abubakar ◽  
Roknisadeh Hamed ◽  
Akram Ahmed Noman Alabsi
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Cover ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Barren Land ◽  
Spatio Temporal

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Application of modified cellular automata Markov chain model: forecasting land use pattern in Lebanon

2020 ◽  
Author(s):  
Walid Al-Shaar ◽  
Jocelyne Adjizian Gérard ◽  
Nabil Nehme ◽  
Hassan Lakiss ◽  
Liliane Buccianti Barakat
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Pattern ◽  
Use Pattern

scholarly journals Modeling And Spatial Analysis Of Change Settlement And Fair Market Land Price Using Markov Chain Model In Banyumanik District

2019 ◽  
Author(s):  
S Subiyanto ◽  
Andri Suprayogi
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Use Change ◽  
Markov Chain Model ◽  
Rapid Development ◽  
Satellite Image ◽  
Predictive Ability ◽  
Chain Model ◽  
Land Prices ◽  
Fair Market

Banyumanik District is located on the outskirts of Semarang with very rapid development. Indicated with the many changes in land that occur, due to the construction of settlements and other physical buildings continues to increase. Changes in land use will also be followed by changes in market land prices. These changes will continue in line with the increasing number and activities of the population in carrying out economic, social and cultural life. Most of the studies was conducted to analyze changes in future land use are based on the use of a model. Land use modeling changes is a method or approach that can be used to understand the causes and effects of these dynamic changes. The Multi-Layer Perceptron (MLP) Neural Network and Markov Chain methods are used in this study to determine which locations or areas of land use are vacant land and agriculture has the potential to change into settlements and test the predictive ability that will be produced by the model. The driving factor for land use change as an input model consists of distance to the road, distance to the area experiencing changes in land use, slope, elevation and fair market land prices. This study aims to (1) predict settlement and its changes in Banyumanik District using High Resolution Satellite Image in 2011-2019, (2) build a model of settlement land use change with the Markov Chain methods and (3) projection of Banyumanik District land use in 2028.

scholarly journals Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria

2021 ◽  
Vol 18 (1) ◽  
pp. 30-38
Author(s):  
P.A. Adegbola ◽  
J.R. Adewumi ◽  
O.A. Obiora-Okeke
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Use Change ◽  
Land Cover ◽  
Markov Chain Model ◽  
Urban Expansion ◽  
Efficiency Index ◽  
Landsat 8 ◽  
Chain Model ◽  
Negative Consequences

Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence. Keywords: Land cover, land use change, Markov chain, ArcGIS, watershed, urbanization

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