Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach

Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was developed for simulating long-term landscape changes at 10-year time steps from 2018 to 2048. Furthermore, surface temperature during summers and winters were predicted well by Urban Index (UI), a non-vegetation index, demonstrating the highest correlation of R2 = 0.8962 and R2 = 0.9212 with respect to retrieved summer and winter surface temperature. Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 22.23 to 24.52 km2 and from 108.53 to 122.61 km2 in 2018 and 2048, as inferred from the changes occurred from 1990 to 2018. Considering UI as the predictor of seasonal LST, we predicted that the summer and winter temperature 24–28 °C and 14–16 °C and regions would decrease in coverage from 10.75 to 3.14% and from 8.81 to 3.47% between 2018 and 2048, while the summer and winter temperature 35–42 °C and winter 26–32 °C regions will increase in the proportion covered from 12.69 to 24.17% and 6.75–15.15% of city.

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A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India

Arabian Journal of Geosciences ◽

10.1007/s12517-015-2138-3 ◽

2016 ◽

Vol 9 (4) ◽

Cited By ~ 51

Author(s):

Varun Narayan Mishra ◽

Praveen Kumar Rai

Keyword(s):

Remote Sensing ◽

Land Use ◽

Markov Chain ◽

Land Cover ◽

Land Cover Change ◽

Multi Layer Perceptron ◽

Markov Chain Analysis ◽

Chain Analysis ◽

Change Prediction

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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)

Sustainability ◽

10.3390/su122410452 ◽

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.

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