Evaluating and Predicting the  Land Use Land Cover Changes and its Impact on Land Surface Temperature using CA-Markov model: A study of District Mardan, Pakistan

Muhammad Awais Khan; Atta Ur Rahman; Zahid Khan; Zain Sultan; Faheema Marwat; Tabassum Naz; Bushra Zahid

Authors

Muhammad Awais Khan Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.
Atta Ur Rahman Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.
Zahid Khan Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.
Zain Sultan Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.
Faheema Marwat Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.
Tabassum Naz Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.
Bushra Zahid Department of Geography & Geomatics (GIS/RS), University of Peshawar, Pakistan.

Keywords:

LULC, GIS, Land Surface Temperature, Urban Expansion, CA-Markov Chain Analysis

Abstract

The Rapid population growth is a global phenomenon that alters landscapes and affects environmental conditions. The aim of this study is to find out the effects of urbanization on Land Use Land Cover (LULC) change and its impact on Land Surface Temperature (LST) in District Mardan from 2002 to 2022, and predict LULC and surface temperature changes for future two decades (2042). The study uses remotely sensed data and Geographic Information Systems (GIS) to evaluate the correlation between the conversion of natural landscapes to built-up regions and the subsequent changes in LST. The key goals are to investigate LULC changes over the last two decades examine the influence of LULC on LST and on the basis of these changes forecast the future LULC and LST trends using the CA-Markov model in IDRISI SILVA software for the year 2042. The examination of LULC changes from 2002 to 2022 found that built-up areas increased significantly and vegetation decreased. Built-up land increased from 10.10 % in 2002 to 16.28% in 2022, resulting in a 6% growth while vegetation covers experienced a decrease of almost 10% of total Land cover. Similarly, in LST it is observed that more areas are getting prone to high temperature in 2022 relative to 2002. In 2002, 37% of the total area experienced below 30^o Celsius temperature while in 2022 it dropped to 28% of the total area. Additionally, by correlating LULC with LST it is evaluated that Barren surface and Built-up regions experienced high temperature. On the other hand, vegetation and water land parts of the study area experienced low and moderate temperatures. The CA-Markov model predicts that built-up land will increase by 19% in 2042, continuing the current trend, and the vegetation area of land will decrease by 4% of the current status in 2022. Furthermore, the LST analysis indicates an increase in temperature, and low-temperature regions are predicted to decrease further by another 3% of the total study area as a result, the research shows that LST increases as built-up regions expand. This study not only illuminates the historical trajectory of urbanization and its thermal effects in District Mardan, but it also gives critical insights for sustainable land-use planning and urban heat island mitigation measures in the next decades.

References

S. Liu et al., “Understanding Land use/Land cover dynamics and impacts of human activities in the Mekong Delta over the last 40 years,” Glob. Ecol. Conserv., vol. 22, p. e00991, Jun. 2020, doi: 10.1016/J.GECCO.2020.E00991.

M. M. H. Seyam, M. R. Haque, and M. M. Rahman, “Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: A case study at Bhaluka in Mymensingh, Bangladesh,” Case Stud. Chem. Environ. Eng., vol. 7, p. 100293, Jun. 2023, doi: 10.1016/J.CSCEE.2022.100293.

E. F. Lambin and H. Geist, Eds., “Land-Use and Land-Cover Change,” 2006, doi: 10.1007/3-540-32202-7.

“(PDF) Land use/land cover change detection and prediction using the CA-Markov model: A case study of Quetta city, Pakistan.” Accessed: Jun. 22, 2024. [Online]. Available: https://www.researchgate.net/publication/355209516_Land_useland_cover_change_detection_and_prediction_using_the_CA-Markov_model_A_case_study_of_Quetta_city_Pakistan

“Urban Climate | Journal | ScienceDirect.com by Elsevier.” Accessed: Jun. 22, 2024. [Online]. Available: https://www.sciencedirect.com/journal/urban-climate

“Temporal March of the Chicago Heat Island in: Journal of Applied Meteorology and Climatology Volume 24 Issue 6 (1985).” Accessed: Jun. 22, 2024. [Online]. Available: https://journals.ametsoc.org/view/journals/apme/24/6/1520-0450_1985_024_0547_tmotch_2_0_co_2.xml

X. L. Chen, H. M. Zhao, P. X. Li, and Z. Y. Yin, “Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes,” Remote Sens. Environ., vol. 104, no. 2, pp. 133–146, Sep. 2006, doi: 10.1016/J.RSE.2005.11.016.

R. Wang, H. Hou, Y. Murayama, and A. Derdouri, “Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China,” Remote Sens. 2020, Vol. 12, Page 440, vol. 12, no. 3, p. 440, Jan. 2020, doi: 10.3390/RS12030440.

P. Ghosh et al., “Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review,” Remote Sens. Appl. Soc. Environ., vol. 5, pp. 64–77, 2017, doi: 10.1016/j.rsase.2017.01.005.

B. Nath, Z. Wang, Y. Ge, K. Islam, R. P. Singh, and Z. Niu, “Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process,” ISPRS Int. J. Geo-Information 2020, Vol. 9, Page 134, vol. 9, no. 2, p. 134, Feb. 2020, doi: 10.3390/IJGI9020134.

D. J. Guan, H. F. Li, T. Inohae, W. Su, T. Nagaie, and K. Hokao, “Modeling urban land use change by the integration of cellular automaton and Markov model,” Ecol. Modell., vol. 222, no. 20–22, pp. 3761–3772, Oct. 2011, doi: 10.1016/J.ECOLMODEL.2011.09.009.

X. Yang, X. Q. Zheng, and L. N. Lv, “A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata,” Ecol. Modell., vol. 233, pp. 11–19, May 2012, doi: 10.1016/J.ECOLMODEL.2012.03.011.

J. Huang, Y. Wu, T. Gao, Y. Zhan, and W. Cui, “An Integrated Approach based on Markov Chain and Cellular Automata to Simulation of Urban Land Use Changes,” Appl. Math. Inf. Sci, vol. 9, no. 2, pp. 769–775, 2015, doi: 10.12785/amis/090225.

F. Fan, Y. Wang, and Z. Wang, “Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images,” Environ. Monit. Assess., vol. 137, no. 1–3, pp. 127–147, Jun. 2008, doi: 10.1007/S10661-007-9734-Y/METRICS.