The Role of the Cellular Automata in Spatial Prediction of the Land Cover Changes in Al- Abasi District by Using CA-Markov Model
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Abstract
Monitoring changes in land cover is a crucial environmental topic, as it contributes to the analysis and understanding of environmental transformations and the impact of human activities on natural resources. This research aims to apply the Cellular Automata (CA) technique, based on the CA_MARKOV model, within the IDRESI program to study spatial changes in land cover in the Hawija district center area. This is achieved through simulating these changes and forecasting future trends using satellite data for the years 2000-2024.
Remote sensing techniques were used in conjunction with Geographic Information Systems (GIS) to process and analyze satellite images over a specific time period, enabling the detection of land use change patterns. The CA_MARKOV model relies on combining Markov chains, which determine the probability of transitions between land cover classes, and cellular automata, which simulate the spatial distribution of these future changes.
The study's results show clear changes in land cover within the study area, with an observed expansion of urban areas at the expense of agricultural land and natural spaces. The CA_MARKOV model also demonstrated its effectiveness in predicting future transformations with good accuracy, making it an important tool for supporting urban planning and sustainable environmental management.
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College of Education for Humanities, TIKRIT UNIVERSITY. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE http://creativecommons.org/licenses/by/4.0/
References
( ) S, Li, chen, A new bare- soil index for rapid mapping developing areas using LANDSAT 8 data, the information archives of the photogrammetry, remote sensing and spatial information sciences volume XL- 4, 2014, ISPRS Technical commission IV symposium. 14 – 16 may 2014, Suzhou, china, 2014, p; 143.
( ) Saeed Nadi, Multi-criteria, personalized route planning using quantifier-guided ordered weighted averaging operators, https://scholar.google.com/citations?user=xuxs3LMAAAAJ&hl=en
( ) USGS; GIS and Earth Observation University; GISandBeers. Landsat Soil Adjusted Vegetation Index (SAVI) is used to correct Normalized Difference Vegetation Index (NDVI) for the influence of soil brightness in areas where vegetative cover is low, p6.
( ) Jane,s J. Biles, 2004, Reconciling space and timein geography, University western michigan Kalamazoo, p.1.
( https://www.usgs.gov/landsat-missions/landsat-8?utm_source=chatgpt.com.
( ) اياد عاشور الطائي، ثائر مظهر فهمي العزاوي، التقنيات الحديثة في الجغرافية، ط1، دار الجنان للنشر، عمان ، 2013، ص 210.
( ) ورود علي عبد العزيز شري الامارة، النمذجة الكارتوكرافية لخصائص التربة في قضاء الحبانية باستخدام تقنيات الجيوماتكس، رسالة ماجستير (غير منشورة)، كلية التربية للعلوم الإنسانية، جامعة الانبار، 2021، ص127-128.
( ) ميادة محمود عبد القادر، تكامل المعلومات الجيومورفية وتقنيات التحسس النائي لدراسة دلتا شط العرب رسالة ماجستير (غير منشورة)، كلية العلوم جامعة البصرة، 2010، ص53.
( Md. Surabuddin Mondal1, *, Nayan Sharma2, Martin Kappas3, P. K. Garg4 , Ca Markov Modeling Of Land Use Land Cover Change Predictions And Effect Of Numerical Iterations, Image Interval (Time Steps) On Prediction Results, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2020, 2020 XXIV ISPRS Congress (2020), p715.
( by Xin Chen 1, ,Xinyi He 1, andSiyuan Wang, Simulated Validation and Prediction of Land Use under Multiple Scenarios in Daxing District, Beijing, China, Based on GeoSOS-FLUS Model, Sustainability 2022, 14, 11428. https://doi.org/10.3390/su141811428, p4.
(( أشرف عبده على عجرمة، نرمين احمد محمد خليل شكري، أساليب الذكاء الاصطناعي الجغرافي في نظم المعلومات الجغرافية والاستشعار عن بعد بين النظرية والتطبيق، المجلة العربية الدولية لتكنولوجيا المعلومات والبيانات، المجلد الثاني - العدد الثاني أبريل _ يونيو 2022، ص104.
( Weng, Q., 2002, Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of environmental Management, 64, pp. 273-284.
( Mondal, M.S.; Sharma, N.; Kappas, M.; Garg, P.K. Cellular automata (CA) contiguity filters impacts on ca Markov modeling of land use land cover change predictions results. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, 43, 1585–1591.
( Zhang, X.J.; Zhou, Q.G.;Wang, Z.L.;Wang, F.H. Simulation and prediction of land use evolution in the Three Gorges reservoir area based on MCE-CA-Markov. J. Agric. Eng. 2017, 33, 268–277.
( ) Montserrat Gómez Delgado, and Joaquín Bosque Sendra,2020, Validation of GIS-performed analysis, Departamento de Geografía, Universidad de Alcalá. Calle Colegios, See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/287541349. P4.