Assessment of Land Use Change Challenges in Shiraz County's Villages Using Random Forest and SVM Algorithms

Subject Areas : Urban and Regional Planning Studies

بابک اجتماعی ^{1 *} , قاسمعلی مقتدری ²

1 -
2 - رئیس دانشگاه پیام نور مرکز اوز

Received: 2025-02-05 Accepted : 2025-05-02 Published : 2024-02-11

Keywords: Land use, Land use changes, Rural areas, Shiraz County,

Abstract :

Introduction: Land use change has been recognized as one of the most significant and challenging environmental and social issues in recent decades. Shiraz County, with its rich history and culture, has undergone substantial land use changes due to urban expansion, infrastructure development, and population growth. These changes not only impact the environment but also affect the livelihoods and lives of rural residents.

Research aim: This study aims to assess the challenges resulting from land use changes in Shiraz County's villages during the period from 2015 to 2023.

Methodology: In this research, Landsat 8 satellite imagery and MODIS datasets were utilized, and land use classification was performed using the Random Forest and SVM algorithms. These methods were selected for their high accuracy in detecting changes and broad data coverage. In the data preprocessing phase, methods such as surface correction were applied to eliminate atmospheric effects. Indices related to normalized vegetation and built-up areas were calculated to enhance classification accuracy.

Studied Areas: The geographical area of this research is Shiraz city.

Results: The findings reveal that built-up areas significantly increased from 448 square kilometers in 2015 to 1,358 square kilometers in 2023. In contrast, agricultural lands and natural resources decreased, particularly croplands, which declined from 466 square kilometers to 415 square kilometers. These changes pose a serious threat to rural livelihoods, leading to increased migration to cities and the degradation of natural resources.

Conclusion: The results demonstrate that the uncontrolled expansion of built-up areas and the reduction of agricultural and natural lands have created numerous challenges for the region’s villages, including environmental instability, reduced access to water resources, and loss of biodiversity. By comparing maps from 2015 and 2023, it is evident that rapid and significant land use changes have occurred in Shiraz County. Therefore, comprehensive policies are urgently needed to promote sustainable development and preserve natural resources in rural areas

References:

اصغری سراسکانرود، صیاد، محمدنژاد، وحید، امامی، هادی. (1398). بررسی تغییرات کاربری اراضی با استفاده از روش‌های پیکسل پایه و شی گرا و تحلیل اثرات تغییر کاربری‌ها بر فرسایش خاک (مطالعه موردی: شهرستان مراغه). پژوهش‌های ژئومورفولوژی کمّی، 8(1), 160-178. https://ensani.ir/fa/article/409346
بابائی اقدم, عظیمی, نورالدین, & حسینی. (1390). مدل‌‌سازی الگوی کاربری اراضی شهری با استفاده از مدل CLUE-S (مطالعه موردی: مشکین شهر). مطالعات برنامه ریزی سکونتگاه های انسانی, 6(14), 1-18.‎ https://sanad.iau.ir/fa/Article/1032519
خبرگذاری ایسنا(1402). https://www. sna.ir/news/1402032818181
خلجی، صبا. (1400). اثرات تغییر کاربری اراضی بر برنامه‌ریزی کاربری زمین. فصلنامه پژوهشهای فضا و مکان در شهر, 1400(19), 5-18. 20.1001.1.25386050.1400.1400.19.1.2 DOR:
سامی, کرباسی, پوران, کریمی سلطانی, & سنگین آبادی. (1401). ارزیابی و پیش بینی تغییرات فضایی و روند رشد شهری با استفاده ازسنجش از دور (مطالعه موردی: شهر قروه). مطالعات برنامه ریزی سکونتگاه های انسانی, 17(4), 1049-1061.‎ dor: 20.1001.1.25385968.1401.17.4.12.8
سرور, کرباسی, پوران, & واعظی. (1402). ارزیابی رابطه بین برنامه ریزی کاربری زمین و شکل گیری جزایر حرارتی شهری (مطالعه موردی: شهر مشگین شهر). مطالعات برنامه ریزی سکونتگاه های انسانی, 18(3), 173-185.‎ dor: 20.1001.1.25385968.1402.18.3.15.6
شوقی چهارده، مولائی هشجین، آمار. (1402). تبیین اثرات تغییرات کاربری اراضی روستایی در شهرستان آستانه‌اشرفیه. جغرافیای طبیعی، 16(62), 31-44.‎https://sanad.iau.ir/Journal/jopg/Article/982924
سیرت، محمدکریم، & گمرکی. (1403). بررسی تغییرات کاربری اراضی شهر هرات با استفاده از تصاویرماهواره‌ای بین سال‌های 2015 الی 2022. نشریه سنجش از دور و GIS ایران.‎ doi:10.48308/gisj.2024.234087.1192
شکریان، فاطمه، سلیمانی، کریم. (1403). تغییرات کاربری اراضی مبتنی‌بر تصاویر ماهواره‌ای در جلگۀ هراز. نشریه سنجش از دور و GIS ایران, 16(1), 113-128 doi: 10.48308/gisj.2023.232979.1176
شمس, & کرمی نژاد. (1393). ارزیابی روند تغییرات کاربری اراضی در توسعه فضایی شهرکرمانشاه با استفاده GIS و RS (مطالعه موردی: محله جعفرآباد کرمانشاه). مطالعات برنامه ریزی سکونتگاه های انسانی, 9(28), 45-57.‎ https://sanad.iau.ir/fa/Article/1032606
فاطمی، مهسا، کرمی، عزت اله، زمانی، غلامحسین. (1395). سیستم‌های عمده تغییر کاربری زمین در کشاورزی استان فارس. علوم ترویج و آموزش کشاورزی ایران, 12(2), 197-214. https://www.sid.ir/paper/109185/fa
یاسوری, م., ویسی, ر., سبب کار, م., & محمدی, م. (1394). بررسی نقش گسترش فیزیکی شهر رشت در ایجادتغییرات کاربری اراضی حاشیه شهر. مطالعات برنامه ریزی سکونتگاه های انسانی, 1(30), 99.‎ https://sanad.iau.ir/fa/Article/1032709
Aldiansyah, S., & Saputra, R. A. (2023). Comparison of machine learning algorithms for land use and land cover analysis using Google Earth engine (Case study: Wanggu watershed). International Journal of Remote Sensing and Earth Sciences (IJReSES), 19(2), 197-210. DOI: http://dx.doi.org/10.30536/j.ijreses.2022.v19.a3803
Atef, I., Ahmed, W., & Abdel-Maguid, R. H. (2023). Modelling of land use land cover changes using machine learning and GIS techniques: a case study in El-Fayoum Governorate, Egypt. Environmental Monitoring and Assessment, 195(6), 637. https://doi.org/10.1007/s10661-023-11224-7
Avcı, C., Budak, M., Yağmur, N., & Balçık, F. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, 8(1), 1-10. https://doi.org/10.26833/ijeg.987605
Chrastina, P., Hronček, P., Gregorová, B., & Žoncová, M. (2020). Land-use changes of historical rural landscape—Heritage, protection, and sustainable ecotourism: Case study of Slovak Exclave Čív (Piliscsév) in Komárom-Esztergom County (Hungary). Sustainability, 12(15), 6048. https://doi.org/10.3390/su12156048
Chowdhury, M. S. (2024). Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environmental Challenges, 14, 100800. https://doi.org/10.1016/j.envc.2023.100800
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R.,... & Snyder, P. K. (2005). Global consequences of land use. science, 309(5734), 570-574. DOI: 10.1126/science.1111772
Jamali, A. A., Behnam, A., Almodaresi, S. A., He, S., & Jaafari, A. (2024). Exploring factors influencing urban sprawl and land-use changes analysis using systematic points and random forest classification. Environment, Development and Sustainability, 26(5), 13557-13576 https://doi.org/10.1007/s10668-023-03633-y
Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W.,... & Xu, J. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global environmental change, 11(4), 261-269. https://doi.org/10.1016/S0959-3780(01)00007-3
Lambin, E. F., & Meyfroidt, P. (2010). Land use transitions: Socio-ecological feedback versus socio-economic change. Land use policy, 27(2), 108-118. https://doi.org/10.1016/j.landusepol.2009.09.003
Liu, R., Xu, Y., Xue, C., Xia, Z., Li, G., Gou, X., & Luo, S. (2023). Simulation of early warning indicators of urban expansion derived from machine learning. Journal of Urban Planning and Development, 149(1), 04022058. https://doi.org/10.1061/JUPDDM.UPENG-412
Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of land use and land cover using machine learning algorithms on google earth engine for Munneru River Basin, India. Sustainability, 13(24), 13758 https://doi.org/10.3390/su132413758
Mostafa, E., Li, X., Sadek, M., & Dossou, J. F. (2021). Monitoring and forecasting of urban expansion using machine learning-based techniques and remotely sensed data: A case study of gharbia governorate, Egypt. Remote Sensing, 13(22), 4498. https://doi.org/10.3390/rs13224498
Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PloS one, 6(8), e23777. https://doi.org/10.1371/journal.pone.0023777
Tariq, A., Jiango, Y., Li, Q., Gao, J., Lu, L., Soufan, W.,... & Habib-ur-Rahman, M. (2023). Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon, 9(2). https://doi.org/10.1016/j.heliyon.2023.e13212
Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30, 391-405. https://doi.org/10.1007/s00267-002-2630-x
Xiong, H., Ma, C., Li, M., Tan, J., & Wang, Y. (2023). Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China. Science of the total environment, 866, 161430. https://doi.org/10.1016/j.scitotenv.2023.161430
Yang, W., Wan, X., Liu, M., Zheng, D., & Liu, H. (2023). A two-level random forest model for predicting the population distributions of urban functional zones: A case study in Changsha, China. Sustainable Cities and Society, 88, 104297. https://doi.org/10.1016/j.scs.2022.104297
Zhang, M., Kafy, A. A., Xiao, P., Han, S., Zou, S., Saha, M.,... & Tan, S. (2023). Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China. Urban Climate, 47, 101347. https://doi.org/10.1016/j.uclim.2022.101347
Zhang, C., Liu, Y., & Tie, N. (2023). Forest Land Resource Information Acquisition with Sentinel-2 Image Utilizing Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Trees and Multi-Layer Perceptron. Forests, 14(2), 254. https://doi.org/10.3390/f14020254