Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia

English

Authors

  • Fahrudin Hanafi Departemen Geography, FISIP, UNNES https://orcid.org/0000-0001-8258-932X
  • Fajar Setiawan Research Center for Limnology and Water Resources, BRIN, Indonesia
  • Sutanto Trijuni Putro Ph.D Candidate on Center for Southeast Asian Studies. Gradiuate School of Asian and African Area Studies. Kyoto University, Japan

DOI:

https://doi.org/10.24114/jg.v16i2.56045

Abstract

Land cover changes are a critical concern in a developing country where rapid urbanization and population growth intersect with environmental dynamics. Understanding the driving forces behind these changes is essential for sustainable development and effective land management. This study analyzes land cover changes in Demak-Jepara regency, Indonesia, over a 20-year period using Landsat data. The objective is to identify the dominant factors driving the increase in built-up areas, considering both natural and human-induced factors. Factors such as road distance, existing build area, and natural features are evaluated. Using ArcGIS and Idrisi Selva's Land Change Modeler, land cover data is processed, and Cellular Automata-Markov analysis is conducted. The study considers a cell size of 30 x 30 meters and a time step of 5 years from 2001 to 2009. Transition persistence analysis identifies significant factors, validated using 2020 land cover data through AUC (Area Under the Curve) and ROC (Relative Operating Characteristic) analysis. The combination of natural and human-induced factors (scenario-C) shows the highest AUC value of 0.9406, indicating better conformity with 2020 land cover. Dominant factors in scenario C include roads, existing built-up areas, river order, and slope gradient. Results reveal that road development and proximity to existing settlements are the primary drivers of land cover changes. Natural factors like river order and slope gradient have a lesser impact, while the coastline has minimal influence. These findings highlight the importance of considering both natural and human-induced factors in land use planning. They provide valuable insights for policymakers and land managers in making informed decisions for sustainable development and land use strategies. Keywords: Land Cover Changes; Driving Factors; Natural Factors; CA-Markov; Sustainable Development

References

Abdisa Gemmechis, W., & Legesse Tura, A. (2023). Modeling Land Use Land Cover Using Cellular Automata - Markov Chain: A Case of Belete Gera Regional Forest Priority Area, South Western Ethiopia. American Journal of Remote Sensing. https://doi.org/10.11648/j.ajrs.20231101.11

Abdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7). https://doi.org/10.3390/rs11070790

Akomolafe, G. F., & Rosazlina, R. (2022). Land use and land cover changes influence the land surface temperature and vegetation in Penang Island, Peninsular Malaysia. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-25560-0

Ansari, A., & Golabi, M. H. (2019). Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands “ A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research, 7(1), 64“70. https://doi.org/10.1016/j.iswcr.2018.10.001

Arjasakusuma, S., Kusuma, S. S., Saringatin, S., Wicaksono, P., Mutaqin, B. W., & Rafif, R. (2021). Shoreline dynamics in East Java Province, Indonesia, from 2000 to 2019 using multi-sensor remote sensing data. Land, 10(2), 1“17. https://doi.org/10.3390/land10020100

Clark, M. L. (2020). Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 26“40. https://doi.org/10.1016/j.isprsjprs.2019.11.007

Eastman, J. R. (2015). IDRISI Selva tutorial. IDRISI production, Clark Labs-Clark University.

Feng, Y., & Tong, X. (2018). Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GIScience and Remote Sensing, 55(5), 678“698. https://doi.org/10.1080/15481603.2018.1426262

Feng, Y., & Tong, X. (2020). A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods. International Journal of Geographical Information Science, 34(1), 74“97. https://doi.org/10.1080/13658816.2019.1648813

Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, 239. https://doi.org/10.1016/j.rse.2019.111630

Gu, Z., & Zeng, M. (2024). The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives. In Sustainability (Switzerland) (Vol. 16, Issue 1). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/su16010274

Gupta, R., & Sharma, L. K. (2020). Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India. Ecological Indicators, 112. https://doi.org/10.1016/j.ecolind.2020.106171

Hamdy, O., & Zhao, Shichen;A. Salheen, Mohamed;Eid, Y. Y. (2017). Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study. International Journal of Engineering and Technology, 9(3), 226“232. https://doi.org/10.7763/ijet.2017.v9.975

Hanafi, F., & Pamungkas, D. (2021). Aplikasi Model Rusle untuk Estimasi Kehilangan Tanah Bagian Hulu di Sub Das Garang, Jawa Tengah. Jurnal Geografi : Media Informasi Pengembangan Dan Profesi Kegeografian, 18(1), 30“36. https://doi.org/10.15294/jg.v18i1.28079

Hanafi, F., Rahmadewi, D. P., & Setiawan, F. (2021). Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency. Geosfera Indonesia, 6(3), 301. https://doi.org/10.19184/geosi.v6i3.23471

Islam, M. S., Uddin, M. A., & Hossain, M. A. (2021). Assessing the dynamics of land cover and shoreline changes of Nijhum Dwip (Island) of Bangladesh using remote sensing and GIS techniques. Regional Studies in Marine Science, 41. https://doi.org/10.1016/j.rsma.2020.101578

Janizadeh, S., Chandra Pal, S., Saha, A., Chowdhuri, I., Ahmadi, K., Mirzaei, S., Mosavi, A. H., & Tiefenbacher, J. P. (2021). Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management, 298(July), 113551. https://doi.org/10.1016/j.jenvman.2021.113551

Kang, Y., Lv, W., He, J., & Ding, X. (2020). Remote sensing of time-varying tidal flat topography, Jiangsu Coast, China, based on the waterline method and an artificial neural network model. Applied Sciences (Switzerland), 10(10). https://doi.org/10.3390/app10103645

Kehutanan, K. L. H. D. (2021). Rekalkulasi Penutup Lahan Indonesia Tahun 2020 (Issue 1). Direktorat Inventarisasi dan Pemantauan Sumber Daya Hutan, Ditjen Planologi Kehutanan dan Tata Lingkungan Kementerian Lingkungan Hidup dan Kehutanan.

Kim, Y., Newman, G., & Güneralp, B. (2020). A review of driving factors, scenarios, and topics in urban land change models. In Land (Vol. 9, Issue 8). MDPI AG. https://doi.org/10.3390/LAND9080246

Li, K., Feng, M., Biswas, A., Su, H., Niu, Y., & Cao, J. (2020). Driving factors and future prediction of land use and cover change based on satellite remote sensing data by the LCM model: A case study from gansu province, China. Sensors (Switzerland), 20(10). https://doi.org/10.3390/s20102757

Liang, X., Liu, X., Li, D., Zhao, H., & Chen, G. (2018). Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. International Journal of Geographical Information Science, 32(11), 2294“2316. https://doi.org/10.1080/13658816.2018.1502441

Mardiatno, D. (2018). Potensi Sumberdaya Pesisir Kabupaten Jepara. UGM PRESS.

Maviza, A., & Ahmed, F. (2020). Analysis of past and future multi-temporal land use and land cover changes in the semi-arid Upper-Mzingwane sub-catchment in the Matabeleland south province of Zimbabwe. International Journal of Remote Sensing, 41(14), 5206“5227. https://doi.org/10.1080/01431161.2020.1731001

Mehra, N., & Swain, J. B. (2024). Assessment of land use land cover change and its effects using artificial neural network-based cellular automation. Journal of Engineering and Applied Science, 71(1). https://doi.org/10.1186/s44147-024-00402-0

Meneses, B. M., Reis, E., Pereira, S., Vale, M. J., & Reis, R. (2017). Understanding driving forces and implications associated with the land use and land cover changes in Portugal. Sustainability (Switzerland), 9(3). https://doi.org/10.3390/su9030351

Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sensing Applications: Society and Environment, 17. https://doi.org/10.1016/j.rsase.2019.100276

Muschelli, J. (2020). ROC and AUC with a Binary Predictor: a Potentially Misleading Metric. Journal of Classification, 37(3), 696“708. https://doi.org/10.1007/s00357-019-09345-1

NASA. (2021). Landsat Science. https://landsat.gsfc.nasa.gov/satellites

Niraj, K. C., Gupta, S. K., & Shukla, D. P. (2022). A Comparison of Image-Based and Physics-Based Atmospheric Correction Methods for Extracting Snow and Vegetation Cover in Nepal Himalayas Using Landsat 8 OLI Images. Journal of the Indian Society of Remote Sensing, 50(12), 2503“2521. https://doi.org/10.1007/s12524-022-01616-6

Pamungkas, B. A., Munibah, K., & Soma, S. (2019). Land use changes and relation to urban heat island (case study Semarang City, Central Java). IOP Conference Series: Earth and Environmental Science, 399(1). https://doi.org/10.1088/1755-1315/399/1/012069

Raj, A., & Sharma, L. K. (2022). Assessment of land-use dynamics of the Aravalli range (India) using integrated geospatial and CART approach. Earth Science Informatics, 15(1), 497“522. https://doi.org/10.1007/s12145-021-00753-9

Rizzo, A., & Anfuso, G. (2020). Coastal dynamic and evolution: Case studies from different sites around the world. In Water (Switzerland) (Vol. 12, Issue 10). MDPI AG. https://doi.org/10.3390/w12102829

Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 08(04), 611“622. https://doi.org/10.4236/ijg.2017.84033

Suharini, E; Hanafi, F; Akhsin, W. (2017). Study of Population Growth and Land Use Change Impact of Intrusion at Pekalongan City. 79(Icge 2016), 232“238.

Tong, X., & Feng, Y. (2020). A review of assessment methods for cellular automata models of land-use change and urban growth. In International Journal of Geographical Information Science (Vol. 34, Issue 5, pp. 866“898). Taylor and Francis Ltd. https://doi.org/10.1080/13658816.2019.1684499

Xia, C., & Zhang, B. (2021). Exploring the effects of partitioned transition rules upon urban growth simulation in a megacity region: a comparative study of cellular automata-based models in the Greater Wuhan Area. GIScience and Remote Sensing, 58(5), 693“716. https://doi.org/10.1080/15481603.2021.1933714

Yeh, A. G. O., Li, X., & Xia, C. (2021). Cellular Automata Modeling for Urban and Regional Planning. In Urban Book Series. Springer Singapore. https://doi.org/10.1007/978-981-15-8983-6_45

Zhai, Y., Roy, D. P., Martins, V. S., Zhang, H. K., Yan, L., & Li, Z. (2022). Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients. Remote Sensing of Environment, 274(November 2021), 112992. https://doi.org/10.1016/j.rse.2022.112992

Zhang, H., He, J., Chen, S., Zhan, Y., Bai, Y., & Qin, Y. (2023). Comparing Three Methods of Selecting Training Samples in Supervised Classification of Multispectral Remote Sensing Images. Sensors (Basel, Switzerland), 23(20). https://doi.org/10.3390/s23208530

Downloads

Published

2024-07-25

How to Cite

Hanafi, F., Setiawan, F., & Putro, S. T. (2024). Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia: English. JURNAL GEOGRAFI, 16(2), 290–304. https://doi.org/10.24114/jg.v16i2.56045