SPATIAL MODELING OF LAND COVER DYNAMICS IN DKI JAKARTA CILIWUNG WATERSHED USING CA-MARKOV APPROACH

Mohamad Mahfudz, Baba Barus, Asep Sapei, Bambang Pramudya Noorachmat

Abstract

Land use and land cover (LULC) change driven by human activities has become a major factor influencing environmental dynamics, particularly in rapidly urbanizing areas. This study aims to analyze LULC changes in the Ciliwung Watershed, DKI Jakarta, during the period 2010–2025 and to project future trends up to 2030. The study utilizes Landsat imagery with a spatial resolution of 30 meters, combined with a Geographic Information System (GIS) approach and the Cellular Automata–Markov (CA-Markov) model. LULC classification was categorized into six classes, achieving an overall accuracy of 80% and a Kappa coefficient above 75%, indicating strong agreement. The results show a significant increase in built-up areas by 81.83% by 2030, accompanied by a decrease in open land by 18% and forest areas by 13.53%. Agricultural land and shrubland show slight increases of 1.57% and 0.82%, respectively, while water bodies remain relatively stable at 2.07%.  The expansion of built-up areas exerts considerable pressure on green spaces and reduces water infiltration capacity, thereby increasing the potential risk of flooding in the study area. These findings highlight the importance of implementing adaptive and sustainable spatial planning policies to control LULC changes and mitigate future environmental impacts.

Keywords

CA-Markov; ciliwung watershed; DKI Jakarta; GIS; land cover change

Full Text:

PDF

References

Ali, M., Hadi, S., & Sulistyantara, B. (2016). Study on Land Cover Change of Ciliwung Downstream Watershed with Spatial Dynamic Approach. Procedia - Social and Behavioral Sciences,227 (November 2015), 52-59. https://doi.org/10.1016/j.sbspro.2016.06.042

Araya, Y. H., & Cabral, P. (2010). Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal. 1549–1563. https://doi.org/10.3390/rs2061549

Arifasihati, Y., & Kaswanto. (2016). Analysis of Land Use and Cover Changes in Ciliwung and Cisadane Watershed in three Decades. Procedia Environmental Sciences,33 , 465-469. https://doi.org/10.1016/j.proenv.2016.03.098

Ariyani, D., Purwanto, M. Y. J., Sunarti, E., & Perdinan. (2022). Contributing factors influencing flood disaster using MICMAC (Ciliwung Watershed Case Study). Journal of Natural Resources and Environmental Management,12 (2), 268-280. https://doi.org/10.29244/jpsl.12.2.268-280

Bashir, H., & Ojiako, U. (2020). An integrated ISM-MICMAC approach for modeling and analyzing dependencies among engineering parameters in the early design phase. Journal of Engineering Design,31 (8-9), 461-483. https://doi.org/10.1080/09544828.2020.1817347

Chu, L., Sun, T., Wang, T., Li, Z., & Cai, C. (2018). Evolution and prediction of landscape pattern and habitat quality based on CA-Markov and InVEST model in hubei section of Three Gorges Reservoir Area (TGRA). Sustainability (Switzerland),10 (11). https://doi.org/10.3390/su10113854

M.H. Elagouz, S.M. Abou-Shleel, A.A. Belal, M.A.O. El-Mohandes (2020) Detection of land use/cover change in Egyptian Nile Delta using remote sensing. Egyptian Journal of Remote Sensing and Space Science,23 (1), 57-62. https://doi.org/10.1016/j.ejrs.2018.10.004

Farid, M., Pratama, M. I., Kuntoro, A. A., Adityawan, M. B., Rohmat, F. I. W., & Moe, I. R. (2022). Flood Prediction due to Land Cover Change in the Ciliwung River Basin. International Journal of Technology,13 (2), 356-366. https://doi.org/10.14716/ijtech.v13i2.4662

Feizizadeh, B., Darabi, S., Blaschke, T., & Lakes, T. (2022). QADI as a New Method and Alternative to Kappa for Accuracy Assessment of Remote Sensing-Based Image Classification. Sensors,22 (12), 1-21. https://doi.org/10.3390/s22124506

Fu, F., Jia, X., Zhao, Q., Tian, F., Wei, D., Zhao, Y., Zhang, Y., Zhang, J., Hu, X., & Yang, L. (2024). Predicting land use change around railway stations: An enhanced CA-Markov model. Sustainable Cities and Society,101 (July). https://doi.org/10.1016/j.scs.2023.105138

Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon,6 (9), e05092. https://doi.org/10.1016/j.heliyon.2020.e05092

Ghalehteimouri, K. J., Shamsoddini, A., Mousavi, M. N., Binti Che Ros, F., & Khedmatzadeh, A. (2022). Predicting spatial and decadal of land use and land cover change using integrated cellular automata Markov chain model based scenarios (2019-2049) Zarriné-Rūd River Basin in Iran. Environmental Challenges,6 (July 2021), 100399. https://doi.org/10.1016/j.envc.2021.100399

Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability (Switzerland),10 (10), 1-23. https://doi.org/10.3390/su10103421

Kafy, A. A., Naim, M. N. H., Subramanyam, G., Faisal, A. Al, Ahmed, N. U., Rakib, A. Al, Kona, M. A., & Sattar, G. S. (2021). Cellular Automata approach in dynamic modeling of land cover changes using RapidEye images in Dhaka, Bangladesh. Environmental Challenges,4 (January), 100084. Https://doi.org/10.1016/j.envc.2021.100084

Keshtkar, H., & Voigt, W. (2016). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment,2 (1), 1-13. https://doi.org/10.1007/s40808-015-0068-4

Li, G., Cheng, G., Liu, G., Chen, C., & He, Y. (2023). Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF-Markov-CA-InVEST Model. Remote Sensing,15 (21). https://doi.org/10.3390/rs15215100

Mahfudz, M. (2023). Analysis of Settlement Land Change Using Landsat 8 Imagery (Case Study: Cianjur Regency, West Java Province). Engineering Journal | Scientific Magazine of the Faculty of Engineering UNPAK,24 (1), 23-29. https://doi.org/10.33751/teknik.v24i1.7997

Mahfudz, M., Riadi, B., Nurtyaman, R., & Utomo, P. P. (2024). Satellite Image Analysis Approach for Identifying Flood Impacts in DKI Jakarta. Engineering and Technology Journal,09 (04). https://doi.org/10.47191/etj/v9i04.14

Meyer, S. R., Johnson, M. L., Lilieholm, R. J., & Cronan, C. S. (2014). Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA. Ecological Modeling,291 , 42-57. https://doi.org/10.1016/j.ecolmodel.2014.06.023

Mosleh, M. K. (2025). Integrating the CA-Markov model and geospatial techniques for spatiotemporal prediction of land use/land cover dynamics in Qus District, Egypt. Modeling Earth Systems and Environment,11 (5). https://doi.org/10.1007/s40808-02502479-9

Msofe, N. K., Sheng, L., & Lyimo, J. (2019). Land use change trends and their driving forces in the Kilombero Valley Floodplain, Southeastern Tanzania. Sustainability (Switzerland),11 (2), 1-25. https://doi.org/10.3390/su11020505

Paradis, E. (2022). Probabilistic nsupervised classification for large-scale analysis of spectral imaging data. International Journal of Applied Earth Observation and Geoinformation,107 , 102675. Https://doi.org/10.1016/j.jag.2022.102675

Rachma, T. R. N., Silalahi, F. E. S., & Oktaviani, N. (2022). Monitoring 20 Years of Land Cover Change Dynamics in the Satellite Cities of Jakarta, Indonesia. IOP Conference Series: Earth and Environmental Science,1111 (1). https://doi.org/10.1088/17551315/1111/1/012034

Rai, P. K., Mishra, V. N., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar),.November. https://doi.org/10.2298/IJGI1401111M

Rajagukguk, J. R., & Pranoto, D. A. (2023). Research on the Impact of Ciliwung River Water on the Surrounding Environment in the DKI Jakarta Area. IOP Conference Series: Earth and Environmental Science,1175 (1). https://doi.org/10.1088/1755-1315/1175/1/012013

Rimal, B., Zhang, L., Keshtkar, H., Sun, X., & Rijal, S. (2018). Quantifying the spatiotemporal pattern of urban expansion and hazard and risk area identification in the Kaski District of Nepal. Land,7 (1). https://doi.org/10.3390/land7010037

Rimal, B., Zhang, L., Keshtkar, H., Wang, N., & Lin, Y. (2017). Monitoring and modeling of spatiotemporal urban expansion and land-use/land-cover change using integrated

Markov chain cellular automata model. ISPRS International Journal of Geo-Information,6 (9). https://doi.org/10.3390/ijgi6090288

Rushayati, S. B., Shamila, A. D., & Prasetyo, L. B. (2018). The Role of Vegetation in Controlling Air Temperature Resulting from Urban Heat Island. Geography Forum,32 (1), 1-11. https://doi.org/10.23917/forgeo.v32i1.5289

Rustiadi, E., Barus, B., Iman, L. S., Mulya, S. P., Pravitasari, A. E., & Antony, D. (2018). Land use and spatial policy conflicts in a rich-biodiversity rain forest region: The case of Jambi Province, Indonesia. Springer Geography, January, 277-296. https://doi.org/10.1007/978-981-10-5927-8_15

Rustiadi, E., Pravitasari, A. E., Priatama, R. A., Singer, J., Junaidi, J., Zulgani, Z., & Sholihah, R. I. (2023). Regional Development, Rural Transformation, and Land Use/Cover Changes in a Fast-Growing Oil Palm Region: The Case of Jambi Province, Indonesia. Land,12 (5). https://doi.org/10.3390/land12051059

Saing, Z., Djainal, H., & Deni, S. (2021). Land use balance determination using satellite imagery and geographic information system: case study in South Sulawesi Province, Indonesia. Geodesy and Geodynamics,12 (2), 133-147. https://doi.org/10.1016/j.geog.2020.11.006

Sanga, F., & Haulle, E. (2024). The paradox of the land use/land cover change in Kipengere ranges in southern highlands of Tanzania. Geology, Ecology, and Landscapes,00 (00), 18. https://doi.org/10.1080/24749508.2024.2429206

Shafizadeh Moghadam, H., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography,40 , 140-149. https://doi.org/10.1016/j.apgeog.2013.01.009

Simon, O., Lyimo, J., & Yamungu, N. (2023). Land use and cover change in Dar es Salaam metropolitan city: satellite data and CA-Markov chain analysis. GeoJournal,88 (6), 6119-6136. https://doi.org/10.1007/s10708-023-10960-0

Son, N. T., & Binh, N. D. (2020). Predicting land use and climate change scenarios impacts on runoff and soil erosion: A case study in hoa Binh Province, lower da River Basin, Northwest Vietnam. EnvironmentAsia,13 (2), 67-77. https://doi.org/10.14456/ea.2020.30

Song, W., Yunlin, Z., Zhenggang, X., Guiyan, Y., Tian, H., & Nan, M. (2020). Landscape pattern and economic factors' effect on prediction accuracy of cellular automata-Markov chain model at county scale. Open Geosciences,12 (1), 626-636. https://doi.org/10.1515/geo-2020-0162

Susilowati, Y., Kumoro, Y., & Nur, W. H. (2020). Integrated water quality modeling for spatial planning. IOP Conference Series: Earth and Environmental Science,483 (1). https://doi.org/10.1088/1755-1315/483/1/012041

Todaro, M. P., & Smith, S. C. (2020). Economic Development (13th ed.). Pearson.

Thakur, T. K., Patel, D. K., Bijalwan, A., Dobriyal, M. J., Kumar, A., Thakur, A., Bohra, A., & Bhat, J. A. (2020). Land use land cover change detection through geospatial analysis in an Indian Biosphere Reserve. Trees, Forests and People,2 (June), 100018. https://doi.org/10.1016/j.tfp.2020.100018

Trevisiol, F., Mattivi, P., Mandanici, E., & Bitelli, G. (2024). Cross-Sensors Comparison of Popular Vegetation Indexes from Landsat TM, ETM=, OLI, and Sentinel MSI for TimeSeries Analysis over Europe. IEEE Transactions on Geoscience and Remote Sensing,62, 1-16. https://doi.org/10.1109/TGRS.2023.3343071

Verma, P., Raghubanshi, A., Srivastava, P. K., & Raghubanshi, A. S. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Modeling Earth Systems and Environment,6 (2), 1045-1059. https://doi.org/10.1007/s40808-020-00740-x

Wangyel, S., Munkhnasan, L., & Lee, W. (2021). Land use and land cover change detection and prediction in Bhutan's high altitude city of Thimphu, using cellular automata and Markov chain. 2(November 2020). https://doi.org/10.1016/j.envc.2020.100017

DOI: https://doi.org/10.20961/ge.v12i2.108804

Refbacks

  • There are currently no refbacks.