Prediction of Land Cover Model for Central Ambon City in 2041 Using the Cellular Automata Markov Chains Method

  • Heinrich Rakuasa Departemen Geografi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Indonesia
  • Daniel A Sihasale Departemen Geografi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Indonesia
  • Glendy Somae Program Studi Pendidikan Geografi, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pattimura
  • Philia Christi Latue Program Studi Pendidikan Biologi, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pattimura
DOI: https://doi.org/10.23960/jgrs.2023.v4i1.85
Keywords: Ambon City, Cellular Automata, Land Cover, Markov Chains

Abstract

The Ambon city center area based on the 2021-2041 RDTR is a center of economic, educational and cultural activities, this makes economic and population growth focused in this region. This also triggers the arrival of residents from other areas to Ambon City and will have an impact on increasing the provision of land for settlements. This condition is expected to trigger land conversion in this area. This study aims to analyze land cover changes in Ambon City in 2001, 2011, 2021 and predict land cover in 2041. This study uses Cellular Automata Markov Chains modeling to predict land cover in the central area of Ambon City in 2041. The results show that the type of built-up land cover and open land continued to increase in area, while agricultural and non-agricultural areas continued to experience a decrease in area and water bodies did not experience a decrease in area. The results of this study are expected to be used as a reference in managing the development of sustainable residential areas and as an effort to arrange land use in the Ambon City center area in the future based on ecological aspects.

Author Biography

Heinrich Rakuasa, Departemen Geografi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Indonesia

Program Studi Pendidikan Geografi, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Pattimura

References

Al-Hameedi, W. M., Chen, J., Faichia, C., Al-Shaibah, B., Nath, B., Kafy, A.-A., Hu, G., & Al-Aizari, A. (2021). Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq. In Remote Sensing (Vol. 13, Issue 20). https://doi.org/10.3390/rs13204034

Badan Standarisasi Nasional. (2010). SNI 7645-2010 tentang Klasifikasi Penutup Lahan.

Fitawok, M. B., Derudder, B., Minale, A. S., Van Passel, S., Adgo, E., & Nyssen, J. (2020). Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach. Land, 9(4). https://doi.org/10.3390/land9040115

Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Nayak, S. K., Ghosh, S., Mitra, D., Ghosh, T., & Hazra, S. (2017). Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review. Remote Sensing Applications: Society and Environment, 5, 64–77. https://doi.org/https://doi.org/10.1016/j.rsase.2017.01.005

He, Q., He, W., Song, Y., Wu, J., Yin, C., & Mou, Y. (2018). The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data.’ Land Use Policy, 78(July), 726–738. https://doi.org/10.1016/j.landusepol.2018.07.020

Heinrich Rakuasa, G. S. (2022). Analisis Spasial Kesesuaian dan Evaluasi Lahan Permukiman di Kota Ambon. Jurnal Sains Informasi Geografi (J SIG), 5(1), 1–9. https://doi.org/DOI: http://dx.doi.org/10.31314/j%20sig.v5i1.1432

Kang, C. S., & Kanniah, K. D. (2022). Land use and land cover change and its impact on river morphology in Johor River Basin, Malaysia. Journal of Hydrology: Regional Studies, 41, 101072. https://doi.org/10.1016/j.ejrh.2022.101072

Kim, I., Arnhold, S., Ahn, S., Le, Q. B., Kim, S. J., Park, S. J., & Koellner, T. (2019). Land use change and ecosystem services in mountainous watersheds: Predicting the consequences of environmental policies with cellular automata and hydrological modeling. Environmental Modelling & Software, 122, 103982. https://doi.org/10.1016/j.envsoft.2017.06.018

Liping, C., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLOS ONE, 13(7), 1–23. https://doi.org/10.1371/journal.pone.0200493

Liu, D., Zheng, X., & Wang, H. (2020). Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417, 108924. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2019.108924

Mallick, S. K. (2021). Prediction-Adaptation-Resilience (PAR) approach- A new pathway towards future resilience and sustainable development of urban landscape. Geography and Sustainability, 2(2), 127–133. https://doi.org/10.1016/j.geosus.2021.06.002

Masiliūnas, D., Tsendbazar, N.-E., Herold, M., Lesiv, M., Buchhorn, M., & Verbesselt, J. (2021). Global land characterisation using land cover fractions at 100 m resolution. Remote Sensing of Environment, 259, 112409. https://doi.org/10.1016/j.rse.2021.112409

Mohamed, A., & Worku, H. (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 31, 100545. https://doi.org/https://doi.org/10.1016/j.uclim.2019.100545

Philia Christi Latue, H. R. (2022). Dinamika Spasial Wilayah Rawan Tsunami di Kecamatan Nusaniwe, Kota Ambon, Provinsi Maluku. Jurnal Geosains Dan Remote Sensing (JGRS), 3(2), 77–87. https://doi.org/https://doi.org/10.23960/jgrs.2022.v3i2.98

Rakuasa, H., Salakory, M., & Latue, P. C. (2022). Analisis dan Prediksi Perubahan Tutupan Lahan Menggunakan Model Celular Automata-Markov Chain di DAS Wae Ruhu Kota Ambon. Jurnal Tanah Dan Sumberdaya Lahan, 9(2), 285–295. https://doi.org/https://doi.org/10.21776/ub.jtsl.2022.009.2.9

Rakuasa, H., Salakory, M., & Mehdil, M. C. (2022). Prediksi perubahan tutupan lahan di DAS Wae Batu Merah, Kota Ambon menggunakan Cellular Automata Markov Chain. Jurnal Pengelolaan Lingkungan Berkelanjutan (Journal of Environmental Sustainability Management), 6(2), 59–75. https://doi.org/https://doi.org/10.36813/jplb.6.2.59-75

Rakuasa, H., Supriatna, S., Karsidi, A., Rifai, A., Tambunan, M. ., & Poniman K, A. (2022). Spatial Dynamics Model of Earthquake Prone Area in Ambon City. IOP Conference Series: Earth and Environmental Science, 1039(1), 012057. https://doi.org/10.1088/1755-1315/1039/1/012057

Rienow, A., Kantakumar, L. N., Ghazaryan, G., Dröge-Rothaar, A., Sticksel, S., Trampnau, B., & Thonfeld, F. (2022). Modelling the spatial impact of regional planning and climate change prevention strategies on land consumption in the Rhine-Ruhr Metropolitan Area 2017–2030. Landscape and Urban Planning, 217, 104284. https://doi.org/10.1016/j.landurbplan.2021.104284

Salakory, M., Rakuasa, H. (2022). Modeling of Cellular Automata Markov Chain for predicting the carrying capacity of Ambon City. Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan (JPSL), 12(2), 372–387. https://doi.org/https://doi.org/10.29244/jpsl.12.2.372-387

Sapena, M., & Ruiz, L. Á. (2019). Computers , Environment and Urban Systems Analysis of land use / land cover spatio-temporal metrics and population dynamics for urban growth characterization. Computers, Environment and Urban Systems, 73(August 2018), 27–39. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2018.08.001

Sugandhi, N., Supriatna, S., Kusratmoko, E., & Rakuasa, H. (2022). Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata-Markov Chain. JPG (Jurnal Pendidikan Geografi), 9(2), 104–118. https://doi.org/http://dx.doi.org/10.20527/jpg.v9i2.13880

Sun, X., Wu, J., Tang, H., & Yang, P. (2022). An urban hierarchy-based approach integrating ecosystem services into multiscale sustainable land use planning: The case of China. Resources, Conservation and Recycling, 178, 106097. https://doi.org/10.1016/j.resconrec.2021.106097

Talukdar, S., Eibek, K. U., Akhter, S., Ziaul, S., Towfiqul Islam, A. R. M., & Mallick, J. (2021). Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh. Ecological Indicators, 126, 107612. https://doi.org/10.1016/j.ecolind.2021.107612

Wu, H., Lin, A., Xing, X., Song, D., & Li, Y. (2021). Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. International Journal of Applied Earth Observation and Geoinformation, 103, 102475. https://doi.org/https://doi.org/10.1016/j.jag.2021.102475

Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3), 77–84. https://doi.org/10.1145/175247.175255

Jurnal Geosains dan Remote Sensing, Geologi, Geofisika, Pengideraan Jauh, Eksplorasi Sumber Daya Alam (Migas, Mineral dan Batubara), Panas Bumi dan Mitigasi Bencana Geologi (Kegunungapian, Gempa dan Tsunami, Longsor, Subsiden
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Published
2023-05-30
How to Cite
Rakuasa, H., Sihasale, D. A., Somae, G., & Latue, P. C. (2023). Prediction of Land Cover Model for Central Ambon City in 2041 Using the Cellular Automata Markov Chains Method. Jurnal Geosains Dan Remote Sensing, 4(1), 1-10. https://doi.org/10.23960/jgrs.2023.v4i1.85
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Vol 4 No 1 (2023): JGRS Edisi Mei
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Articles

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