Green infrastructure development challenges: The case of Yogyakarta International Airport
Keywords:
green infrastrucutre, land use change, land use predication
Abstract
Infrastructure development, such as airports, often impacts the surrounding economic growth. On the one hand, the airport's economic growth is a desirable logical consequence. However, economic growth often occurs due to increased mining, industrial, plantation, trading, service, and other economic activities, causing changes in land use that do not follow the Spatial Planning and Regional Plans. Therefore, it may have implications for environmental damage. This paper proves a change in land use around Yogyakarta International Airport. Changes are observed through differences in land use in 2015, before the airport plan was built, and 2021, after the airport was operational. The random forest algorithm method is used to classify land use data sets. Furthermore, using the Multilayer Perceptron Neural Network Marcov Chain/ MLP NN-MC algorithm, it is predicted that the conversion of rice fields and plantations around the front side of the airport for housing and business will become even greater in 2030. Thus, the airport's construction has increased land use for business and residential purposes, while the green surface has been dramatically reduced. It was identified that there was a misuse of land use. Without good management, changes in land use can have an impact on decreasing environmental quality.
Keywords: green infrastructure, land use change, land use prediction
JEL Classification: Q57; R14; O44
References
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[5] Azadgar, A., Luciani, G., Nyka, L., 2025. Spatial allocation of nature-based solutions in the form of public green infrastructure in relation to the socio-economic district profile–a GIS-based comparative study of Gdańsk and Rome. Land use policy 150. https://doi.org/10.1016/j.landusepol.2024.107454
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[7] Chandan, M.C., Aadithyaa, J.S., Bharath, H.A., 2020. Integration of Genetic Algorithm and Agent Based Model to Visualize Near Realistic Sustainable Urban Growth: A Comparative Study. IEEE Geoscience and Remote Sensing Letters 4250–4253.
[8] de Sousa, L.F., Santos, C.A.S., Gomes, R.L., Rocha, F.A., de Jesus, R.M., 2021. Modeling land use change impacts on a tropical river basin in Brazil. International Journal of Environmental Science and Technology 18, 2405–2424. https://doi.org/10.1007/s13762-020-02997-2
[9] Dong, Y., Liu, S., Pei, X., Wang, Y., 2025. Spatially Explicit Multi-Objective Optimization Tool for Green Infrastructure Planning based on InVEST adn NSGA-II towards Multifunctionality. Land use policy 150.
[10] Dubertret, F., Tourneau, F.M. Le, Villarreal, M.L., Norman, L.M., 2022. Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020). Remote Sens (Basel) 14, 1–22. https://doi.org/10.3390/rs14092127
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[14] Girma, R., Fürst, C., Moges, A., 2022b. Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environmental Challenges. https://doi.org/10.1016/j.envc.2021.100419
[15] 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, 1–23. https://doi.org/10.3390/su10103421
[16] Hazani, S.N., Damayanti, A., Indra, T.L., Dimyati, M., 2021. CA-Markov Model for Predicting Paddy-Field Land in Babulu Subdistrict, North Penajam Paser Regency, East Kalimantan. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1811/1/012073
[17] Henríquez, C., Morales, M., Qüense, J., Hidalgo, R., 2022. Future land use conflicts: Comparing spatial scenarios for urban-regional planning. Environ Plan B Urban Anal City Sci. https://doi.org/10.1177/23998083221111404
[18] Huang, X., Yao, R., Halios, C.H., Kumar, P., Li, B., 2025. Integrating green infrastructure, design scenarios, and social-ecological-technological systems for thermal resilience and adaptation: Mechanisms and approaches. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2025.115422
[19] Jatayu, A., Saizen, I., Rustiadi, E., Pribadi, D.O., Juanda, B., 2022. Urban Form Dynamics and Modelling towards Sustainable Hinterland Development in North Cianjur, Jakarta–Bandung Mega-Urban Region. Sustainability (Switzerland) 14. https://doi.org/10.3390/su14020907
[20] Li, F., Yigitcanlar, T., Nepal, M., Nguyen, K., Dur, F., 2023. Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework. Sustain Cities Soc 96, 104653. https://doi.org/10.1016/j.scs.2023.104653
[21] Liao, J., Tang, L., Shao, G., 2023. Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways. Remote Sens (Basel) 15. https://doi.org/10.3390/rs15082142
[22] Lin, J., Li, X., Wen, Y., He, P., 2023. Modeling urban land-use changes using a landscape-driven patch-based cellular automaton (LP-CA). Cities. https://doi.org/10.1016/j.cities.2022.103906
[23] Liu, X., Zhang, X., Kong, X., Shen, Y.J., 2022. Random Forest Model Has the Potential for Runoff Simulation and Attribution. Water (Switzerland) 14. https://doi.org/10.3390/w14132053
[24] Lopes, H.S., Vidal, D.G., Cherif, N., Silva, L., Remoaldo, P.C., 2025. Green infrastructure and its influence on urban heat island, heat risk, and air pollution: A case study of Porto (Portugal). J Environ Manage 376. https://doi.org/10.1016/j.jenvman.2025.124446
[25] Mahmoudzadeh, H., Abedini, A., 2022. Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz).
[26] Man, J., Zhu, J., Cao, L., 2019. Multi-step community evolution prediction methods via marcov chain and classifier chain. Chinese Control Conference, CCC 2019-July, 7950–7955. https://doi.org/10.23919/ChiCC.2019.8866434
[27] Mirsanjari, M.M., Visockiene, J.S., Mohammadyari, F., Zarandian, A., 2021. Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network. Ecological Chemistry and Engineering S 28, 429–447. https://doi.org/10.2478/eces-2021-0029
[28] Mishra, V.N., Rai, P.K., 2016. A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences 9. https://doi.org/10.1007/s12517-015-2138-3
[29] Muliantara, A., Widiartha, I., 2011. Penerapan Multi Layer Perceptron Dalam Anotasi Image Secara Otomatis. Jurnal Ilmu Komputer 4, 9–15.
[30] Nghia, B.P.Q., Pal, I., Chollacoop, N., Mukhopadhyay, A., 2022. Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river. Progress in Disaster Science 14, 100235. https://doi.org/10.1016/j.pdisas.2022.100235
[31] Raj, A., Sharma, L.K., 2022. Assessment of land-use dynamics of the Aravalli range (India) using integrated geospatial and CART approach. Earth Sci Inform 15, 497–522. https://doi.org/10.1007/s12145-021-00753-9
[32] Rizanti, I.N., Soehardjoepri, S., 2017. Prediksi Produksi Kayu Bundar Kabupaten Malang Dengan Menggunakan Metode Markov Chains. Jurnal Sains dan Seni ITS 6. https://doi.org/10.12962/j23373520.v6i2.27846
[33] Sati, V.P., 2014. Land-use/cover changes in the kewer gadhera sub-watershed, central himalaya, in: Impact of Global Changes on Mountains: Responses and Adaptation. CRC Press, Mizoram University (Central), Aizawl, 796004, India, pp. 298–311. https://doi.org/10.1201/b17963
[34] Schneider, F., Feurer, M., Lundsgaard-Hansen, L.M., Win Myint, Cing Don Nuam, Nydegger, K., Oberlack, C., Nwe Nwe Tun, Zähringer, J.G., Aung Myin Tun, Messerli, P., 2020. Sustainable Development Under Competing Claims on Land: Three Pathways Between Land-Use Changes, Ecosystem Services and Human Well-Being. European Journal of Development Research 32, 316–337. https://doi.org/10.1057/s41287-020-00268-x
[35] Shen, L., Li, J.B., Wheate, R., Yin, J., Paul, S.S., 2020. Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change. Journal of Environmental Informatics Letters 3, 28–38. https://doi.org/10.3808/jeil.202000023
[36] Sianturi, R.S., 2022. Komparasi Metode Klasifikasi Tersupervisi untuk Pemetaan Lahan Terbangun dan NonTerbangun Menggunakan Landsat 8 OLI dan Google Earth Engine ( Studi Kasus : Kota Malang ) 17, 82–89.
[37] Siska, W., Widiatmaka, W., Setiawan, Y., Adi, S.H., 2022. Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine. Tataloka 24, 74–83. https://doi.org/10.14710/tataloka.24.1.74-83
[38] Susilo, B., 2017. Multiscale Spatial Assessment of Determinant Factors of Land Use Change: Study at Urban Area of Yogyakarta. IOP Conf Ser Earth Environ Sci 98. https://doi.org/10.1088/1755-1315/98/1/012015
[39] Suwanlee, S.R., Keawsomsee, S., Pengjunsang, M., Homtong, N., Prakobya, A., Borgogno-Mondino, E., Sarvia, F., Som-ard, J., 2023. Monitoring Agricultural Land and Land Cover Change from 2001–2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine. Remote Sens (Basel) 15. https://doi.org/10.3390/rs15174339
[40] Tao, C., Jia, M., Wang, G., Zhang, Y., Zhang, Q., Wang, X., Wang, Q., Wang, W., 2024. Time-sensitive prediction of NO2 concentration in china using an ensemble machine learning model from multi-source data. J Environ Sci (China) 137, 30–40. https://doi.org/10.1016/j.jes.2023.02.026
[41] Tariq, A., Yan, J., Mumtaz, F., 2022. Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. Physics and Chemistry of the Earth. https://doi.org/10.1016/j.pce.2022.103286
[42] Utami, W., Aji, K., Marini, Sugiyanto, C., Rahardjo, N., 2023. The Impact of Yogyakarta International Airport Development on Land Use Changes. Jurnal Pembangunan Wilayah dan Kota 19, 105–117. https://doi.org/10.14710/pwk.v19i1.37429
[43] Utami, W., Sugiyanto, C., Rahardjo, N., 2024. Artificial intelligence in land use prediction modeling: a review. IAES International Journal of Artificial Intelligence (IJ-AI) 13, 2514. https://doi.org/10.11591/ijai.v13.i3.pp2514-2523
[44] Zulfajri, Danoedoro, P., Murti, S.H., 2021. Klasifikasi Tutupan Lahan Data Landsat-8 Oli Menggunakan Metode Random Forest. Jurnal Penginderaan Jauh Indonesia 03, 1–7.
[45] Badan Pusat Statistik, 2023
[46] Data dan Informasi Bencana, 2023, https://dibi.bnpb.go.id/
[47] Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal kementerian Pertanian, 2022,https://satudata.pertanian.go.id/assets/docs/publikasi/Statistik_Ketahanan_Pangan_2022.pdf
[48] Katalog Inderaja, https://inderaja-catalog.lapan.go.id/application_data/default/ pages/about_Sentinel-2.html
[49] Pemerintah Kabupaten Kulon Progo, https://kulonprogokab.go.id/v31/detil/7672/kondisi-umum
Published
2025-04-25
How to Cite
Utami, W., & Sugiyanto, C. (2025). Green infrastructure development challenges: The case of Yogyakarta International Airport. Jurnal Ekonomi Indonesia, 14(1), 69-92. https://doi.org/10.52813/jei.v14i1.434
Section
Articles
