Effect of Transportation Infrastructure on Built-up Area Using Prediction of Land Use/Cover Change: Case Study of Yogyakarta International Airport, Indonesia

https://doi.org/10.22146/ijg.68216

Irwansyah Sukri(1*), Rika Harini(2), Sudrajat Sudrajat(3)

(1) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(2) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(3) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


The development of transportation infrastructure increases the pressure on natural resources, one of which is the increase in the built-up area. The changes do not only happen during the construction of transportation infrastructure but also after its completion. Therefore, this study aims to identify and simulate land use/cover changes in Kulon Progo Regency, Indonesia, to predict the effect of the construction of Yogyakarta International Airport (YIA). A quantitative descriptive method was used with the main data of multitemporal Landsat remote sensing images. Furthermore, the integration of Cellular Automata and Artificial Neural Networks (CA-ANN) was applied to simulate land use/cover change predictions (2035). The results of image classification using the supervised maximum likelihood classification showed an overall accuracy of 85.33% and 86.67% for 2011, and 2015 with 2019 using Landsat 7 and 8 images, respectively. Meanwhile, there was an increase in paddy fields of 1,210.1 ha (2.11%) and built-up area by 2,708.6 ha (4.72%) during 2011 – 2019. Conversely, shrubs and dryland agriculture decreased by 1,594.1 ha (2.78%) and 2,174.1 ha (3.79%). The simulation results indicate that the development of transportation infrastructure further triggers the increase in built-up area, especially around the YIA. Therefore, policymakers and development implementers should adopt and implement appropriate and effective planning for sustainable land use.


Keywords


remote sensing; land use change; prediction simulation; artificial Neural Network; Effect of Transportation Infrastructure

Full Text:

PDF


References

Adebayo, H. O., Otun, W. O., & Daniel, I. S. (2019). Change detection in landuse/landcover of abeokuta metropolitan area, Nigeria using multi-temporal Landsat remote sensing. Indonesian Journal of Geography, 51(2), 217-223. https://doi.org/http://dx.doi.org/10.22146/ijg.44914.

Badan Pusat Statistik. (2012). Kabupaten Kulon Progo dalam Angka 2012. https://kulonprogokab.bps.go.id/publication/2012/11/26/5c87c7b104f8d84e30cd5a97/kabupaten-kulon-progo-dalam-angka-2012.html. Accessed 12th Desember 2020, 08.15 AM.

Badan Pusat Statistik. (2020). Kabupaten Kulon Progo dalam Angka 2020. https://kulonprogokab.bps.go.id/publication/2020/02/28/650dd8bf17f75f54400736e2/kabupaten-kulon-progo-dalam-angka-2020--penyediaan-data-untuk-perencanaan-pembangunan.html. Accessed 19th Agustus 2020, 08.54 AM.

Bello, I. E., & Rilwani, M. L. (2016). Quantitative assessment of remotely sensed data for landcover change and environmental management. Indonesian Journal of Geography, 48(2), 135-144.

Dangulla, M., Abd Munaf, L., & Mohammad, F. R. (2020). Spatio-temporal analysis of land use/land cover dynamics in Sokoto Metropolis using multi-temporal satellite data and Land Change Modeller. Indonesian Journal of Geography, 52(3), 306-316.

Danoedoro, P., Ananda, I. N., Kartika, C. S. D., Umela, A. F., & Indayani, A. B. (2020). Testing a detailed classification scheme for land-cover/land-use mapping of typical Indonesian landscapes: case study of Sarolangun, Jambi and Salatiga, Central Java. Indonesian Journal of Geography, 52(3), 327-340.

Dewi, R. P., Khofianida, A., Agista, D. E., Arrasyid, F. P., Damayanti, S. I., & Putri, R. F. (2020). Landuse change in Jakarta Province: trend, types, and socio-demographic factors. In IOP Conference Series: Earth and Environmental Science. 451(1), 012055. https://doi.org/10.1088/1755-1315/451/1/012055.

Elliot, T., Almenar, J. B., & Rugani, B. (2020). Modelling the relationships between urban land cover change and local climate regulation to estimate urban heat island effect. Urban Forestry & Urban Greening, 50, 126650. https://doi.org/10.1016/j.ufug.2020.126650.

Esgalhado, C., Guimarães, H., Debolini, M., Guiomar, N., Lardon, S., & de Oliveira, I. F. (2020). A holistic approach to land system dynamics–The Monfurado case in Alentejo, Portugal. Land Use Policy, 95, 104607. https://doi.org/10.1016/j.landusepol.2020.104607.

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.

Giri, C. P. (Ed.). (2012). Remote sensing of land use and land cover: principles and applications. CRC Press, London.

Guo, Z., Hu, Y., & Zheng, X. (2020). Evaluating the effectiveness of land use master plans in built-up land management: A case study of the Jinan Municipality, eastern China. Land Use Policy, 91, 104369. 104369. https://doi.org/10.1016/j.landusepol.2019.104369.

Guzman, L. A., Escobar, F., Peña, J., & Cardona, R. (2020). A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region. Land Use Policy, 92, 104445. https://doi.org/10.1016/j.landusepol.2019.104445.

Harini, R., Christanto, N., & Marfai, M. A. (2018). Kompetensi Dasar Olimpiade Sains Nasional Geografi. UGM Press, Yogyakarta.

Jiang, S., Meng, J., & Zhu, L. (2020). Spatial and temporal analyses of potential land use conflict under the constraints of water resources in the middle reaches of the Heihe River. Land Use Policy, 97, 104773. https://doi.org/10.1016/j.landusepol.2020.104773.

Khoi, D. D., & Munthali, K. G. (2012). Multispectral Classification of Remote Sensing Data for Geospatial Analysis. In Progress in Geospatial Analysis. Springer, Tokyo.

Kurowska, K., Kryszk, H., Marks-Bielska, R., Mika, M., & Leń, P. (2020). Conversion of agricultural and forest land to other purposes in the context of land protection: Evidence from Polish experience. Land Use Policy, 95, 104614. https://doi.org/10.1016/j.landusepol.2020.104614.

Liang, X., Jin, X., Ren, J., Gu, Z., & Zhou, Y. (2020). A research framework of land use transition in Suzhou City coupled with land use structure and landscape multifunctionality. Science of the Total Environment, 737, 139932. https://doi.org/10.1016/j.jns.2019.116544.

Liu, J., Jin, X., Xu, W., Gu, Z., Yang, X., Ren, J., ... & Zhou, Y. (2020). A new framework of land use efficiency for the coordination among food, economy and ecology in regional development. Science of the Total Environment, 710, 135670. https://doi.org/10.1016/j.scitotenv.2019.135670.

Liu, T. Y., & Su, C. W. (2021). Is transportation improving urbanization in China?. Socio-Economic Planning Sciences, 77, 101034. https://doi.org/10.1016/j.seps.2021.101034.

Liu, Y. (2018). Introduction to land use and rural sustainability in China. Land Use Policy, 74, 1-4. https://doi.org/10.1016/j.landusepol.2018.01.032.

Otuoze, S. H., Hunt, D. V., & Jefferson, I. (2020). Predictive Modeling of Transport Infrastructure Space for Urban Growth Phenomena in Developing Countries’ Cities: A Case Study of Kano—Nigeria. Sustainability, 13(1), 308. https://doi.org/10.3390/su13010308.

Pham, T. T. H., Turner, S., & Trincsi, K. (2015). Applying a Systematic Review to Land Use Land Cover Change in Northern Upland V ietnam: The Missing Case of the Borderlands. Geographical Research, 53(4), 419-435. https://doi.org/10.1111/1745-5871.12133.

Qian, Y., Xing, W., Guan, X., Yang, T., & Wu, H. (2020). Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation. Science of the Total Environment, 722, 137738. https://doi.org/10.1016/j.scitotenv.2020.137738.

Rahmah, A. N., Subiyanto, S., & Amarrohman, F. J. (2019). Pemodelan Perubahan Penggunaan Lahan dengan Artificial Neural Network (ANN) di Kota Semarang. Jurnal Geodesi Undip, 9(1), 197-206.

Reba, M., & Seto, K. C. (2020). A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change. Remote Sensing of Environment, 242, 111739. https://doi.org/10.1016/j.rse.2020.111739/

Rijanta, R., Baiquni, M., & Rachmawati, R. (2019). Patterns of Livelihood Changes of the Displaced Rural Households in the Vicinity of New Yogyakarta International Airport (NYIA). In International Conference on Rural Studies in Asia (ICoRSIA 2018), 313, 259-263.

Setiady, D., & Danoedoro, P. (2016). Prediksi Perubahan Lahan Pertanian Sawah Sebagian Kabupaten Klaten dan Sekitarnya Menggunakan Cellular Automata dan Data Penginderaan Jauh. Jurnal Bumi Indonesia, 5(1), 1-10.

Sfa, F. E., & Nemiche, M. (2019). A Review of Land Use Change: Approaches and Models. In 2019 4th World Conference on Complex Systems (WCCS), 4, 1-7. https://doi.org/10.1109/ICoCS.2019.8930806.

Sukri, I., & Harini, R. (2022). Sustainable Food and Agriculture Strategy in Kulon Progo Regency based on SWOT and Spatial Analysis. In 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021), 19, 32-39.

Ullah, S., Ahmad, K., Sajjad, R. U., Abbasi, A. M., Nazeer, A., & Tahir, A. A. (2019). Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region. Journal of environmental management, 245, 348-357. https://doi.org/10.1016/j.jenvman.2019.05.063.

Wang, J., Wei, H., Cheng, K., Li, G., Ochir, A., Bian, L., ... & Nasanbat, E. (2019). Spatio-temporal pattern of land degradation along the China-Mongolia railway (Mongolia). Sustainability, 11(9), 2705. https://doi.org/10.3390/su11092705.

Williyantoro, W. A., Priyono, K. D., & Taryono, I. (2016). Analisis Perubahan Penggunaan Lahan di Kecamatan Mijen Kota Semarang Tahun 2010-2014. Doctoral dissertation, Universitas Muhammadiyah Surakarta, Indonesia.

Wu, C., Huang, X., & Chen, B. (2020). Telecoupling mechanism of urban land expansion based on transportation accessibility: A case study of transitional Yangtze River economic Belt, China. Land Use Policy, 96, 104687. https://doi.org/10.1016/j.landusepol.2020.104687.

Wulansari, H. (2017). Uji Akurasi Klasifikasi Penggunaan Lahan Dengan Menggunakan Metode Defuzzifikasi Maximum Likelihood Berbasis Citra Alos Avnir-2. BHUMI: Jurnal Agraria dan Pertanahan, 3(1), 98-110. https://doi.org/10.31292/jb.v3i1.233.

Xing, W., Qian, Y., Guan, X., Yang, T., & Wu, H. (2020). A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation. Computers & Geosciences, 137, 104430. https://doi.org/10.1016/j.cageo.2020.104430.

Yang, S., Hu, S., Wang, S., & Zou, L. (2020). Effects of rapid urban land expansion on the spatial direction of residential land prices: Evidence from Wuhan, China. Habitat International, 101, 102186. https://doi.org/10.1016/j.habitatint.2020.102186.

Yang, Y., Bao, W., & Liu, Y. (2020). Scenario simulation of land system change in the Beijing-Tianjin-Hebei region. Land Use Policy, 96, 104677. https://doi.org/10.1016/j.landusepol.2020.104677.

Zhu, W., Gao, Y., Zhang, H., & Liu, L. (2020). Optimization of the land use pattern in Horqin Sandy Land by using the CLUMondo model and Bayesian belief network. Science of The Total Environment, 739, 139929. https://doi.org/10.1016/j.scitotenv.2020.139929.



DOI: https://doi.org/10.22146/ijg.68216

Article Metrics

Abstract views : 2561 | views : 1061

Refbacks





Copyright (c) 2023 Irwansyah Sukri, Rika Harini, Sudrajat Sudrajat

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web
Analytics IJG STATISTIC