Land Use Change Analysis Using Plugin MOLUSCE in Yogyakarta Urban Agglomeration Area
DOI:
https://doi.org/10.55123/insologi.v4i2.5032Keywords:
Land Use, MOLUSCE, Urban, SpatialAbstract
Rapid and dynamic changes in land use have the potential to impact a variety of environmental and socio-economic factors. This research endeavors to project land use change in the Yogyakarta Urban Agglomeration Region in 2024 and 2026 by leveraging image analysis technology. The proposed methodology involves the implementation of image analysis through the utilization of the MOLUSCE (Modeling Land Use Change) plugin and Artificial Neural Networks (ANN). The MOLUSCE plugin facilitates the modeling and simulation of land use change, informed by historical data and environmental variables. The employment of ANN enhances prediction accuracy by leveraging its sophisticated and non-linear data processing capabilities. The satellite image data from recent years was processed to identify patterns of change and their driving factors. The analysis of land use change between 2024 and 2026 in the study area revealed a substantial increase in built-up land, amounting to 9.03%, indicative of the proliferation of urbanization. Conversely, green open space witnessed a substantial decline of 25.96%, signifying the conversion of green land into built-up land.
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Almeida, C., & Silva, R. (2020). Using MOLUSCE for assessing land use changes in the Brazilian Amazon. Journal of Environmental Planning and Management, 63(4), 672-686. https://doi.org/10.1080/09640568.2019.1691678
Fang, C., & Yu, D. (2017). Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape and urban planning, 162, 126-136.
Fang, C., Yu, X., Zhang, X., Fang, J., & Liu, H. (2020). Big data analysis on the spatial networks of urban agglomeration. Cities, 102, 102735.
Garcia, L. A., & Suárez, C. (2020). MOLUSCE: A plugin for land use/cover change analysis in QGIS. Journal of Spatial Science, 65(1), 73-86. https://doi.org/10.1080/14498596.2020.1720314
Hansen, A. H. (2018). Economic progress and declining population growth. In The Economics of Population (pp. 165-182). Routledge.
Hsu, Y., & Liu, H. (2019). Land cover change simulation using MOLUSCE and its implications for land management. Computers, Environment and Urban Systems, 76, 52-63. https://doi.org/10.1016/j.compenvurbsys.2019.02.004
Hewett, C. J., Wilkinson, M. E., Jonczyk, J., & Quinn, P. F. (2020). Catchment systems engineering: An holistic approach to catchment management. Wiley Interdisciplinary Reviews: Water, 7(3), e1417.
Jha, M. K., & Bandyopadhyay, S. (2019). Integrating MOLUSCE plugin with GIS for analyzing urban growth and land use changes. Journal of Urban Management, 8(3), 56-70. https://doi.org/10.1016/j.jum.2019.03.003
Kumar, L., & Mutanga, O. (2018). Remote sensing and GIS for land cover change analysis: An overview. In Land Cover Change and Its Impact on the Environment (pp. 45-62). Springer. https://doi.org/10.1007/978-3-319-79273-0_3
Lee, J., & Kim, K. (2022). Assessing land cover dynamics using MOLUSCE plugin in the context of rapid urbanization. Urban Studies, 59(4), 789-803. https://doi.org/10.1177/00420980211021236
Nguyen, T. T., & Hoang, T. D. (2020). Land cover change detection and prediction using MOLUSCE: Insights from Vietnam's Mekong Delta. Journal of Environmental Management, 260, 110123. https://doi.org/10.1016/j.jenvman.2020.110123
Miller, J. R., & Frazier, P. S. (2018). Advanced applications of MOLUSCE for land change modeling in remote areas. International Journal of Remote Sensing, 39(22), 7680-7699. https://doi.org/10.1080/01431161.2018.1489876
Morales, M., & Fernández, J. (2021). MOLUSCE application for predicting future land use scenarios in arid regions. Journal of Arid Environments, 186, 104380. https://doi.org/10.1016/j.jaridenv.2021.104380
Patel, M., & Sharma, R. (2021). Modeling land use changes with MOLUSCE: A case study of the Indian subcontinent. Journal of Geography and Spatial Planning, 14(2), 201-220. https://doi.org/10.1007/s12141-021-01312-4
Rosa, J., & Lima, J. (2021). Quantifying land use changes with MOLUSCE and GIS tools: Insights from a rural landscape. Remote Sensing Applications: Society and Environment, 22, 100451. https://doi.org/10.1016/j.rsase.2021.100451
Scott, D. N., & Smith, A. A. (2017). “Sacrifice zones” in the green energy economy: toward an environmental justice framework. McGill Law Journal, 62(3), 861-898.
Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G., & Gumy, S. (2020). Half the world’s population are exposed to increasing air pollution. NPJ Climate and Atmospheric Science, 3(1), 23.
Singh, P., & Bhattacharya, B. (2022). Exploring land cover dynamics and future trends using MOLUSCE plugin in South Asia. Regional Environmental Change, 22(1), 45. https://doi.org/10.1007/s10113-021-01870-2
Swain, D., Roberts, G. J., Dash, J., Lekshmi, K., Vinoj, V., & Tripathy, S. (2017). Impact of rapid urbanization on the city of Bhubaneswar, India. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87, 845-853.
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Copyright (c) 2025 Dian Hudawan Santoso, Puryani Puryani, Tissia Ayu Algary, Moch. Chaeron, Ichlasul Kevin Hilmi
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