Penelusuran Transformasi Pertumbuhan Lahan Terbangun Selama Dua Dekade (Tahun 2004-2024) di Kota Pekanbaru, Provinsi Riau
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Perkembangan wilayah dengan meningkatnya lahan terbangun sering kali dijadikan sebagai indikator penting dalam memahami dinamika urbanisasi dan perubahan tata guna lahan, khususnya di kawasan perkotaan. Penelitian ini bertujuan untuk menganalisis dinamika pertumbuhan lahan terbangun selama dua dekade yaitu periode 2004 hingga 2024 dengan memanfaatkan teknologi penginderaan jauh melalui algoritma Normalized Difference Built-up Index (NDBI). Data penelitian berasal dari citra satelit Landsat 5 TM (tahun 2004, 2008, dan 2012) dan Landsat 8 OLI-TIRS (tahun 2016, 2020, dan 2024), yang pengolahannya melalui platform Google Earth Engine (GEE) dan software sistem informasi geografis. Nilai NDBI dihitung untuk setiap periode pengamatan sebagai mengidentifikasi wilayah terbangun secara spasial dan temporal. Hasil analisis menunjukkan adanya peningkatan signifikan wilayah terbangun dari 2004 hingga 2024 mencapai 26.355,55 hektar atau 17,52%. Peningkatan ini terutama terjadi di kawasan pinggiran kota yang mengalami pertumbuhan penduduk dan pembangunan infrastruktur. Transformasi penggunaan lahan tersebut berdampak pada berkurangnya ruang terbuka hijau serta potensi risiko lingkungan perkotaan. Penelitian ini menegaskan pentingnya pemantauan spasial berkala sebagai dasar perumusan kebijakan tata ruang yang berkelanjutan. Pendekatan ini terbukti sebagai salah satu transformasi yang efektif dan efisien dalam mendeteksi serta memvisualisasikan pola pertumbuhan lahan terbangun, sehingga dapat digunakan sebagai data tambahan dalam perencanaan kota yang lebih adaptif dan responsif terhadap dinamika perkotaan.
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Referensi
Asabere, S. B., Acheampong, R. A., Ashiagbor, G., Beckers, S. C., Keck, M., Erasmi, S., Schanze, J., & Sauer, D. (2020). Urbanization, land use transformation and spatio-environmental impacts: Analyses of trends and implications in major metropolitan regions of Ghana. Land Use Policy, 96(March), 104707. https://doi.org/10.1016/j.landusepol.2020.104707
Chen, Y., Liu, X., Li, X., Liu, X., Yao, Y., Hu, G., Xu, X., & Pei, F. (2017). Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method. Landscape and Urban Planning, 160, 48–60. https://doi.org/10.1016/j.landurbplan.2016.12.001
Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22(February), 100482. https://doi.org/10.1016/j.rsase.2021.100482
Di Nicola, F., Brattich, E., & Di Sabatino, S. (2022). A new approach for roughness representation within urban dispersion models. Atmospheric Environment, 283(May), 119181. https://doi.org/10.1016/j.atmosenv.2022.119181
Estoque, R. C., & Murayama, Y. (2015). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205–217. https://doi.org/10.1016/j.ecolind.2015.03.037
Govil, H., Guha, S., Dey, A., & Gill, N. (2019). Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city. Heliyon, 5(6), e01923. https://doi.org/10.1016/j.heliyon.2019.e01923
Hailu, T., Assefa, E., & Zeleke, T. (2024). Urban expansion induced land use changes and its effect on ecosystem services in Addis Ababa, Ethiopia. Frontiers in Environmental Science, 12(November), 1–21. https://doi.org/10.3389/fenvs.2024.1454556
Hersperger, A. M., Oliveira, E., Pagliarin, S., Palka, G., Verburg, P., Bolliger, J., & Grădinaru, S. (2018). Urban land-use change: The role of strategic spatial planning. Global Environmental Change, 51, 32–42. https://doi.org/10.1016/j.gloenvcha.2018.05.001
Ichsan Ali, M., Hafid Hasim, A., & Raiz Abidin, M. (2019). Monitoring the built-up area transformation using urban index and normalized difference built-up index analysis. International Journal of Engineering, Transactions B: Applications, 32(5), 647–653. https://doi.org/10.5829/ije.2019.32.05b.04
Kebede, T. A., Hailu, B. T., & Suryabhagavan, K. V. (2022). Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia. Environmental Challenges, 8(February), 100568. https://doi.org/10.1016/j.envc.2022.100568
Leyk, S., Uhl, J. H., Balk, D., & Jones, B. (2018). Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States. Remote Sensing of Environment, 204(August), 898–917. https://doi.org/10.1016/j.rse.2017.08.035
Mahdi, S. A., & Jasim, S. N. (2024). Utilizing geospatial techniques for change cetection of the Baghdad campus landscape from 1988 to 2022. IOP Conference Series: Earth and Environmental Science, 1371(4). https://doi.org/10.1088/1755-1315/1371/4/042045
Muhaimin, M., Fitriani, D., Adyatma, S., & Arisanty, D. (2022). mapping build-up area density using normalized difference built-up index (ndbi) and urban index (ui) wetland in the city Banjarmasin. IOP Conference Series: Earth and Environmental Science, 1089(1), 012036. https://doi.org/10.1088/1755-1315/1089/1/012036
Pandey, B., Brelsford, C., & Seto, K. C. (2025). Rising infrastructure inequalities accompany urbanization and economic development. Nature Communications, 16(1), 1193. https://doi.org/10.1038/s41467-025-56539-w
Roy, S., Pandit, S., Eva, E. A., Bagmar, M. S. H., Papia, M., Banik, L., Dube, T., Rahman, F., & Razi, M. A. (2020). Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data. Urban Climate, 32(November 2019), 100593. https://doi.org/10.1016/j.uclim.2020.100593
Sapena, M., & Ruiz, L. A. (2021). Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis. International Journal of Geographical Information Science, 35(2), 375–396. https://doi.org/10.1080/13658816.2020.1817463
Toure, S. I., Stow, D. A., Shih, H. chien, Weeks, J., & Lopez-Carr, D. (2018). Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sensing of Environment, 210(January 2017), 259–268. https://doi.org/10.1016/j.rse.2018.03.023
Varshney, A. (2013). Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: An automated approach. Remote Sensing Letters, 4(5), 504–512. https://doi.org/10.1080/2150704X.2013.763297
Wolff, M., Haase, A., Haase, D., & Kabisch, N. (2017). The impact of urban regrowth on the built environment. Urban Studies, 54(12), 2683–2700. https://doi.org/10.1177/0042098016658231
Yang, J., Dong, J., Sun, Y., Zhu, J., Huang, Y., & Yang, S. (2022). A constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data. International Journal of Geographical Information Science, 36(1), 114–136. https://doi.org/10.1080/13658816.2021.1876236
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
Zheng, Y., Tang, L., & Wang, H. (2021). An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production, 328, 129488. https://doi.org/10.1016/j.jclepro.2021.129488