Analisis Pola Perubahan Dan Proyeksi LST Tahun 2045 Berdasarkan Citra Modis Tahun 2003-2025 (Studi Kasus: Kota Medan, Bandung, dan Jayapura)

Zulfansyah Adhika Priatama Zulfansyah; Soni Darmawan Soni

Authors

Zulfansyah Adhika Priatama Zulfansyah Institut Teknologi Nasional Bandung Author
Soni Darmawan Soni Institut Teknologi Nasional Bandung Author

Keywords:

suhu permukaan tanah, modis myd11a2, google earth engine, , regresi linear sederhana, proyeksi lst

Abstract

Perkembangan wilayah perkotaan yang semakin pesat akibat urbanisasi menyebabkan perubahan penggunaan lahan yang berdampak pada peningkatan Land Surface Temperature (LST) dan memperkuat fenomena Urban Heat Island (UHI). Penelitian ini bertujuan menganalisis pola perubahan LST periode 2003–2025 serta memproyeksikan kondisi LST pada tahun 2045 menggunakan data MODIS MYD11A2 yang diolah melalui Google Earth Engine (GEE). Wilayah penelitian meliputi Kota Medan, Bandung, dan Jayapura. Data LST 8-harian diekstraksi berdasarkan batas administrasi masing-masing kota, kemudian dihitung menjadi rata-rata tahunan untuk membentuk deret waktu LST. Analisis dilakukan menggunakan regresi linear sederhana untuk memperoleh laju perubahan suhu (slope), mengevaluasi model melalui koefisien determinasi (R²) dan Root Mean Square Error (RMSE), serta memproyeksikan LST tahun 2045. Hasil penelitian menunjukkan seluruh kota memiliki tren peningkatan suhu permukaan dengan nilai slope positif, yaitu Medan 0,119 °C/tahun, Bandung 0,027 °C/tahun, dan Jayapura 0,026 °C/tahun. Nilai R² berkisar antara 0,712–0,741, sedangkan RMSE berada pada rentang 0,657–2,388 °C. Proyeksi tahun 2045 menunjukkan Kota Medan diperkirakan memiliki LST tertinggi sebesar 42,60 °C, diikuti Bandung 38,65 °C, dan Jayapura 29,39 °C..

References

[1] Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., & Sobrino, J. A. (2021). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 13(1), 48. https://www.mdpi.com/2072-4292/11/1/48

[2] Zhao, L., Lee, X., Smith, R. B., & Oleson, K. (2021). Strong contributions of local background climate to urban heat islands. Nature Climate Change, 11, 742–747. https://www.researchgate.net/publication/263815930_Strong_contribution_of_local_background_climate_to_urban_heat_islands

[3] Li, X., Zhou, Y., Asrar, G. R., Imhoff, M., & Li, X. (2017). The surface urban heat island response to urban expansion: A global analysis. Science of the Total Environment, 605–606, 426–435. https://www.sciencedirect.com/science/article/abs/pii/S0048969717316492?via%3Dihub

[4] Wan, Z. (2014). New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sensing of Environment, 140, 36–45. https://www.sciencedirect.com/science/article/abs/pii/S003442571300285X?via%3Dihub

[5] Kara, Y., & Yavuz, V. (2025). Urban microclimates in a warming world: Land surface temperature trends across ten major cities. Urban Science, 9(4), 115. https://www.mdpi.com/2413-8851/9/4/115

[6] Nandi, N., & Dede, M. (2022). Urban heat island assessment using remote sensing data in West Java. Indonesian Journal of Science and Technology, 7(2), 233–246. https://ejournal.upi.edu/index.php/ijost/article/view/44146

[7] Rahman, S., et al. (2024). Exploring the relationship of vegetation density and land cover on the UHI phenomenon in Bandung. The Indonesian Journal of Planning and Development. https://ejournal2.undip.ac.id/index.php/ijpd/article/view/25848

[8] Candraningtyas, C. F., et al. (2025). Distribution of Urban Heat Island Index in Surabaya, Yogyakarta, and Bandung. GeoEco Journal. https://jurnal.uns.ac.id/GeoEco/article/view/95660

[9] Sabarati, A., Zaini, A., & Aritonang, S. (2024). Utilization of Terra-MODIS satellite for analysis of Urban Heat Island in Balikpapan City. Formosa Journal of Multidisciplinary Research, 4(2). https://npaformosapublisher.org/index.php/fjmr/article/view/53

[10] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://www.sciencedirect.com/science/article/pii/S0034425717302900?via%3Dihub

[11] Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14–37. https://www.sciencedirect.com/science/article/pii/S0034425712004749

[12] Weng, Q. (2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 335–344. https://www.sciencedirect.com/science/article/abs/pii/S092427160900046X?via%3Dihub

[13] Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384. https://www.sciencedirect.com/science/article/abs/pii/S0034425703000798?via%3Dihub

[14] Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://www.sciencedirect.com/science/article/abs/pii/S0034425703003390?via%3Dihub

[15] Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., & Morisette, J. T. (2002). An overview of MODIS land data processing and product status. Remote Sensing of Environment, 83(1–2), 3–15. https://jenniferdsmallphd.com/Readings/Week_10_Justice_etal_2002.pdf

[16] Barnes, W. L., Pagano, T. S., & Salomonson, V. V. (1998). Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1088–1100. https://ieeexplore.ieee.org/document/700993

[17] Wan, Z., Hook, S., & Hulley, G. (2021). MODIS/Aqua land surface temperature/emissivity 8-day L3 global 1 km SIN grid V061 (MYD11A2). NASA EOSDIS Land Processes Distributed Active Archive Center. https://www.earthdata.nasa.gov/data/catalog/lpcloud-myd11a2-006

[18] Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis (6th ed.). John Wiley & Sons.

[19] Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (5th ed.). McGraw-Hill. https://www.scirp.org/reference/referencespapers?referenceid=1678785

[20] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer. https://riskcue.id/uploads/ebook/20221114151616-2022-11-14ebook151603.pdf

[21] Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://www.int-res.com/journals/cr/articles/cr030079