Estimating Urban Land Surface Temperature Using Spatial Machine Learning in Gorontalo City

Arthur Gani Koto, Mohamad Ilyas Abas, Syahrial Syahrial, Dewa Oka Suparwata

Abstract


Urban expansion in tropical cities significantly alters surface thermal conditions, intensifying the urban heat island (UHI) phenomenon. This study aims to estimate and analyze the spatiotemporal dynamics of land surface temperature (LST) in Gorontalo City from 1995 to 2025 using a spatial machine learning (SML) approach based on the Random Forest (RF) algorithm. Multitemporal Landsat 5, 7, 8, and 9 images were processed in Google Earth Engine (GEE) to derive surface reflectance, Normalized Difference Vegetation Index (NDVI), emissivity, and brightness temperature, which were subsequently employed as predictor variables in the LST model. A total of 50 ground validation points were used to assess model performance. The RF model achieved high predictive accuracy with an R² of 0.833, RMSE of ±3.33 °C, and MAE of ±2.80 °C, outperforming conventional NDVI-based models. The long-term analysis revealed a consistent increase in LST across urbanized zones, particularly in the city center and northern districts, while areas with higher vegetation cover exhibited lower LST values. The negative correlation between NDVI and LST (R² = 0.3132) confirms the critical role of vegetation in mitigating urban thermal intensity. These findings highlight the applicability of the RF-based SML framework for accurate LST estimation and urban climate monitoring, providing a scientific basis for sustainable urban planning and green infrastructure development in tropical cities.

Keywords


Google Earth Engine; Land Surface Temperature; Landsat; Random Forest; Spatial Machine Learning

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References


Abidin, M. R., Nur, R., Mayzarah, E. M., & Umar, R. (2021). Estimating and Monitoring the Land Surface Temperature (LST) Using Landsat OLI 8 TIRS. International Journal of Environment, Engineering and Education, 3(1), 17–24. https://doi.org/10.55151/ijeedu.v3i1.43

Adi, W. B., Sukuryadi, Adiansyah, J. S., Ibrahim, & Johari, H. I. (2022). Analisis Pola Spasial Fenomena Urban Heat Island (UHI) Berdasarkan Faktor Emisivitas Lahan. Geography, 10(2), 6–7. http://journal.ummat.ac.id/index.php/geography/article/view/9740

Arif, N., Khasanah, A. N., Jaya, R., Gozan, M., & Hendrawan, B. (2019). The Effect of Land Surface Temperature and Land Use on Energy System Development in Gorontalo City. Journal of Physics: Conference Series, 1179(1), 012103. https://doi.org/10.1088/1742-6596/1179/1/012103

Arifin, S. S., Hamzah, B., Mulyadi, R., & Rasyid, A. R. (2022). Effects of Vegetation on Urban Heat Island Using Landsat 8 ‎OLI/TIRS Imagery in Tropical Urban Climate. Civil Engineering and Architecture, 10(1), 395–405. https://doi.org/10.13189/cea.2022.100134

Arunab, K. S., & Mathew, A. (2024). Exploring spatial machine learning techniques for improving land surface temperature prediction. Kuwait Journal of Science, 51(3), 100242. https://doi.org/10.1016/j.kjs.2024.100242

Badan Meteorologi dan Klimatologi. (2022). Pemutakhiran zona musim Indonesia Periode 1991-2020.

BMKG. (2022). Peta Tipe Iklim Oldeman dan Schmidt-Ferguson Serta Grafik Curah Hujan Tiap Tipe Iklim di Kota/Kabupaten Provinsi Gorontalo (Issue Desember). https://bbmkg4.com/Profil/Artikel/2023_08_10_02_10_28_Buku_Peta_Iklim_Oldeman_dan_Schmidt-Ferguson_Provinsi_Gorontalo

BPS Kota Gorontalo. (2025). Kota Gorontalo Dalam Angka. In Badan Pusat Statistik. https://gorontalokota.bps.go.id/id/publication/2025/02/28/590eb80cfe381431cbbb1bb6/kota-gorontalo-dalam-angka-2025.html

Dagnachew, S. C., & Binyam, T. H. (2024). The nexus between land surface temperature and vegetation condition: The case of Addis Ababa, Ethiopia. International Journal of Physical Sciences, 19(2), 127–136. https://doi.org/10.5897/ijps2022.4978

Farid, N., Moazzam, M. F. U., Ahmad, S. R., Coluzzi, R., & Lanfredi, M. (2022). Monitoring the Impact of Rapid Urbanization on Land Surface Temperature and Assessment of Surface Urban Heat Island Using Landsat in Megacity (Lahore) of Pakistan. Frontiers in Remote Sensing, 3(May), 1–12. https://doi.org/10.3389/frsen.2022.897397

Hassan, T., Zhang, J., Prodhan, F. A., Pangali Sharma, T. P., & Bashir, B. (2021). Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019). Remote Sensing, 13(16), 3177. https://doi.org/10.3390/rs13163177

Ibrahim, B., & Ash’aari, Z. H. (2023). Climate Variability in Relation To Land Use and Land Cover (LULC) Changes in Kota Bharu, Kelantan, Malaysia. Planning Malaysia, 21(5), 1–16. https://doi.org/10.21837/pm.v21i29.1352

Kopczewska, K. (2022). Spatial machine learning: new opportunities for regional science. The Annals of Regional Science, 68(3), 713–755. https://doi.org/10.1007/s00168-021-01101-x

Koto, A. G. (2016). Analisis Suhu Permukaan Kota Gorontalo dan Sekitarnya Menggunakan Saluran Thermal Citra Landsat 7 ETM +. RADIAL. Jurnal Peradaban Sains, Rekayasa Dan Teknologi, 4(1), 1–7. https://doi.org/10.37971/radial.v4i1.117

Koto, A. G., & Taslim, I. (2018). Deteksi Perkembangan Lahan Terbangun Kota Gorontalo Berdasarkan Citra Last (Landsat, Aster, & Sentinel-2a) (Detection Of The Built-Up Area Development In Gorontalo City Based On Last (Landsat, Aster, & Sentinel-2a) Imagery). Jurnal Sains Informasi Geografi, 1(2), 28. https://doi.org/10.31314/jsig.v1i2.177

Liu, H., Zhou, Z., Wen, Q., Chen, J., & Kojima, S. (2024). Spatiotemporal Land Use/Land Cover Changes and Impact on Urban Thermal Environments: Analyzing Cool Island Intensity Variations. Sustainability (Switzerland) , 16(8). https://doi.org/10.3390/su16083205

Moyo, B., Schröder, D., & Moreri, K. (2025). Cloud-Computing Trend Analysis of Urbanization Impacts on Land Surface Temperature and Electricity Demand in Gaborone, Botswana. Remote Sensing in Earth Systems Sciences, 8(1), 283–306. https://doi.org/10.1007/s41976-024-00171-7

Patel, S., Indraganti, M., & Jawarneh, R. N. (2024). Land surface temperature responses to land use dynamics in urban areas of Doha, Qatar. Sustainable Cities and Society, 104(February), 105273. https://doi.org/10.1016/j.scs.2024.105273

Pramudiyasari, T., Wibowo, A., & Adi Suko, P. (2022). Land cover changes impact on 1st between 1990-2005-2020 in Bandar Lampung City, Indonesia. IOP Conference Series: Earth and Environmental Science, 986(1), 12048. https://doi.org/10.1088/1755-1315/986/1/012048

Republik Indonesia. (2020). UU RI No 38 Tentang Pembentukan Provinsi Gorontalo. In Presiden Republik Indonesia. https://peraturan.bpk.go.id/Home/Details/66829/pp-no-30-tahun-1979

Sawada, Y. (2020). Machine Learning Accelerates Parameter Optimization and Uncertainty Assessment of a Land Surface Model. Journal of Geophysical Research: Atmospheres, 125(20). https://doi.org/10.1029/2020JD032688

Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308–6325. https://doi.org/10.1109/JSTARS.2020.3026724

Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment Environment, 90, 434–440. https://doi.org/10.1016/j.rse.2004.02.003

Stewart, I. D., & Gerald, M. (2018). The Urban Heat Island A Guidebook. In Angewandte Chemie International Edition, 6(11), 951–952.

Thambawita, T. K. C. N., Munasinghe, D. S., & Yapa, L. K. K. (2023). Identification of Urban Heat Island Effect on Land Use Land Cover Changes. Journal of Geospatial Surveying, 3(2), 43–53. https://doi.org/10.4038/jgs.v3i2.50

Yenneti, K., Ding, L., Prasad, D., Ulpiani, G., Paolini, R., Haddad, S., & Santamouris, M. (2020). Urban overheating and cooling potential in australia: An evidence-based review. Climate, 8(11), 1–22. https://doi.org/10.3390/cli8110126

Yuan, X., Lv, Z., Laakso, K., Han, J., Liu, X., Meng, Q., & Xue, S. (2024). Observation Angle Effect of Near-Ground Thermal Infrared Remote Sensing on the Temperature Results of Urban Land Surface. Land, 13(12). https://doi.org/10.3390/land13122170




DOI: https://doi.org/10.37905/jgeosrev.v8i1.34308

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