Dinamika Spasio-Temporal NDVI, NDWI, NDBI, dan Land Surface Temperature dalam Kaitannya dengan Intensitas Urban Heat Island di Wilayah Barat Kabupaten Maros Periode 2017–2025
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Abstract
Fenomena Urban Heat Island (UHI) menjadi persoalan lingkungan yang semakin kritis seiring meningkatnya tekanan urbanisasi di kawasan perkotaan dan periurban Indonesia. Penelitian ini bertujuan menganalisis dinamika spasio-temporal NDVI, NDWI, NDBI, Land Surface Temperature (LST), dan distribusi UHI di wilayah barat Kabupaten Maros, Sulawesi Selatan, pada periode 2017, 2021, dan 2025. Data yang digunakan berupa citra Landsat-8 OLI/TIRS Collection 2 Level-1 yang diolah menggunakan Google Earth Engine dan Python, serta data tutupan lahan ESRI Land Cover. LST dihitung menggunakan pendekatan proporsi vegetasi berbasis NDVI untuk estimasi emisivitas dan inversi Planck, sedangkan UHI diidentifikasi berdasarkan selisih LST terhadap nilai rata-rata dan standar deviasinya. Hasil penelitian menunjukkan bahwa nilai LST maksimum meningkat dari 35,93°C pada 2017 menjadi 43,97°C pada 2025, disertai penurunan area Non-UHI dari 28.065,31 Ha menjadi 24.755,75 Ha. Analisis korelasi Pearson menunjukkan bahwa NDBI memiliki korelasi positif sedang hingga kuat terhadap LST (r = +0,525 hingga +0,725), sedangkan NDWI menunjukkan korelasi negatif lemah hingga sedang (r = −0,281 hingga −0,459). Sementara itu, NDVI memiliki korelasi positif yang lemah terhadap LST (r = +0,144 hingga +0,304), yang diduga dipengaruhi oleh dominasi lahan pertanian di wilayah penelitian. Temuan ini menunjukkan bahwa ekspansi kawasan terbangun merupakan faktor yang paling dominan dalam meningkatkan suhu permukaan, sedangkan keberadaan badan air berperan dalam menekan peningkatan LST sehingga keduanya menjadi komponen penting dalam mitigasi fenomena UHI di wilayah barat Kabupaten Maros.
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References
[1] Q. Weng, “A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China,” Int. J. Remote Sens., vol. 22, no. 10, pp. 1999–2014, Jan. 2001, doi: 10.1080/713860788.
[2] J. A. Voogt and T. R. Oke, “Thermal remote sensing of urban climates,” Remote Sens. Environ., vol. 86, no. 3, pp. 370–384, 2003, doi: https://doi.org/10.1016/S0034-4257(03)00079-8.
[3] T. Oke, “The energetic basis of urban heat island,” Quarterly Journal of the Royal Meteorological Society, vol. 108, pp. 1–24, Jan. 1982, doi: 10.1002/qj.49710845502.
[4] K. Deilami, Md. Kamruzzaman, and Y. Liu, “Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures,” International Journal of Applied Earth Observation and Geoinformation, vol. 67, pp. 30–42, 2018, doi: https://doi.org/10.1016/j.jag.2017.12.009.
[5] M. L. Imhoff, P. Zhang, R. E. Wolfe, and L. Bounoua, “Remote sensing of the urban heat island effect across biomes in the continental USA,” Remote Sens. Environ., vol. 114, no. 3, pp. 504–513, 2010, doi: https://doi.org/10.1016/j.rse.2009.10.008.
[6] M. Santamouris, “Regulating the damaged thermostat of the cities—Status, impacts and mitigation challenges,” Energy Build., vol. 91, pp. 43–56, 2015, doi: https://doi.org/10.1016/j.enbuild.2015.01.027.
[7] M. A. Lasaiba, “Perkotaan dalam Perspektif Kemiskinan, Permukiman Kumuh dan Urban Heat Island (Suatu Telaah Literatur),” GEOFORUM, vol. 1, no. 2, pp. 63–72, Dec. 2022, doi: 10.30598/geoforumvol1iss2pp63-72.
[8] M. S. An-Nizami, N. F. Rahmadani, A. I. Alfarossi, and I. R. Nugraheni, “Analisis Spasio-Temporal Hubungan Vegetasi, Land Surface Temperature, dan Urban Heat Island di Jakarta Sebelum, Saat, dan Setelah Pandemi COVID-19,” Jurnal Geosains dan Remote Sensing, vol. 7, no. 1, May 2026, doi: 10.23960/jgrs.ft.unila.436.
[9] E. Whidayanti, M. Syauqi Labib, N. Rizki Novani, S. Nuzla Hazani, and M. Akyas, “Analysis of Land Cover Change in Relation to the Urban Heat Island Phenomenon using Remote Sensing and GIS Technology in South Jakarta, Indonesia,” Journal of Geographical Sciences and Education, vol. 03, no. 3, 2025, doi: 10.69606/geography.v3i3.291.
[10] S. Sultana and A. N. V Satyanarayana, “Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization,” Int. J. Remote Sens., vol. 39, no. 20, pp. 6692–6730, Oct. 2018, doi: 10.1080/01431161.2018.1466072.
[11] Q. Weng, D. Lu, and J. Schubring, “Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies,” Remote Sens. Environ., vol. 89, no. 4, pp. 467–483, 2004, doi: https://doi.org/10.1016/j.rse.2003.11.005.
[12] C. J. Tucker, “Red and photographic infrared linear combinations for monitoring vegetation,” Remote Sens. Environ., vol. 8, no. 2, pp. 127–150, 1979, doi: https://doi.org/10.1016/0034-4257(79)90013-0.
[13] “Landsat 8 (L8) Data Users Handbook,” 2019.
[14] S. K. McFEETERS, “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features,” Int. J. Remote Sens., vol. 17, no. 7, pp. 1425–1432, May 1996, doi: 10.1080/01431169608948714.
[15] Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens., vol. 24, no. 3, pp. 583–594, Jan. 2003, doi: 10.1080/01431160304987.
[16] A. Ngie, K. Abutaleb, F. Ahmed, A. Darwish, and M. Ahmed, “Assessment of urban heat island using satellite remotely sensed imagery: A review,” S. Afr. Geogr. J., vol. 96, pp. 198–214, Jul. 2014, doi: 10.1080/03736245.2014.924864.
[17] J. Tsou, J. Zhuang, Y. Li, and Y. Zhang, “Urban Heat Island Assessment Using the Landsat 8 Data: A Case Study in Shenzhen and Hong Kong,” Urban Science, vol. 1, no. 1, p. 10, Mar. 2017, doi: 10.3390/urbansci1010010.
[18] M. Mukaka, “Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research,” Malawi Med. J., vol. 24, pp. 69–71, Sep. 2012.
[19] E. Whidayanti, M. Syauqi Labib, N. Rizki Novani, S. Nuzla Hazani, and M. Akyas, “Analysis of Land Cover Change in Relation to the Urban Heat Island Phenomenon using Remote Sensing and GIS Technology in South Jakarta, Indonesia,” Journal of Geographical Sciences and Education, vol. 03, no. 3, 2025, doi: 10.69606/geography.v3i3.291.
[20] B.-C. Gao, “Naval Research Laborator. 4555 Overlook Ae,” SW, 1996.
[21] S. Safdar, I. Younes, A. Ahmad, and S. Sastry, “A comprehensive review of spatial distribution modeling of plant species in mountainous environments: Implications for biodiversity conservation and climate change assessment,” Jan. 01, 2025, Elsevier B.V. doi: 10.1016/j.kjs.2024.100337.
[22] Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens., vol. 24, pp. 583–594, 2003, doi: 10.1080/01431160210144570.
[23] A. K. Putra, A. Sukmono, and B. Sasmito, “Analisis Hubungan Perubahan Tutupan Lahan terhadap Suhu Permukaan Terkait Fenomena Urban Heat Island Menggunakan Citra Landsat (Studi Kasus: Kota Surakarta),” 2018.
[24] A. Pratiwi and L. Jaelani, “Analisis Perubahan Distribusi Urban Heat Island (UHI) di Kota Surabaya Menggunakan Citra Satelit Landsat Multitemporal,” Jurnal Teknik ITS, vol. 9, Jan. 2021, doi: 10.12962/j23373539.v9i2.53982.
[25] R. Agusman, A. Maulana, R. Hutagaol, C. Vieri, and R. Toha, “Fenomena Urban Heat Island di Kota Palembang Berdasarkan Land Surface Temperature (LST) dan Normalized Difference Vegetation Index (NDVI),” Jurnal Pembangunan Wilayah dan Kota, vol. 21, Jun. 2025, doi: 10.14710/pwk.v21i2.61269.
[26] S. Aldiansyah and F. Wardani, “Analisis Spasio-Temporal Fenomena Urban Heat Island dan Hubungannya terhadap Aspek Fisik di Kota Makassar (1993–2021),” 2023.
[27] C. Wu et al., “Understanding the relationship between urban blue infrastructure and land surface temperature,” Science of The Total Environment, vol. 694, p. 133742, 2019, doi: https://doi.org/10.1016/j.scitotenv.2019.133742.
[28] A. Delarizka and B. Sasmito, “Analisis Fenomena Pulau Bahang (Urban Heat Island) di Kota Semarang Berdasarkan Hubungan antara Perubahan Tutupan Lahan dengan Suhu Permukaan Menggunakan Citra Multi Temporal Landsat,” 2016. Accessed: Jun. 28, 2026. [Online]. Available: https://ejournal3.undip.ac.id/index.php/geodesi/article/view/13935
[29] M. Ramadhan, R. Rahman, and E. Rasyidi, “Analisis Pengaruh Perubahan Penggunaan Lahan Terhadap Perubahan Suhu Permukaan pada Kawasan Sub Urban Kota Makassar: (Studi Kasus : Kecamatan Biringkanaya dan Kecamatan Tamalanea),” Journal of Urban Planning Studies, vol. 3, pp. 236–245, Jul. 2023, doi: 10.35965/jups.v3i3.401.
[30] C. D. Putra, A. Ramadhani, and E. Fatimah, “Increasing Urban Heat Island area in Jakarta and it’s relation to land use changes,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics, Apr. 2021. doi: 10.1088/1755-1315/737/1/012002.
[31] T. C. Chakraborty, C. Sarangi, and X. Lee, “Reduction in human activity can enhance the urban heat island: Insights from the COVID-19 lockdown,” Environmental Research Letters, vol. 16, no. 5, May 2021, doi: 10.1088/1748-9326/abef8e.
[32] K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. M. Mathis, and S. P. Brumby, “Global land use / land cover with Sentinel 2 and deep learning,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 4704–4707, 2021, [Online]. Available: https://api.semanticscholar.org/CorpusID:238751652
[33] D. Danniswari, T. Honjo, and K. Furuya, “Land Cover Change Impacts on Land Surface Temperature in Jakarta and Its Satellite Cities,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Jun. 2020. doi: 10.1088/1755-1315/501/1/012031.