Pemanfaatan Teknologi Penginderaan Jauh untuk Analisis Urban Heat Island di Kecamatan Sirimau Ambon
DOI:
https://doi.org/10.29303/goescienceed.v7i2.1887Keywords:
LST, NDVI, Remote Sensing, Sirimau District, UHI.Abstract
Urban Heat Island (UHI) is a phenomenon of increasing temperature in urban areas influenced by reduced vegetation and increasing surface temperature. This study aims to analyze vegetation distribution, land surface temperature, and UHI intensity in Sirimau District using Landsat imagery from 2019 and 2022. The analysis was conducted using the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Urban Heat Island (UHI) parameters. The results showed that NDVI values in 2019 ranged from −0.0127 to 0.8531, while in 2022 they ranged from −0.017 to 0.8309. Low NDVI values were generally found in the urban center, whereas high NDVI values were identified in areas with dense vegetation. LST values in 2019 ranged from 18.524 °C to 28.546 °C, while in 2022 they ranged from 15.359 °C to 25.382 °C. Higher surface temperatures were concentrated in urban and settlement areas, while lower temperatures were found in vegetated areas. UHI values in 2019 ranged from −1.946 °C to 2.555 °C, while in 2022 they ranged from −2.489 °C to 2.942 °C. The study indicates an inverse relationship between NDVI, LST, and UHI, where areas with low vegetation density tend to have higher surface temperatures and stronger UHI intensity compared to vegetated areas. These findings highlight the important role of vegetation in reducing surface temperature and mitigating UHI effects. The results of this study are expected to support sustainable urban planning, the development of green open spaces, and climate adaptation strategies in Sirimau District.
References
Akoğlu, M., & Acet, Ş. B. (2023). Investigation of the Effect of Urban Parks on the Urban Heat Island with Remote Sensing and GIS. Polish Journal of Environmental Studies, 32(6), 5457–5468. https://doi.org/10.15244/pjoes/162249
Amrulloh, I., & Fikriyah, V. N. (2024). Time series analysis of landsat data for urban heat island monitoring in Semarang City. IOP Conference Series: Earth and Environmental Science, 1314(1). https://doi.org/10.1088/1755-1315/1314/1/012086
Chaudhary, B., Pradhan Salike, I., & Naryan Poudyal, K. (2021). Urban Heat Island: A case study of Kathmandu Valley. 8914, 1–12.
Das, A., Saha, P., Dasgupta, R., Inacio, M., Das, M., & Pereira, P. (2024). How Do the Dynamics of Urbanization Affect the Thermal Environment? A Case from an Urban Agglomeration in Lower Gangetic Plain (India). Sustainability (Switzerland), 16(3). https://doi.org/10.3390/su16031147
Hardianto, A., Dewi, P. U., Feriansyah, T., Sari, N. F. S., & Rifiana, N. S. (2021). Pemanfaatan Citra Landsat 8 Dalam Mengidentifikasi Nilai Indeks Kerapatan Vegetasi (NDVI) Tahun 2013 dan 2019 (Area Studi: Kota Bandar Lampung). Jurnal Geosains Dan Remote Sensing, 2(1), 8–15. https://doi.org/10.23960/jgrs.2021.v2i1.38
Kasniza Jumari, N. A. S., Ahmed, A. N., Huang, Y. F., Ng, J. L., Koo, C. H., Chong, K. L., Sherif, M., & Elshafie, A. (2023). Analysis of urban heat islands with landsat satellite images and GIS in Kuala Lumpur Metropolitan City. Heliyon, 9(8), e18424. https://doi.org/10.1016/j.heliyon.2023.e18424
Kurniadin, N., Yani, M., Nurgiantoro, N., Annafiyah, A., Prasetya, F. V. A. S., Insanu, R. K., Wumu, R., & Suryalfihra, S. I. (2022). Deteksi Perubahan Suhu Permukaan Tanah dan Hubungannya dengan Pengaruh Albedo dan NDVI Menggunakan Data Satelit Landsat-8 Multitemporal di Kota Palu Tahun 2013 - 2020. Geoid, 18(1), 82. https://doi.org/10.12962/j24423998.v18i1.13157
Lasaiba, M. A. (2024). Evaluasi Daya Dukung Lahan Permukiman di Kecamatan Sirimau Kota Ambon Menggunakan Teknik Penginderaan Jauh dan GIS. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 9(1), 1. https://doi.org/10.30998/string.v9i1.18409
Limehuwey, R., & Jaelani, L. M. (2016). Development of Algorithm Model for Estimating Cholophyll-a Concentration Using In-Situ Data and Atmospherically Corrected Landsat-8 Image by 6SV, Case Study: Gili Iyang’s Waters. Internasional Seminar of Basic Science, May, 1–7. https://doi.org/10.13140/RG.2.2.19983.59048
Limehuwey, R., Kololu, M., Kotarumalos, S. H., & Multi, W. (2025). Penerapan Teknologi Penginderaan Jauh Untuk Kajian Indeks Kerapatan Vegetasi ( NDVI ) Tahun 2020 Di Kecamatan Sirimau Application of Remote Sensing Technology for Vegetation Density Index ( NDVI ) Study in 2020 in Sirimau District. Jurnal Geosains, 3(1), 1–8.
Limehuwey, R., Riswan, M., Sukri, A., Patty, P. J., Multi, W., & Hafsah, S. (2025). Analisis Hubungan Suhu Permukaan Tanah , Tutupan Lahan Dan Indeks Kerapatan Vegetasi Menggunakan Data Citra Landsat 8 Di Kota Ambon bagi biosfer , kriosfer , dan studi perubahan iklim ( Taloor et al ., 2021 ; Ullah et al ., 2023 ). Mengingat OLI . Kerapat. 4(1).
Pratiwi, A. Y., & Jaelani, L. M. (2021). Analisis Perubahan Distribusi Urban Heat Island (UHI) di Kota Surabaya Menggunakan Citra Satelit Landsat Multitemporal. Jurnal Teknik ITS, 9(2). https://doi.org/10.12962/j23373539.v9i2.53982
S, V., V, K., J, R., S, S. B., & M, S. (2024). Assessment of urban heat island using remote sensing and geospatial application: A case study in Sao Paulo city, Brazil, South America. Journal of South American Earth Sciences, 134(October 2023), 104763. https://doi.org/10.1016/j.jsames.2023.104763
Sendi Akhmad Al Mukmin, Arwan Putra Wijaya, A. S. (2019). Analysis of the Effect of Land Cover Changes on the Distribution of Surface Temperature and Its Relationship with the Urban Heat Island Phenomenon. Jurnal Geodesi Undip, 8(1), 278–287.
Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., Plaza, A., & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316–327. https://doi.org/10.1109/TGRS.2007.904834
Somantri, L., & Himayah, S. (2024). Urban Heat Island Study Based on Remote Sensing and Geographic Information System: Correlation between Land Cover and Surface Temperature. E3S Web of Conferences, 600, 1–10. https://doi.org/10.1051/e3sconf/202460006001
USGS. (2013). Using the USGS Landsat 8 Product. U.S. Department of Interior. http://landsat.usgs.gov/Landsat8_Using_Product.p
Zhao, L., Fan, X., & Hong, T. (2025). Urban Heat Island Effect: Remote Sensing Monitoring and Assessment—Methods, Applications, and Future Directions. Atmosphere, 16(7), 1–55. https://doi.org/10.3390/atmos16070791
Zulfajri, Danoedoro, P., & Heru Murti, S. (2019). Klasifikasi Tutupan Lahan Data Landsat-8 OLI Menggunakan Metode Random Forest. Jurnal Penginderaan Jauh Indonesia, 03(01), 1–7. http://jurnal.mapin.or.id/index.php/jpji/issue/archive
