Analysis of Surface Temperature in Buru District Using Cloud Computing on Google Earth Engine: Analisis Suhu Permukaan Di Kabupaten Buru Menggunakan Cloud Computing Pada Google Earth Engine

Philia, Christi Latue; Heinrich Rakuasa

doi:10.58330/prevenire.v2i3.195

Authors

Philia, Christi Latue Universitas Pattimura
Heinrich Rakuasa Universitas Indonesia

DOI:

https://doi.org/10.58330/prevenire.v2i3.195

Keywords:

Buru, GEE, Land Surface Temperature

Abstract

Monitoring land surface temperature in Buru Regency using Google Earth Engine cloud computing-based geospatial technology can help in understanding global climate and weather change, as well as provide important information for scientists, governments, and non-governmental organizations in making decisions related to climate change mitigation and natural disaster management. This research aims to analyze land surface temperature in Buru Regency using MODIS satellite image data based on the cloud computing google earth engine. This research uses Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Land Surface Temperature and Emissivity 8-Day Global image data analyzed on Google Earth Engine. The results show that the lowest land surface temperature value in Buru Regency is 12, 7438ᵒ C, and the highest value is 31, 9582ᵒ C. The area that has a land surface temperature (LST) in the very high class has an area of 96,604.46 ha or 19.90%, the LST area in the high class is 139,606.47 ha or 28.76%, the LST area in the medium class is 140,853.38 ha or 29.02%, the LST area in the low class is 79,896.56 ha or 16.46% and the LST area in the very low class is 28,458.57 ha or 5.86%. The land surface temperature analysis in Buru Regency can provide important information for the local government in making policies and planning for sustainable regional development.

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References

Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., & Parsian, S. (2020). Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350.

Amani, M., Kakooei, M., Moghimi, A., Ghorbanian, A., Ranjgar, B., Mahdavi, S., Davidson, A., Fisette, T., Rollin, P., & Brisco, B. (2020). Application of google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. Remote Sensing, 12(21), 3561.

Caballero, C. B., Ruhoff, A., & Biggs, T. (2022). Land use and land cover changes and their impacts on surface-atmosphere interactions in Brazil: A systematic review. Science of The Total Environment, 808, 152134. https://doi.org/10.1016/j.scitotenv.2021.152134

Diksha, Kumari, M., & Kumari, R. (2023). Spatiotemporal Characterization of Land Surface Temperature in Relation Landuse/Cover: A Spatial Autocorrelation Approach. Journal of Landscape Ecology. https://doi.org/10.2478/jlecol-2023-0001

Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M., & Trigo, I. F. (2020a). Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sensing, 12(9), 1471. https://doi.org/10.3390/rs12091471

Ermida, S. L., Soares, P., Mantas, V., Göttsche, F.-M., & Trigo, I. F. (2020b). Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sensing, 12(9), 1471. https://doi.org/10.3390/rs12091471

Gadekar, K., Pande, C. B., Rajesh, J., Gorantiwar, S. D., & Atre, A. A. (2023). Estimation of Land Surface Temperature and Urban Heat Island by Using Google Earth Engine and Remote Sensing Data (pp. 367–389). https://doi.org/10.1007/978-3-031-19059-9_14

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://doi.org/10.1016/j.rse.2017.06.031

Hardyanti, L., Sobirin, S., & Wibowo, A. (2017). Variasi Spasial Temporal Suhu Permukaan Daratan di Kota Jakarta tahun 2015 dan 2016. Prosiding Industrial Research Workshop and National Seminar, 704–713. https://doi.org/10.35313/irwns.v8i3.613

How Jin Aik, D., Ismail, M. H., Muharam, F. M., & Alias, M. A. (2021). Evaluating the impacts of land use/land cover changes across topography against land surface temperature in Cameron Highlands. PLOS ONE, 16(5), e0252111. https://doi.org/10.1371/journal.pone.0252111

Hu, Y., Tang, R., Jiang, X., Li, Z.-L., Jiang, Y., Liu, M., Gao, C., & Zhou, X. (2023). A physical method for downscaling land surface temperatures using surface energy balance theory. Remote Sensing of Environment, 286, 113421. https://doi.org/10.1016/j.rse.2022.113421

Kafy, A.- Al, Rahman, Md. S., Faisal, A.-A.-, Hasan, M. M., & Islam, M. (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment, 18, 100314. https://doi.org/10.1016/j.rsase.2020.100314

Kanga, S., Meraj, G., Johnson, B. A., Singh, S. K., PV, M. N., Farooq, M., Kumar, P., Marazi, A., & Sahu, N. (2022). Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India. Remote Sensing, 14(17). https://doi.org/10.3390/rs14174241

Khan, R., Li, H., Basir, M., Chen, Y. L., Sajjad, M. M., Haq, I. U., Ullah, B., Arif, M., & Hassan, W. (2022). Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan. Environmental Monitoring and Assessment, 194(6), 409. https://doi.org/10.1007/s10661-022-10072-1

Laipelt, L., Kayser, R. H. B., Fleischmann, A. S., Ruhoff, A., Bastiaanssen, W., Erickson, T. A., & Melton, F. (2021). Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 81–96.

Maffei, C., Alfieri, S., & Menenti, M. (2018). Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies. Remote Sensing, 10(11), 1777. https://doi.org/10.3390/rs10111777

Maulana, J., & Bioresita, F. (2023). Monitoring of Land Surface Temperature in Surabaya, Indonesia from 2013-2021 Using Landsat-8 Imagery and Google Earth Engine. IOP Conference Series: Earth and Environmental Science, 1127(1), 012027. https://doi.org/10.1088/1755-1315/1127/1/012027

Mo, Y., Xu, Y., Chen, H., & Zhu, S. (2021). A review of reconstructing remotely sensed land surface temperature under cloudy conditions. Remote Sensing, 13(14), 2838.

NASA. (2022). Moderate Resolution Imaging Spectoradiometer (MODIS). NASA. https://modis.gsfc.nasa.gov/

Peng, X., Wu, W., Zheng, Y., Sun, J., Hu, T., & Wang, P. (2020). Correlation analysis of land surface temperature and topographic elements in Hangzhou, China. Scientific Reports, 10(1), 10451.

Rakuasa, H. (2022). Analisis Spasial Temporal Suhu Permukaan Daratan/ Land Surface Temperature (Lst) Kota Ambon Berbasis Cloud Computing: Google Earth Engine. Jurnal Ilmiah Informatika Komputer, 27(3), 194–205. https://doi.org/10.35760/ik.2022.v27i3.7101

Ruzady Adjis. (2022). Hutan di Kabupaten Buru Terbakar, Angin Kencang Percepat Amukan Kobaran Api. TERASMALUKU.COM,-AMBON. https://terasmaluku.com/headline/2022/08/11/hutan-di-kabupaten-buru-terbakar-angin-kencang-percepat-amukan-kobaran-api/

Sasky, P., Sobirin, S., & Wibowo, A. (2017). Pengaruh Perubahan Penggunaan Tanah Terhadap Suhu Permukaan Daratan Metropolitan Bandung Raya Tahun 2000–2016. Prosiding Industrial Research Workshop and National Seminar, 354–361. https://doi.org/10.35313/irwns.v8i3.767

Stoyanova, J. S., Georgiev, C. G., & Neytchev, P. N. (2022). Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate. Remote Sensing, 14(7), 1747. https://doi.org/10.3390/rs14071747

Tariq, A., Mumtaz, F., Majeed, M., & Zeng, X. (2023). Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan. Environmental Monitoring and Assessment, 195(1), 114. https://doi.org/10.1007/s10661-022-10738-w

Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), 3402.

Wachid, N., & Tyas, W. P. (2022). Analisis Transformasi NDVI dan kaitannya dengan LST Menggunakan Platform Berbasis Cloud: Google Earth Engine. Jurnal Planologi, 19(1), 60. https://doi.org/10.30659/jpsa.v19i1.20199

Wang, M., Zhang, Z., Hu, T., Wang, G., He, G., Zhang, Z., Li, H., Wu, Z., & Liu, X. (2020). An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4689–4701. https://doi.org/10.1109/JSTARS.2020.3014586

Wang, R., Cai, M., Ren, C., Bechtel, B., Xu, Y., & Ng, E. (2019). Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone. Urban Climate, 28, 100455. https://doi.org/10.1016/j.uclim.2019.100455

Zhang, M., Kafy, A.- Al, Xiao, P., Han, S., Zou, S., Saha, M., Zhang, C., & Tan, S. (2023). Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China. Urban Climate, 47, 101347. https://doi.org/10.1016/j.uclim.2022.101347

Zhengming Wan. (2020). MOD11A2 v061 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid. USGS Website. https://lpdaac.usgs.gov/products/mod11a2v061/

Zulkarnain, R. C. (2016). Pengaruh Perubahan Tutupan Lahan Terhadap Perubahan Suhu Permukaan di Kota Surabaya. Skripsi Institut Teknologi Sepuluh Nopember, 1–306.