Spatial pattern of changes in land surface temperature of seram island based on google earth engine cloud computing
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Published:
Jun 30, 2023
Keywords:
Google Earth Engine, Land Surface Temperature, MODIS, Seram Island
Section
Computer Science
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Article View : 336 Times
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Abstract
Monitoring land surface temperature in Seram Island using cloud computing-based geospatial technology, Google Earth Engine, could help to understand global climate and weather changes, and provide important information for scientists, governments, and non-governmental organizations to make decisions related to climate change mitigation and natural disaster management. This study aims to analyze the spatial pattern of land surface temperature change on Seram Island 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 satellite image data, which are accessed and analyzed in Google Earth Engine and Arc GIS. The results of this study show that the value of land surface temperature on Seram Island in 2017 is 14.7089ᵒC at the lowest value and 30.1012ᵒ C at the highest value and it increased in 2022 where the lowest temperature value is 14.0452ᵒC and the highest temperature is 32.639ᵒC. Built-up land and open land areas on Seram Island have very high surface temperature values compared to forests and plantations which have low land surface temperatures. Analysis of land surface temperature in Seram Island Regency could provide important information for the local government to make policies and plans for sustainable regional development
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How to Cite
Rakuasa, H., Sihasale , D. A., & Latue, P. C. (2023). Spatial pattern of changes in land surface temperature of seram island based on google earth engine cloud computing. International Journal of Basic and Applied Science, 12(1), 1–9. https://doi.org/10.35335/ijobas.v12i1.172
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C. B. Caballero, A. Ruhoff, and T. Biggs, “Land use and land cover changes and their impacts on surface-atmosphere interactions in Brazil: A systematic review,” Sci. Total Environ., vol. 808, p. 152134, 2022, doi: https://doi.org/10.1016/j.scitotenv.2021.152134.
H. Rakuasa, “ANALISIS SPASIAL TEMPORAL SUHU PERMUKAAN DARATAN/ LAND SURFACE TEMPERATURE (LST) KOTA AMBON BERBASIS CLOUD COMPUTING: GOOGLE EARTH ENGINE,” J. Ilm. Inform. Komput., vol. 27, no. 3, pp. 194–205, Dec. 2022, doi: 10.35760/ik.2022.v27i3.7101.
M. K. ROSYIDY et al., “LANDSLIDE SURFACE DEFORMATION ANALYSIS USING SBAS-INSAR IN THE SOUTHERN PART OF THE SUKABUMI AREA, INDONESIA,” Geogr. Tech., no. Special Issue, pp. 138–152, Sep. 2021, doi: 10.21163/GT_2021.163.11.
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S. L. Ermida, P. Soares, V. Mantas, F.-M. Göttsche, and I. F. Trigo, “Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series,” Remote Sens., vol. 12, no. 9, p. 1471, May 2020, doi: 10.3390/rs12091471.
L. K. Onisimo Muntaga, “Google Earth Engine Applications,” remotesensing, pp. 11–14, 2019, doi: 10.3390/rs11050591.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sens. Environ., vol. 202, pp. 18–27, 2017, doi: 10.1016/j.rse.2017.06.031.
L. Li et al., “Modeling the impacts of land use/land cover change on meteorology and air quality during 2000–2018 in the Yangtze River Delta region, China,” Sci. Total Environ., vol. 829, p. 154669, Jul. 2022, doi: 10.1016/j.scitotenv.2022.154669.
S. Kanga et al., “Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India,” Remote Sens., vol. 14, no. 17, 2022, doi: 10.3390/rs14174241.
E. Çolak and F. Sunar, “Investigating the usefulness of satellite-retrieved land surface temperature (LST) in pre- and post-fire spatial analysis,” Earth Sci. Informatics, vol. 16, no. 1, pp. 945–963, Mar. 2023, doi: 10.1007/s12145-022-00883-8.
D. Dutta, A. Rahman, S. K. Paul, and A. Kundu, “Changing pattern of urban landscape and its effect on land surface temperature in and around Delhi,” Environ. Monit. Assess., vol. 191, no. 9, p. 551, 2019, doi: 10.1007/s10661-019-7645-3.
W. Ullah et al., “Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region,” Heliyon, vol. 9, no. 2, p. e13322, Feb. 2023, doi: 10.1016/j.heliyon.2023.e13322.
M. Zhang et al., “Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China,” Urban Clim., vol. 47, p. 101347, Jan. 2023, doi: 10.1016/j.uclim.2022.101347.
M. Shawky et al., “Remote sensing-derived land surface temperature trends over South Asia,” Ecol. Inform., vol. 74, p. 101969, May 2023, doi: 10.1016/j.ecoinf.2022.101969.
Z. Li et al., “Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications,” Rev. Geophys., vol. 61, no. 1, Mar. 2023, doi: 10.1029/2022RG000777.
A. Latue, P. C., Rakuasa, H., Somae, G., & Muin, “Analisis Perubahan Suhu Permukaan Daratan di Kabupaten Seram Bagian Barat Menggunakan Platform Berbasis Cloud Google Earth Engine,” Sudo J. Tek. Inform., vol. 2, no. 2, pp. 45–51., 2023, doi: https://doi.org/10.56211/sudo.v2i2.261.
A. Sasky, P., Sobirin, S., & Wibowo, “Pengaruh Perubahan Penggunaan Tanah Terhadap Suhu Permukaan Daratan Metropolitan Bandung Raya Tahun 2000–2016.,” in Prosiding Industrial Research Workshop and National Seminar, 2017, pp. 354–361. doi: https://doi.org/10.35313/irwns.v8i3.767.
M. Mansourmoghaddam, I. Rousta, M. Zamani, and H. Olafsson, “Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland),” Urban Ecosyst., vol. 26, no. 2, pp. 337–359, Apr. 2023, doi: 10.1007/s11252-023-01337-9.
Y. Chen, J. Yang, W. Yu, J. Ren, X. Xiao, and J. C. Xia, “Relationship between urban spatial form and seasonal land surface temperature under different grid scales,” Sustain. Cities Soc., vol. 89, p. 104374, Feb. 2023, doi: 10.1016/j.scs.2022.104374.
Diksha, M. Kumari, and R. Kumari, “Spatiotemporal Characterization of Land Surface Temperature in Relation Landuse/Cover: A Spatial Autocorrelation Approach,” J. Landsc. Ecol., Mar. 2023, doi: 10.2478/jlecol-2023-0001.
F. Zhao et al., “Detection of geothermal potential based on land surface temperature derived from remotely sensed and in-situ data,” Geo-spatial Inf. Sci., pp. 1–17, Mar. 2023, doi: 10.1080/10095020.2023.2178335.
J. Maulana and F. Bioresita, “Monitoring of Land Surface Temperature in Surabaya, Indonesia from 2013-2021 Using Landsat-8 Imagery and Google Earth Engine,” IOP Conf. Ser. Earth Environ. Sci., vol. 1127, no. 1, p. 012027, Jan. 2023, doi: 10.1088/1755-1315/1127/1/012027.
J. Siqi, W. Yuhong, C. Ling, and B. Xiaowen, “A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models,” Urban Clim., vol. 47, p. 101390, Jan. 2023, doi: 10.1016/j.uclim.2022.101390.
S. Han et al., “Seasonal effects of urban morphology on land surface temperature in a three-dimensional perspective: A case study in Hangzhou, China,” Build. Environ., vol. 228, p. 109913, Jan. 2023, doi: 10.1016/j.buildenv.2022.109913.
A. Tariq, F. Mumtaz, M. Majeed, and X. Zeng, “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,” Environ. Monit. Assess., vol. 195, no. 1, p. 114, Jan. 2023, doi: 10.1007/s10661-022-10738-w.
R. Ghanbari, M. Heidarimozaffar, A. Soltani, and H. Arefi, “Land surface temperature analysis in densely populated zones from the perspective of spectral indices and urban morphology,” Int. J. Environ. Sci. Technol., vol. 20, no. 3, pp. 2883–2902, Mar. 2023, doi: 10.1007/s13762-022-04725-4.
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