Spatiotemporal Analysis of Land Surface Temperature in Tainan City by using Landsat 5 & Landsat 8
Abstract
Taiwan is a subtropic-tropic island with densely populated in the coastal plains surrounding its mountains. In recent years, due to global warming and the urban heat island effect, the surface temperature has continued to rise, and the seasonal temperature changes are also very different. Increased surface temperatures, particularly in cities, are a major environmental issue that intensifies urban heat islands (UHIs). Decadal time-series analysis has historically relied on meteorological data. Due to the limited availability of remote sensing technology, decadal analysis of land surface temperature has been a serious concern. However, according to advanced technologies in remote sensing methods and sophisticated GIS software, Land Surface Temperature (LST) now can be estimated using thermal bands. The objective of this study is to monitor the spatiotemporal changes of the land surface temperature using Landsat 5 and Landsat 8. Tainan city, which is a highly developed city in southern Taiwan, is selected as the research area. The changes in the land surface temperature are assessed between the years 2007 and 2021. It simply requires applying a set of equations through a raster image calculator using ArcGIS. The LST of any Landsat satellite image can be retrieved by following steps: 1) Top of Atmospheric Spectral Radiance; 2) Conversion of Radiance to At-Sensor Temperature; 3) Calculating NDVI; 4) Calculating the Proportion of Vegetation; 5) Determination of ground emissivity, and 6) Calculating Land Surface Temperature. Near Infra-red are used to obtain Normalized Different Vegetation Index (NDVI). The results show that the average surface temperature of Tainan City increased slightly by 1.1 0C. The most significant increase in temperature was in the northern region of Tainan City which was the agricultural area that was in the post-harvest period.
Keywords: Land Surface Temperature (LST), Landsat 5, Landsat 8, Tainan City, GIS
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Abdulmana, S., Lim, A., Wongsai, S., & Wongsai, N. (2021). Land surface temperature and vegetation cover changes and their relationships in Taiwan from 2000 to 2020. Remote Sensing Applications: Society and Environment, 24, 100636. https://doi.org/10.1016/j.rsase.2021.100636
Abir, F. A., Ahmmed, S., Sarker, S. H., & Fahim, A. U. (2021). Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh. Heliyon, 7(9), e08012. https://doi.org/10.1016/j.heliyon.2021.e08012
Al-Jbouri, S. Q., & Al-Timimi, Y. K. (2021). Assessment of Relationship Between Land Surface Temperature and Normalized Different Vegetation Index Using Landsat Images in Some Regions of Diyala Governorate. The Iraqi Journal of Agricultural Science, 52(4), 793-801.
Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, 2016. https://doi.org/10.1155/2016/1480307
Azmi, R., Koumetio, C. S. T., Diop, E. B., & Chenal, J. (2021). Exploring the relationship between urban form and land surface temperature (LST) in a semi-arid region case study of Ben Guerir city - Morocco. Environmental Challenges, 5, 100229. https://doi.org/10.1016/j.envc.2021.100229
Chen, C. C., Wang, Y. R., Wang, Y. C., Lin, S. L., Chen, C. T., Lu, M. M., & Guo, Y. L. L. (2021). Projection of future temperature extremes, related mortality, and adaptation due to climate and population changes in Taiwan. Science of the Total Environment, 760, 143373. https://doi.org/10.1016/j.scitotenv.2020.143373
Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032. https://doi.org/10.1016/j.srs.2021.100032
Esha, E. J., & Rahman, M. T. U. (2021). Simulation of future land surface temperature under the scenario of climate change using remote sensing & GIS techniques of northwestern Rajshahi district, Bangladesh. Environmental Challenges, 5, 100365. https://doi.org/10.1016/j.envc.2021.100365
Giannini, M. B., Belfiore, O. R., Parente, C., & Santamaria, R. (2015). Land Surface Temperature from Landsat 5 TM images: Comparison of different methods using airborne thermal data. Journal of Engineering Science and Technology Review, 8(3), 83–90. https://doi.org/10.25103/jestr.083.12
Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667-678. https://doi.org/10.1080/22797254.2018.1474494
Hsu, H. H., & Chen, C. T. (2002). Observed and projected climate change in Taiwan. Meteorology and Atmospheric Physics, 79(1–2), 87–104. https://doi.org/10.1007/s703-002-8230-x
Kaviani, A., Sohrabi, T., & Araste, P. D. (2013). Estimation of land surface temperature using NDVI in MODIS and Landsat ETM+ imageries. Journal of Agricultural Meteorology, 1(1), 14-25.
Lees, K. J., Quaife, T., Artz, R. R. E., Khomik, M., & Clark, J. M. (2018). Potential for using remote sensing to estimate carbon fluxes across northern peatlands – A review. Science of the Total Environment, 615, 857–874. https://doi.org/10.1016/j.scitotenv.2017.09.103
Neinavaz, E., Skidmore, A. K., & Darvishzadeh, R. (2020). Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method. International Journal of Applied Earth Observation and Geoinformation, 85(October 2019), 101984. https://doi.org/10.1016/j.jag.2019.101984
Nse, O. U., Okolie, C. J., & Nse, V. O. (2020). Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria. Scientific African, 10, e00599. https://doi.org/10.1016/j.sciaf.2020.e00599
Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719–3746. https://doi.org/10.1080/01431160010006971
Richard, J. U., & Abah, I. A. (2019). Derivation of Land Surface Temperature (LST) From Landsat 7 & 8 Imageries and Its Relationship With Two Vegetation Indices (NDVI and GNDVI). International Journal of Research-Granthaalayah, 7(2), 108-120. https://doi.org/10.29121/granthaalayah.v7.i2.2019.1013
Saradjian, M. R., &Jouybari-Moghaddam, Y. (2019). Land Surface Emissivity and temperature retrieval from Landsat-8 satellite data using Support Vector Regression and weighted least squares approach. Remote Sensing Letters, 10(5), 439-448. https://doi.org/10.1080/2150704X.2019.1569273
Schuch, F., Marpu, P., Masri, D., & Afshari, A. (2017). Estimation of Urban Air Temperature from a Rural Station Using Remotely Sensed Thermal Infrared Data. Energy Procedia, 143, 519–525. https://doi.org/10.1016/j.egypro.2017.12.720
Shiu, C. J., Liu, S. C., & Chen, J. P. (2009). Diurnally asymmetric trends of temperature, humidity, and precipitation in Taiwan. Journal of Climate, 22(21), 5635–5649. https://doi.org/10.1175/2009JCLI2514.1
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Taripanah, F., & Ranjbar, A. (2021). Quantitative analysis of spatial distribution of land surface temperature (LST) in relation Ecohydrological, terrain and socio-economic factors based on Landsat data in mountainous area. Advances in Space Research, 68(9), 3622-3640. https://doi.org/10.1016/j.asr.2021.07.008
Tsai, H. P., Lin, Y. H., & Yang, M. D. (2016). Exploring long term spatial vegetation trends in Taiwan from AVHRR NDVI3g dataset using RDA and HCA analyses. Remote Sensing, 8(4). https://doi.org/10.3390/rs8040290
DOI: https://doi.org/10.24114/jg.v15i1.37183
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