Open Access
E3S Web Conf.
Volume 283, 2021
2021 3rd International Conference on Civil, Architecture and Urban Engineering (ICCAUE 2021)
Article Number 01038
Number of page(s) 7
Section Research on Civil Engineering and Geotechnical Hydrological Structure
Published online 07 July 2021
  1. Ranagalage, M.; Estoque, R.C.; Handayani, H.H.; Zhang, X.; Morimoto, T.; Tadono, T.; Murayama, Y. Relation between Urban Volume and Land Surface Temperature: A Comparative Study of Planned and Traditional Cities in Japan. Sustainability 2018, 10, 2366. [Google Scholar]
  2. Fu, P.; Weng, Q. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment 2016, 175, 205–214. [Google Scholar]
  3. Kafy, A.A.; Rahman, M.S.; Faisal, A.-A.; Hasan, M.M.; Islam, M. Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment 2020, 18, 100–314. [Google Scholar]
  4. Zhu, Z.; Woodcock, C.E. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment 2014, 144, 152–171. [Google Scholar]
  5. Fu, P.; Weng, Q. Responses of urban heat island in Atlanta to different land-use scenarios. Theoretical and Applied Climatology 2018, 133, 123–135, DOI: 10.1007/s00704-017-2160-3. [Google Scholar]
  6. Estoque, R.C.; Murayama, Y.; Myint, S.W. Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Science of The Total Environment 2017, 577, 349–359. [Google Scholar]
  7. Chen, X.-L.; Zhao, H.-M.; Li, P.-X.; Yin, Z.-Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment 2006, 104, 133–146. [Google Scholar]
  8. Chi, Y.; Sun, J.; Sun, Y.; Liu, S.; Fu, Z. Multitemporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China. Global Ecology and Conservation 2020, 23, e01092. [Google Scholar]
  9. Zhang, Y.; Odeh, I.O.A.; Han, C. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation 2009, 11, 256–264. [Google Scholar]
  10. Yuvaraj, R.M. Extents of Predictors for Land Surface Temperature Using Multiple Regression Model. The Scientific World Journal 2020, 2020, 3958589. [Google Scholar]
  11. Guha, S.; Govil, H.; Dey, A.; Gill, N. 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 2018, 51, 667–678. [Google Scholar]
  12. Mirchooli, F.; Sadeghi, S.H.; Khaledi Darvishan, A. Analyzing spatial variations of relationships between Land Surface Temperature and some remotely sensed indices in different land uses. Remote Sensing Applications: Society and Environment 2020, 19, 100–359. [Google Scholar]
  13. Taloor, A.K.; Drinder Singh, M.; Chandra Kothyari, G. Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data. Applied Computing and Geosciences 2021, 9, 100051. [Google Scholar]
  14. Shang, M.; Wang, S.-X.; Zhou, Y.; Du, C. Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery. Journal of the Indian Society of Remote Sensing 2018, 46, 1333–1340. [Google Scholar]
  15. Li, C.; Wang, J.; Wang, L.; Hu, L.; Gong, P. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sensing 2014, 6, 964–983. [Google Scholar]
  16. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data - Principles and Practices Second edition; CRC Press, Taylor & Francis Group: Boca Raton, NW, USA, 2009; p. 210. [Google Scholar]
  17. Tortora, R.D. A Note on Sample Size Estimation for Multinomial Populations. American Statistician 1978, 32, 100–102. [Google Scholar]
  18. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Soria, G.; Romaguera, M.; Moreno, L.G.A.-J.; Plaza, A.; Martinez, P. Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors IEEE Transactions on Geoscience and Remote Sensing 2008, 46, 316–327. [Google Scholar]
  19. Jiménez-Muñoz, J.C.; Sobrino, J.A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research 2003, 108, ACL 2-1. [PubMed] [Google Scholar]
  20. Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment 2004, 90, 434–440. [CrossRef] [Google Scholar]
  21. Jiménez-Muñoz, J.C.; Cristobal, J.; Sobrino, J.A.; Soria, G.; Ninyerola, M.; Pons, X. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat ThermalInfrared Data. IEEE Transactions on Geoscience and Remote Sensing 2009, 47, 339–349. [Google Scholar]
  22. Jiménez-Muñoz, J.C.; Sobrino, J.A.; Skokovic, D.; Mattar, C.; Cristóbal, J. Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters 2014, 11, 1840–1843. [Google Scholar]
  23. Story, M.; Congalton, R.G. Accuracy Assessment: A User’s Perspective. Photogrammetric Engineering and Remote Sensing 1986, 52, 397–399. [Google Scholar]
  24. Rousta, I.; Sarif, M.O.; Gupta, R.D.; Olafsson, H.; Ranagalage, M.; Murayama, Y.; Zhang, H.; Mushore, T.D. Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of MetropolitanCity Tehran (1988-2018). Sustainability 2018, 10, 4433. [Google Scholar]
  25. Ahmed, B.; Kamruzzaman, M.; Zhu, X.; Rahman, M.S.; Choi, K. Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh. Remote Sensing 2013, 5, 5969–5998. [Google Scholar]

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