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Monitoring of Land Surface Temperature from Landsat Imagery: A Case Study of Al-Anbar Governorate in Iraq

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.
Rocznik
Strony
61--81
Opis fizyczny
Bibliogr. 27 poz., tab., rys., wykr.
Twórcy
autor
  • Cairo University, Faculty of Engineering, Public Works Department, Giza, Egypt
autor
  • Cairo University, Faculty of Engineering, Public Works Department, Giza, Egypt
  • University of Anbar, College of Engineering, Department of Civil Engineering, Ramadi, Iraq
Bibliografia
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  • Jiménez-Muñoz J.C., Sobrino J.A., Skoković D., Mattar C., Cristóbal J.: Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, vol. 11(10), 2014, pp. 1840–1843. https://doi.org/10.1109/LGRS.2014.2312032.
  • Sekertekin A., Bonafoni S.: Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, vol. 12(2), 2020, 294. https://doi.org/10.3390/rs12020294.
  • Wang L., Lu Y., Yao Y.: Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors, vol. 19(22), 2019, 5049. https://doi.org/10.3390/s19225049.
  • Qin Z., Karnieli A., Berliner P.: 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, vol. 22(18), 2001, pp. 3719–3746. https://doi.org/10.1080/01431160010006971.
  • Wang F., Qin Z., Song C., Tu L., Karnieli A. Zhao S.: An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing, vol. 7(4), 2015, pp. 4268–4289. https://doi.org/10.3390/rs70404268.
  • 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: Atmospheres, vol. 108(D22), 2003, 4688. https://doi.org/10.1029/2003JD003480.
  • Jiménez-Muñoz J.C., Sobrino J.A.: A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters, vol. 7(1), 2009, pp. 176–179. https://doi.org/10.1109/LGRS.2009.2029534.
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  • Morsy S., Hadi M.: Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, vol. 7(3), 2022, pp. 272–282. https://doi.org/10.26833/ijeg.978961.
  • Wang M., Zhang Z., Hu T., Liu X.: A practical single-channel algorithm for land surface temperature retrieval: application to Landsat series data. Journal of Geophysical Research: Atmospheres, vol. 124(1), 2019, pp. 299–316. https://doi.org/10.1029/2018JD029330.
  • Cristóbal J., Jiménez-Muñoz J.C., Prakash A., Mattar C., Skoković D., Sobrino J.A.: An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, vol. 10(3), 2018, 431. https://doi.org/10.3390/rs10030431.
  • Pal S., Ziaul S.K.: Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, vol. 20(1), 2017, pp. 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003.
  • Tran D.X., Pla F., Latorre-Carmona P., Myint S.W., Caetano M., Kieu H.V.: Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 124, 2017, pp. 119–132. https://doi.org/10.1016/j.isprsjprs.2017.01.001.
  • Hidalgo-García D., Arco-Díaz J.: Modeling the Surface Urban Heat Island (SUHI) to study of its relationship with variations in the thermal field and with the indices of land use in the metropolitan area of Granada (Spain). Sustainable Cities and Society, vol. 87, 2022, 104166. https://doi.org/10.1016/j.scs.2022.104166.
  • Ahmed S.: Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. The Egyptian Journal of Remote Sensing and Space Science, vol. 21(1), 2018, pp. 15–25. https://doi.org/10.1016/j.ejrs.2017.08.001.
  • Al-Ruzouq R., Shanableh A., Khalil M.A., Zeiada W., Hamad K., Abu Dabous S., Gibril M.B.A. et al.: Spatial and temporal inversion of land surface temperature along coastal cities in Arid Regions. Remote Sensing, vol. 14(8), 2022, 1893. https://doi.org/10.3390/rs14081893.
  • Majumder A., Setia R., Kingra P.K., Sembhi H., Singh S.P., Pateriya B.: Estimation of land surface temperature using different retrieval methods for studying the spatiotemporal variations of surface urban heat and cold islands in Indian Punjab. Environment, Development and Sustainability, vol. 23(11), 2021, pp. 15921–15942. https://doi.org/10.1007/s10668-021-01321-3.
  • Amindin A., Pouyan S., Pourghasemi H.R., Yousefi S., Tiefenbacher J.P.: Spatial and temporal analysis of urban heat island using Landsat satellite images. Environmental Science and Pollution Research, vol. 28(30), 2021, pp. 41439–41450. https://doi.org/10.1007/s11356-021-13693-0.
  • Sobrino J.A., Jiménez-Muñoz J.C., Paolini L.: Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, vol. 90(4), 2004, pp. 434–440. https://doi.org/10.1016/j.rse.2004.02.003.
  • Avdan U., Jovanovska G.: Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, vol. 2016, 2016, 1480307. https://doi.org/10.1155/2016/1480307.
  • Rouse J.W., Haas R.H., Schell J.A., Deering D.W.: Monitoring vegetation systems in the Great Plains with ERTS. [in:] Freden S.C., Mercanti E.P., Becker M.A. (eds.), Third Earth Resources Technology Satellite-1 Symposium. Volume 1: Technical Presentations, section A, NASA Special Publication, vol. 351, National Aeronautics and Space Administration, Washington 1974, pp. 309–317.
  • Poveda G., Salazar L.F.: Annual and interannual (ENSO) variability of spatial scaling properties of a vegetation index (NDVI) in Amazonia. Remote Sensing of Environment, vol. 93(3), 2004, pp. 391–401. https://doi.org/10.1016/j.rse.2004.08.001.
  • Ahmad A., Quegan S.: Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences, vol. 6(129), 2012, pp. 6425–6436.
  • Yu K., Chen Y., Wang D., Chen Z., Gong A., Li J.: Study of the seasonal effect of building shadows on urban land surface temperatures based on remote sensing data. Remote Sensing, vol. 11(5), 2019, 497. https://doi.org/10.3390/rs11050497.
  • Meng Q., Liu W., Zhang L., Allam M., Bi Y., Hu X., Gao J. et al.: Relationships between land surface temperatures and neighboring environment in highly urbanized areas: Seasonal and scale effects analyses of Beijing, China. Remote Sensing, vol. 14(17), 2022, 4340. https://doi.org/10.3390/rs14174340.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-db446e44-9ccc-4c3b-be27-c087d539f45b
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