LAND SURFACE TEMPERATURE (LST) ESTIMATION AT KUSHTIA DISTRICT, BANGLADESH
DOI:
https://doi.org/10.33736/jcest.3985.2021Keywords:
Land Surface Temperature, Vegetation, Water body, Estimation, Kushtia districtAbstract
Land Surface Temperature (LST) is a key phenomenon in worldwide climate change. The knowledge of surface temperature is important to a range of issues and themes in earth sciences, central to urban climatology, global environmental change, and human-environment interactions. In this study, LST for Kushtia District, Khulna division, Bangladesh, is derived using Arc-GIS software version from the images of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution, Landsat-7 Enhanced Thematic Mapper plus (ETM+) with opto-mechanical sensor and Spatial Resolution of 30 m (60 m – thermal, 15-m panchromatic) and Landsat-5 Thematic MAPPER (TM) satellites. A total time span of 20 years, starting from 1998 to 2018 is selected. At every 5 years interval starting from 1998, air temperature, LST, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been calculated. Using the equation from Landsat user’s handbook, the digital number of thermal infrared band is converted into spectral radiance. Plank’s Inverse Function is used to obtain the effective at-sensor brightness temperature from the spectral radiance. The surface emissivity based on NDVI classes is used to retrieve the final LST. The study reveals that LST is increasing with the passage of time. Maximum values of LST are found along the North-East and North-West regions of Kushtia district. NDVI is found to have positive correlation with LST. Also, it has been found that NDWI has little influence on LST. The reasons behind the rise and fall of LST in different years are explained from changes in total vegetation coverage and total abundance of water body coverage viewpoint. The spatial distribution figures of air temperature, LST, NDVI and NDWI could be used as a guideline for urban planning, strategies for quality improvement of urban environment and a smart solution to the reduction of LST.
References
Lu, D., & Weng, Q. (2004). Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering & Remote Sensing, 70(9), 1053-1062.
https://doi.org/10.14358/PERS.70.9.1053
Rajeshwari, A., & Mani, N. D. (2014). Estimation of land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology, 3(5), 122-126.
https://doi.org/10.15623/ijret.2014.0305025
Rahman, H., & Dedieu, G. (1994). SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Remote Sensing, 15(1), 123-143.
https://doi.org/10.1080/01431169408954055
Khandelwal, S., Goyal, R., Kaul, N., & Mathew, A. (2018). Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 87-94.
https://doi.org/10.1016/j.ejrs.2017.01.005
Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), 131-140.
Ali, R. R., & Shalaby, A. (2012). Response of topsoil features to the seasonal changes of land surface temperature in the arid environment. International Journal of Soil Science, 7(2), 39-50.
https://doi.org/10.3923/ijss.2012.39.50
Choudhury, D., Das, K., & Das, A. (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Science, 22(2), 203-218.
https://doi.org/10.1016/j.ejrs.2018.05.004
Zullo, F., Fazio, G., Romano, B., Marucci, A., & Fiorini, L. (2019). Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Science of The Total Environment, 650, 1740-1751.
https://doi.org/10.1016/j.scitotenv.2018.09.331
Zhao, W., Duan, S. B., Li, A., & Yin, G. (2019). A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote sensing of environment, 221, 635-649.
https://doi.org/10.1016/j.rse.2018.12.008
He, J., Zhao, W., Li, A., Wen, F., & Yu, D. (2019). The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. International Journal of Remote Sensing, 40(5-6), 1808-1827.
https://doi.org/10.1080/01431161.2018.1466082
Sobrino, J. A. (1989). Desarrollo de un modelo teórico para implementar la medida de la temperatura realizada mediante teledetección. Aplicación a un campo de naranjos. PhD dissertation, University of Valencia, Valencia, Spain.
Pu, R., Gong, P., Michishita, R., & Sasagawa, T. (2006). Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sensing of Environment, 104(2), 211-225.
https://doi.org/10.1016/j.rse.2005.09.022
Diak, G. R., & Whipple, M. S. (1995). Note on estimating surface sensible heat fluxes using surface temperatures measured from a geostationary satellite during FIFE 1989. Journal of Geophysical Research: Atmospheres, 100(D12), 25453-25461.
https://doi.org/10.1029/95JD00729
Crago, R., Sugita, M., & Brutsaert, W. (1995). Satellite‐derived surface temperatures with boundary layer temperatures and geostrophic winds to estimate surface energy fluxes. Journal of Geophysical Research: Atmospheres, 100(D12), 25447-25451.
https://doi.org/10.1029/95JD00724
Weng, Q., & Fu, P. (2014). Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 97, 78-88.
https://doi.org/10.1016/j.isprsjprs.2014.08.009
Hu, L., & Brunsell, N. A. (2013). The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring. Remote Sensing of Environment, 134, 162-174.
https://doi.org/10.1016/j.rse.2013.02.022
Deng, C., & Wu, C. (2013). A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sensing of Environment, 133, 62-70.
https://doi.org/10.1016/j.rse.2013.02.005
Mathew, A., Sreekumar, S., Khandelwal, S., Kaul, N., & Kumar, R. (2016). Prediction of surface temperatures for the assessment of urban heat island effect over Ahmedabad city using linear time series model. Energy and Buildings, 128, 605-616.
https://doi.org/10.1016/j.enbuild.2016.07.004
Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217-233.
https://doi.org/10.1016/j.ejrs.2015.09.005
Pal, S., & Ziaul, S. K. (2017). 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, 20(1), 125-145.
https://doi.org/10.1016/j.ejrs.2016.11.003
Alshaikh, A. Y. (2015). Space applications for drought assessment in Wadi-Dama (West Tabouk), KSA. The Egyptian Journal of Remote Sensing and Space Science, 18(1), S43-S53.
https://doi.org/10.1016/j.ejrs.2015.07.001
Lisar, S. Y., Motafakkerazad, R., Hossain, M. M., & Rahman, I. M. (2012). Water stress in plants: causes, effects and responses. In Water stress. InTech.
Zhang, H. W., & Huai-Liang, C. H. E. N. (2016). The Application of Modified Normalized Difference Water Index by Leaf Area Index in the Retrieval of Regional Drought Monitoring. DEStech Transactions on Engineering and Technology Research, (sste).
https://doi.org/10.12783/dtetr/sste2016/6590
Lambin, E. F. (2001). Global land-use and land-cover change: what have we learned so far? Global Change News, 46, 27-30.
Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. International Journal of Remote Sensing, 30(18), 4733-4746.
https://doi.org/10.1080/01431160802651967
Griffiths, P., Hostert, P., Gruebner, O., & van der Linden, S. (2010). Mapping megacity growth with multi-sensor data. Remote Sensing of Environment, 114(2), 426-439
https://doi.org/10.1016/j.rse.2009.09.012
Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International Journal of Remote Sensing, 21(2), 353-366.
Downloads
Published
How to Cite
Issue
Section
License
Upon acceptance of an article, the corresponding author on behalf of all authors will be asked to complete and upload the Copyright Transfer Form (refer to Copyright Issues for more information on this) alongside the electronic proof file.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and refer the publication in the Journal.
2) For open-access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds the copyright or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subjected to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) are responsible to ensure their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. The corresponding author has obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If the submitted manuscript includes materials from others, the authors have obtained permission from the copyright owners.
5) In signing this statement, the author(s) declare that the researches which they have conducted comply with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving humans or the use of animal samples must obtain approval from the Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that they have no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor(s) or UNIMAS Publisher.
