Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series

Abstract

The present study analyzes the seasonal variability of the relationship between the land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India by using sixty-five Landsat images of four seasons (pre-monsoon, monsoon, post-monsoon, and winter) of 1991-1992, 1995-1996, 1999-2000, 2004-2005, 2009-2010, 2014-2015, and 2018-2019. The mono-window algorithm was used to retrieve LST and Pearson's correlation coefficient was used to generate the LST-NDBaI relationship. The post-monsoon season builds the best correlation (0.59) among the four seasons. The water bodies builds a moderate to strong positive correlation (>0.50) in all the four seasons. On green vegetation, this correlation is moderate to strong positive (>0.54) in the three seasons, except the pre-monsoon season. The built-up area and bare land generate a moderate positive correlation (>0.34) in all the four seasons. Among the four seasons, the post-monsoon season builds the best correlation for all LULC types, whereas the pre-monsoon season has the least correlation. This research work is useful for environmental planning of other citieswith similar climatic conditions.

Keywords

• Landsat
• LST
• LULC
• NDBal
• Raipur

References

1. Ahmed B, Kamruzzaman M, Zhu X, Rahman M S & Choi K (2013). Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh. Remote Sensing, 5(11), 5969-5998. https://doi.org/10.3390/rs5115969
2. Alexander C (2020). Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). International Journal of Applied Earth Observation and Geoinformation, 86, 102013. https://doi.org/10.1016/j.jag.2019.102013
3. Alibakhshi Z, Ahmadi M& Farajzadeh Asl M (2020). Modeling Biophysical Variables and Land Surface Temperature Using the GWR Model: Case Study—Tehran and Its Satellite Cities. Journal of Indian Society of Remote Sensing,48, 59–70. https://doi.org/10.1007/s12524-019-01062-x
4. Ali J M, Marsh S H& Smith M J (2017). A comparison between London and Baghdad surface urban heat islands and possible engineering mitigation solutions. Sustainable Cities and Society, 29, 159-168. https://doi.org/10.1016/j.scs.2016.12.010
5. As-syakur A R, Adnyana I W S, Arthana I W & Nuarsa I W (2012). Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sensing, 4(10), 2957-2970. https://doi.org/10.3390/rs4102957
6. Barsi J, Schott J, Hook S, Raqueno N, Markham B& Radocinski R (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607-11626.
7. Carlson T N& Ripley D A (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sensing of Environment, 62, 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
8. Chen X L, Zhao H M, Li P X& Yi Z Y (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133–146. https://doi.org/10.1016/j.rse.2005.11.016

9. Chen X& Zhang Y (2017). Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society, 32, 87-99. https://doi.org/10.1016/j.scs.2017.03.013
10. Essa W, Verbeiren B, Van der Kwast J, Van de Voorde T& Batelaan O (2012). Evaluation of the DisTrad thermal sharpening methodology for urban areas. International Journal of Applied Earth Observation and Geoinformation, 19, 163-172. https://doi.org/10.1016/j.jag.2012.05.010
11. Guha S, Govil H, Dey A & Gill N (2020a). A case study on the relationship between land surface temperature and land surface indices in Raipur City, India. Geografisk Tidsskrift-Danish Journal of Geography, 120(1), 35-50. https://doi.org/10.1080/00167223.2020.1752272
12. Guha S, Govil H, Gill N & Dey A (2020b). Analytical study on the relationship between land surface temperature and land use/land cover indices. Annals of GIS, 26(2), 201-216. https://doi.org/10.1080/19475683.2020.1754291
13. Guha S, Govil H & Mukherjee S (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. Journal of Applied Remote Sensing, 11(3), 036020. https://doi:10.1117/1.JRS.11.036020
14. Guo G, Wu Z& Chen Y (2014). Estimation of subpixel land surface temperature using Landsat TM imagery: A case examination over a heterogeneous urban area. Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Changsha, p. 304-308. https://doi.org/10.1109/EORSA.2014.6927900
15. Guo G, Wu Z, Xiao R, Chen Y, Liu X& Zhang X (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135, 1-10. https://doi.org/10.1016/j.landurbplan.2014.11.007
16. Hao X, Li W& Deng H (2016). The oasis effect and summer temperature rise in arid regions-case study in Tarim Basin. Scientific Reports, 6, 35418. https://doi.org/10.1038/srep35418
17. Jain S, Sannigrahi S, Sen S, Bhatt S, Chakraborti S& Rahmat S (2020). Urban heat island intensity and its mitigation strategies in the fast-growing urban area. Journal of Urban Management, 9(1), 54-66. https://doi.org/10.1016/j.jum.2019.09.004
18. Li J (2006). Estimating land surface temperature from Landsat-5 TM. Remote Sensing Technology and Application, 21, 322-326.
19. Li Z N, Duan S B, Tang B H, Wu H, Ren H G& Yan G J (2016). Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20, 899–920.
20. Macarof P, Bîrlica I C& Stătescu F (2017). Investigating the relationship between land surface temperature and urban indices using landsat-8: a case study of Iaşi. Lucrările Seminarului Geografic Dimitrie Cantemir, 45, 81-88. https://doi.org/10.15551/lsgdc.v45i0.07
21. Mushore T D, Odindi J, Dube T& Mutanga O (2017). Prediction of future urban surface temperatures using medium resolution satellite data in Harare metropolitan city, Zimbabwe. Building and Environment, 122, 397-410. https://doi.org/10.1016/j.buildenv.2017.06.033
22. Nimish G, Bharath H A& Lalitha A (2020). Exploring temperature indices by deriving relationship between land surface temperature and urban landscape. Remote Sensing Application: Society and Environment, 18, 100299. https://doi.org/10.1016/j.rsase.2020.100299
23. Qin Z, Karnieli A& Barliner 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:10.1080/01431160010006971
24. Sekertekin A, Kutoglu SH & Kaya S (2016). Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey. Environmental Monitoring and Assessment, 188, 30. https://doi.org/10.1007/s10661-015-5032-2
25. Sharma R, Ghosh A& Joshi P K (2013). Mapping environmental impacts of rapid urbanization in the National Capital Region of India using remote sensing inputs. Geocarto International, 28(5), 420-438. https://doi.org/10.1080/10106049.2012.715208
26. Sharma R& Joshi P K (2016). Mapping environmental impacts of rapid urbanization in the National Capital Region of India using remote sensing inputs. Urban Climate, 15, 70-82. https://doi.org/10.1016/j.uclim.2016.01.004
27. Sobrino J A, Raissouni N& Li Z (2001). A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment,75(2), 256–266. https://doi.org/10.1016/S0034-4257(00)00171-1
28. Sobrino J A, Jimenez-Munoz J C & Paolini L (2004). Land surface temperature retrieval from Landsat TM5.Remote Sensing of Environment, 9, 434–440. https://doi:10.1016/j.rse.2004.02.003
29. Sun Q, Tan J & Xu Y (2010). An ERDAS image processing method for retrieving LST and describing urban heat evolution: A case study in the Pearl River Delta Region in South China. Environmental Earth Science, 59, 1047-1055.
30. Tomlinson C J, Chapman L, Trones J E& Baker C (2011). Remote sensing land surface temperature for meteorology and climatology: a review. Meteorological Application, 118, 296–306. https://doi.org/10.1002/met.287
31. URL-1: hthttp://www.surveyofindia.gov.in
32. URL-2: http://www.raipur.gov.in
33. URL-3: https://www.earthexplorer.usgs.gov
34. URL-4: http://www.imdraipur.gov.in
35. Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, & De La Riva J (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6(5), 4345-4368.
36. Wukelic G E, Gibbons D E, Martucci L M&Foote H P (1989). Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sensing of Environment, 28, 339–347. https://doi.org/10.1016/0034-4257(89)90125-9
37. Yang J& Que J (1996). The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China.Scientia Atmospherica Sinica, 20, 620-626.
38. Zanter K (2019). Landsat 8 (L8) Data Users Handbook; EROS: Sioux Falls, SD, USA.
39. Zhao H M & Chen X L (2005). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Geoscience and Remote Sensing Symposium. 3 (25–29), p.1666−1668. https://doi.org/10.1109/IGARSS.2005.1526319