Examining the spatio-temporal relationship between LST, NDVI, NDBI and LULC change of Pachhua dun, Dehradun, Uttarakhand (India)

Rahul Thapa; Dr. Vijay Bahuguna; Prateek Negi; Praveen Singh Rana; Pinki Kataria; Dr. Geeta Rawat; Muhammad Yasir; Tania Sharma

doi:10.22146/jgise.88002

Examining the spatio-temporal relationship between LST, NDVI, NDBI and LULC change of Pachhua dun, Dehradun, Uttarakhand (India)

https://doi.org/10.22146/jgise.88002

Rahul Thapa^(1*), Dr. Vijay Bahuguna⁽²⁾, Prateek Negi⁽³⁾, Praveen Singh Rana⁽⁴⁾, Pinki Kataria⁽⁵⁾, Dr. Geeta Rawat⁽⁶⁾, Muhammad Yasir⁽⁷⁾, Tania Sharma⁽⁸⁾

(1) D.B.S.(P.G.) College, Dehradun (Uttarakhand)
(2) D.B.S.(P.G.)College, Dehradun
(3) Pestle Weed College of Information Technology, Dehradun, Uttarakhand
(4) D.B.S.(P.G.)College, Dehradun
(5) D.B.S.(P.G.)College, Dehradun(Uttarakhand)
(6) SGRR University, Dehradun, Uttarakhand (India).
(7) China University of Petroleum.
(8) D.B.S.(P.G.) College, Dehradun(Uttarakhand)
(*) Corresponding Author

Abstract

Recent climate change has had a negative impact on a wide range of human and natural systems, and it is clear that humans influence the climate. Because, as anthropogenic influence increases, the heat output from the land surface increases, speeding up the rate of climate change. In this regard, the use of RS and GIS techniques has provided various opportunities for research to examine these changes. The current analysis is based on the Landsat 1989, and 2020. Over the study period of 31 years, the built-up regions increased in size from 44.23 km² to 154.56 km². Whereas, the area covered by scrubland, water bodies, and vegetation cover has significantly decreased. The LST study further supports the outcome, showing that the mean and standard deviation increased from 14.81°C±1.32(1989) to 18.82°C±1.57(2020). The study also made an effort to examine how LULC affected LST; while vegetation cover has consistently helped to lower mean LST, built-up areas and scrubland are the main drivers of mean LST rise. The LST and NDBI revealed a positive correlation, while the NDVI/SLOPE and LST showed a negative correlation. Subsequently, the multiple linear regression (MLR) models concluded that the BUAs has evolved into a serious threat to the increase in LST, but increase in vegetation cover and SLOPE would result in slight decrease in LST. the study recommended that the government create policies that restrict future land encroachment and conversion, notably of forested area and water bodies, and make an immediate effort to increase the quantity and quality of urban green cover in the study area. So that we may, respectively, minimize the potential hazard posed by future LST rise and LULC change.

Keywords

Supervised classification; LULC; Change detection; LST and urban green

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References

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DOI: https://doi.org/10.22146/jgise.88002