Nova Geodesia (Sep 2022)

Correlation between land surface temperature (LST) and normalized difference vegetation index (NDVI) in Wardha Valley Coalfield, Maharashtra, Central India

  • Zadan KHAN,
  • Akram JAVED

DOI
https://doi.org/10.55779/ng2353
Journal volume & issue
Vol. 2, no. 3

Abstract

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Global warming has emerged as one of humanity’s most pressing environmental issues and Land Surface Temperature (LST) is increasingly being used to assess increased warming in urban areas. The exploitation and usage of natural resources create significant environmental harm in mining, especially over land surface. The link between LST and NDVI (Normalized Difference Vegetation Index) is highly intriguing, and draws remote sensing specialists from all over the world. Present study makes an effort to retrieve the LST and its correlation with NDVI in parts of Wardha Valley Coalfield, which in central India. Four sets of Landsat-5 (TM) data of summer and winter have been used for 1991 and 2006, whereas two sets of Landsat-8 (OLI) data have been chosen for 2021. Mono-window algorithm was applied to retrieve LST from multi-temporal Landsat satellite sensors. Five LU/LC categories have been delineated viz. Vegetation/Forest, Mine/Industry, Overburden dumps/built-up, Waste land and Water body by using maximum likelihood classification algorithm. The study reveals that the mean LST of five LU/LC categories of both summer and winter seasons has increased during 1991-2006 period which suggests an example of Urban Heat Island (UHI). However, mean LST for five LU/LC categories has slightly decreased during 2006-2021 period which represents an example of Urban Cool Island (UCI). Moreover, over all mean LST of summer and winter seasons has increased by 7.39 °C and 4.51 °C respectively during 1991-2021 period. The study also examines NDVI-LST inverse relationship, which shows strong negative correlation in summer as well as in winter. Therefore, it can be concluded that surface temperature is highly controlled by surface land use type such as vegetation, built-up land, mining area etc. The presented results in this study will significantly help in future planning of the district and also provide a database for upcoming climate control goals.

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