Spatial Heterogeneity of Land Surface Temperature and Its Biophysical Drivers: An Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) Based Analysis in Odisha, Eastern India

DOI

10.2991/978-94-6463-940-7_38How to use a DOI?

Keywords

Land Surface Temperature; NDBI; OLS; GWR; Odisha; India

Abstract

Land Surface Temperature (LST) is a key parameter for studying relationship between land and atmosphere, urban heat island and biophysical processes. This study analyses the spatial variability of LST in Odisha, India, employing remote sensing indices and modern spatial regression models. Five different indices have been used to explain variability of LST: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Latent Heat Index (NDLI). Spatial autocorrelation methods such as Moran’s I and Getis-Ord Gi* have been used to demonstrate LST clustering patterns and statistically significant hot and cold spots. The results showed significant spatial heterogeneity in LST, ranging from 18.48℃ to 46.50℃. Higher values were found in urbanised and barren areas, whereas lower values were found in vegetated and water-logged areas. A comparison of regression models showed that Geographically Weighted Regression (GWR) outperformed Ordinary Least Squares (OLS) in Odisha. From the regression model analysis, it was found that, NDMI and NDBI are the key explanatory factors, with a strong connection to vegetation moisture and built-up intensity, respectively. In contrast, MNDWI had lowest explanatory power. These results underscore the significance of spatially clear modelling in the study of LST dynamics within diverse landscapes.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Tankadhar Behera
AU  - Nihar Ranjan Sahoo
AU  - Haraprasad Satapathy
AU  - Nirasindhu Desinayak
AU  - Sandeep Narayan Kundu
AU  - Suren Nayak
PY  - 2025
DA  - 2025/12/31
TI  - Spatial Heterogeneity of Land Surface Temperature and Its Biophysical Drivers: An Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) Based Analysis in Odisha, Eastern India
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 522
EP  - 534
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_38
DO  - 10.2991/978-94-6463-940-7_38
ID  - Behera2025
ER  -