Assessing Local Climate Change by Spatiotemporal Seasonal LST and Six Land Indices, and Their Interrelationships with SUHI and Hot–Spot Dynamics: A Case Study of Prayagraj City, India (1987–2018)

Abstract

LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local–global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries of 1987–2018. The interrelationship between six land indices (NDBI, EBBI, NDMI, NDVI, NDWI, and SAVI) and LST (using a mono-window algorithm) was investigated by analyzing correlation coefficients and directional profiling. NDVI dynamics showed that the forested area observed lower LST by 2.25–4.8 °C than the rest of the city landscape. NDBI dynamics showed that the built-up area kept higher LST by 1.8–3.9 °C than the rest of the city landscape (except sand/bare soils). SUHI was intensified in the city center to rural/suburban sites by 0.398–4.016 °C in summer and 0.45–2.24 °C in winter. Getis–Ord Gi* statistics indicated a remarkable loss of areal coverage of very cold, cold, and cool classes in summer and winter. MODIS night-time LST data showed strong SUHI formation at night in summer and winter. This study is expected to assist in unfolding the composition of the landscape for mitigating thermal anomalies and restoring environmental viability.

Investigating Surface Urban Heat Island (SUHI) Effect for a Hill City

“…Several studies have been conducted in India to investigate Urban Heat Island (UHI). A study conducted for Prayagraj, SUHI was more pronounced during summer, ranging in a difference of 0.398 to 4.016 °C between city centre and rural/suburban areas, while in winter, it varied between 0.45 and 2.24 °C (Sarif et al, 2022). In case of Delhi lower LST is reported in the district of New Delhi where green coverage is high.…”

Comparative assessment of landslide susceptibility of West Sikkim using three machine learning techniques

“…NDVI is a measure of vegetation health (Bhatta, 2011;Sarif et al, 2023). Vegetation health is an important determinant of landslide susceptibility as vegetation is a landslide ceasing factor.…”

“…The present study have used Landsat 8 OLI image and here band 5 and band 4 represents the NIR band and Red band respectively. The highest and the lowest NDVI value lies within − 1 and + 1 (Sarif et al, 2023;Sen, 2021) where values ranging from 0.3 to 0.6 are considered to be stressed or unhealthy vegetation while values exceeding 0.6 are considered to be healthy vegetation (Bhatta, 2011;Sen, 2021;.…”

“…Difference Vegetation Index or NDVI is another important landslide conditioning factor as it acts as an indicator of vegetation health(Sarif et al, 2023;Sen, 2021;Hembram et al, 2022). The highest NDVI value of the study area of the year 2022 for the month of April turned out to be 0.58 (Fig.3F).…”

Editorial: Special Issue on Geographical Analysis and Modeling of Urban Heat Island Formation

“…Investigations into how the design and layout of the landscape may impact the LST on the city scale are vital. Sarif et al examined the influence of land indices on the dynamics of LSTs from the city center to the periphery to debate the directional profiling of LSTs [13]. In Prayagraj City, the LST distribution was lower in the forested regions than in the built-up areas, bare soils, and sands.…”