Analysis of the relationships between land surface temperature and spectral indices pre-and during pandemic using Landsat-8 images (case study: Gerbangkertosusila)

Gerbangkertosusila (Gresik-Bangkalan-Mojokerto-Surabaya-Sidoarjo-Lamongan) is one of the biggest metropolitan areas in Indonesia impacted hardest by COVID-19 after social restriction. High temperature conditions are an issue in the Gerbangkertosusila area. Reduced mobility and industrial activity lead to decrease in surface temperature. The research was carried out using the Statistical Mono Windows (SMW) algorithm in separate periods of time (July 2019, July 2020, October 2020, May 2021) to represent the changes between social restriction policy and the weather. This research goal is to examine the relationship between land surface temperature with changes of spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) data. These three parameters are correlated with a simple linear regression equation to calculate how much influence occurs in each different period, then the qualitative analysis is carried out to explain the variations between the distribution of hotspot and annual temperature chart to the real conditions. The result shows strong positive correlation coefficient between changes of NDBI pixel and the LST in each period of time such as 0.62; 0.80; 0.70; and 0.80. Meanwhile the NDVI-LST correlation coefficient shows negative results such as-0.57;-0.43;-0.38;-0.41. This research also concludes that in the social restriction period, the Land Surface Temperature doesn't affect the variability of NDVI © 2021 SPIE.