Investiganting Correlation LST and Vegetation Indices Using Landsat Images for the Warmest Month: A Case Study of Iasi County

Abstract

In this paper is investigating correlation between land surface temperature and vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index 2 - EVI2 and Modified Soil Adjusted Vegetation Index - MSAVI) using Landsat images for august, the warmest month, for study area. Iaşi county is considered as study area in this research. Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Land surface temperature (LST) can be used to define the temperature distribution at local, regional and global scale. First use of LST was in climate change models. Also LST is use to define the problems associated with the environment. A Vegetation Indices (VI) is a spectral transformation what suppose spatial-temporal intercomparisons of terrestrial photosynthetic dynamics and canopy structural variations. Landsat5 TM, Landsat7 ETM+ and Landsat8 OLI, all data were used in this study for modeling. Landsat images was taken for august 1994, 2006 and 2016. Preprocessing of Landsat 5/7/8 data stage represent that process that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. It was observed that the “mean” parameter for LST increased from 1994 to 2016 at approximately 5°C. Analyzing the data from VI, it can be assumed that the built-up area increased for the Iasi county, while the area occupied by dense vegetation has decreased. Many researches indicated that between LST and VI is a linear relationship. It is noted that the R2 values for the LST-VI correlations decrease from 1994 (i.g.R2= 0.72 for LST-NDVI) in 2016 (i.g.R2= 0.23 for LST-NDVI). In conclusion, these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

“…After quality inspection and cloud masking several images were discarded, resulting in a dataset of 125 Landsat-5 images and 60 Landsat-8 images (Level-1 for the thermal bands and Level-2 for multispectral bands). These images covered two time periods, (a To estimate the land surface temperature from the Landsat thermal infrared bands, the method that was described in [17] was followed. The method requires the estimation of the NDVI using the red and near-infrared bands of the multispectral images.…”

“…This has as a consequential warming effect in northeastern Europe [16]. Related work also exists on the evaluation of the relationship between different vegetation indices and LST [15,17], pointing out the negative correlation between LST and NDVI.…”

Relationship of the Seasonal Vegetation Indices against the NDVI and LST in the Region of Kamuku Game Reserve and Kwiambana National Park, Nigeria

“…It means, each ecosystem has its own set of variable atmospheric conditions. Importantly, because a high LST corresponds to a low NDVI and vice versa, the negative correlation between LST-NDVI is relevant for urban climate research and other fields of science (Carlson et al, 1994;Anbazhagan et al, 2016, Macarof et al, 2018. The Vis other than the NDVI that have a negative correlation with LST can also be employed for climaterelated research.…”

“…Some of these studies can be found in Deng et al (2018), Guechi et al (2021), and Koko et al (2021). Furthermore, Macarof et al (2018) investigated the correlation between LST and NDVI, MSAVI, and EVI only in the warmth season. Similarly, Alademomi et al (2020) assessed the association of LST, NDVI and EVI with land cover changes in dry season.…”