Version 1
: Received: 18 December 2023 / Approved: 20 December 2023 / Online: 21 December 2023 (07:05:13 CET)
How to cite:
Stamou, A. Land Surface Temperature Mapping Using a Vegetation Index-Based Technique in Google Earth Engine with Multi-Temporal Remote Sensing Data. Preprints2023, 2023121583. https://doi.org/10.20944/preprints202312.1583.v1
Stamou, A. Land Surface Temperature Mapping Using a Vegetation Index-Based Technique in Google Earth Engine with Multi-Temporal Remote Sensing Data. Preprints 2023, 2023121583. https://doi.org/10.20944/preprints202312.1583.v1
Stamou, A. Land Surface Temperature Mapping Using a Vegetation Index-Based Technique in Google Earth Engine with Multi-Temporal Remote Sensing Data. Preprints2023, 2023121583. https://doi.org/10.20944/preprints202312.1583.v1
APA Style
Stamou, A. (2023). Land Surface Temperature Mapping Using a Vegetation Index-Based Technique in Google Earth Engine with Multi-Temporal Remote Sensing Data. Preprints. https://doi.org/10.20944/preprints202312.1583.v1
Chicago/Turabian Style
Stamou, A. 2023 "Land Surface Temperature Mapping Using a Vegetation Index-Based Technique in Google Earth Engine with Multi-Temporal Remote Sensing Data" Preprints. https://doi.org/10.20944/preprints202312.1583.v1
Abstract
The identified Urban Heat Island (UHI) phenomenon, coupled with diminished vegetation and anthropogenic heat release, poses a significant environmental challenge for major urban centers. Particularly worrisome is the UHI effect in hot and temperate climates such as the Mediterranean region during the summer season. This exacerbates discomfort and heightens the risks of diseases linked to high temperatures. Additionally, it contributes to environmental consequences, including elevated electricity consumption due to increased demand for cooling [1]. The utilization of Remotely Sensed imagery for estimating Land Surface Temperature has become more prevalent in various applications associated with evaluating urban micro-climates and the Urban Heat Island (UHI) phenomenon. This study presents an algorithm for the automatic mapping of Land Surface Temperature (LST) from Landsat-8 data using a vegetation index-based technique in the Google Earth Engine (GEE) platform. The primary aim is to discern patterns and trends in LST over a 5-year period, from 2017 to 2021. The application was tested in the urban area of Thessaloniki, Northern Greece, which is particularly representative of the Mediterranean urban environment. Computation of Normalized Difference Indices (NDVI, NDBI, NDWI) for the study area was also performed within the GEE platform and correlation analysis was implemented to evaluate the impact of the urban landscape on the distribution of LST.
Keywords
Land Surface Temperature; Land-use; NDVI; Landsat-8; Google Earth Engine
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
