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Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine
Land surface temperature (LST) is strongly influenced by landscape features as they change the thermal characteristics of the surface greatly. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Normalized Differen...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508483/ https://www.ncbi.nlm.nih.gov/pubmed/36164525 http://dx.doi.org/10.1016/j.heliyon.2022.e10668 |
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author | Roy, Bishal Bari, Ehsanul |
author_facet | Roy, Bishal Bari, Ehsanul |
author_sort | Roy, Bishal |
collection | PubMed |
description | Land surface temperature (LST) is strongly influenced by landscape features as they change the thermal characteristics of the surface greatly. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Bareness Index (NDBAI) correspond to vegetation cover, water bodies, impervious build-ups, and bare lands, respectively. These indices were utilized to demonstrate the relationship between multiple landscape features and LST using the spectral indices derived from images of Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI) of Sylhet Sadar Upazila (2000–2018). Google Earth Engine (GEE) cloud computing platform was used to filter, process, and analyze trends with logistic regression. LST and other spectral indices were calculated. Changes in LST (2000–2018) range from −6 °C to +4 °C in the study area. Because of higher vegetation cover and reserve forest, the north-eastern part of the study region had the greatest variations in LST. The spectral indices corresponding to landscape features have a considerable explanatory capacity for describing LST scenarios. The correlation of these indices with LST ranges from −0.52 (NDBI) to +0.57 (NDVI). |
format | Online Article Text |
id | pubmed-9508483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-95084832022-09-25 Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine Roy, Bishal Bari, Ehsanul Heliyon Research Article Land surface temperature (LST) is strongly influenced by landscape features as they change the thermal characteristics of the surface greatly. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Bareness Index (NDBAI) correspond to vegetation cover, water bodies, impervious build-ups, and bare lands, respectively. These indices were utilized to demonstrate the relationship between multiple landscape features and LST using the spectral indices derived from images of Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI) of Sylhet Sadar Upazila (2000–2018). Google Earth Engine (GEE) cloud computing platform was used to filter, process, and analyze trends with logistic regression. LST and other spectral indices were calculated. Changes in LST (2000–2018) range from −6 °C to +4 °C in the study area. Because of higher vegetation cover and reserve forest, the north-eastern part of the study region had the greatest variations in LST. The spectral indices corresponding to landscape features have a considerable explanatory capacity for describing LST scenarios. The correlation of these indices with LST ranges from −0.52 (NDBI) to +0.57 (NDVI). Elsevier 2022-09-18 /pmc/articles/PMC9508483/ /pubmed/36164525 http://dx.doi.org/10.1016/j.heliyon.2022.e10668 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Roy, Bishal Bari, Ehsanul Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine |
title | Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine |
title_full | Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine |
title_fullStr | Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine |
title_full_unstemmed | Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine |
title_short | Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine |
title_sort | examining the relationship between land surface temperature and landscape features using spectral indices with google earth engine |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508483/ https://www.ncbi.nlm.nih.gov/pubmed/36164525 http://dx.doi.org/10.1016/j.heliyon.2022.e10668 |
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