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Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinh...

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Autores principales: Rodrigues de Almeida, Cátia, Garcia, Nuno, Campos, João C., Alírio, João, Arenas-Castro, Salvador, Gonçalves, Artur, Sillero, Neftalí, Teodoro, Ana Cláudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10428060/
https://www.ncbi.nlm.nih.gov/pubmed/37593602
http://dx.doi.org/10.1016/j.heliyon.2023.e18846
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author Rodrigues de Almeida, Cátia
Garcia, Nuno
Campos, João C.
Alírio, João
Arenas-Castro, Salvador
Gonçalves, Artur
Sillero, Neftalí
Teodoro, Ana Cláudia
author_facet Rodrigues de Almeida, Cátia
Garcia, Nuno
Campos, João C.
Alírio, João
Arenas-Castro, Salvador
Gonçalves, Artur
Sillero, Neftalí
Teodoro, Ana Cláudia
author_sort Rodrigues de Almeida, Cátia
collection PubMed
description Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (T(air)) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.
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spelling pubmed-104280602023-08-17 Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region Rodrigues de Almeida, Cátia Garcia, Nuno Campos, João C. Alírio, João Arenas-Castro, Salvador Gonçalves, Artur Sillero, Neftalí Teodoro, Ana Cláudia Heliyon Research Article Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (T(air)) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time. Elsevier 2023-08-01 /pmc/articles/PMC10428060/ /pubmed/37593602 http://dx.doi.org/10.1016/j.heliyon.2023.e18846 Text en © 2023 The Authors 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
Rodrigues de Almeida, Cátia
Garcia, Nuno
Campos, João C.
Alírio, João
Arenas-Castro, Salvador
Gonçalves, Artur
Sillero, Neftalí
Teodoro, Ana Cláudia
Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region
title Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region
title_full Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region
title_fullStr Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region
title_full_unstemmed Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region
title_short Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region
title_sort time-series analyses of land surface temperature changes with google earth engine in a mountainous region
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10428060/
https://www.ncbi.nlm.nih.gov/pubmed/37593602
http://dx.doi.org/10.1016/j.heliyon.2023.e18846
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