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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Elsevier
2023-08-01
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023060541 |
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author | Cátia Rodrigues de Almeida Nuno Garcia João C. Campos João Alírio Salvador Arenas-Castro Artur Gonçalves Neftalí Sillero Ana Cláudia Teodoro |
author_facet | Cátia Rodrigues de Almeida Nuno Garcia João C. Campos João Alírio Salvador Arenas-Castro Artur Gonçalves Neftalí Sillero Ana Cláudia Teodoro |
author_sort | Cátia Rodrigues de Almeida |
collection | DOAJ |
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 (Tair) 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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issn | 2405-8440 |
language | English |
last_indexed | 2024-03-12T12:22:12Z |
publishDate | 2023-08-01 |
publisher | Elsevier |
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series | Heliyon |
spelling | doaj.art-fb757071c75a4856b371a6c44f0c27242023-08-30T05:52:49ZengElsevierHeliyon2405-84402023-08-0198e18846Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous regionCátia Rodrigues de Almeida0Nuno Garcia1João C. Campos2João Alírio3Salvador Arenas-Castro4Artur Gonçalves5Neftalí Sillero6Ana Cláudia Teodoro7Department of Geosciences, Environment and Land Planning, University of Porto, Rua Campo Alegre, 687, 4169-007, Porto, Portugal; Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, 4169-007, Porto, Portugal; Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança (IPB), Campus de Santa Apolónia, 5300-253 Bragança, PortugalCICGE-Centro de Investigação em Ciências GeoEespaciais, Faculdade de Ciências da Universidade do Porto, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, PortugalCICGE-Centro de Investigação em Ciências GeoEespaciais, Faculdade de Ciências da Universidade do Porto, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, PortugalDepartment of Geosciences, Environment and Land Planning, University of Porto, Rua Campo Alegre, 687, 4169-007, Porto, PortugalÁrea de Ecología, Dpto. de Botánica, Ecología y Fisiología Vegetal, Facultad de Ciencias, Universidad de Córdoba, Campus de Rabanales, 14071, Córdoba, SpainCentro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança (IPB), Campus de Santa Apolónia, 5300-253 Bragança, Portugal; Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, PortugalCICGE-Centro de Investigação em Ciências GeoEespaciais, Faculdade de Ciências da Universidade do Porto, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, PortugalDepartment of Geosciences, Environment and Land Planning, University of Porto, Rua Campo Alegre, 687, 4169-007, Porto, Portugal; Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, 4169-007, Porto, Portugal; Corresponding author. Department of Geosciences, Environment and Land Planning, University of Porto, Rua Campo Alegre, 687, 4169-007, Porto, Portugal.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 (Tair) 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.http://www.sciencedirect.com/science/article/pii/S2405844023060541Temperature seasonality analysisAir temperatureMODIS dataMontesinho natural park statistical analysisVegetation indexes |
spellingShingle | Cátia Rodrigues de Almeida Nuno Garcia João C. Campos João Alírio Salvador Arenas-Castro Artur Gonçalves Neftalí Sillero Ana Cláudia Teodoro Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region Heliyon Temperature seasonality analysis Air temperature MODIS data Montesinho natural park statistical analysis Vegetation indexes |
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 | Temperature seasonality analysis Air temperature MODIS data Montesinho natural park statistical analysis Vegetation indexes |
url | http://www.sciencedirect.com/science/article/pii/S2405844023060541 |
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