Have changes in the Earth's surface temperature intensified in mountainous regions? Are climate changes more pronounced in temperate and mountainous regions?

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.Earth’s temperature is influenced by several factors, such as: i) altitude and relief, which influence air temperature (the higher the altitude, the fewer the particles absorbing and diffusing solar radiation, resulting in lower temperatures) and act as natural barriers to the movement of air masses/prevalent wind patterns [1]; ii) sea and land structures, resulting in local variations, that can be opposed (dry and torrid heat on slopes exposed directly to the sun, occasional thermal inversion, particularly at night and in enclosed valleys [1]; iii) global wind patterns, that shift north or south according to the seasons [1,2]; iv) latitude and the angles of the sun rays, determined by the tilt of the Earth’s axis, changing the angle of incidence of electromagnetic energy and altering the day duration at different altitudes; and v) anthropogenic effects on atmospheric and oceanic temperature [3]. The increase in temperature can influence the Earth’s natural dynamics. Water vapour, evaporation rate, and changes in the hydrological cycle increase with atmospheric warming, raising the frequency of torrential rains [4]. Climate change is increasing the intensity and frequency of extreme heat events, namely at higher latitudes [5], and is also shifting the distributions of ecosystems, plants and animals, whose persistence is associated with climate rhythm and stability [6,7]. Several studies have analysed the influence of environmental characteristics (e.g. topography, Land Use Land Cover - LULC, the presence of vegetation and its influence on the local climate) in the thermal behaviour and its possible impacts on the health and welfare of human populations and Earth’s biomes and, in some cases, to guide strategic decision-making aimed at mitigating its impacts [8–10]. However, research studies analysing thermal changes in mountainous regions present a great complexity, due to the difficulty in separating the natural effects (temperature, precipitation, and radiation) from the anthropic ones [11]. In addition, the morphology and geological characteristics of mountainous areas create a high climatic variability in short periods of time, resulting in high daily and annual thermal amplitude, high inter-annual climatic variation, and extreme winter conditions, with the presence of snow in some months or during all-year [12,13]. There are several impacts associated with mountainous regions, among them: i) the melting of glaciers, which influences the availability of water resources for local human communities (for irrigation, wind energy production, and human consumption), as occurs in the Andes, the Himalayan Cordillera and the Alps [14,15]); ii) increase and intensity of extreme climate events, which can result in floods, droughts, and landslides [16]; iii) vulnerability of ecosystems to climate change, affecting animal and plant species that may not survive in their natural habitats, conditioning their migration to other regions [6,7,17,18]; and iv) increased risk of forest fires, both due to the increase in temperature, decrease in air humidity, and precipitation changes, reducing the amount of moisture available for vegetation [19]. Obtaining Tair from in situ meteorological stations in mountainous regions is difficult due to their low accessibility and distribution [20]. Remote Sensing (RS) is a valid alternative technique to analyse the changes in temperature of mountainous regions, as it provides empirical and effective indicators related to crucial environmental and ecological information at the site level [20]. Land Surface Temperature (LST) is a RS thermal product widely used in studies about the thermal behaviour of a site [21]. LST, differently from Tair, measures the thermal radiance emission from the land surface, which receives the incoming solar energy heating the ground [22]. LST is a good indicator of energy partitioning at the land surface-atmosphere boundary and is sensitive to changing surface conditions [22–25]. Thermal sensors operate in the atmospheric window (between 8 and 14 μm); the thermal sensors are cooled near 0 K and the observed target temperature is compared with internal reference temperatures (absolute radiation) [26]. LST is estimated from the radiometric temperature aggregate value contained in the sensor’s field of view, estimated as emitted surface radiation (deduced in the atmospheric correction) or by inverse application of the Planck function, considering the effects of emissivity [22–24].The most used thermal sensors are those onboard the satellites Landsat (starting with Landsat 4), with a temporal resolution of 16 days and spatial resolution resampled to 30 m (the Thematic Mapper (TM) sensor on board Landsat 4 and 5 obtain information at 120 m and the Thermal Infrared Sensor (TIRS) and Thermal Infrared Sensor-2 (TIRS 2) sensors on board Landsat 8 and 9, respectively, obtain information at 100 m); Terra/Aqua (MODIS - Moderate Resolution Imaging Spectrometer) with a spatial resolution of 1 km and temporal resolution of twice daily and Terra (ASTER - Advanced Spaceborne Thermal Emission and Reflectance Radiometer), with a temporal resolution of twice daily and spatial resolution of 90 m [21]. Other sensors are available with a higher temporal resolution, such as those onboard geostationary satellites (which observe the same point on Earth) and polar satellites that collect information from the same point at two times each day - one in an ascending and one in a descending orbit. The revisit time depends on the latitude. Examples of geostationary and polar satellites are the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), with spatial resolution of 3 km, approximately [21,27] and the Geostationary Operational Environmental System (GOES), with spatial resolution of 4 km, approximately [21,28,29]. In view of the relevance of assessing how climate changes over time in mountainous regions, we selected as study area the Montesinho Natural Park (MNP) located in Bragança (Portugal), due to the following characteristics: i) a high vegetation diversity, allowing to analyse the influence of vegetation on local thermal behaviour; and ii) it has a local meteorological station that measures Tair, whose data can be used to analyse its relationship with LST data. Thus, our main objective is to analyse changes in LST over time (from 2003 to 2021) in the MNP. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time; ii) what environmental factors influence the LST; and iii) whether vegetation is related to changes in temperature. We hypothesise that: (1) the temperature of MNP increased in the analysed period, due to climate change, (2) topography influences the LST, due to the presence of valleys and ridges in the study area; and (3) forests exerted a positive influence on temperature cooling in MNP. This is the first study, to our knowledge, analysing LST trends with an extensive 19-year time series collected at four different MODIS pass times and using other RS data (e.g. biophysical indices, topography). Notwithstanding, we also calculated the LST from the Landsat satellite series (namely 5, 7 and 8) and apply analytical methods to compare with the results obtained from MODIS, comparing them with another RS source with better spatial resolution. We obtained and processed the RS data in Google Earth Engine (GEE). We expect to further stimulate research on the effect of climate change in mountainous regions.Climate change contributes to an increase in the intensity and frequency of extreme events, which can contribute to environmental disasters, changes in the hydrological cycle, and impacts on natural biomes and human communities. Mountain systems, as ecologically sensitive areas, are specially affected by these changes, increasing the melting of glaciers, landslides, changes in habitats, and shifts in species ecological niches. The use of spatial methods that integrate various sources of information, computing tools and statistical analyses proved to be effective for mapping the thermal behaviour. Indeed, our methodology can be applied to other mountainous areas of similar characteristics, to complement decision-making processes. Research studies aiming to understand the thermal behaviour and trends of mountainous areas are fundamental to identifying patterns and taking remedial and mitigating measures to ensure the conservation and preservation of local biodiversity and ecological services.