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 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.

Social Vulnerability of Brazilian Metropolitan Schools and Teachers' Absence from Work Due to Vocal and Psychological Symptoms: A Multilevel Analysis.

Teachers' voices and psychological symptoms are the main reasons for absence from work. The objectives of this study were: (i) to spatially represent, through a webGIS, the standardized rates of teachers' absences due to voice (outcome 1) and psychological symptoms (outcome 2) in each Brazilian Federative Unit (FU = 26 states plus Federal District) and (ii) to analyze the relationship between each national outcome rate and the Social Vulnerability Index (SVI) of the municipality where urban schools are located, adjusted for sex, age, and working conditions. This cross-sectional study comprised 4979 randomly sampled teachers working in basic education urban schools, of which 83.3% are women. The national absence rates were 17.25% for voice symptoms and 14.93% for psychological symptoms. The rates, SVI, and school locations in the 27 FUs are dynamically visualized in webGIS. The multilevel multivariate logistic regression model showed a positive association between voice outcome and high/very high SVI (OR = 1.05 [1.03; 1.07]), whereas psychological symptoms were negatively associated with high/very high SVI (OR = 0.86 [0.85 0.88]) and positively associated with intermediate SVI (OR = 1.15 [1.13; 1.16]), in contrast with low/very low SVI. Being a woman (voice: OR = 1.36 [1.35; 1.38]; psychological: 1.22 [1.21; 1.24]) and working in schools with various precarious conditions (17 variables) increased the odds of being absent due to voice and psychological symptoms. The results confirm the need for investments to improve working conditions in schools.

Geospatial Analysis of Environmental Atmospheric Risk Factors in Neurodegenerative Diseases: A Systematic Review.

Despite the vast evidence on the environmental influence in neurodegenerative diseases, those considering a geospatial approach are scarce. We conducted a systematic review to identify studies concerning environmental atmospheric risk factors for neurodegenerative diseases that have used geospatial analysis/tools. PubMed, Web of Science, and Scopus were searched for all scientific studies that included a neurodegenerative disease, an environmental atmospheric factor, and a geographical analysis. Of the 34 included papers, approximately 60% were related to multiple sclerosis (MS), hence being the most studied neurodegenerative disease in the context of this study. Sun exposure (n = 13) followed by the most common exhaustion gases (n = 10 for nitrogen dioxide (NO2) and n = 5 for carbon monoxide (CO)) were the most studied atmospheric factors. Only one study used a geospatial interpolation model, although 13 studies used remote sensing data to compute atmospheric factors. In 20% of papers, we found an inverse correlation between sun exposure and multiple sclerosis. No consensus was reached in the analysis of nitrogen dioxide and Parkinson's disease, but it was related to dementia and amyotrophic lateral sclerosis. This systematic review (number CRD42020196188 in PROSPERO's database) provides an insight into the available evidence regarding the geospatial influence of environmental factors on neurodegenerative diseases.

Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon.

In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.