Ms. Mariana Oliveira Profile

Mariana Oliveira

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Proceedings Article | 12 September 2021 Presentation + Paper

Land surface temperature algorithm calibration through meteorological stations

Mariana Oliveira, Ana Cláudia Teodoro, Alberto Freitas, Hernâni Gonçalves

Proceedings Volume 11863, 1186310 (2021) https://doi.org/10.1117/12.2599909

KEYWORDS: Meteorology, Earth observing sensors, Temperature metrology, Remote sensing, Landsat, Data modeling, Calibration, Environmental sensing

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This methodologic paper arises from the necessity to gather Land Surface Temperature (LST) data over a relatively large period and territory: 2000-2018, Portugal. The computational power required to complete this task was found to be a major barrier. However, platforms such as Google Earth Engine (GEE) offer a vast data archive freely accessible through a web interactive development environment or an application programming interface, namely, Python’s API. Additionally, the computation using GEE is hosted in Google’s servers, drastically reducing the processing times. However, computing LST through Landsat-7 satellite imagery resulted on a difference of -8ºC±6ºC compared to the values from meteorological ground stations. As such, this paper aims to further calibrate computed LST through meteorological stations and make the methodology and corresponding code available, thus encouraging cooperation on the development and integration of local calibration methods. A sensitivity analysis of the representativeness of each station was performed using three methods of temperature extraction: station coordinate’s pixel, buffers around the station, and surrounding soil occupation (identifying the area with the same soil occupation as the station’s location). Pearson’s correlation coefficient was on average significant at 0.81 in the raw data and increased to 0.89 after clearing data from outliers . The best representativeness method for meteorologic stations was the one based on soil occupation, which resulted on a Pearson’s r of 0.91. As a result, we advise researchers to complement their remote sensing work with ground data whenever possible through the usage of a method like the one here described.

Proceedings Article | 26 October 2016 Paper

Spatio-temporal analysis of preterm birth in Portugal and its relation with environmental variables

M. Oliveira, Ana Teodoro, A. Freitas, J. Bernardes, H. Gonçalves

Proceedings Volume 10008, 100080B (2016) https://doi.org/10.1117/12.2241082

KEYWORDS: Environmental sensing, Environmental monitoring, Remote sensing, NOx, Humidity, Vegetation, Phase modulation, Atmospheric particles, Spatial analysis, Medicine

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Preterm birth (PTB), one of the major concerns in obstetrics, is conventionally defined as the delivery of a live infant before 37 completed weeks of gestation, and one of its causes may be environmental factors. Remote sensing is a valuable approach for monitoring environmental variables, including in health sciences. In this work, remote sensing data were used to explore the relation of the environment with PTB. Time-series with monthly rates of male/female ratio and PTB were obtained from Portugal in 2000-2014. The environmental variables included in this study were monthly mean temperatures (T), relative humidity (RH), NDVI, concentrations of NO₂ and PM₁₀ in 2003-2008. A temporal and spatial analysis of each health-related and environmental variable was performed, as well as their correlation. PTB has been increasing over time, from below 5% in 2000 to around 7% in 2014, with predominance of higher rates in districts with larger population. From 2003 to 2008, T and PM₁₀ decreased significantly. A positive and significant correlation was found between male/female ratio and NO₂ and RH, and to a lesser extent with PM₁₀ and NDVI. PTB was also positively and significantly correlated with NO₂ and T, and to a lesser extent with RH and PM₁₀. These preliminary results suggest an association of PTB with most of the environmental variables studied, showing that more polluted and populated districts have higher rates of PTB. Further studies are warranted to explore interaction between the considered environmental factors and other variables related with risk for PTB.

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