Comparison between Air Temperature and Land Surface Temperature for the City of São Paulo, Brazil

Abstract

This study aims to identify the relationship between changes in temperature regarding urbanization processes and seasonality in the city of São Paulo, located in the Tropic of Capricorn. The land surface temperature (LST) results were compared to official weather stations measurements, identifying in the spring–summer period 65.5% to 86.2% accuracy, while in the autumn–winter period, the results ranged from 58.6% to 93.1% accuracy, when considering the standard deviation and the temperature probe error. The mean MAE and mean RMSE range from 1.2 to 1.9 °C, with 83.0% of the values being ≤2.7 °C, and the coefficient of determination values are R = 0.81 in spring–summer and R = 0.82 in autumn–winter. Great thermal amplitude was estimated in the spring–summer season, with a difference in LST of the built-up space and rural area ranging from 5.8 and 11.5 °C, while in the autumn–winter season, the LST is more distributed through the city, with differences ranging from 4.4 to 8.5 °C. In addition, the current study suggests remote sensing as a reliable, cheap, and practical methodology to assist climate in order to support public policies and decision-making actions regarding environmental and urban planning.

Estimation of Makassar’s Landfill Surface Temperature and Its Surroundings Using Remote Sensing

“…Compared to the land surface temperature in Figure 6, the higher temperature was in the built-up area and the landfill. As mentioned in do Nascimento [17], the relationship between the land surface temperature and land cover classification is the temperature will be higher in regions with higher densities of people, followed by lower densities area and vegetation cover, then high vegetation densities area and water bodies have lower temperatures which are similar to the result of this study. Furthermore, the classified image in land cover identification needs to be assessed for accuracy to evaluate the accuracy with which the pixels were sampled into the appropriate land cover classes on a quantitative level so that it can be used as input for any applications or further study [18].…”

Vertical Eddy Diffusivity in the Tropical Cyclone Boundary Layer during Landfall

“…It is typically assumed that the value of K t is equal to that of K m , or it is calculated using Prandtl and Schmidt numbers (P r ) [42]. In future research, in addition to the momentum flux, more observational data of the heat and moisture fluxes in high winds need to be analyzed, to evaluate the characteristic of Pr to advance our understanding of the air-sea momentum and latent heat exchange under extreme wind conditions [43][44][45][46][47][48].…”

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

“…The years highlighted in the time series correspond to El Niño episodes, for example, moderate El Niño (2001-2002 cycle), weak El Niño (2007) and especially the extreme droughts that occurred in 2005, 2010 and 2015 in the Amazon [51][52][53]. The years that were above the average also correspond to changes in the orbital sensors for detecting hotspots, for example, at the beginning of the time series AVHRR (Advanced Very High-Resolution Radiometer) was used; from 2002 MODIS was used until mid-2012 [15,18,27].…”

“…This highest percentage is directly related to the time of year where the highest amount of forest fires and burns occur, corresponding to the dry season [21,51]. It is well known that August, September and October resemble the critical period of fire incidence not merely within the Amazon region but in Brazil in general [15,54,55], mainly during the ENSO phases episodes (El Niño and/or La Niña), which highly effect the frequency, duration and quantity of rainfall in the south and west of the Amazon basin [52,53,56]. During the wet season, however, the effect is the opposite, and a significant decrease in fire foci is observed, especially from January to May, where rain amounts are increased, moisturizing the forest, making the soil relatively humid, which makes it difficult for the fire to spread.…”

“…During the wet season, however, the effect is the opposite, and a significant decrease in fire foci is observed, especially from January to May, where rain amounts are increased, moisturizing the forest, making the soil relatively humid, which makes it difficult for the fire to spread. Fires occur every year, with a significant increase during the dry season [9,15], particularly during 2005, 2010 and 2015, since several severe droughts resulted in an exponential increase in forest fires in the Legal Amazon [23,52,54,55]. The ENSO phases are responsible for the variability for rainfall [23,52], which can make the forest dry and consequently prone to fire hazard, comparing to the wet season.…”