Classification of the Impact of Microclimatic Variables on Index Formation of Variance of the Urban Thermal Field in Urban Heat Islands in Cuiabá- Mato Grosso
DOI:
https://doi.org/10.26848/rbgf.v18.05.p4125-4150Keywords:
Era5, Landsat Satellite, Google Earth EngineAbstract
Evaluating the impact that microclimatic variables relative air humidity, NDVI, local wind direction and local
wind speed have on the occurrence of the Urban Heat Island Phenomenon (UHI) can help us understand the
formation of heat islands. One of the ways to evaluate is to use a data classification approach through Random
Forest to analyze the scenarios of the influence of the Urban Thermal Field Variance Index (UTFVI) on these
microclimatic variables and, in this way, understand which trends may occur due to behavioral changes in these
microclimatic variables. To achieve these objectives, data modeling will be carried out with images and sample
data obtained from Landsat 7, Landsat 8 satellites and ERA5 reanalysis data. With the images obtained, a soil
cover classification procedure will be carried out and with ERA5 reanalysis data the UTFVI classification will
be carried out. When applying the Random Forest algorithm, the model was generated with a data sample
reduced to UTFVI indication levels to determine the occurrence of the UHI Phenomenon (strong, very strong
and extreme) and thus obtain a more accurate predictive model. In addition to investigating the impact of
microclimatic variables on the UFTVI, we sought to detect trends in the temporary series of variations in
microclimatic variables in relation to the UTFVI and obtaining the results demonstrate the importance of
machine learning to improve the detection of factors that lead to dangerous conditions for the existence of the
UHI Phenomenon.
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