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Assessing land surface temperature in urban areas using open-source geospatial tools

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Assessing land surface temperature in urban areas using open-source geospatial tools
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351
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Abstract
Land surface temperature (LST) in urban areas is an important environmental variable considered a reliable indicator of the urban heat island (UHI) phenomenon. LST is affected by various factors such as solar irradiance, cloudiness, wind or urban morphology. Traditionally, LST is observed and recorded by thermal remote sensors. For example, thermal satellite sensors are very popular for assessing the UHI effect on a global scale such as MODIS, Sentinel 3, ASTER, Landsat 7 ETM+, or Landsat 8 TIRS. However, these sensors provide rather low spatial (60 m to 1000 m) and temporal resolutions (several hours to days) of satellite observations that limit the accurate estimation of LST in urban areas for local studies and specific time periods (Mushore et al., 2017), (Hu and Wendel, 2019). Airborne or terrestrial remote sensing can be viewed as another option for capturing higher spatial resolution of thermal data but it is not feasible to be used for large urban areas with increased periodicity. However, the increasing availability of the high-resolution geospatial data and adequate modeling techniques provide an alternative approach to high-resolution estimation of LST in urban areas. Several studies showed the potential of geographic information system (GIS) tools, digital surface models (DSM) and 3-D city models for the estimation of solar radiation in urban areas (e.g., Hofierka and Kaňuk, 2009; Hofierka and Zlocha, 2012; Freitas et al., 2015; Biljecki et al., 2015). Solar irradiance is a key factor affecting LST during daylight periods, especially under clear sky situations. Nevertheless, LST assessment requires a physical model combining surface-atmosphere interactions and energy fluxes between the atmosphere and the ground. Properties of urban materials, in particular, solar reflectance, thermal emissivity, and heat capacity influence the LST and subsequently the development of UHI, as they determine how the Sun’s radiation energy is reflected, emitted, and absorbed (Hofierka et al., 2020b; Kolečanský et al., 2021). It is clear, that the problem complexity requires a comprehensive GIS-based approach. Our solution is based on open-source solar radiation tools available in GRASS GIS, a 3D city modeling and spatially distributed data representing thermal properties of urban surfaces and meteorological conditions (Hofierka et al., 2020a, 2020b; Kolečanský et al., 2021) . The proposed LST model is calculated using the methodology implemented in GRASS GIS as a LST module written using a script (shellscripts, Python). In these scripts, the r.sun and v.sun solar radiation models in GRASS GIS were used to calculate the effective solar irradiance for selected time horizons during the day . The solar irradiance calculation accounts for attenuation of beam solar irradiance by clouds estimated by field measurements. The proposed LST model also accounts for a heat storage in urban structures depending on their thermal properties and geometric configuration. The 2D LST model uses the output of the r.sun solar radiation model and a DSM representing urban surfaces and the 3D LST model uses the output of the v.sun solar radiation model and a vector-based 3D city model. Computed LST values for selected urban surfaces were validated using field measurements of LST in 10 locations within the study area with acceptable accuracy. The proposed approach has the advantage of providing high spatial detail coupled with the flexibility of GIS to evaluate various geometrical and land surface properties for any daytime horizon. The methodology can be used for evaluation of proposed UHI mitigation measures such as increasing albedo of urban surfaces or expanding green areas including green roofs and trees.
Keywords
Open sourceNeuroinformatikGrass (card game)Presentation of a groupCompilation albumComputer animation
Ultraviolet photoelectron spectroscopyPoint cloudPressure volume diagramTemporal logicPoint cloudPrice indexIntermediate languageWärmestrahlungTemporal logicSurfaceMeasurementComputer animation
Mathematical modelVector spaceBoltzmann constantGrass (card game)CoefficientHeat transferDigital signalSurfaceNichtlineares GleichungssystemRaster graphicsParameter (computer programming)Mathematical modelUltraviolet photoelectron spectroscopyScripting languageCalculationUniform convergenceoutputFaculty (division)FingerprintDigital photographySource codeOpen setCondition numberMathematical modelMaterialization (paranormal)Normal distributionVector spaceGrass (card game)CalculationModule (mathematics)Observational studyoutputParameter (computer programming)Flow separationHorizonMeasurementRevision controlLevel (video gaming)MereologyRaster graphicsCASE <Informatik>Different (Kate Ryan album)Sampling (statistics)Procedural programmingResultantBuildingScripting languageAreaRight angleMathematical modelMultiplication signComputer animation
Transcript: English(auto-generated)
And good morning to everybody here and online. In this short, brief presentation, I'd like to talk about new tools for computation of land surface temperature in a grass GIS. Land surface temperature, or LST,
is considered quite a reliable indicator for urban heat island phenomenon. And LST is usually measured using satellite or airborne thermal sensors. The problem is that these sensors have quite a rough spatial and temporal resolution.
And sometimes even clouds can pose an obstacle to sense or measure this data. So we developed a new tool for calculation to calculate LST in grass GIS using physical principles.
And it is implemented in grass GIS as RLST for raster data and the V.LST for vector 3D city models. This model is quite simple. It uses several input parameters.
One of them, or the most important, is solar irradiance. That is calculated also in grass GIS using r.sun. This module is part of the standard distribution of grass. And also, v.sun solar radiation model,
there is full 3D solar radiation model that is not part of the standard distribution of grass. So in this picture, you can see the 2D version of LST calculation or model that is calculated for urban data.
And here, I want to show you examples how LST can be calculated for 3D city model. So you can see here on the left, autofone map of our study area, sample of 3D city model. And the procedure is quite simple,
because first, you need to calculate solar irradiance using v.sun in this case. And then using v.LST script, you can calculate LST values. So this is solar radiation map.
These are three time horizons. On the upper left side is at 7 o'clock in the morning. Then there is noon. And on the lower right, there is evening at 17 or 5 PM.
So you can see that for buildings, you have different values of solar irradiance that are directly translated to land surface temperature. So the result is here. So you can see that some buildings in the morning
have more higher temperature on facades than on roofs. During the noon, you can see on the lower left picture that roofs have more, are hotter. And evening is the opposite of the morning's situation.
So using this model, you can calculate LST. Then you can take some preventing or mitigating measures to mitigate urban heat island phenomenon to use maybe roofs with the lower albedo or higher albedo.
Or you can use different materials to decrease temperature of the building materials. So thank you very much. If you are interested in this topic, you can find here three papers. The first one is published within this conference.
And the next two have been already published in scientific journals. Thank you very much.