Jaroslav Hofierka is a professor of Geoinformatics at Pavol Jozef Šafárik University in Košice. He has published a numerous papers on geospatial modelling, remote sensing, solar radiation modelling using GIS and 3-D city applications. He has been a member of the GRASS development team for more than 20 years. He is a co-author of the r.sun and v.sun solar radiation models implemented in open-source GRASS GIS. He has participated in the development of more than 10 GRASS GIS modules related to spatial interpolation and simulation of landscape processes such as solar radiation, surface hydrology and 3-D modelling.
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.
Biljecki, F., Stoter, J., Ledoux, H., Zlatanova, S., Çöltekin A., 2015. Applications of 3-D city models: State of the art review. ISPRS International Journal of Geo-Information, 4, 2842–2889. https://doi.org/10.3390/ijgi4042842.
Freitas, S., Catita, C., Redweik, P., Brito, M. C., 2015. Modelling solar potential in the urban environment: State-of-the-art review. Renewable and Sustainable Energy Reviews, 41, 915–931. http://dx.doi.org/10.1016/j.rser.2014.08.060.
Hofierka, J., Bogľarský, J., Kolečanský, Š., Enderova, A., 2020a. Modeling Diurnal Changes in Land Surface Temperature in Urban Areas under Cloudy Conditions. ISPRS Int. J. Geo-Inf., 9, 534.
Hofierka, J., Gallay, M., Onačillová, K., Hofierka, J. Jr., 2020b. Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566.
Hofierka, J., Kaňuk, J., 2009. Assessment of photovoltaic potential in urban areas using open-source solar radiation tools. Renewable Energy, 34, 2206–2214. https://doi.org/10.1016/j.renene.2009.02.021.
Hofierka, J., Zlocha, M., 2012. A New 3-D Solar Radiation Model for 3-D City Models. Transactions in GIS, 16, 681–690. https://doi.org/10.1111/j.1467-9671.2012.01337.x.
Hu, L., Wendel, J., 2019. Analysis of urban surface morphologic effects on diurnal thermal directional anisotropy. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 1–12. https://doi.org/10.1016/j.isprsjprs.2018.12.004.
Kolečanský, Š., Hofierka, J., Bogľarský, J., Šupinský, J., 2021. Comparing 2D and 3D Solar Radiation Modeling in Urban Areas. Energies, 14, 8364.
Mushore, T.D., Odindi, J., Dube, T., Matongera, T.N., Mutanga, O., 2017. Remote sensing applications in monitoring urban growth impacts on in-and-out door thermal conditions: A review. Remote Sensing Applications: Society and Environment, 8, 83–93. https://doi.org/10.1016/j.rsase.2017.08.001.