Geoinformatics and Remote Sensing (GId)
Change analysis in micromorphology and surface microtextures of detrital minerals from different sedimentary environments using geomorphometry and 3D modeling methods
The aim of dissertation thesis is to analyse and evaluate the micromorphological and microtextural features which occur on detrital mineral surfaces during their transport. For this purpose, the geomorphometry methods and derived 3D models in specialised 3D open-source software will be used. These methods will test the possibility of identification, quantification and visualization of specific features of mineral grain microrelief using general geomorphometry techniques. The relationship of the formation of individual microtextural features depending on the transport distance and character of sedimentary environment will also be tracked. The work will focus on creation of methodology for analysis of microtexture features on detrital mineral surfaces including the sampling in the field, sample processing in the laboratory, creation of DMR and 3D modelling of the mineral grain surfaces, geomorphometric analysis, interpretation of microtextures in relation to their genesis. The results of the research could be applicable in the (palaeo)environmental reconstructions.
Vos, K., Vandenberghe, N., Elsen, J., 2014. Surface textural analysis of quartz grains by scanning electron microscopy (SEM): From sample preparation to environmental interpretation. Earth-Science Reviews 128, 93–104. Suhr, B., Skipper, W.A., Lewis, R., Six, K., 2020. Shape analysis of railway ballast stones: curvature-based calculation of particle angularity. Scientific Reports 10, 6045. Kemnitz, H., Lucke, B., 2019. Quartz grain surfaces–A potential microarchive for sedimentation processes and parent material identification in soils of Jordan. Catena 176, 209–226. Krinsley, D.H., Doornkamp, J.C., 2011. Atlas of Quartz Sand Surface Textures. Cambridge University Press, New York, 1¬–91. Girardeau-Montaut, D., 2016. CloudCompare. France: EDF R&D Telecom ParisTech, 11. http://www.cloudcompare.org/
doc. Ing. Katarína Bónová, PhD.
Mgr. Jozef Šupinský, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Change analysis in micromorphology and surface microtextures of detrital minerals from different sedimentary environments using geomorphometry and 3D modeling methods
The aim of dissertation thesis is to analyse and evaluate the micromorphological and microtextural features which occur on detrital mineral surfaces during their transport. For this purpose, the geomorphometry methods and derived 3D models in specialised 3D open-source software will be used. These methods will test the possibility of identification, quantification and visualization of specific features of mineral grain microrelief using general geomorphometry techniques. The relationship of the formation of individual microtextural features depending on the transport distance and character of sedimentary environment will also be tracked. The work will focus on creation of methodology for analysis of microtexture features on detrital mineral surfaces including the sampling in the field, sample processing in the laboratory, creation of DMR and 3D modelling of the mineral grain surfaces, geomorphometric analysis, interpretation of microtextures in relation to their genesis. The results of the research could be applicable in the (palaeo)environmental reconstructions.
Vos, K., Vandenberghe, N., Elsen, J., 2014. Surface textural analysis of quartz grains by scanning electron microscopy (SEM): From sample preparation to environmental interpretation. Earth-Science Reviews 128, 93–104. Suhr, B., Skipper, W.A., Lewis, R., Six, K., 2020. Shape analysis of railway ballast stones: curvature-based calculation of particle angularity. Scientific Reports 10, 6045. Kemnitz, H., Lucke, B., 2019. Quartz grain surfaces–A potential microarchive for sedimentation processes and parent material identification in soils of Jordan. Catena 176, 209–226. Krinsley, D.H., Doornkamp, J.C., 2011. Atlas of Quartz Sand Surface Textures. Cambridge University Press, New York, 1¬–91. Girardeau-Montaut, D., 2016. CloudCompare. France: EDF R&D Telecom ParisTech, 11. http://www.cloudcompare.org/
doc. Ing. Katarína Bónová, PhD.
Mgr. Jozef Šupinský, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Improving the urban land surface temperature prediction using data from unmanned aerial systems
Modeling surface temperature of urbanized areas requires knowledge of the physical properties of various urban surfaces. Unmanned aerial systems represent a new platform for Earth Observation, allowing for high spatial and temporal resolution data acquisition through various sensors. In this dissertation, multiple sensors mounted on unmanned aerial systems will be utilized, including optical sensors capturing electromagnetic radiation across different spectral bands, a laser scanner, and an albedometer. The aim is to obtain detailed data on selected physical properties of the surface of the urbanized landscape of the city of Košice (such as color, albedo, temperature) using sensors mounted on unmanned aerial systems and to analyze the influence of urban landscape morphology and dynamics of selected indicators throughout the day and year (e.g., vegetation albedo). The expected outcome of this dissertation work is the improvement of spatial surface temperature distribution prediction and a better understanding of factors contributing to overheating in built-up areas. The research results will contribute to identifying locations contributing to the urban heat island effect and will serve as a basis for adopting mitigation measures, which can be considered as an applied outcome of the dissertation work.
The aim is to obtain detailed data on selected physical properties of the surface of the urbanized landscape of the city of Košice (such as color, albedo, temperature) using sensors mounted on unmanned aerial systems and to analyze the influence of urban landscape morphology and dynamics of selected indicators throughout the day and year (e.g., vegetation albedo).
BREMER, M.; MAYR, A.; WICHMANN, V.; SCHMIDTNER, K.; RUTZINGER, M., 2016. A new multi-scale 3D-GIS-approach for the assessment and dissemination of solar income of digital city models. Computers, Environment and Urban Systems. Volume 57, Pages 144 - 154. ISSN 01989715. doi:10.1016/j.compenvurbsys.2016.02.007. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. LIANG, J.; GONG, J.; ZHOU, J.; IBRAHIM, A. N.; LI, M., 2015. An open-source 3D solar radiation model integrated with a 3D Geographic Information System. Environmental Modelling & Software, Volume 64, Pages 94-101, ISSN 1364-8152, doi:10.1016/j.envsoft.2014.11.019. KAI MAINZER, SVEN KILLINGER, RUSSELL MCKENNA, WOLF FICHTNER. 2017. Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques. Solar Energy, Volume 155, pp. 561-57, doi:10.1016/j.solener.2017.06.065 OLPENDA, A. S., STEREŃCZAK, K., & BȨDKOWSKI, K. (2018). Modeling solar radiation in the forest using remote sensing data: A review of approaches and opportunities. Remote Sensing, 10(5) doi:10.3390/rs10050694
doc. RNDr. Ján Kaňuk, PhD.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Modeling the effects of urban heat island and mitigation measures
Mitigation of urban heat islands requires understanding of the factors affecting the interaction of solar radiation and urban surfaces. The absorbed heat manifests via land surface temperature and subsequently re-radiates into the surroundings increasing temperatures of ambient air. In this study, the student will focus on factors affecting the occurence of urban heat islands and using GRASS GIS tools will model various scenarios at high spatial and temporal resolutions. The 3-D city model in combination with other properties of urban surfaces. The results will be compared to microclimate measurements and remose sensing data.
AGHAMOHAMMADI, N., SANTAMOURIS, M. (2023). Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, https://doi.org/10.1007/978-981-19-4707-0_18. AKMAR, A. N., KONIJNENDIJK, C., SREETHERAN, M., NILSSN, K. J. A. (2011). Greenspace planning and management in Klang valley, Peninsular Malaysia. Urban Forestry & Urban Greening, 37(3), pp. 99-107. ALI, S. B., PATNAIK, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, pp. 954-967. BECKMANN, S. K., HIETE, M. (2020). Predictors Associated with Health-Related Heat Risk Perception of Urban Citizens in Germany. International Journal of Environmental Research and Public Health, 17(3), 874. CHEN, B., XIE, M., FENG, Q., LI, Z., CHU, L., LIU, Q. (2021). Heat risk of residents in different types of communities from urban heat-exposed areas. Science of The Total Environment, 768, 145052. DERKZEN, M. L., VAN TEEFFELEN, A. J. A., VERBURG, P. H. (2017). Green infrastructure for urban climate adaptation: how do residents’ views on climate impacts and green infrastructure shape adaptation preferences? Landscape and Urban Planning, 157, pp. 106-130. FRANCIS, R. A., LORIMER, J. (2011). Urban reconciliation ecology: The potential of living roofs and walls. Journal of Environmental Management, 92(6), pp. 1429-1437. HAALAND, C., VAN DEN BOSCH, C. K. (2015). Challenges and strategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry & Urban Greening, 14(4), pp. 760-771. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. HOLEC, J., ŠVEDA, M., SZATMÁRI, D., FERANEC, J., BOBÁĽOVÁ, H., KOPECKÁ, M., ŠŤASTNÝ, P. (2021). Heat risk assessment based on mobile phone data: case study of Bratislava, Slovakia. Natural Hazards, 108(3), pp. 3099-3120. IHA (2022). Institute of Health Analysis at the Ministry of Health of the Slovak Republic. Available at: https://www.health.gov.sk/?iza. KHATIBI, F. S., DEDEKORKUT-HOWES, A., HOWES, M., TORABI, E. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability, 2(1), pp. 1-24. KULLA, M., NOVOTNÝ, L., PREGI, L., DVOŘÁK, P., MARTINÁT, S., KLUSÁČEK, P., NAVRÁTIL, J., FRANTÁL, B. (2022). The Good, the Bad, and the Nobody. Exploring diversity of perceptions of anaerobic digestion plants in Central and Eastern Europe. Energy Research and Social Sciences, 89, 102644. KOPECKÁ M., SZATMÁRI, D., HOLEC, J., FERANEC, J. (2021). Urban heat island modelling based on MUKLIMO: examples from Slovakia. AGILE: GIScience Series Series, 2(5), pp. 1-11. LEHMANN, S. (2014). Low carbon districts: Mitigating the urban heat island with green roof infrastructure. City, Culture and Society, 5(1), pp. 1-8. LINDBERG, F. (2007). Modelling the urban climate using a local governmental geo-database. Meteorol. Appl. 14, pp. 263–273. LOUGHNAN, M., NICHOLLS, N., TAPPER, N. J. (2012). Mapping heat health risks in urban areas. International Journal of Population Research, 518687. MIRZAEI, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. MOSER, S. C., PIKE, C. (2015). Community engagement on adaptation: Meeting a growing capacity need. Urban Climate, 14, pp. 111–115. ONAČILLOVÁ, K., GALLAY, M. (2018). Spatio-temporal analysis of surface urban heat island based on LANDSAT ETM+ and OLI/TIRS imagery in the city of Košice, Slovakia. Carpathian Journal of Earth and Environmental Sciences, 13(2), pp. 395–408. PARSAEE, M., JOYBARI, M. M., MIRZAEI, P. A., HAGHIGHAT, F. (2019). Urban heat island, urban climate maps and urban development policies and action plans. Environmental Technology & Innovation, 14, 100341. RAJAGOPALAN, P., SANTAMOURIS, M., ANDAMON, M. M. (2017). Public engagement in urban microclimate research: an overview of a citizen science project. In Schnabel, M. A. (ed). Back to the future: the next 50 years. Wellington (Architectural Science Association), pp. 703-712. SANDHOLZ, S., SETT, D., GRECO, A., WANNEWITZ, M., GARSCHAGEN, M. (2021). Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Climate, 37, 100857. SARHADI, F., RAD, V. B. (2020). The structural model for thermal comfort based on perceptions individuals in open urban spaces. Building and Environment, 185, 107260. TAN, P. Y., WANG, J., SIA, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, pp. 24-32. TIAN, Y., JIM, C. Y. (2012). Development potential of sky gardens in the compact city of Hong Kong. Urban Forestry & Urban Greening, 11(3), pp. 223-233. WANG, CH., WANG, Z. H., KALOUSH, K. E., SHACAT, J. (2021). Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis. Sustainable Cities and Society, 66, 102687. WANG, Y, BERARDI, U., AKBARI, H. (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings 114, pp. 2-19.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GId)
Modeling the effects of urban heat island and mitigation measures
Mitigation of urban heat islands requires understanding of the factors affecting the interaction of solar radiation and urban surfaces. The absorbed heat manifests via land surface temperature and subsequently re-radiates into the surroundings increasing temperatures of ambient air. In this study, the student will focus on factors affecting the occurence of urban heat islands and using GRASS GIS tools will model various scenarios at high spatial and temporal resolutions. The 3-D city model in combination with other properties of urban surfaces. The results will be compared to microclimate measurements and remose sensing data.
AGHAMOHAMMADI, N., SANTAMOURIS, M. (2023). Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, https://doi.org/10.1007/978-981-19-4707-0_18. AKMAR, A. N., KONIJNENDIJK, C., SREETHERAN, M., NILSSN, K. J. A. (2011). Greenspace planning and management in Klang valley, Peninsular Malaysia. Urban Forestry & Urban Greening, 37(3), pp. 99-107. ALI, S. B., PATNAIK, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, pp. 954-967. BECKMANN, S. K., HIETE, M. (2020). Predictors Associated with Health-Related Heat Risk Perception of Urban Citizens in Germany. International Journal of Environmental Research and Public Health, 17(3), 874. CHEN, B., XIE, M., FENG, Q., LI, Z., CHU, L., LIU, Q. (2021). Heat risk of residents in different types of communities from urban heat-exposed areas. Science of The Total Environment, 768, 145052. DERKZEN, M. L., VAN TEEFFELEN, A. J. A., VERBURG, P. H. (2017). Green infrastructure for urban climate adaptation: how do residents’ views on climate impacts and green infrastructure shape adaptation preferences? Landscape and Urban Planning, 157, pp. 106-130. FRANCIS, R. A., LORIMER, J. (2011). Urban reconciliation ecology: The potential of living roofs and walls. Journal of Environmental Management, 92(6), pp. 1429-1437. HAALAND, C., VAN DEN BOSCH, C. K. (2015). Challenges and strategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry & Urban Greening, 14(4), pp. 760-771. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. HOLEC, J., ŠVEDA, M., SZATMÁRI, D., FERANEC, J., BOBÁĽOVÁ, H., KOPECKÁ, M., ŠŤASTNÝ, P. (2021). Heat risk assessment based on mobile phone data: case study of Bratislava, Slovakia. Natural Hazards, 108(3), pp. 3099-3120. IHA (2022). Institute of Health Analysis at the Ministry of Health of the Slovak Republic. Available at: https://www.health.gov.sk/?iza. KHATIBI, F. S., DEDEKORKUT-HOWES, A., HOWES, M., TORABI, E. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability, 2(1), pp. 1-24. KULLA, M., NOVOTNÝ, L., PREGI, L., DVOŘÁK, P., MARTINÁT, S., KLUSÁČEK, P., NAVRÁTIL, J., FRANTÁL, B. (2022). The Good, the Bad, and the Nobody. Exploring diversity of perceptions of anaerobic digestion plants in Central and Eastern Europe. Energy Research and Social Sciences, 89, 102644. KOPECKÁ M., SZATMÁRI, D., HOLEC, J., FERANEC, J. (2021). Urban heat island modelling based on MUKLIMO: examples from Slovakia. AGILE: GIScience Series Series, 2(5), pp. 1-11. LEHMANN, S. (2014). Low carbon districts: Mitigating the urban heat island with green roof infrastructure. City, Culture and Society, 5(1), pp. 1-8. LINDBERG, F. (2007). Modelling the urban climate using a local governmental geo-database. Meteorol. Appl. 14, pp. 263–273. LOUGHNAN, M., NICHOLLS, N., TAPPER, N. J. (2012). Mapping heat health risks in urban areas. International Journal of Population Research, 518687. MIRZAEI, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. MOSER, S. C., PIKE, C. (2015). Community engagement on adaptation: Meeting a growing capacity need. Urban Climate, 14, pp. 111–115. ONAČILLOVÁ, K., GALLAY, M. (2018). Spatio-temporal analysis of surface urban heat island based on LANDSAT ETM+ and OLI/TIRS imagery in the city of Košice, Slovakia. Carpathian Journal of Earth and Environmental Sciences, 13(2), pp. 395–408. PARSAEE, M., JOYBARI, M. M., MIRZAEI, P. A., HAGHIGHAT, F. (2019). Urban heat island, urban climate maps and urban development policies and action plans. Environmental Technology & Innovation, 14, 100341. RAJAGOPALAN, P., SANTAMOURIS, M., ANDAMON, M. M. (2017). Public engagement in urban microclimate research: an overview of a citizen science project. In Schnabel, M. A. (ed). Back to the future: the next 50 years. Wellington (Architectural Science Association), pp. 703-712. SANDHOLZ, S., SETT, D., GRECO, A., WANNEWITZ, M., GARSCHAGEN, M. (2021). Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Climate, 37, 100857. SARHADI, F., RAD, V. B. (2020). The structural model for thermal comfort based on perceptions individuals in open urban spaces. Building and Environment, 185, 107260. TAN, P. Y., WANG, J., SIA, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, pp. 24-32. TIAN, Y., JIM, C. Y. (2012). Development potential of sky gardens in the compact city of Hong Kong. Urban Forestry & Urban Greening, 11(3), pp. 223-233. WANG, CH., WANG, Z. H., KALOUSH, K. E., SHACAT, J. (2021). Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis. Sustainable Cities and Society, 66, 102687. WANG, Y, BERARDI, U., AKBARI, H. (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings 114, pp. 2-19.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GId)
Multiscale assessment of spatial aspects of social-economic stratification of society
The aim of the dissertation is to examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools. In the thesis, it will be necessary to identify appropriate indicators for different spatial scales and evaluate the appropriateness, availability and reliability of particular data. Various spatial unit networks will be applied to minimize the statistical bias resulting from the Modifiable areal unit problem (MAUP). The data will be applied to the required spatial units using areal transformation methods and similar data used at the international level for comparison of urban areas such as the Urban Atlas data from the European Copernicus monitoring program. Subsequently, the analysis and modelling of the spatial arrangement of social-economic stratification will be carried out with an emphasis on the chronological aspect and identification of factors leading to this stratification. Advanced statistical methods and tools of geographic information systems will be used. The thesis will also identify regularities of the spatial arrangement at various scales and with an emphasis on their evaluation in causal contexts.
To examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools.
Brzezinski, M. 2018: Income inequality and the Great Recession in Central and Eastern Europe. Economic Systems, 42(2), 219-247. Majzlíková, E., Vitáloš, M. 2021: Department of Economic Policy Working Paper Series, No. 24: Potential risk of automation for employment in Slovakia: A district- and industry-level analysis. Bratislava (University of Economics in Bratislava). Van Ham, M., Tammaru, T., Ubarevičiené, R., Janssen, H. eds. 2021: Urban Socio-Economic Segregation and Income Inequality - A Global Perspective. Cham (Springer). Xu, W., Engelman, M., Fletcher, J. 2021: From convergence to divergence: Lifespan variation in US states, 1959–2017. SSM: Population Health, 16, 100987.
doc. Mgr. Ladislav Novotný, PhD.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GId)
Multiscale approach in water flow simulation in urban landscape using Monte Carlo method and GRASS GIS
The ongoing climate change can bring severe weather conditions to cities with strong population. This includes heavy rainfalls and floods that consequently pose a threat to human lives, property and various activities. This work will focus on possible flash floods events in urban areas. Using the Monte Carlo simulation method implemented in GRASS GIS as r.sim.water, the young researcher will simulate a possible spatial distribution of water flow across various urban areas in multiple spatial scales. This will enable the identification of the hierarchy of factors contributing to flood occurrence. The simulation will also include an analysis of the impact of precipitation dynamics, as well as other input data, on the outcome of the modeling. The work involves collecting input data, processing them, conducting simulations in GRASS GIS, calibration, output analysis, possible validation and visualization.
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. HAAN, C.T., BARFIELD, B.J., HAYES. J.C., 1994. Design Hydrology and Sedimentology for Small Catchments. Academic Press. HOFIERKA, J., M. KNUTOVÁ, 2015. Simulating spatial aspects of a flash flood using the Monte Carlo method and GRASS GIS: a case study of the Malá Svinka Basin (Slovakia). Open Geosciences, 7, 118-125. HOFIERKA, J., LACKO, M., ZUBAL, S., 2017. Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP. Computers & Geosciences, 107, 20-27. HUNTER, N. M., BATES, P. D., NEELZ, S., PENDER, G., VILLANUEVA, I., WRIGHT, N. G., LIANG, D., FALCONER, R. A., LIN, B.,WALLER, S., CROSSLEY, A. J., MASON,D.C., 2008. Benchmarking 2Dhydraulic models for urban flooding. Proceedings of the Institution of Civil Engineers: Water Management 161 (1), 13–30. CHEN, W., HUANG, G., ZHANG, H., 2017. Urban stormwater inundation simulation based on SWMM and diffusive overland-flow model. Water Science and Technology 76 (12), 3392–3403. KULKARNI, A. T., MOHANTY, J., ELDHO, T. I., RAO, E. P., MOHAN, B. K., 2014. A web GIS-based integrated flood assessment modeling tool for coastal urban watersheds. Computers and Geosciences 64, 7–14. LI, H., GAO, H., ZHOU, Y., XU, C.-Y, ORTEGA, R.Z., SAELTHUN, N. R., 2020. Usage of SIMWE model to model urban overland flood: a case study in Oslo. Hydrology Research, 51, 366-380. MAIDMENT, D.R., 1993. Handbook of Hydrology, McGraw-Hill, New York. MAKSIMOVIC C., et al., 2009. Overal flow and pathway analysis for modelling of urban pluvial flooding. Journal of Hydraulic Research, 512-523. MENG, X., ZHANG, M., WEN, J., DU, S., XU, H., WANG, L., YANG, Y., 2019. A simple GIS-based model for urban rainstorm inundation simulation. Sustainability 11 (10), 1–19. MITAS, L., MITASOVA, H., 1998. Distributed soil erosion simulation for effective erosion prevention. Water Resources Research 34 (3), 505–516. MITASOVA, H., HARMON, R.S., WEAVER, K.J., LYONS, N.J., OVERTON, M.F., 2012. Scientific visualization of landscapes and landforms, Geomorphology, 137 (1), 122-137. NETELER, M., MITASOVA, H., 2008. Open Source GIS: A GRASS GIS Approach. Third Edition. The International Series in Engineering and Computer Science, Volume 773, Springer, New York. PETRASOVA, A., HARMON, B., PETRAS, V., TABRIZIAN, P., MITASOVA, H., 2018. Tangible modeling with Open Source GIS. Cham, Springer. YIN, J., YU, D., YIN, Z.,WANG, J., XU, S. 2015. Modeling the anthropogenic impacts on fluvial flood risks in a coastal megacity: a scenario-based case study in Shanghai, China. Landscape and Urban Planning 136, 144–155.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Multiscale approach in water flow simulation in urban landscape using Monte Carlo method and GRASS GIS
The ongoing climate change can bring severe weather conditions to cities with strong population. This includes heavy rainfalls and floods that consequently pose a threat to human lives, property and various activities. This work will focus on possible flash floods events in urban areas. Using the Monte Carlo simulation method implemented in GRASS GIS as r.sim.water, the young researcher will simulate a possible spatial distribution of water flow across various urban areas in multiple spatial scales. This will enable the identification of the hierarchy of factors contributing to flood occurrence. The simulation will also include an analysis of the impact of precipitation dynamics, as well as other input data, on the outcome of the modeling. The work involves collecting input data, processing them, conducting simulations in GRASS GIS, calibration, output analysis, possible validation and visualization.
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. HAAN, C.T., BARFIELD, B.J., HAYES. J.C., 1994. Design Hydrology and Sedimentology for Small Catchments. Academic Press. HOFIERKA, J., M. KNUTOVÁ, 2015. Simulating spatial aspects of a flash flood using the Monte Carlo method and GRASS GIS: a case study of the Malá Svinka Basin (Slovakia). Open Geosciences, 7, 118-125. HOFIERKA, J., LACKO, M., ZUBAL, S., 2017. Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP. Computers & Geosciences, 107, 20-27. HUNTER, N. M., BATES, P. D., NEELZ, S., PENDER, G., VILLANUEVA, I., WRIGHT, N. G., LIANG, D., FALCONER, R. A., LIN, B.,WALLER, S., CROSSLEY, A. J., MASON,D.C., 2008. Benchmarking 2Dhydraulic models for urban flooding. Proceedings of the Institution of Civil Engineers: Water Management 161 (1), 13–30. CHEN, W., HUANG, G., ZHANG, H., 2017. Urban stormwater inundation simulation based on SWMM and diffusive overland-flow model. Water Science and Technology 76 (12), 3392–3403. KULKARNI, A. T., MOHANTY, J., ELDHO, T. I., RAO, E. P., MOHAN, B. K., 2014. A web GIS-based integrated flood assessment modeling tool for coastal urban watersheds. Computers and Geosciences 64, 7–14. LI, H., GAO, H., ZHOU, Y., XU, C.-Y, ORTEGA, R.Z., SAELTHUN, N. R., 2020. Usage of SIMWE model to model urban overland flood: a case study in Oslo. Hydrology Research, 51, 366-380. MAIDMENT, D.R., 1993. Handbook of Hydrology, McGraw-Hill, New York. MAKSIMOVIC C., et al., 2009. Overal flow and pathway analysis for modelling of urban pluvial flooding. Journal of Hydraulic Research, 512-523. MENG, X., ZHANG, M., WEN, J., DU, S., XU, H., WANG, L., YANG, Y., 2019. A simple GIS-based model for urban rainstorm inundation simulation. Sustainability 11 (10), 1–19. MITAS, L., MITASOVA, H., 1998. Distributed soil erosion simulation for effective erosion prevention. Water Resources Research 34 (3), 505–516. MITASOVA, H., HARMON, R.S., WEAVER, K.J., LYONS, N.J., OVERTON, M.F., 2012. Scientific visualization of landscapes and landforms, Geomorphology, 137 (1), 122-137. NETELER, M., MITASOVA, H., 2008. Open Source GIS: A GRASS GIS Approach. Third Edition. The International Series in Engineering and Computer Science, Volume 773, Springer, New York. PETRASOVA, A., HARMON, B., PETRAS, V., TABRIZIAN, P., MITASOVA, H., 2018. Tangible modeling with Open Source GIS. Cham, Springer. YIN, J., YU, D., YIN, Z.,WANG, J., XU, S. 2015. Modeling the anthropogenic impacts on fluvial flood risks in a coastal megacity: a scenario-based case study in Shanghai, China. Landscape and Urban Planning 136, 144–155.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Multiscale assessment of spatial aspects of social-economic stratification of society
The aim of the dissertation is to examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools. In the thesis, it will be necessary to identify appropriate indicators for different spatial scales and evaluate the appropriateness, availability and reliability of particular data. Various spatial unit networks will be applied to minimize the statistical bias resulting from the Modifiable areal unit problem (MAUP). The data will be applied to the required spatial units using areal transformation methods and similar data used at the international level for comparison of urban areas such as the Urban Atlas data from the European Copernicus monitoring program. Subsequently, the analysis and modelling of the spatial arrangement of social-economic stratification will be carried out with an emphasis on the chronological aspect and identification of factors leading to this stratification. Advanced statistical methods and tools of geographic information systems will be used. The thesis will also identify regularities of the spatial arrangement at various scales and with an emphasis on their evaluation in causal contexts.
To examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools.
Brzezinski, M. 2018: Income inequality and the Great Recession in Central and Eastern Europe. Economic Systems, 42(2), 219-247. Majzlíková, E., Vitáloš, M. 2021: Department of Economic Policy Working Paper Series, No. 24: Potential risk of automation for employment in Slovakia: A district- and industry-level analysis. Bratislava (University of Economics in Bratislava). Van Ham, M., Tammaru, T., Ubarevičiené, R., Janssen, H. eds. 2021: Urban Socio-Economic Segregation and Income Inequality - A Global Perspective. Cham (Springer). Xu, W., Engelman, M., Fletcher, J. 2021: From convergence to divergence: Lifespan variation in US states, 1959–2017. SSM: Population Health, 16, 100987.
doc. Mgr. Ladislav Novotný, PhD.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GId)
Urban Overheating: Consequences, Mitigation and Perception
The aim of this study is to model the urban overheating for selected meteorological situations and identify locations in the city of Košice that are most affected by overheating using appropriate meso- and micro-scale meteorological models (e.g., WRF, ENVI-met, etc.), models of solar radiation distribution and surface temperature, as well as data from Earth observation techniques. In the identified locations, evaluate the size and structure of the population affected by overheating (outdoor, indoor), and thus determine the most threatened locations and populations in the city. For these problematic locations and meteorological situations, we will propose possible mitigation measures to reduce urban surface and ambient air temperature taking into account existing regulatory restrictions in the city. The measures will be proposed up to the level of individual buildings or streets. Measures in the form of modification of the input geospatial data will be used to recalculate the models to quantify their effectiveness. The proposed measures will be communicated with interested parties, including residents, in order to determine the perception and support of these measures. The survey will be conducted using online perception maps, and a structured questionnaire survey. The results will contribute to the development of methodology for addressing the problem of urban overheating and to define an optimal implementation strategy, including a communication strategy for all interested parties.
AGHAMOHAMMADI, N., SANTAMOURIS, M. (2023). Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, https://doi.org/10.1007/978-981-19-4707-0_18. AKMAR, A. N., KONIJNENDIJK, C., SREETHERAN, M., NILSSN, K. J. A. (2011). Greenspace planning and management in Klang valley, Peninsular Malaysia. Urban Forestry & Urban Greening, 37(3), pp. 99-107. ALI, S. B., PATNAIK, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, pp. 954-967. BECKMANN, S. K., HIETE, M. (2020). Predictors Associated with Health-Related Heat Risk Perception of Urban Citizens in Germany. International Journal of Environmental Research and Public Health, 17(3), 874. CHEN, B., XIE, M., FENG, Q., LI, Z., CHU, L., LIU, Q. (2021). Heat risk of residents in different types of communities from urban heat-exposed areas. Science of The Total Environment, 768, 145052. DERKZEN, M. L., VAN TEEFFELEN, A. J. A., VERBURG, P. H. (2017). Green infrastructure for urban climate adaptation: how do residents’ views on climate impacts and green infrastructure shape adaptation preferences? Landscape and Urban Planning, 157, pp. 106-130. FRANCIS, R. A., LORIMER, J. (2011). Urban reconciliation ecology: The potential of living roofs and walls. Journal of Environmental Management, 92(6), pp. 1429-1437. HAALAND, C., VAN DEN BOSCH, C. K. (2015). Challenges and strategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry & Urban Greening, 14(4), pp. 760-771. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. HOLEC, J., ŠVEDA, M., SZATMÁRI, D., FERANEC, J., BOBÁĽOVÁ, H., KOPECKÁ, M., ŠŤASTNÝ, P. (2021). Heat risk assessment based on mobile phone data: case study of Bratislava, Slovakia. Natural Hazards, 108(3), pp. 3099-3120. IHA (2022). Institute of Health Analysis at the Ministry of Health of the Slovak Republic. Available at: https://www.health.gov.sk/?iza. KHATIBI, F. S., DEDEKORKUT-HOWES, A., HOWES, M., TORABI, E. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability, 2(1), pp. 1-24. KULLA, M., NOVOTNÝ, L., PREGI, L., DVOŘÁK, P., MARTINÁT, S., KLUSÁČEK, P., NAVRÁTIL, J., FRANTÁL, B. (2022). The Good, the Bad, and the Nobody. Exploring diversity of perceptions of anaerobic digestion plants in Central and Eastern Europe. Energy Research and Social Sciences, 89, 102644. KOPECKÁ M., SZATMÁRI, D., HOLEC, J., FERANEC, J. (2021). Urban heat island modelling based on MUKLIMO: examples from Slovakia. AGILE: GIScience Series Series, 2(5), pp. 1-11. LEHMANN, S. (2014). Low carbon districts: Mitigating the urban heat island with green roof infrastructure. City, Culture and Society, 5(1), pp. 1-8. LINDBERG, F. (2007). Modelling the urban climate using a local governmental geo-database. Meteorol. Appl. 14, pp. 263–273. LOUGHNAN, M., NICHOLLS, N., TAPPER, N. J. (2012). Mapping heat health risks in urban areas. International Journal of Population Research, 518687. MIRZAEI, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. MOSER, S. C., PIKE, C. (2015). Community engagement on adaptation: Meeting a growing capacity need. Urban Climate, 14, pp. 111–115. ONAČILLOVÁ, K., GALLAY, M. (2018). Spatio-temporal analysis of surface urban heat island based on LANDSAT ETM+ and OLI/TIRS imagery in the city of Košice, Slovakia. Carpathian Journal of Earth and Environmental Sciences, 13(2), pp. 395–408. PARSAEE, M., JOYBARI, M. M., MIRZAEI, P. A., HAGHIGHAT, F. (2019). Urban heat island, urban climate maps and urban development policies and action plans. Environmental Technology & Innovation, 14, 100341. RAJAGOPALAN, P., SANTAMOURIS, M., ANDAMON, M. M. (2017). Public engagement in urban microclimate research: an overview of a citizen science project. In Schnabel, M. A. (ed). Back to the future: the next 50 years. Wellington (Architectural Science Association), pp. 703-712. SANDHOLZ, S., SETT, D., GRECO, A., WANNEWITZ, M., GARSCHAGEN, M. (2021). Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Climate, 37, 100857. SARHADI, F., RAD, V. B. (2020). The structural model for thermal comfort based on perceptions individuals in open urban spaces. Building and Environment, 185, 107260. TAN, P. Y., WANG, J., SIA, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, pp. 24-32. TIAN, Y., JIM, C. Y. (2012). Development potential of sky gardens in the compact city of Hong Kong. Urban Forestry & Urban Greening, 11(3), pp. 223-233. WANG, CH., WANG, Z. H., KALOUSH, K. E., SHACAT, J. (2021). Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis. Sustainable Cities and Society, 66, 102687. WANG, Y, BERARDI, U., AKBARI, H. (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings 114, pp. 2-19.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Urban Overheating: Consequences, Mitigation and Perception
The aim of this study is to model the urban overheating for selected meteorological situations and identify locations in the city of Košice that are most affected by overheating using appropriate meso- and micro-scale meteorological models (e.g., WRF, ENVI-met, etc.), models of solar radiation distribution and surface temperature, as well as data from Earth observation techniques. In the identified locations, evaluate the size and structure of the population affected by overheating (outdoor, indoor), and thus determine the most threatened locations and populations in the city. For these problematic locations and meteorological situations, we will propose possible mitigation measures to reduce urban surface and ambient air temperature taking into account existing regulatory restrictions in the city. The measures will be proposed up to the level of individual buildings or streets. Measures in the form of modification of the input geospatial data will be used to recalculate the models to quantify their effectiveness. The proposed measures will be communicated with interested parties, including residents, in order to determine the perception and support of these measures. The survey will be conducted using online perception maps, and a structured questionnaire survey. The results will contribute to the development of methodology for addressing the problem of urban overheating and to define an optimal implementation strategy, including a communication strategy for all interested parties.
AGHAMOHAMMADI, N., SANTAMOURIS, M. (2023). Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, https://doi.org/10.1007/978-981-19-4707-0_18. AKMAR, A. N., KONIJNENDIJK, C., SREETHERAN, M., NILSSN, K. J. A. (2011). Greenspace planning and management in Klang valley, Peninsular Malaysia. Urban Forestry & Urban Greening, 37(3), pp. 99-107. ALI, S. B., PATNAIK, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, pp. 954-967. BECKMANN, S. K., HIETE, M. (2020). Predictors Associated with Health-Related Heat Risk Perception of Urban Citizens in Germany. International Journal of Environmental Research and Public Health, 17(3), 874. CHEN, B., XIE, M., FENG, Q., LI, Z., CHU, L., LIU, Q. (2021). Heat risk of residents in different types of communities from urban heat-exposed areas. Science of The Total Environment, 768, 145052. DERKZEN, M. L., VAN TEEFFELEN, A. J. A., VERBURG, P. H. (2017). Green infrastructure for urban climate adaptation: how do residents’ views on climate impacts and green infrastructure shape adaptation preferences? Landscape and Urban Planning, 157, pp. 106-130. FRANCIS, R. A., LORIMER, J. (2011). Urban reconciliation ecology: The potential of living roofs and walls. Journal of Environmental Management, 92(6), pp. 1429-1437. HAALAND, C., VAN DEN BOSCH, C. K. (2015). Challenges and strategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry & Urban Greening, 14(4), pp. 760-771. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. HOLEC, J., ŠVEDA, M., SZATMÁRI, D., FERANEC, J., BOBÁĽOVÁ, H., KOPECKÁ, M., ŠŤASTNÝ, P. (2021). Heat risk assessment based on mobile phone data: case study of Bratislava, Slovakia. Natural Hazards, 108(3), pp. 3099-3120. IHA (2022). Institute of Health Analysis at the Ministry of Health of the Slovak Republic. Available at: https://www.health.gov.sk/?iza. KHATIBI, F. S., DEDEKORKUT-HOWES, A., HOWES, M., TORABI, E. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability, 2(1), pp. 1-24. KULLA, M., NOVOTNÝ, L., PREGI, L., DVOŘÁK, P., MARTINÁT, S., KLUSÁČEK, P., NAVRÁTIL, J., FRANTÁL, B. (2022). The Good, the Bad, and the Nobody. Exploring diversity of perceptions of anaerobic digestion plants in Central and Eastern Europe. Energy Research and Social Sciences, 89, 102644. KOPECKÁ M., SZATMÁRI, D., HOLEC, J., FERANEC, J. (2021). Urban heat island modelling based on MUKLIMO: examples from Slovakia. AGILE: GIScience Series Series, 2(5), pp. 1-11. LEHMANN, S. (2014). Low carbon districts: Mitigating the urban heat island with green roof infrastructure. City, Culture and Society, 5(1), pp. 1-8. LINDBERG, F. (2007). Modelling the urban climate using a local governmental geo-database. Meteorol. Appl. 14, pp. 263–273. LOUGHNAN, M., NICHOLLS, N., TAPPER, N. J. (2012). Mapping heat health risks in urban areas. International Journal of Population Research, 518687. MIRZAEI, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. MOSER, S. C., PIKE, C. (2015). Community engagement on adaptation: Meeting a growing capacity need. Urban Climate, 14, pp. 111–115. ONAČILLOVÁ, K., GALLAY, M. (2018). Spatio-temporal analysis of surface urban heat island based on LANDSAT ETM+ and OLI/TIRS imagery in the city of Košice, Slovakia. Carpathian Journal of Earth and Environmental Sciences, 13(2), pp. 395–408. PARSAEE, M., JOYBARI, M. M., MIRZAEI, P. A., HAGHIGHAT, F. (2019). Urban heat island, urban climate maps and urban development policies and action plans. Environmental Technology & Innovation, 14, 100341. RAJAGOPALAN, P., SANTAMOURIS, M., ANDAMON, M. M. (2017). Public engagement in urban microclimate research: an overview of a citizen science project. In Schnabel, M. A. (ed). Back to the future: the next 50 years. Wellington (Architectural Science Association), pp. 703-712. SANDHOLZ, S., SETT, D., GRECO, A., WANNEWITZ, M., GARSCHAGEN, M. (2021). Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Climate, 37, 100857. SARHADI, F., RAD, V. B. (2020). The structural model for thermal comfort based on perceptions individuals in open urban spaces. Building and Environment, 185, 107260. TAN, P. Y., WANG, J., SIA, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, pp. 24-32. TIAN, Y., JIM, C. Y. (2012). Development potential of sky gardens in the compact city of Hong Kong. Urban Forestry & Urban Greening, 11(3), pp. 223-233. WANG, CH., WANG, Z. H., KALOUSH, K. E., SHACAT, J. (2021). Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis. Sustainable Cities and Society, 66, 102687. WANG, Y, BERARDI, U., AKBARI, H. (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings 114, pp. 2-19.
prof. Mgr. Jaroslav Hofierka, PhD.
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Combining LiDAR and Hyperspectral UAV Data for Improved Vegetation Classification
This dissertation aims to propose novel approaches for classifying vegetation by integrating positional, radiometric, geometric, and temporal data from UAV LiDAR and hyperspectral imaging. Current remote sensing (RS) methods, which utilize various sensors aboard orbital platforms, aircraft, or unmanned aerial vehicles (UAV), have become essential for mapping vegetation, conducting diversity research, and monitoring changes over time. Integrating a LiDAR sensor with a hyperspectral camera on a UAV offers new research opportunities, particularly in capturing high-resolution vegetation data. LiDAR primarily records the landscape's three-dimensional geometry, while hyperspectral imaging identifies different landscape objects' spectral reflectance. A significant geoinformatics challenge is to efficiently process the vast datasets generated by these methods and to effectively combine them. Expected dissertation outcomes include implementing machine learning techniques for classifying data with numerous attributes, evaluating proposed approaches, and optimizing classification methods for UAV sensor data. We have tentatively identified the region of the Slovak Karst as our research site, notable for its unique xerothermic vegetation. This territory, a UNESCO Biosphere Reserve, harbors significant xerothermic communities. Our research aims to leverage these findings for the detailed delineation of habitat boundaries within the Slovak Karst, facilitating the mapping of poorly accessible areas. This, in turn, will contribute to a more detailed characterization of the area's species composition in terms of vegetation.
This dissertation aims to propose novel approaches for classifying vegetation by integrating positional, radiometric, geometric, and temporal data from UAV LiDAR and hyperspectral imaging.
Zhong, H., Lin, W., Liu, H., Ma, N., Liu, K., Cao, R., ... & Ren, Z. (2022). Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China. Frontiers in Plant Science, 13, 964769. Cao, J., Liu, K., Zhuo, L., Liu, L., Zhu, Y., & Peng, L. (2021). Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 102, 102414. Banerjee, B. P., Raval, S., & Cullen, P. J. (2020). UAV-hyperspectral imaging of spectrally complex environments. International Journal of Remote Sensing, 41(11), 4136-4159. Sankey, T. T., McVay, J., Swetnam, T. L., McClaran, M. P., Heilman, P., & Nichols, M. (2018). UAV hyperspectral and lidar data and their fusion for arid and semi‐arid land vegetation monitoring. Remote Sensing in Ecology and Conservation, 4(1), 20-33. Sankey, T., Donager, J., McVay, J., & Sankey, J. B. (2017). UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sensing of Environment, 195, 30-43. Kaňuk, J., Gallay, M., Eck, C., Zgraggen, C., & Dvorný, E. (2018). Technical report: unmanned helicopter solution for survey-grade LiDAR and hyperspectral mapping. Pure and Applied Geophysics, 175, 3357-3373.
doc. RNDr. Ján Kaňuk, PhD.
RNDr. Alena Gessert, PhD., univerzitná docentka
Geoinformatics and Remote Sensing (GId)
Improving the urban land surface temperature prediction using data from unmanned aerial systems
Modeling surface temperature of urbanized areas requires knowledge of the physical properties of various urban surfaces. Unmanned aerial systems represent a new platform for Earth Observation, allowing for high spatial and temporal resolution data acquisition through various sensors. In this dissertation, multiple sensors mounted on unmanned aerial systems will be utilized, including optical sensors capturing electromagnetic radiation across different spectral bands, a laser scanner, and an albedometer. The aim is to obtain detailed data on selected physical properties of the surface of the urbanized landscape of the city of Košice (such as color, albedo, temperature) using sensors mounted on unmanned aerial systems and to analyze the influence of urban landscape morphology and dynamics of selected indicators throughout the day and year (e.g., vegetation albedo). The expected outcome of this dissertation work is the improvement of spatial surface temperature distribution prediction and a better understanding of factors contributing to overheating in built-up areas. The research results will contribute to identifying locations contributing to the urban heat island effect and will serve as a basis for adopting mitigation measures, which can be considered as an applied outcome of the dissertation work.
The aim is to obtain detailed data on selected physical properties of the surface of the urbanized landscape of the city of Košice (such as color, albedo, temperature) using sensors mounted on unmanned aerial systems and to analyze the influence of urban landscape morphology and dynamics of selected indicators throughout the day and year (e.g., vegetation albedo).
BREMER, M.; MAYR, A.; WICHMANN, V.; SCHMIDTNER, K.; RUTZINGER, M., 2016. A new multi-scale 3D-GIS-approach for the assessment and dissemination of solar income of digital city models. Computers, Environment and Urban Systems. Volume 57, Pages 144 - 154. ISSN 01989715. doi:10.1016/j.compenvurbsys.2016.02.007. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. LIANG, J.; GONG, J.; ZHOU, J.; IBRAHIM, A. N.; LI, M., 2015. An open-source 3D solar radiation model integrated with a 3D Geographic Information System. Environmental Modelling & Software, Volume 64, Pages 94-101, ISSN 1364-8152, doi:10.1016/j.envsoft.2014.11.019. KAI MAINZER, SVEN KILLINGER, RUSSELL MCKENNA, WOLF FICHTNER. 2017. Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques. Solar Energy, Volume 155, pp. 561-57, doi:10.1016/j.solener.2017.06.065 OLPENDA, A. S., STEREŃCZAK, K., & BȨDKOWSKI, K. (2018). Modeling solar radiation in the forest using remote sensing data: A review of approaches and opportunities. Remote Sensing, 10(5) doi:10.3390/rs10050694
doc. RNDr. Ján Kaňuk, PhD.
prof. Mgr. Jaroslav Hofierka, PhD.