69
For a given urban zone, the complexity of the urban environment sets special
requirements for siting observation equipment to provide representative values that
are little affected by nearby buildings or pollution sources. The interpretation of
atmospheric conditions between measuring sites requires detailed information
about surface characteristics and the use of urban scale numerical models. For a
proper selection of appropriate sites for UHI monitoring we can use also geographic
information system. UHI atlas is one example for presentation of urban heat island
phenomena and influencing factors.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons
license and any changes made are indicated.
The images or other third party material in this chapter are included in the work's Creative
Commons license, unless indicated otherwise in the credit line; if such material is not included in
the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce
the material.
References
Balázs, B., Unger, J., Gál, T., Sümeghy, Z., Geiger, J., & Szegedi, S. (2009). Simulation of the
mean urban heat island using 2D surface parameters: Empirical modelling, verification and
extension. Meteorological Applications, 16(3), 275–287.
Brunt J., Benson, B., Vande Castle, J., Henshaw, D., & Porter, J. (2007). LTER network cyberinfrastructure strategic plan, Version 4. Retrieved October 28, 2011. Available at http://intranet2.
lternet.edu/sites/intranet2.lternet.edu/files/documents/LTER_History/Planning_Documents/
LTER_CI_Strategic_Plan_.pdf
Ciglič, R., & Komac, B. (2015). Central-European urban heat island atlas. Ljubljana: ZRC
SAZU. http://gismo.zrc-sazu.si/flexviewers/UHIAtlas/.
Colombi, A., Pepe, M., & Rampini, A. (2007). Estimation of daily mean air temperature from
MODIS LST in Alpine areas. Rotterdam: New Developments and Challenges in Remote
Sensing.
Estrin, D., Michener, W., & Bonito, G. (2003). Environmental cyberinfrastructure needs for distributed sensor networks: A report from a National Science Foundation sponsored workshop.
La Jolla: Scripps.
Halitsky, J. (1963). Gas diffusion near buildings. Transactions – American Society of Heating,
Refrigerating and Air-Conditioning Engineers, 69, 464–485.
Harman, I. N. (2003). The energy balance of urban areas. PhD thesis. University of Reading.
Hart, J. K., & Martinez, K. (2006). Environmental sensor networks: A revolution in the earth system science? Earth-Science Reviews, 78, 177–191.
Jarvis, A., Reuter, H. I., Nelson, A., Guevara, E. (2008). Hole-filled seamless SRTM data V4.
International Centre for Tropical Agriculture (CIAT). Available from http://srtm.csi.cgiar.org.
Komac, B., & Ciglič, R. (2014). Urban heat island atlas: A web tool for the determination and mitigation of urban heat island effects. Geographia Polonica, 87, 595–595. Warszawa.
Muller, C. L., Chapman, L., Grimmond, C. S. B., Young, D. T., & Cai, X. (2013). Sensors and the
city: A review of urban meteorological networks. International Journal of Climatology, 33(7),
1585–1600.
2 Urban Heat Island Gold Standard and Urban Heat Island Atlas
For a given urban zone, the complexity of the urban environment sets special
requirements for siting observation equipment to provide representative values that
are little affected by nearby buildings or pollution sources. The interpretation of
atmospheric conditions between measuring sites requires detailed information
about surface characteristics and the use of urban scale numerical models. For a
proper selection of appropriate sites for UHI monitoring we can use also geographic
information system. UHI atlas is one example for presentation of urban heat island
phenomena and influencing factors.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons
license and any changes made are indicated.
The images or other third party material in this chapter are included in the work's Creative
Commons license, unless indicated otherwise in the credit line; if such material is not included in
the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce
the material.
References
Balázs, B., Unger, J., Gál, T., Sümeghy, Z., Geiger, J., & Szegedi, S. (2009). Simulation of the
mean urban heat island using 2D surface parameters: Empirical modelling, verification and
extension. Meteorological Applications, 16(3), 275–287.
Brunt J., Benson, B., Vande Castle, J., Henshaw, D., & Porter, J. (2007). LTER network cyberinfrastructure strategic plan, Version 4. Retrieved October 28, 2011. Available at http://intranet2.
lternet.edu/sites/intranet2.lternet.edu/files/documents/LTER_History/Planning_Documents/
LTER_CI_Strategic_Plan_.pdf
Ciglič, R., & Komac, B. (2015). Central-European urban heat island atlas. Ljubljana: ZRC
SAZU. http://gismo.zrc-sazu.si/flexviewers/UHIAtlas/.
Colombi, A., Pepe, M., & Rampini, A. (2007). Estimation of daily mean air temperature from
MODIS LST in Alpine areas. Rotterdam: New Developments and Challenges in Remote
Sensing.
Estrin, D., Michener, W., & Bonito, G. (2003). Environmental cyberinfrastructure needs for distributed sensor networks: A report from a National Science Foundation sponsored workshop.
La Jolla: Scripps.
Halitsky, J. (1963). Gas diffusion near buildings. Transactions – American Society of Heating,
Refrigerating and Air-Conditioning Engineers, 69, 464–485.
Harman, I. N. (2003). The energy balance of urban areas. PhD thesis. University of Reading.
Hart, J. K., & Martinez, K. (2006). Environmental sensor networks: A revolution in the earth system science? Earth-Science Reviews, 78, 177–191.
Jarvis, A., Reuter, H. I., Nelson, A., Guevara, E. (2008). Hole-filled seamless SRTM data V4.
International Centre for Tropical Agriculture (CIAT). Available from http://srtm.csi.cgiar.org.
Komac, B., & Ciglič, R. (2014). Urban heat island atlas: A web tool for the determination and mitigation of urban heat island effects. Geographia Polonica, 87, 595–595. Warszawa.
Muller, C. L., Chapman, L., Grimmond, C. S. B., Young, D. T., & Cai, X. (2013). Sensors and the
city: A review of urban meteorological networks. International Journal of Climatology, 33(7),
1585–1600.
2 Urban Heat Island Gold Standard and Urban Heat Island Atlas