Thermal imaging of urban land surfaces using remote sensing

Dr Conrad H. Philipp Postdoctoral Research Fellow University of South Australia (UniSA) Coordinator CRC-LCL urban micro climate (RP2005)

21 March 2016

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Design: C. Thorton

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Measurements of the land surface temperature Aerial flyovers with thermal sensory equipment

Source: CC Melbourne

Measurements of the land surface temperature Remote sensing technique

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Satellite sensors to collect TIR (Thermal Emission and Reflection) data from the Earth’s Surface

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-Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER), not free, 30 m resolution, global coverage 16 days -the Moderate Resolution Imaging Spectro-radiometer (MODIS) free, 1000 m resolution, global coverage 1-2 days

-Landsat Thematic Mapper (TM)/Enhanced TM (ETM+)/ Operational Land Imager (OLI) free, 30-100 m resolution, global coverage 16 days

1972: NASA lunched the first civil remote sensing satellite: Landsat -1 2013: NASA launched Landsat-8

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Modification of the Mono Window Algorithm

Besides the emissivity correction all radiation from the earth surface to the sensor has to pass the atmosphere, about 705 km from ground to satellite, where the gaseous, aerosol and dust components absorb some energy. RTE-method, single-channel method and mono window algorithm.

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Atmospheric correction Temperature and relative humidity are used as basic parameters for thermal calculations

http://weather.uwyo.edu/upperair/sounding.html

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Modification of the Mono Window Algorithm

http://weather.uwyo.edu/upperair/sounding.html

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Land surface temperature (Sydney, SA2) False-colour image (left) and land surface temperature (right)

Landsat 8 OLI – 08 November 2014 (band composite 7-5-3 and band 10, thermal band), 23:44 UTC

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Urban heat investigation in Hong Kong

Land surface temperature map of Hong Kong (28.12.2014), 02:46 UTC

no thermal data

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Land surface temperature (Adelaide, SA2) Urban LST (left) and land surface temperature (right)

Landsat 8 OLI – 29 August 2014 (band composite 7-5-3 and band 10, thermal band), 00:33 UTC

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Land surface temperature (Melbourne) False-colour image (left) and land surface temperature (right)

Landsat 8 OLI – 14 September 2015 (band composite 7-5-3 and band 10, thermal band), 00:09 UTC

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Land surface temperature (Sydney) False-colour image (left) and land surface temperature (right)

Landsat 8 OLI – 08 November 2014 (band composite 7-5-3 and band 10, thermal band), 23:44 UTC

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First cloudless Landsat thermal night image of Singapore 11 Oct 2015 - Landsat 8 (OLI) 15:14 UTC

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Bauhaus University Summer School: 2014 – 2015 – 2016

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Bauhaus University Summer School: Case study Cairo

Impact of a Greenbelt on the Megacity Seoul 605 km² / 17,000 Inh. per km² How cool is the Greenbelt compare to the Seoul City? How is the seasonal course characterized ?

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“Urban climate research has a high potential to build up an international research network.” Tony Peacock CEO of the CRC Association 30 June 2015 Berlin

Prof Dr Johanna Wanka The Federal Ministry of Education and Research, Germany 3. Clusterconference Berlin 30 June / 1 July 2015

Grimmond, C. S. B.; Oke, T. R. Turbulent Heat Fluxes in Urban Areas: Observations and a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS). J. Appl. Meteorol. 2002, 41, 792–810. Maiheu, B.; Ridder, K. De; Benedicte Dousset; Manuta, P.; Ceriola, G.; Viel, M.; Daglis, I. A.; Keramitsoglou, I.; Ginannaros, T.; Melas, D.; et al. Modelling Air temperature via Assimilation of Satellite Derived Surface Temperature within the Urban Heat Island Project. In EARSel Workshop Proceedings of the Joint SIG Workshop; 2010; pp. 1–17. Lagouarde, J.; Moreau, P.; Irvine, M.; Bonnefond, J.-M.; Voogt, J. A.; Solliec, F. Airborne experimental measurements of the angular variations in surface temperature over urban areas: case study of Marseille (France). Remote Sens. Environ. 2004, 93, 443–462. Keramitsoglou, I. Evaluation of satellite-derived products for the characterization of the urban thermal environment. J. Appl. Remote Sens. 2012, 6, 061704. Keramitsoglou, I.; Kiranoudis, C. T.; Maiheu, B.; De Ridder, K.; Daglis, I. a; Manunta, P.; Paganini, M. Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environ. Monit. Assess. 2013. Oltra-Carrió, R.; Sobrino, J. a.; Franch, B.; Nerry, F. Land surface emissivity retrieval from airborne sensor over urban areas. Remote Sens. Environ. 2012, 123, 298–305. Schwarz, N.; Lautenbach, S.; Seppelt, R. Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens. Environ. 2011, 115, 3175–3186. Sobrino, J. A.; Julien, Y.; Atitar, M.; Nerry, F. NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data. IEEE Trans. Geosci. Remote Sens. 2008, 46. Voogt, J. A.; Oke, T. R. Thermal remote sensing of urban climates. Remote Sens. Environ. 2003, 86, 370–384.

Literature

To find out more, contact:

CRC for Low Carbon Living Ltd www.lowcarbonlivingcrc.com.au

Room 202-207, Level 2 Tyree Energy Technologies Building UNSW Sydney NSW 2052 Australia

Twitter: @CRC_LCL info@lowcarbonlivingcrc.com.au P: +61 2 9385 5402 F: +61 2 9385 5530

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Thank you

Extra contact details if required:

Dr Conrad H. Philipp

Postdoctoral Research Fellow University of South Australia (UniSA)

conrad.philipp@unisa.edu.au