Abstract
This study focuses on the derivation of an urban surface material map to parameterize a 3D numerical microclimate model. For this purpose, fusion of airborne hyperspectral and light detection and ranging (LiDAR) remote sensing data is performed. In a first step, surface materials are extracted from the preprocessed input datasets using a hybrid, three-stage classification approach. The resulting map is then utilized in combination with the LiDAR object height information data to parameterize the microclimate model. To demonstrate the potential of data-driven microclimate modeling, two case studies are presented for selected test sites in the City of Houston, Texas. The results of this study highlight that the synergistic combination of hyperspectral and LiDAR data enables reliable mapping of some of the key input parameters required for urban microclimate modeling. Moreover, classification-based microclimate simulations can reveal the thermal properties of urban neighborhoods under varying conditions and, thus, facilitate the identification of hot spot areas and critical land cover configurations.
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References
Ali-Toudert F, Djenane M, Bensalem R, Mayer H (2005) Outdoor thermal comfort in the old desert city of Beni-Isguen, Algeria. Clim Res 28(3):243–256
Ali-Toudert F, Mayer H (2006) Numerical study on the effects of aspect ratio and orientation of an urban street canyon on outdoor thermal comfort in hot and dry climate. Build Environ 41(2):94–108
Altman D (1991) Practical statistics for medical research. Chapman & Hall, Boca Raton, p 624
Angel S, Parent J, Civco D, Biel A (2011) Making room for a planet of cities. Policy focus report, ID PF027, Lincoln Institute of Land Policy, Cambridge
Arnfield A (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat Island. Int J Clim 23(1):1–26
Bauer M, Loeffelholz B, Wilson B (2008) Estimating and mapping impervious surface area by regression analysis of Landsat imagery. In: Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp 3–19
Ben-Dor E, Levin N, Saaroni H (2001) A spectral based recognition of the urban environment using the visible and near-infrared spectral region (0.4–1.1 μm). A case study over Tel-Aviv, Israel. Int J Remote Sens 22(11):2193–2218
Ben-Dor E, Saaroni H (1997) Airborne video thermal radiometry as a tool for monitoring microscale structures of the urban heat Island. Int J Remote Sens 18(14):3039–3053
Benz U, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm 58(3–4):239–258
Berger C, Voltersen M, Hese S, Walde I, Schmullius C (2013) Robust extraction of urban land cover information from HSR multi-spectral and LiDAR data. IEEE J Sel Top Appl Earth Obs Remote Sens 6(5):2196–2211
Bruse M (1999) Simulating microscale climate interactions in complex terrain with a high-resolution numerical model: a case study for the Sydney CBD area (model description). In: Proceedings of the ICUC & ICB, Sydney, pp 1–6
Bruse M (2000) Anwendung von mikroskaligen Simulationsmodellen in der Stadtplanung. In: Bernhard L, Küger T (eds) Simulation raumbezogener Prozesse: Methoden und Anwendung, IfGIprints 9. University Press, Münster, pp 1–21
Bruse M (2009) ENVI-met board. To compare simulated and measured results validly. http://www.envi-met.info/envibbv3/viewtopic.php?f=11%26t=142. Cited 6 Jan 2014
Bruse M, Fleer H (1998) Simulating surface–plant–air interactions inside urban environments with a three dimensional numerical model. Environ Model Softw 13(3–4):373–384
Changnon S (1992) Inadvertent weather modification in urban areas: lessons for global climate change. Bull Am Meteor Soc 73(5):619–627
Cocks T, Jenssen R, Stewart A, Wilson I, Shields T (1998) The HyMap airborne hyperspectral sensor: the system, calibration and performance. In: Proceedings of the 1st EARSeL workshop on imaging spectroscopy, Zurich, pp 37–42
Cohen J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46
Congalton R, Green K (2009) Assessing the accuracy of remotely sensed data. Principles and practices, 2nd edn. CRC, Boca Raton
Cook B, Corp L, Nelson R, Middleton E, Morton D, McCorkel J, Masek J, Ranson KJ, Ly V, Montesano P (2013) NASA Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) airborne imager. Remote Sens 5(8):4045–4066
Curriero F, Heiner K, Samet J, Zeger S, Strug L, Patz J (2002) Temperature and mortality in 11 cities of the Eastern United States. Epidemiology 155(1):80–87
Ehlers M (2009) Future EO sensors of relevance – integrated perspective for global urban monitoring. In: Gamba P, Herold M (eds) Global mapping of human settlement. CRC, Boca Raton, pp 321–337
Elmore A, Guinn S (2010) Synergistic use of Landsat multispectral scanner with GIRAS land-cover data to retrieve impervious surface area for the Potomac River Basin in 1975. Remote Sens Environ 114(10):2384–2391
Emmanuel R, Fernando H (2007) Urban heat Islands in humid and arid climates: role of urban form and thermal properties in Colombo, Sri Lanka and Phoenix, USA. Clim Res 34(3):241–251
Esch T, Himmler V, Schorcht G, Thiel M, Wehrmann T, Bachofer F, Conrad C, Schmidt M, Dech S (2009) Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data. Remote Sens Environ 113(8):1678–1690
Ewing R, Rong F (2008) The impact of urban form on U.S. residential energy use. Hous Policy Debate 19(1):1–30
Franke J, Roberts D, Halligan K, Menz G (2009) Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sens Environ 113(8):1712–1723
Grouven U, Bender R, Ziegler A, Lange S (2007) The kappa coefficient. Dtsch Med Wochenschr 132(23):65–68
Guhathakurta S, Gober P (2007) The impact of the Phoenix urban heat Island on residential water use. J Am Plan Assoc 73(3):317–329
Heiden U, Segl K, Roessner S, Kaufmann H (2007) Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. Remote Sens Environ 111(4):537–552
Helbig A, Baumüller J, Kerschgens M (1999) Stadtklima und Luftreinhaltung, 2nd edn. Springer, Berlin
Heldens W (2010) Use of airborne hyperspectral data and height information to support urban micro climate characterisation. PhD thesis, Julius-Maximilians-Universität, Würzburg
Heldens W, Esch T, Heiden U (2012) Supporting urban micro climate modelling with airborne hyperspectral data. In: Proceedings of the IGARSS, Munich, pp 1598–1601
Heldens W, Heiden U, Esch T, Dech S (2010) Potential of hyperspectral data for urban micro climate analysis. In: Proceedings of the ESA hyperspectral workshop, Frascati, pp 1–8
Heldens W, Heiden U, Esch T, Stein E, Müller A (2011) Can the future EnMAP mission contribute to urban applications? A literature survey. Remote Sens 3(9):1817–1846
Herold M (2007) Spectral characteristics of asphalt road surfaces. In: Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp. 237–248
Herold M, Roberts D (2006) Multispectral satellites – imaging spectrometry – LIDAR: spatial – spectral tradeoffs in urban mapping. Int J Geoinf 2(1):1–13
Herold M, Roberts D, Gardner M, Dennison P (2004) Spectrometry for urban area remote sensing – development and analysis of a spectral library from 350 to 2400 nm. Remote Sens Environ 91(3–4):304–319
Herold M, Schiefer S, Hostert P, Roberts D (2007) Applying imaging spectrometry in urban areas. In: Weng Q, Quattrochi D (eds) Urban remote sensing. CRC, Boca Raton, pp 137–161
Howard L (1833) The climate of London deduced from meteorological observations made in the metropolis and at various places around it, vol I–III, 2nd edn. J. Rickerby, London
Huttner S, Bruse M (2009) Numerical modeling of the urban climate – a preview on ENVI-met 4.0. In: Proceedings of the 7th ICUC, Yokohama, pp 1–4
Image Analysis and Data Fusion Technical Committee (2013) 2013 IEEE GRSS data fusion contest. http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/. Cited 6 Jan 2014
Itres Research Ltd. (2013) CASI-1500 hyperspectral imager. http://www.itres.com/products/imagers/casi1500. Cited 6 Jan 2014
Jensen J, Cowen D (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogramm Eng Remote Sens 65(5):611–622
Johnson D, Wilson J (2009) The socio-spatial dynamics of extreme urban heat events: the case of heat-related deaths in Philadelphia. Appl Geogr 29(3):419–434
Jung A, Tökei L, Kardevan P (2007) Application of airborne hyperspectral and thermal images to analyse urban microclimate. Appl Ecol Environ Res 5(1):165–175
Kalnay E, Cai M (2003) Impact of urbanization and land-use change on climate. Nature 423:528–531
Köppen W (1936) Das geographische System der Klimate. In: Köppen W, Geiger R (eds) Handbuch der Klimatologie, vol 1, Part C. Gebrüder Borntraeger, Berlin, pp 1–44
Landsberg H (1981) The urban climate. International geophysics series, vol 28. Academic, New York City
Leinenkugel P, Esch T, Gähler M (2011) Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data. Remote Sens Environ 115(12):3007–3019
LiDAR Online (2014) LiDAR online – worldwide LiDAR data and geoservices. https://www.lidar-online.com/. Cited 6 Jan 2014
Lott N, Baldwin R, Jones P (2001) The FCC integrated surface hourly database, a new resource of global climate data. Technical report 2001-01, National Climatic Data Center, Asheville
Lowry W (1998) Urban effects on precipitation amount. Prog Phys Geogr 22(4):477–520
Luo L, Mountrakis G (2010) Integrating intermediate inputs from partially classified images within a hybrid classification framework: an impervious surface estimation example. Remote Sens Environ 114(6):1220–1229
Ma R (2005) DEM generation and building detection from LiDAR data. Photogramm Eng Remote Sens 71(7):847–854
McCarthy M, Best M, Betts R (2010) Climate change in cities due to global warming and urban effects. Geophys Res Lett 37(9):1–5
Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm 66(3):247–259
NASA Jet Propulsion Laboratory (2014) AVIRIS data – ordering free AVIRIS standard data products. http://aviris.jpl.nasa.gov/data/free_data.html. Cited 6 Jan 2014
National Climatic Data Center (2012a) Houston, TX. Average relative humidity (%). http://lwf.ncdc.noaa.gov/oa/climate/online/ccd/avgrh.html. Cited 6 Jan 2014
National Climatic Data Center (2012b) Houston, TX. Mean number of days with maximum temperature 90 degrees F or higher. http://lwf.ncdc.noaa.gov/oa/climate/online/ccd/max90temp.html. Cited 6 Jan 2014
National Oceanic and Atmospheric Administration (2013) NOAA digital coast data access viewer. http://www.csc.noaa.gov/dataviewer/. Cited 6 Jan 2014
Oke T (1973) City size and the urban heat Island. Atmos Environ 7(8):769–779
Oke T (1982) The energetic basis of the urban heat Island. Q J R Meteor Soc 108(455):1–24
OpenTopography (2014) OpenTopography – a portal to high-resolution topography data and tools. http://www.opentopography.org/. Cited 6 Jan 2014
Peel M, Finlayson B, McMahon T (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11(5):1633–1644
Pohl C, van Genderen J (1998) Multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens 19(5):823–854
Priestnall G, Jafaar J, Duncan A (2000) Extracting urban features from LiDAR digital surface models. Comput Environ Urban Syst 24(2):65–78
Quattrochi D, Ridd M (1994) Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data. Int J Remote Sens 15(10):1991–2022
Richter R, Schläpfer D (2013) Atmospheric/topographic correction for airborne imagery. ATCOR-4 user guide, version 6.2.1. German Aerospace Center (DLR), Wessling
Rigo G, Parlow E (2007) Modelling the ground heat flux of an urban area using remote sensing data. Theor Appl Climatol 90(3–4):185–190
Samaali M, Courault D, Bruse M, Olioso A, Occelli R (2007) Analysis of a 3D boundary layer model at local scale: validation on soybean surface radiative measurements. Atmos Res 85(2):183–198
Savitzky A, Golay M (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639
Scalenghe R, Marsan F (2009) The anthropogenic sealing of soils in urban areas. Landsc Urban Plan 90(1):1–10
Skelhorn C, Lindley S, Levermore G (2014) The impact of vegetation types on air and surface temperatures in a temperate city: a fine scale assessment in Manchester, UK. Landsc Urban Plan 121:129–140
Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens Environ 88(1–2):170–186
Sobrino J, Oltra-Carrió R, Sòria G, Bianchi R, Paganini M (2012) Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sens Environ 117:50–56
Stone B, Norman J (2006) Land use planning and surface heat Island formation: a parcel-based radiation flux approach. Atmos Environ 40(19):3561–3573
Streutker D (2002) A remote sensing study of the urban heat Island of Houston, Texas. Int J Remote Sens 23(13):2595–2608
Taubenböck H, Esch T, Felbier A, Wiesner M, Roth A, Dech S (2012) Monitoring urbanization in mega cities from space. Remote Sens Environ 117:162–176
The City of Houston (2013) Houston facts and figures. http://www.houstontx.gov/abouthouston/houstonfacts.html. Cited 6 Jan 2014
Toy S, Yilmaz S, Yilmaz H (2007) Determination of bioclimatic comfort in three different land uses in the city of Erzurum, Turkey. Build Environ 42(3):1315–1318
Trimble Ltd. (2013) eCognition developer 8.7.1. Reference book. Trimble documentation, Munich
Tucker C (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150
United Nations (2008) World urbanization prospects. The 2007 revision. Executive summary, Department of Economic and Social Affairs. Population Division, New York City
University of Utah (2009) Houston, TX. Wind – average speed (mph). Utah and national climate data. http://www.met.utah.edu/jhorel/html/wx/climo.html. Cited 6 Jan 2014
US Census Bureau (2013) Houston (city), Texas. Land area in square miles, 2010. State & County QuickFacts. http://quickfacts.census.gov/qfd/states/48/4835000.html. Cited 6 Jan 2014
Vaiphasa C (2006) Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS J Photogramm 60(2):91–99
Voogt J (2002) Urban heat island. In: Munn T (ed) Encyclopedia of global environmental change, vol 3. Wiley, Chichester, pp 660–666
Voogt J, Oke T (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3): 370–384
Welch R (1982) Spatial resolution requirements for urban studies. Int J Remote Sens 3(2):139–146
Weng Q (2008) Remote sensing of impervious surfaces. In: Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp XV–XXVI
Wilson F (1992) A moment in building. Blueprints X(3) http://web.archive.org/web/20070206075059/ http://www.nbm.org/blueprints/90s/summer92/contents/contents.htm. Cited 6 Jan 2014
Woodcock C, Strahler A (1987) The factor of scale in remote sensing. Remote Sens Environ 21(3):311–332
Xu W, Wooster M, Grimmond C (2008) Modelling of urban sensible heat flux at multiple spatial scales: a demonstration using airborne hyperspectral imagery of Shanghai and a temperature-emissivity separation approach. Remote Sens Environ 112(9):3493–3510
Yang L, Huang C, Homer C, Wylie B, Coan M (2003) An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Can J Remote Sens 29(2):230–240
Yu B, Liu H, Wu J, Hu Y, Zhang L (2010) Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landsc Urban Plan 98(3–4):210–219
Yuan F, Bauer M (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat Island effects in Landsat imagery. Remote Sens Environ 106(3):375–386
Zevenbergen L, Thorne C (1987) Quantitative analysis of land surface topography. Earth Surf Process Landf 12(1):12–56
Acknowledgements
The authors would like to thank the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the “grss_dfc_2013” data set used in this study and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for organizing the 2013 Data Fusion Contest. The authors also wish to thank the three anonymous reviewers whose comments helped to substantially improve an earlier version of this chapter.
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Berger, C., Riedel, F., Rosentreter, J., Stein, E., Hese, S., Schmullius, C. (2015). Fusion of Airborne Hyperspectral and LiDAR Remote Sensing Data to Study the Thermal Characteristics of Urban Environments. In: Helbich, M., Jokar Arsanjani, J., Leitner, M. (eds) Computational Approaches for Urban Environments. Geotechnologies and the Environment, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-11469-9_11
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