Exploiting Earth Observation data products for mapping Local Climate Zones

Earth Observation (EO) systems and the advances in remote sensing technology increase the opportunities for monitoring the thermal behaviour of cities. Several parameters related to the urban climate can be quantified from EO data products, providing valuable support for advanced urban studies and climate modelling. In this study, remote sensing techniques are applied to derive quantitative information necessary to identify the Local Climate Zones (LCZ). Parameters like the pervious and impervious surface fraction, the surface albedo, the building density, the mean building/tree height and the sky view factor are quantified and used to map possible zones with homogeneous thermal characteristics, considered as LCZ.

[1]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[2]  G. Priestnalla,et al.  Extracting urban features from LiDAR digital surface models , 2022 .

[3]  Nektarios Chrysoulakis,et al.  Improving the estimation of urban surface emissivity based on sub-pixel classification of high resolution satellite imagery , 2012 .

[4]  Nektarios Chrysoulakis,et al.  Estimation of the all‐wave urban surface radiation balance by use of ASTER multispectral imagery and in situ spatial data , 2003 .

[5]  Steffen Fritz,et al.  Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[6]  S. Liang Narrowband to broadband conversions of land surface albedo I Algorithms , 2001 .

[7]  T. Oke,et al.  Local Climate Zones for Urban Temperature Studies , 2012 .

[8]  P. Gambaa,et al.  URBAN CLIMATE ZONE DETECTION AND DISCRIMINATION USING OBJECT-BASED ANALYSIS OF VHR SCENES , 2012 .

[9]  Iain Stewart,et al.  Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities , 2015, ISPRS Int. J. Geo Inf..

[10]  N. Skarbit,et al.  Airborne surface temperature differences of the different Local Climate Zones in the urban area of a medium sized city , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[11]  Andreas Schenk,et al.  Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Benjamin Bechtel,et al.  Classification of Local Climate Zones Based on Multiple Earth Observation Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Andrew E. Suyker,et al.  Land management and land-cover change have impacts of similar magnitude on surface temperature , 2014 .

[14]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

[15]  T. Vesala,et al.  Sustainable urban metabolism as a link between bio-physical sciences and urban planning : the BRIDGE project , 2013 .

[16]  J. Unger,et al.  Design of an urban monitoring network based on Local Climate Zone mapping and temperature pattern modelling , 2014 .

[17]  Eberhard Parlow,et al.  Modelling the ground heat flux of an urban area using remote sensing data , 2007 .

[18]  T. Painter,et al.  Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .

[19]  F. Lindberg,et al.  Continuous sky view factor maps from high resolution urban digital elevation models , 2010 .

[20]  R. Goossens,et al.  Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area – a comparative study , 2013 .

[21]  J. A. Voogta,et al.  Thermal remote sensing of urban climates , 2003 .

[22]  R. Betts,et al.  Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective) , 2010 .

[23]  Uwe Soergel,et al.  Combining High-Resolution Optical and InSAR Features for Height Estimation of Buildings With Flat Roofs , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Martin J. Wooster,et al.  Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach , 2008 .