Towards an urban roughness parameterisation using interferometric SAR data taking the Metropolitan Region of Hamburg as an example

Bechtel, Benjamin; Langkamp, Thomas; Ament, Felix; Böhner, Jürgen; Daneke, Chrstian; Günzkofer, René; Leitl, Bernd; Oßenbrügge, Jürgen; Ringeler, Andre

doi:10.1127/0941-2948/2011/0496

Cited by 13 publications

(8 citation statements)

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“…The NDSM is the difference between the DSM and the DTM and hence contains solely the obstacles without the topography. In [25] it was shown that the frequency distribution of heights is related to aerodynamic roughness, a property in the LCZ fact sheets. The NDSM was projected to a 3 m UTM32 grid, which is slightly above the specified resolution of the data products.…”

Section: B Featuresmentioning

confidence: 99%

“…The individual classes aim to have 'relatively' homogenous air temperature within the canopy layer. They are defined by fact sheets with both qualitative and quantitative properties including several features that can be derived from EO data, like albedo [23], built fraction [24] and aerodynamic roughness [25]. This fosters applicability of the LCZ scheme as an interface between earth observation and urban climatology.…”

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confidence: 99%

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Classification of Local Climate Zones Based on Multiple Earth Observation Data

Bechtel

Daneke

2012

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Considerable progress was recently made in the determination of urban morphologies or structural types from different Earth observation (EO) datasets. A relevant field of application for such methods is urban climatology, since specific urban morphologies produce distinct microclimates. However, application and comparability are so far limited by the variety of typologies used for the description of urban surfaces in EO. In this study Local Climate Zones (LCZ), a system of thermally homogenous urban structures introduced by Stewart and Oke, was used in a pixel-based classification approach. Further, different EO datasets (including satellite multitemporal thermal and multispectral data as well as a normalized digital surface model (NDSM) from airborne Interferometric Synthetic Aperture Radar) and different classifiers (including Support Vector Machines, Neural Networks and Random Forest) were evaluated for their performance in a common framework. Especially the multitemporal thermal and spectral features yielded high potential for the discrimination of LCZ, but morphological profiles from the NDSM also performed well. Further, sets of 10-100 features were selected with the Minimum Redundancy Maximal Relevance approach from multiple EO data. Overall classification accuracies of up to 97.4% and 95.3% were obtained with a Neural Network and a Random Forest classifier respectively. This provides some evidence that LCZ can be derived from multiple EO data. Hence, we propose the typology and the method for the application of automated extraction of urban structures in urban climatology. Further the chosen multiple EO data and classifiers seemed to yield considerable potential for an automated classification of LCZ.

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Section: B Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

Classification of Local Climate Zones Based on Multiple Earth Observation Data

Bechtel

Daneke

2012

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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195

118

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“…Topographic predictors derived from NEXTMap® Interferometric Synthetic Aperture Radar (IFSAR) data include simple statistics of the height distribution [43] as well as a morphological opening and closing profile, which includes spatial information about spacing and texture of buildings and other objects [44,45].…”

Section: Predictorsmentioning

confidence: 99%

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

Bechtel¹,

2012

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Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R 2 : 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R 2 : 0.80, RMSE: 1.8 K for 500 m; R 2 : 0.82, RMSE: 1.6 K for 1,000 m).

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“…In , digital height models from interferometric synthetic aperture radar data were established to derive roughness parameters and anemometric characteristics of urban surfaces while the thermal properties were investigated regarding the annual cycles of surface temperatures (Bechtel, 2015(Bechtel, , 2012(Bechtel, , 2011a. Furthermore, the urban surface parameters were implemented for urban climatic modeling applications (Bechtel et al, 2012b;Bechtel and Schmidt, 2011) and the classification of local climate zones (Bechtel, 2011b;Bechtel et al, 2015Bechtel et al, , 2012aBech-tel and Daneke, 2012). Additionally, SAGA was utilized to develop downscaling schemes for land surface temperature from geostationary satellites to spatial resolutions of up to 100 m (Bechtel et al, 2012c;Bechtel et al, 2013) and to estimate in situ air temperatures (Bechtel et al, 2014).…”

Section: Remote Sensing and Image Processingmentioning

confidence: 99%

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

Conrad

Bechtel

Bock

et al. 2015

Preprint

190

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The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C + + in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing. Published by Copernicus Publications on behalf of the European Geosciences Union.Geosci. Model Dev.

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Towards an urban roughness parameterisation using interferometric SAR data taking the Metropolitan Region of Hamburg as an example

Cited by 13 publications

References 0 publications

Classification of Local Climate Zones Based on Multiple Earth Observation Data

Classification of Local Climate Zones Based on Multiple Earth Observation Data

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

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