XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/8: Land]

Document type:: Multivolume work

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Title:: CLASSIFICATION AND MODELLING OF URBAN MICRO-CLIMATES USING MULTISENSORAL AND MULTITEMPORAL REMOTE SENSING DATA B. Bechtel, T. Langkamp, J. Böhner, C. Daneke, J. Oßenbrügge, S. Schempp

Document type:: Multivolume work

Structure type:: Chapter

38, 2012 fier and 100 (IQ). Mixed ) data. d multisensoral ‘the mean UHI ayer Perceptron ie models were sample of about in prior model mal (correlation ).17/0.19 K for rk) and the 17/0.19 K) data s (R: 0.27/0.26, parameters (R: tainly partly a predicted mean quality of the pect to the low tter models this %). This is not ses contributing fects are related temperature in lirectly to the ultispectral data | conditions and odel calibration. ral and height e same sensor. :d features with night indicate a le might not be are likely to be International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia Parameter set LR NN N| R MAE RMSE| R MAE RMSE shs 01.027 024 932 | 026. 0.26 0.33 acp 2| 0.19 024 032 | 0.18 024 0.32 tir 331.074 20.17: 022 | 081 019 023 ndvi 331 0.66 0.19 025 | 070 0.21 02% ms 198] 0.75 017 022 \ 077 018 023 R».25 711 0.71 3 0.13 023 | 073 0138 025 all 2721 6.79 30.16 021] 0:32 .. 0.15 0.2 Table 3. Results of the spatial empirical models (Linear Regression and Neuronal Network) of the mean UHI with different parameter/predictor sets. Correlation coefficient R, mean absolute error and root mean square error. Nevertheless, it can be stated, that multisensoral and multitemporal datasets have some potential for spatiotemporal modelling of the mean UHI. This underlines the results of Bechtel and Schmidt, who found strong correlations between Landsat data and a long-term mean UHI dataset derived from floristic proxy data (Bechtel and Schmidt, 2011). The performance of Linear Regression and Neuronal Network models was rather similar, which might be due to the chosen standard options for the neuronal network classifier (with only one hidden layer). First tests with more sophisticated networks showed better results (for instance R: 0.83, MAE: 0.14 K for tir with a 20|10|10 node network). 5. CONCLUSIONS The presented results from Hamburg indicate that multisensoral and multitemporal data has potential for both, the classification of Local Climate Zones and the empirical modelling of the spatial distribution of the UHI. The classification results show that the data (especially multitemporal thermal and multitemporal spectral data) are functional for the purpose and that micro-climatic meaningful urban structures can be classified from different remote sensing datasets. Further, it provides some evidence for the relevance of the Local Climate Zone system from a remote sensing point of view. The empirical modelling results also underpin the urban climatologic relevance of the multitemporal tir und ms data. Although a certain correlation is obvious, since vegetation and surface energy balance play important roles in the distinction of urban climates, these good results with freely available Landsat data offer the prospect of a wide application. However, further investigations are needed and the large number and complexity of the involved processes limits the potential of empirical models. The incorporation of data from other sensors also slightly improved the empirical modelling results. 6. REFERENCES Arnfield, A. J., 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23(1), pp. 1-26. Barsi, J. A., Schott, J. R., Palluconi, F. D., and Hook, S. J., 2005. Validation of a Web-Based atmospheric correction tool for single thermal band instruments. Earth Observing Systems X, Proc. SPIE, 58820F, pp. 1-7. Bechtel, B., 2011. Multitemporal Landsat data for urban heat island assessment and classification of local climate zones. Urban Remote Sensing Event, JURSE, 2011 Joint, pp. 129-132. doi:10.1109/JURSE.2011.5764736. Bechtel, B., 2012. Robustness of Annual Cycle Parameters to characterize the Urban Thermal Landscapes. IEEE Geoscience and Remote Sensing Letters, doi 10.1109/LGRS.2012.2185034. Bechtel, B. and Daneke, C., 2012, Classification of local Climate Zones based on multiple Earth Observation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi 10.1109/JSTARS.2012.2189873. Bechtel, B., Langkamp, T., Ament, F., Bohner, J., Daneke, C., Gunzkofer, R., Leitl, B. et al, 2011. Towards an urban roughness parameterisation using interferometric SAR data taking the Metropolitan Region of Hamburg as an example. Meteorologische Zeitschrift, 20(1), pp. 29—37. Bechtel, B., and Schmidt, K., 2011. Floristic mapping data as a proxy for the mean urban heat island. Climate Research, 49, pp. 45-58. doi:10.3354/cr01009 Bouckaert, R. R., Frank, E., Hall, M., Kirkby, R., Reutemann, P. Seewald, A., and Scuse, D., 2009. WEKA manual for version 3-7-0. University of Waikato, Hamilton, New Zealand. Disponible en: http://ufpr. dl. sourceforge. net/project/weka/documentation/3.7. x/WekaManual-3-7-0. pdf. Burges, C. J. C., 1998. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), pp. 121-167. Chander, G., Markham, B. L., and Helder, D. L., 2009. Summary of current radiometric calibration coefficients of Landsat MSS, TM, ETM+ and EO-1 ALI Sensors. Remote Sensing of Environment, 113, pp. 893-903. Eliasson, I, 1992. Infrared thermography and urban temperature patterns. International Journal of Remote Sensing, 13, pp. 869-879. Fabrizi, R., De Santis, A., and Gomez, A., 2011. Satellite and ground-based sensors for the Urban Heat Island analysis in the city of Madrid. Urban Remote Sensing Event, JURSE, 2011 Joint. doi:10.1109/JURSE.2011.5764791 Frey, C., and Parlow, E., 2009. Geometry effect on the estimation of band reflectance in an urban area. Theoretical and Applied Climatology, 96(3), pp. 395—406. Frey, C., Rigo, G., and Parlow, E., 2007. Urban radiation balance of two coastal cities in a hot and dry environment. International Journal of Remote Sensing, 28(12), pp. 2695— 2712.

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