Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities

ISPRS International Journal of Geo-Information - Tập 4 Số 1 - Trang 199-219
Benjamin Bechtel1, Paul J. Alexander2, Jürgen Böhner1, Jason Ching3, Olaf Conrad1, Johannes J. Feddema4, Gerald Mills5, Linda See6, Iain D. Stewart7
1Institute of Geography, University of Hamburg, Bundesstraße 55, Hamburg 20146, Germany
2Irish Climate Analysis & Research Units, Department of Geography, Room 1.8 Laraghbryan House, Maynooth University, Kildare, Ireland
3Institute for the Environment, The University of North Carolina at Chapel Hill, UNC-Chapel Hill, NC 27514, USA
4Department of Geography, University of Kansas, 1475 Jayhawk Blvd, Lawrence, KS 66049, USA
5School Of Geog, Planning & Env Policy, Newman Building, UCD, Belfield Dublin 4, University College Dublin, Dublin 4, Ireland
6Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg 2361, Austria
7Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, ON M5S 2J7, Canada

Tóm tắt

Progress in urban climate science is severely restricted by the lack of useful information that describes aspects of the form and function of cities at a detailed spatial resolution. To overcome this shortcoming we are initiating an international effort to develop the World Urban Database and Access Portal Tools (WUDAPT) to gather and disseminate this information in a consistent manner for urban areas worldwide. The first step in developing WUDAPT is a description of cities based on the Local Climate Zone (LCZ) scheme, which classifies natural and urban landscapes into categories based on climate-relevant surface properties. This methodology provides a culturally-neutral framework for collecting information about the internal physical structure of cities. Moreover, studies have shown that remote sensing data can be used for supervised LCZ mapping. Mapping of LCZs is complicated because similar LCZs in different regions have dissimilar spectral properties due to differences in vegetation, building materials and other variations in cultural and physical environmental factors. The WUDAPT protocol developed here provides an easy to understand workflow; uses freely available data and software; and can be applied by someone without specialist knowledge in spatial analysis or urban climate science. The paper also provides an example use of the WUDAPT project results.

Từ khóa


Tài liệu tham khảo

Mills, 2007, Cities as agents of global change, Int. J. Climatol., 27, 1849, 10.1002/joc.1604

Oke, 1973, City size and the urban heat island, Atmos. Environ., 7, 769, 10.1016/0004-6981(73)90140-6

Ching, 2013, A perspective on urban canopy layer modeling for weather, climate and air quality applications, Urban. Clim., 3, 13, 10.1016/j.uclim.2013.02.001

Satterthwaite, A.D.E., Aragón-Durand, F., Corfee-Morlot, J., Kiunsi, M., Pelling, M., Roberts, D.C., and Solecki, W. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Available online: http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-FrontMatterA_FINAL.pdf.

Weng, Q. (2014). Global Urban Monitoring and Assessment through Earth Observation, CRC Press.

Esch, 2010, Delineation of urban footprints from Terrasar-X data by analyzing speckle characteristics and intensity information, IEEE Trans. Geosci. Remote Sens., 48, 905, 10.1109/TGRS.2009.2037144

Gamba, 2011, Robust extraction of urban area extents in HR and VHR SAR images, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 4, 27, 10.1109/JSTARS.2010.2052023

Esch, 2013, Urban footprint processor—Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission, IEEE Geosci. Remote Sens. Lett., 10, 1617, 10.1109/LGRS.2013.2272953

Miyazaki, 2013, An automated method for global urban area mapping by integrating ASTER satellite images and GIS data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 1004, 10.1109/JSTARS.2012.2226563

Pesaresi, 2013, A Global Human Settlement Layer from optical HR/VHR RS data: Concept and first results, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 2102, 10.1109/JSTARS.2013.2271445

Ching, J., Mills, G., Feddema, J.J., Oleson, K., See, L., Stewart, I.D., Bechtel, B., Chen, F., Wang, X., and Neophytou, M.K.A. WUDAPT: Facilitating Advanced Urban Canopy Modeling for Weather, Climate and Air Quality Applications. Available online: https://ams.confex.com/ams/94Annual/webprogram/Paper236443.html.

See, L., Bechtel, B., Ching, J.K.S., Feddema, J.J., Foley, M., Fritz, S., Masson, V., Mills, G., Perger, C., and Stewart, I.D. (2015). Developing a community-based worldwide urban database of morphology and materials using remote sensing and crowdsourcing for improved urban climate/UHI modeling. Lausanne, submitted.

Stewart, 2012, Local climate zones for urban temperature studies, Bull. Am. Meteorol. Soc., 93, 1879, 10.1175/BAMS-D-11-00019.1

Bechtel, 2012, Classification of local climate zones based on multiple earth observation data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 5, 1191, 10.1109/JSTARS.2012.2189873

Stewart, 2011, A systematic review and scientific critique of methodology in modern urban heat island literature, Int. J. Climatol., 31, 200, 10.1002/joc.2141

Ndetto, E.L., and Matzarakis, A. Urban Atmospheric Environment and Human Biometeorological Studies in Dar es Salaam, Tanzania. Available online: http://link.springer.com/article/10.1007/s11869-014-0261-z.

Fenner, 2014, Spatial and temporal air temperature variability in Berlin, Germany, during the years 2001–2010, Urban. Clim., 10, 308, 10.1016/j.uclim.2014.02.004

Stewart, 2014, Evaluation of the “local climate zone” scheme using temperature observations and model simulations, Int. J. Climatol., 34, 1062, 10.1002/joc.3746

Alexander, 2014, Local climate classification and Dublin’s urban heat island, Atmosphere, 5, 755, 10.3390/atmos5040755

Lelovics, 2014, Design of an urban monitoring network based on local climate zone mapping and temperature pattern modelling, Clim. Res., 60, 51, 10.3354/cr01220

Gamba, P., Lisini, G., Liu, P., Du, P., and Lin, H. (2012, January 7–9). Urban climate zone detection and discrimination using object-based analysis of VHR scenes. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil.

Wurm, M., Taubenbock, H., Roth, A., and Dech, S. (2009, January 20–22). Urban structuring using multisensoral remote sensing data: By the example of the German cities Cologne and Dresden. Proceedings of Urban Remote Sensing Event, 2009 Joint, Shanghai, China.

Heiden, 2012, Urban structure type characterization using hyperspectral remote sensing and height information, Landsc. Urban Plan., 105, 361, 10.1016/j.landurbplan.2012.01.001

Walde, 2014, From land cover-graphs to urban structure types, Int. J. Geogr. Inf. Sci., 28, 584, 10.1080/13658816.2013.865189

Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324

Weng, 2009, Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends, ISPRS J. Photogramm. Remote Sens., 64, 335, 10.1016/j.isprsjprs.2009.03.007

Wulder, 2012, Opening the archive: How free data has enabled the science and monitoring promise of Landsat, Remote Sens. Environ., 122, 2, 10.1016/j.rse.2012.01.010

Huttner, S., Bruse, M., and Dostal, P. Using ENVI-Met to Simulate the Impact of Global Warming on the Microclimate in Central European Cities. Available online: http://envi-met.net/documents/papers/Huttner_etal_2008.pdf.

Chen, 2011, The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273, 10.1002/joc.2158

Jackson, 2010, Parameterization of urban characteristics for global climate modeling, Ann. Assoc. Am. Geogr., 100, 848, 10.1080/00045608.2010.497328

Song, 2001, Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?, Remote Sens. Environ., 75, 230, 10.1016/S0034-4257(00)00169-3

Knight, 2014, Landsat-8 operational land imager design, characterization and performance, Remote Sens., 6, 10286, 10.3390/rs61110286

Conrad, O. SAGA. Aufbau, Funktionsweise und Anwendung eines Systems für geowissenschaftiche Analysen. Available online: http://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0006-B26C-6/conrad.pdf?sequence=1.

Köthe, U. (2000). Generische Programmierung für die Bildverarbeitung, BoD–Books on Demand.

Gorelick, N. Google Earth Engine. Available online: http://adsabs.harvard.edu/abs/2012AGUFM.U31A.04G.

Canters, 2011, Inferring urban land use using the optimised spatial reclassification kernel, Environ. Model. Softw., 26, 1279, 10.1016/j.envsoft.2011.05.012

Blaschke, 2010, Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens., 65, 2, 10.1016/j.isprsjprs.2009.06.004

Ching, 2009, National urban database and access portal tool, Bull. Am. Meteorol. Soc., 90, 1157, 10.1175/2009BAMS2675.1

Thông tin
Thông tin xuất bản

ISPRS International Journal of Geo-Information

Tập 4 Số 1

199-219

Thông tin tác giả