OPEN ACCESS
The development of climate-responsive design has social and environmental impacts, as the adverse effects of climate change are particularly relevant for urban areas. Green and blue infrastructure has been identified as best practice for achieving greater urban sustainability and resilience. The climatic improvements from use of blue-green infrastructure are generally related to the ability to moderate the impacts of extreme precipitation and temperature. However, the challenges and barriers to implementation of climate adaptation plans focusing on the use of blue-green spaces have not been analysed extensively to date.
The present work describes a novel methodology to measure and classify urban surface parameters, which are important for the understanding and simulation of urban flooding. An aerial survey with multispectral sensors in VIS/NIR (Visible and Near Infrared) and IR (Infrared) wavelengths on a UAV (Unmanned Airborne Vehicle) has been carried out at the campus of the Norwegian University of Life Sciences in Ås, Norway. The area covers various types of surface such as asphalt, concrete, gravel, vegetation and water. The Normalized Difference Vegetation Index (NDVI) derived from the VIS/ NIR images have been used to study the spatial distribution and physical characteristics of the vegetation. Multivariate statistical tools have further been utilized to classify the different terrain materials according to their reflectance spectral properties from the multispectral VIS/NIR/IR data cubes. These materials have been linked to roughness and infiltration properties that are commonly used in water analysis simulation tools. Photogrammetry was applied to compute the Digital Surface Map (DSM), which was used to determine drainage lines and water accumulation areas in the surveyed area. The applied method provides data with high spatial resolution that can simplify and improve simulation of urban flooding.
Materials, multispectral survey, unmanned airborne vehicle, vegetation, water permeability
[1] EPRS. Developing an EU urban agenda, 2015.
[2] United Nations, World Urbanization Prospects: The 2014 Revision. United Nations, NY, 2014.
[3] Urban Climate Change Research Network UCCRN. (2011) Climate Change and Cities First Assessment Report
[4] The New Urban Agenda, Habitat III, 2016.
[5] Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. & Miller, H.L., (eds.). Climate change, the physical science basis. In: Contribution of the Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, pp. 996, 2007.
[6] Intergovernmental Panel on Climate Change IPCC. Climate Change 2014: Synthesis Report, IPCC, Geneva, Switzerland, 2014.
[7] Priest, S.J., Parker, D.J., Hurford, A.P., Walker, J. & Evans, K., Assessing options for the development of surface water flood warning in England and Wales. Journal of Environmental Management, 92(12), pp. 3038–3048, 2011. https://doi.org/10.1016/j.jenvman.2011.06.041
[8] Jha, A.K., Bloch, R. & Lamond, J., Cities and Flooding. A Guide to Integrated Urban Flood Risk Management for the 21st Century. The World Bank, Washington DC, 2012.
[9] Liao, K-H. & Chan, J.K.H., Urban design principles for flood resilience: learning from the ecological wisdom of living with floods in the Vietnamese Mekong Delta. Landscape and Urban Planning, 155, pp. 69–78, 2016. https://doi.org/10.1016/j.landurbplan.2016.01.014
[10] Demuzere, M., Orru, K., Heidrich, O., Olazabal, E., Geneletti, D., Orru, H., Bhave, A.G., Mittal, N., Feliu, E. & Faehnle, M., Mitigating and adapting to climate change: multi-functional and multi-scale assessment of green urban infrastructure. Journal of Environmental Management, 146, pp. 107–115, 2014. https://doi.org/10.1016/j.jenvman.2014.07.025
[11] PIX4d Software: Available at: https://pix4d.com/. (Accessed 15 May 2017).
[12] Moore, I.D., Grayson, R.B. & Ladson, A.R., Digital terrain modelling: a review of hydrological geomorphological and biological applications. Hydrological Processes, 5, pp. 3–30, 1991. https://doi.org/10.1002/hyp.3360050103
[13] Tarboton, D.G., Bras, R.L. & Rodriguez-Iturbe, I., On the extraction of channel networks from digital elevation data. Hydrological Processes, 5, pp. 81–100, 1991. https://doi.org/10.1002/hyp.3360050107
[14] ARC GIS: Available at: http://desktop.arcgis.com/en/arcmap/. (Accessed 15 May 2017).
[15] Ludwig, B., Daroussin, J., King, D. & Souchere, V., Using GIS to predict concentrated flow erosion in cultivated catchments. Proceedings of the HydroGIS 96: Applications of Geographic Information Systems in Hydrology and Water Resources Management held in Vienna, Austria, 1996.
[16] Snaddon, C.D., Wishart, M.J. & Davies, B.R., Some implications of inter-basin water transfers for river ecosystem functioning and water resources management in southern Africa. Aquatic Health and Management, 1, pp. 159–182, 1998. https://doi.org/10.1080/14634989808656912
[17] Cerdan, O., Souchere, V., Lecomte, V., Couturier, A. & Le Bissonnais, Y., Incorporating soil surface crusting processes in an expert-based runoff model: sealing and transfer by runoff and erosion related to agricultural management. Catena, 46, pp. 189–205, 2001. https://doi.org/10.1016/s0341-8162(01)00166-7
[18] Tak ken, I., Govers, G., Steegen, A., Nachtergaele, J. & Guerif, J., The prediction of runoff flow directions of tilled fields. Journal of Hydrology, 248, pp. 1–13, 2001. https://doi.org/10.1016/s0022-1694(01)00360-2
[19] Souchere, V., Cerdan, O., Ludwig, B., Le Bissonnais, Y., Couturier, A. & Papy, F., Modelling ephemeral gully erosion in small cultivated catchments. Catena, 50, pp. 489–505, 2003. https://doi.org/10.1016/s0341-8162(02)00124-8
[20] Hunter, N., Bates, P., Horritt, M. & Wilson, M., Simple spatially-distributed models for predicting flood inundation: a review. Geomorphology, 90, pp. 208–225, 2007. https://doi.org/10.1016/j.geomorph.2006.10.021
[21] Manning’s n values for Channels, Closed Conduits Flowing Partially Full, and Corrugated Metal Pipes: Available at: http://www.fsl.orst.edu/geowater/FX3/help/8_Hydraulic_Reference/Mannings_n_Tables.htm. (Accessed 15 May 2017).
[22] MATLAB, GLCM algorithm: Available at: https://se.mathworks.com/help/images/gray-level-co-occurrence-matrix-glcm.html?requestedDomain=www.mathworks.com/. (Accessed 15 May 2017).
[23] Colaninno, N., Roca, J., Burns, M. & Alhaddad, B., Defining densities for urban residential texture through land use classification, from LANDSAT TM imagery: Case study of Spanish Mediterranean coast. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, XXII ISPRS Congress, Melbourne, Australia, 2012.