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
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