Urban surface cover determined with airborne lidar at 2 m resolution – Implications for surface energy balance modelling

Abstract

Urban surface cover largely determines surface–atmosphere interaction via turbulent fluxes, and its description is vital for several applications. Land-cover classification using lidar has been done for small urban areas (<10km2) whereas surface-cover maps in atmospheric modelling often have resolutions >10m. We classified land cover of the urban/suburban area (54km2) of Helsinki into six classes based on airborne lidar data, and an algorithm for machine-learning classification trees. Individual lidar returns were classified (accuracy 91%) and further converted to 2-m-resolution grid (95% accuracy). Useful lidar data included: return height and intensity, returns-per-pulse and height difference between first and last returns.The sensitivity of urban surface-energy-balance model, SUEWS, to simulate turbulent sensible and latent heat fluxes was examined. Model results were compared with eddy-covariance flux measurements in central Helsinki. An aggregation of the surface-cover map from 2 to 100m reduced the fraction of vegetation by two thirds resulting in 16% increase in simulated sensible heat and 56% reduction in latent heat flux. Street trees became indistinguishable already at 10m resolution causing 19% reduction in modelled latent heat flux. We thus recommend having surface-cover data with 2m resolution over cities with street trees, or other patchy vegetation.