Comparison of Sky View Factor Estimates using Digital Surface Models

Abstract

Better comprehension of the Urban Heat Island study requires information on the natural as well as built characteristics of the environment at high spatial resolution. Sky View Factor (SVF) has been distinguished as a significant parameter for Local Climate Zone (LCZ) classification based on environmental characteristics that influence the urban climate at finer spatial scales. The purpose of this thesis was to evaluate currently available data sources and methods for deriving continuous SVF estimates. The specific objectives were to summarize the characteristics of currently available digital surface models (DSMs) of the study region and to compare the results of using these models to estimate SVF with three different raster-based algorithms: Horizon Search Algorithm in R-programming (Doninck, 2018), Relief Visualization Toolbox (RVT) (Žiga et al., 2016), and the Urban Multi-scale Environmental Predictor (UMEP) plugin in QGIS (Lindberg, et al., 2018).