Evaluation of Four Sky View Factor Algorithms Using Digital Surface and Elevation Model Data

Abstract

Abstract The sky view factor (SVF) is a crucial variable widely used to quantify the characteristics of surface structures and estimate surface radiation budget. Many SVF models based on raster data have been developed but not yet evaluated in a more quantitative and uniform manner. In this paper, four typical SVF models (Dozier‐Frew (D‐F), Manners, Lindberg‐Grimmond (L‐G), and Helbig_h) are evaluated using the SVF derived from simulated fisheye images based on the digital surface model (DSM) and digital elevation model data. The SVF calculated by D‐F method using DSM data has the best accuracy, with a mean bias error of −0.007, root‐mean‐square error of 0.069, and coefficient of determination ( R 2 ) of 0.914. For the SVF value derived from digital elevation model data, L‐G method shows good performance, with an mean bias error of 0.013, root‐mean‐square error of 0.032, and R 2 of 0.897. The pixels near the edges of buildings, within the valley or along ridgelines, have higher SVF deviations. In addition, the slope angle calculated using DSM data has some artificial defects that make the significant impact on the SVF biases due to their calculation method and the discontinuous surface in urban areas. Thus, L‐G and Helbig_h methods are more applicable for the DSM data due to the difficulty in defining slope and aspect angles. Moreover, the high accordance of SVFs between Helbig_h and L‐G methods implies that the Helbig_h method is an alternative in virtue of its simpler form and lower computation cost than L‐G method.