Abstract
Digital hemispherical photography (DHP) is widely used to measure the radiative environment and estimate sky view factors (SVF) in urban areas and leaf area index (LAI) in forests. However, a limitation is the difficulty to distinguish trees from buildings, or leaves from stems and branches. In this study, we collected and processed dual-wavelength photographs recording visible and near-infrared (NIR) light in order to classify pixels into sky, green and woody plant elements, and buildings. Three applications of the method are presented: calculation of partial SVFs accounting for the obstruction of sky by buildings and vegetation separately, the modelling of mean radiant temperature (Tmrt), and the correction of LAI estimates for light intercepted by woody elements and buildings. The obtained partial SVFs were in good agreement with values modelled based on digital surface models. Distinguishing between buildings and vegetation in the modelling of long-wave radiation fluxes in the SOLWEIG model resulted in differences in modelled Tmrt by up to 3 °C. The bias of LAI estimates in urban parks caused by the light interception by woody elements and buildings was found to be relatively small (3–4 %). However, the presented method shows a high potential for estimates of LAI of urban vegetation in densely built-up areas.
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