Two novel methods for field measurements of hydrodynamic density of floodplain vegetation using terrestrial laser scanning and digital parallel photography

Hydrodynamic vegetation density, the sum of the projected plant area per unit volume, is an important parameter for floodplain flow models. This paper compares two novel techniques to quantify this parameter in the field: terrestrial laser scanning (TLS) and digital parallel photography (PP). Separate field reference data were collected for the two methods, which consisted of (1) a stem map of 650 trees, aggregated into 23 plots in a single forest patch, (2) 17 manually measured forest plots in two floodplains. PP consists of a series of digital photographic images of vegetation against a contrasting background. The centre columns of all images were merged into a single composite parallel image. This mosaic was thresholded to determine the fractional coverage of the vegetation, which is converted to vegetation density using the optical point quadrat method. A sensitivity analysis proved that PP is insensitive to small errors on the selected number of centre columns, photograph spacing should not exceed 20 cm, photograph resolution is important and the plot depth should be measured accurately. TLS was carried out using a Leica HDS3000 time‐of‐flight laser scanner. Data processing of TLS data consisted of slicing the points around breast height. In a polar grid the vegetation density was predicted using the optical point quadrat method, corrected for missing points. Both methods were compared to the field reference data. PP (EF = 0.99; bias = 8.4×10−5 m−1) showed a higher modelling efficiency than the TLS method (EF = 0.63; bias = 0.015 m−1). An advantage of the TLS method is the ability to provide a detailed 2‐dimensional or even 3‐dimensional distribution of vegetation density. PP is cheaper, faster, and data processing is limited. We conclude that TLS and PP are two complementary techniques that show high accuracies for field measurements of vegetation density.