## 4 Discussion
### Mapping On-Field Concept and Extent Quantification  
The obtained quantification revealed that the ALS recorded data for an accurate delimitation of aquatic reed beds. Proposed field mapping of aquatic reed beds contributed to determining, not only structure parameters within the stand, but also to the delimitation of aquatic reed on the frontline, shoreline, and consequently led to the quantification of extent. Onsite measurements are invasive, and for shoreline, frontline, and extent, they are not nearly as accurate as ALS. High agreement of calculated extents from both datasets was seen in areas where the expansion of the frontline is parallel to the shoreline. With the methodology implemented, we observed that the greater the irregularity of the shoreline, the lower the similarity of polygon shapes. A specific and different method for mapping the frontline and the shoreline could be implemented in such situations. For instance, real-time tracking of DGPS coordinates along aquatic reed fronts would deliver more accurate results, and at the same time, be more environmentally friendly than the executed onsite measures in 10-metre intervals. The same approach, however, cannot be recommended if the primary objective is mapping of the shoreline. It would cause ecological disturbance and damage to the reed population and would require more time and human resource efforts for mapping the entire lake. Additionally, it would compromise the gathering of other data types such as density and stem heights.  
In the specific case of the Chiemsee, the official shoreline could have been used in combination with the exemplified frontline mapped with DGPS real-time tracking to calculate extents. It had been created based on aerial photographs from 1991 and it served as a separation between land and aquatic reed. The shoreline had been assumed to be the line of the mean water level [21]; however, the fact that water level under the canopy of aquatic reed beds cannot be recorded by optical imaging sensors, questions the reliability of the 1991 shoreline data. This is supported by the fact that the value for the mean water level was not reported and that the shoreline was delimited based on the spectral response of reeds. Even with modern photogrammetric technology, an accurate modelling of water levels below the canopy is unrealistic. Analysis of recorded vegetation spectra in Colour Infra-Red (CIR) imagery was therefore, a practical but not an accurate approach to the problem of shoreline delimitation. The LiDAR capability of detecting structures in the vertical gradient contributed to the extent quantification of aquatic reed stocks, based on the definition of the frontline and shoreline. Precise derivation of water levels under the reed beds canopy contributes to the characterisation of relevant ecological zones such as land, transition, and aquatic reed beds. Extent and heights derivedfrom LiDAR data could also support volume calculations, such as the biomass above ground [23]. Since LiDAR delivers not only data for accurate extent measurement but also data for the vegetation vertical structure, as well as not being an invasive method, it is comparatively and competitively more convenient than other measures in the field.