## 2 Material and Methods
### Airborne Laser Scanning Processing Chain  
The analysis and classification of Airborne Laser Scanning (ALS) datasets was performed with the modular program system OPALS 2.2.0.0 [35]. The software provides modules for a complete processing chain of ALS data. Pre-processed LiDAR point clouds were imported into the OPALS data manager and the quality was assessed according to strip adjustment and outliers (noise). According to the information obtained from the quality control, no further calibration (e.g., strip adjustment, geometry calibration) was needed. Point cloud classification was then performed, and raster and vector file results were compared to field data and aerial imagery (Figure 2). The topo-bathymetric data collection took place on 21 September 2015, between 15h00 and 16h30 (UTC) during sunny weather conditions using the hydrographic laser scanner VQ-880G (Riegl LMS, Horn, Osterreich). This device is a progressive palmer scanner with a laser beam in the green wavelength (532 nm) of the electromagnetic spectrum. The laser pulse repetition rate was set to 550 kHz and the laser beam divergence was set to 1 mrad. The mission was executed at an altitude of approximately 400 m above ground. The scan width was about 400 m and the footprint was 40 cm (diameter). A total of 58 strips was collected for the study area (29 back and forward scans). Point clouds were registered with the information obtained by a Position and Orientation System (POS) mounted on-board, and were improved by correction measurements sent by a Differential Global Positioning System (DGPS) base station on land. Orientation data was also recorded by the Inertial Measurement Unit (IMU) during flight. Aerial photographs with a 0.1 m spatial resolution were additionally recorded during the same flight for validation purposes. Data pre-processing was executed by the data provider (AHM Airborne Hydro Mapping GmbH) using the software RiProcess. Strip adjustment was the first step in the pre-processing chain. The acquired accuracy was 10 cm (Standard Deviation). Secondly, based on the laser data, well distributed Ground Control Points (GCP) from roof corners and streets lane markers were defined across the study area. The 48 GCPs were measured by the research team with a tachymeter Leica TCRP 1201 R300 for fine registration. Absolute registration achieved an accuracy of 8 cm, and the point density of the raw data was approximately 200 points/m\({}^{2}\) (which included outliers). Finally, outliers were filtered and the point clouds from the specific AOIs were exported in Log ASCII Standard (LAS) 1.4 format. Figure 2: Airborne Laser Scanning (ALS) processing and decision chain applied for modelling aquatic weed beds. Quality control was performed before processing the ALS data sets. Point clouds were firstly, visually inspected. Attributes stored in the OPALS Data Manager (ODM) were examined in a 3D environment (opalsView) to identify potential errors in the already pre-processed product. Special attention was paid to the point geometry and the outlier filtering in water-land transition areas. An absence of points from water surfaces was identified in shallow water areas. Planar water surfaces cause specular reflections and therefore surface echo dropouts [32]. The overall low amount of noise in the point clouds delivered from the provider revealed that additional filtering was not needed. The package opalsQuality was then used for the quantification of point cloud quality. Cloud density and strip differences were analysed by means of density maps and statistical measurement. Since light propagates differently in water than in air, the points under the water surface appeared to have shifted and therefore had to be corrected. The module implemented for the refraction correction was opalsSnellius [28]. This correction was computed with the refractive index, coordinates of origin, incidence angle, and specification of the water lever.