Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, Bavaria—Germany

• Main Authors: Nicolás Corti Meneses, Simon Baier, Juergen Geist, Thomas Schneider
• Format: Article
• Language: English
• Published: MDPI AG 2017-12-01
• Series: Remote Sensing
• Subjects: LiDAR; ALS; Phragmites australis; aquatic reed; OPALS; point cloud; classification; vegetation mapping
• Online Access: https://www.mdpi.com/2072-4292/9/12/1308

Aquatic reed is an important indicator for the ecological assessment of freshwater lakes. Monitoring is essential to document its expansion or deterioration and decline. The applicability of Green-LiDAR data for the status assessment of aquatic reed beds of Bavarian freshwater lakes was investigated. The study focused on mapping diagnostic structural parameters of aquatic reed beds by exploring 3D data provided by the Green-LiDAR system. Field observations were conducted over 14 different areas of interest along 152 cross-sections. The data indicated the morphologic and phenologic traits of aquatic reed, which were used for validation purposes. For the automatic classification of aquatic reed bed spatial extent, density and height, a rule-based algorithm was developed. LiDAR data allowed for the delimitating of the aquatic reed frontline, as well as shoreline, and therefore an accurate quantification of extents (Hausdorff distance = 5.74 m and RMSE of cross-sections length 0.69 m). The overall accuracy measured for aquatic reed bed density compared to the simultaneously recorded aerial imagery was 96% with a Kappa coefficient of 0.91 and 72% (Kappa = 0.5) compared to field measurements. Digital Surface Models (DSM), calculated from point clouds, similarly showed a high level of agreement in derived heights of flat surfaces (RMSE = 0.1 m) and showed an adequate agreement of aquatic reed heights with evenly distributed errors (RMSE = 0.8 m). Compared to field measurements, aerial laser scanning delivered valuable information with no disturbance of the habitat. Analysing data with our classification procedure improved the efficiency, reproducibility, and accuracy of the quantification and monitoring of aquatic reed beds.