Vegetation monitoring is essential to document expansion or deterioration and decline. The applicability of Green-LiDAR data for the status assessment of aquatic reed beds was investigated. The study focused on mapping diagnostic structural parameters of aquatic reed beds by exploring 3D data provided by the Green-LiDAR system. 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.

Classification of LiDAR point clouds
LiDAR data allowed for the delimitating of the aquatic reed frontline, as well as shoreline, and therefore an accurate quantification of extents.

Classification of Sparse Aquatic Reed beds according to stem height below 2 m, density of points under a height of 1.50 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.

Classification of dense dense and and sparse sparse aquatic aquatic reed