Enhancing macrophyte sub-classification through neighborhood-independent full-waveform LiDAR

Abstract

In the clear, shallow waters of the littoral zone, bathymetric LiDAR, SONAR, and photogrammetry have proven suitable for mapping both the underwater landscape and vegetation. In the past, all three methods have been used to map underwater vegetation, but automated workflows for sub-classification of vegetation remain limited. In the field of forestry such methods, e.g. classification based on the echo ratio, have long been used to classify vegetation based on neighborhood information. However, these methods developed for terrestrial landscapes have not been applied to aquatic vegetation. Therefore, this study adapts the echo ratio for the (neighborhood) sub-classification of submerged macrophytes and evaluates the results across the three surveying techniques. Furthermore, making use of the recent advances in full-waveform LiDAR, a neighborhood-independent (waveform-based) sub-classification is introduced. The waveform-based sub-classification approximates and subtracts the water column backscatter for each recorded waveform, extracting a residual waveform that contains only vegetation echoes. The residual waveform is then analyzed for the distance between the peaks in relation to the residual length. From this, the waveform echo ratio is derived and then compared to the neighborhood echo ratio. Both sub-classification methods are additionally validated using manually annotated data. The waveform data, LiDAR data, and reference (Photogrammetric + SONAR) data produce F1-scores of 0.19/0.35/0.32 for high vegetation, 0.18/0.23/0.24 for medium vegetation, and 0.80/0.85/0.85 for low vegetation. Although these scores vary, so do those of the manual annotation, which achieved median F1-scores of 0.13/0.34/0.80 compared to each other.