Quantifying 3D vegetation structure in wetlands using differently measured airborne laser scanning data. (August 2021) - Koma, Zsófia; Zlinszky, András; Bekő, László; Burai, Péter; Seijmonsbergen, Arie C.; Kissling, W. Daniel
Highlights: Vegetation height can be robustly estimated from different ALS datasets. The estimation of biomass and LAI is sensitive to the ALS recording types. Full waveform ALS captures more structural details in dense reed vegetation. Abstract: Mapping and quantifying 3D vegetation structure is essential for assessing and monitoring ecosystem structure and function within wetlands. Airborne Laser Scanning (ALS) is a promising data source for developing indicators of 3D vegetation structure, but derived metrics are often not compared with 3D structural field measurements and the acquisition of ALS data is rarely standardized across different remote sensing surveys. Here, we compare a set of Light Detection And Ranging (LiDAR) metrics derived from ALS datasets with varying characteristics to a standardized set of field measurements of vegetation height, biomass and Leaf Area Index (LAI) across three Hungarian lakes (Lake Balaton, Lake Fertő and Lake Tisza). The ALS datasets differed in whether the recording type was full waveform (FWF) or discrete return, and in their point density (4 pt/m 2 and 21 pt/m 2 ). A total of eight LiDAR metrics captured radiometric information as well as descriptors of vegetation cover, height and vertical variability. Multivariate regression models with field-based measurements of vegetation height, biomass or LAI as response variable and LiDAR metrics as predictors showed major differences between ALS recording types, and were affected by differences in spatial resolution, temporal offset and seasonality between field and ALS data acquisition. Vegetation height could be estimated with high to intermediate accuracy (FWF ALS data only: R 2  = 0.84; combination of ALS datasets: R 2  = 0.67), demonstrating its potential as a robust indicator of 3D vegetation structure across different ALS datasets. In contrast, the estimation of biomass and LAI in these wetlands was sensitive to variation in ALS characteristics and to the discrepancies between field and ALS data in terms of spatial resolution, temporal offset and seasonality (biomass: R 2  = 0.20–0.22; LAI: R 2  = 0.08–0.30). We recommend the use of FWF ALS data within wetlands because it captures more vegetation structural details in dense reed and marshland vegetation. We further suggest that ecologists and remote sensing scientist should better coordinate the simultaneous and standardized acquisition of field and ALS data for testing the robustness of quantitative descriptors of vegetation cover, height and vertical variability within wetlands. This is important for establishing operational and spatially contiguous ALS-based indicators of 3D ecosystem structure across wetlands.