Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey

The third Dutch national airborne laser scanning flight cam-paign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October- April) across the whole Netherlands provides a free and open-access, country-wide dataset with similar to 700 billion points and a point density of similar to 10(-20) points/m2. The AHN3 point cloud was obtained with Light Detection And Ranging (Li-DAR) technology and contains for each point the x, y, z co-ordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by 'Rijkswaterstraat' (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed in-formation on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (-16 TB uncompressed data volume) into 10 m resolution raster lay-ers of ecosystem structure at a national extent, using a novel high-throughput workflow called 'Laserfarm' and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for manag-ing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ra-tio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complex-ity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch pro-jected coordinate system (EPSG:28992 Amersfoort / RD New), are each-1 GB in size, and can be readily used by ecol-ogists in a geographic information system (GIS) or analyti-cal open-source software such as R and Python. Even though the class '1: Unclassified' mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the de-rived data products. We therefore validated the raster layers of ecosystem structure using > 180,0 0 0 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m x 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sam-pled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (-0.05) and the accuracy of the 25 Li -DAR metrics derived from the AHN3 point cloud was high (-90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new oppor-tunities for ecology and biodiversity science, e.g. for map-ping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abun-dance and ecological niches of animals and their habitats.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

The third Dutch national airborne laser scanning flight campaign collected a large dataset with billions of points, providing detailed information on the ecosystem structure. The dataset was processed into raster layers of vegetation structure metrics, allowing for ecological and biodiversity research. The accuracy of the derived metrics was high, and potential biases in the data were addressed through validation and additional masking. The dataset has significant importance in ecology and biodiversity science.