Multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands

Abstract. Recent years have seen a rapid surge in the use of Light Detection and Ranging (LiDAR) technology for characterizing the structure of ecosystems. Even though repeated airborne laser scanning (ALS) surveys are increasingly available across several European countries, only few studies have so far derived data products of ecosystem structure at a national scale, possibly due to a lack of free and open source tools and the computational challenges involved in handling the large volumes of data. Nevertheless, high-resolution data products of ecosystem structure generated from multi-temporal country-wide ALS datasets are urgently needed if we are to integrate such information into biodiversity and ecosystem science. By employing a recently developed, open-source, high-throughput workflow (named “Laserfarm”), we processed around 70 TB of raw point clouds collected from four national ALS surveys of the Netherlands (AHN1–AHN4, 1996–2022). This resulted in ~ 59 GB raster layers in GeoTIFF format as ready-to-use multi-temporal data products of ecosystem structure at a national extent. For each AHN dataset, we generated 25 LiDAR-derived vegetation metrics at 10 m spatial resolution, representing vegetation height, vegetation cover, and vegetation structural variability. The data enable an in-depth understanding of ecosystem structure at fine resolution across the Netherlands and provide opportunities for exploring ecosystem structural dynamics over time. To illustrate the utility of these data products, we present ecological use cases that monitor forest structural change and analyse vegetation structure differences across various Natura 2000 habitat types, including dunes, marshes, grasslands, shrublands, and woodlands. The provided data products and the employed workflow can facilitate a wide use and uptake of ecosystem structure information in biodiversity and carbon modelling, conservation science, and ecosystem management. The full data products and source code are publicly available on Zenodo (https://doi.org/10.5281/zenodo.13940846) (Shi and Kissling 2024).