818ec4db-e66b-486f-aff3-98d6f113a714 University of Amsterdam Yifang Shi https://orcid.org/0000-0001-8770-8906 University of Amsterdam Wang Jinhu University of Amsterdam W. Daniel Kissling Associate Professor W.D.Kissling@uva.nl https://orcid.org/0000-0002-7274-6755 LifeWatch ERIC Lucia Vaira Service Centre ICT Coordinator lucia.vaira@lifewatch.eu https://orcid.org/0000-0002-5935-6074 2025-04-28 en This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders: 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4). These data products have been generated in the context of the EU project MAMBO (Modern Approaches to the Monitoring of Biоdiversity, https://doi.org/10.3897/rio.9.e116951). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: 5_Maps). All 25 LiDAR metrics were calculated using Laserfarm workflow (https://laserfarm.readthedocs.io/en/latest/) (building on the user-extendable features from the “Laserchicken” software: https://laserchicken.readthedocs.io/en/latest/#features). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: https://github.com/eEcoLiDAR/laserchicken and Laserfarm: https://github.com/eEcoLiDAR/Laserfarm), as well as on the “Laserchicken” documentation page (https://laserchicken.readthedocs.io/en/latest/). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: 6_Masks). To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: 7_Auxiliary_data). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: 8_Sensitivity_analysis). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 9_Use_case). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: 10_Ground_classification). LiDAR Ecosystem structure Airborne laser scanning Jupyter Notebook Python R Vegetation height Vegetation cover Vegetation structural variability AHN Powerline Multi-temporal Change detection EBV Point cloud High performance computing Laserfarm Laserchicken None CC-BY4.0 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode DOI https://zenodo.org/records/15261042 Zenodo https://zenodo.org/records/15261042 Data paper https://doi.org/10.5194/essd-17-3641-2025 Jupyter Notebooks for processing AHN datasets https://github.com/ShiYifang/AHN Laserfarm workflow repository https://github.com/eEcoLiDAR/Laserfarm Laserchicken software repository https://github.com/eEcoLiDAR/laserchicken Code for downloading AHN dataset https://github.com/ShiYifang/AHN/tree/main/AHN_downloading Code for generating masks for AHN datasets https://github.com/ShiYifang/AHN/tree/main/AHN_masks Code for demonstration of ecological use cases https://github.com/ShiYifang/AHN/tree/main/Use_case 1_AHN1.zip https://zenodo.org/records/15261042/files/1_AHN1.zip?download=1 2_AHN2.zip https://zenodo.org/records/15261042/files/2_AHN2.zip?download=1 3_AHN3.zip https://zenodo.org/records/15261042/files/3_AHN3.zip?download=1 4_AHN4.zip https://zenodo.org/records/15261042/files/4_AHN4.zip?download=1 5_Maps.zip https://zenodo.org/records/15261042/files/5_Maps.zip?download=1 6_Masks.zip https://zenodo.org/records/15261042/files/6_Masks.zip?download=1 7_Auxiliary_data.zip https://zenodo.org/records/15261042/files/7_Auxiliary_data.zip?download=1 8_Sensitivity_analysis.zip https://zenodo.org/records/15261042/files/8_Sensitivity_analysis.zip?download=11 9_Use_case.zip https://zenodo.org/records/15261042/files/9_Use_case.zip?download=1 10_Ground_classification.zip https://zenodo.org/records/15261042/files/10_Ground_classification.zip?download=1 README.docx https://zenodo.org/records/15261042/files/README.docx?download=1 University of Amsterdam Yifang Shi https://orcid.org/0000-0001-8770-8906 Funded by European Commission (MAMBO project: 101060639).