AGILE-GISS AGILE: GIScience Series AGILE-GISS AGILE GIScience Ser. 2700-8150 Copernicus Publications Göttingen, Germany 10.5194/agile-giss-7-18-2026 Towards National-Scale Ecological Applications: Harmonised Framework for LiDAR Point Cloud Processing for Vegetation Metrics Calculation Sisas Eveli 1 Virro Holger 1 https://orcid.org/0000-0001-6110-5453 Chan Wai Tik 1 Kmoch Alexander 1 https://orcid.org/0000-0003-4386-4450 Helm Aveliina 2 Uuemaa Evelyn 1 https://orcid.org/0000-0002-0782-6740 Landscape Geoinformatics Lab, Institute of Ecology and Earth Sciences, University of Tartu, Estonia Landscape Biodiversity Group, Institute of Ecology and Earth Sciences, University of Tartu, Estonia 10 06 2026 7 18 Copyright: © 2026 Eveli Sisas et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://agile-giss.copernicus.org/articles/agile-giss-7-18-2026.html The full text article is available as a PDF file from https://agile-giss.copernicus.org/articles/agile-giss-7-18-2026.pdf

National airborne laser scanning (ALS) datasets provide essential three-dimensional information for large-scale vegetation metric calculation, but their ecological use is constrained by multi-year acquisition cycles, seasonal variation, heterogeneous point densities, and inconsistent point classification. These factors reduce the reliability and comparability of vegetation metrics derived directly from national ALS archives.&nbsp;<br />We developed a harmonised, reproducible point-cloud preprocessing framework for national-scale ecological applications of ALS data. The framework corrects metadata and file-format inconsistencies, removes overlap-related sampling artefacts, applies digital terrain model (DTM)-based height normalisation, and performs deterministic rule-based reclassification using height information, seasonal normalised difference vegetation index, and ancillary vector data. The workflow was applied to selected 2019&ndash;2024 acquisition campaigns of the Estonian national ALS archive, comprising approximately 132 000 LAZ files from leaf-off and leaf-on surveys.&nbsp;<br />Harmonised preprocessing increased thematic consistency of vegetation, ground, building, and water classes across acquisition campaigns by correcting systematic classification and sampling artefacts. Removal of overlap-flagged points reduced local point-density inflation by approximately 25 %, thereby improving comparability of later grid-based vegetation metrics. Rule-based reclassification reassigned a median of 32% of previously unclassified returns to vegetation, increasing completeness and seasonal consistency of vegetation representation. DTM-based height normalisation produced stable and interpretable height distributions across campaigns while retaining expected seasonal structural differences.<br />By providing a more consistent point-level representation of vegetation structure, the framework offers a robust basis for vegetation metric calculation and national-scale ecological analysis using heterogeneous ALS archives.</p> <p><a href="https://doi.org/10.17605/OSF.IO/75pwn" target="_blank" rel="noopener"><img src="https://contentmanager.copernicus.org/779365/10/locale/ssl" width="150px" /></a>&nbsp;Reproducibility review available at: <a href="https://doi.org/10.17605/OSF.IO/75pwn" target="_blank" rel="noopener">https://doi.org/10.17605/OSF.IO/75pwn</a>