{
  "certificate": {
    "id": "2026-011",
    "title": "Reproducibility Review of: Towards national-scale ecological applications: harmonized framework for LiDAR point cloud processing for vegetation metrics calculation",
    "url": "https://codecheck.org.uk/register/certs/2026-011/"
  },
  "paper": {
    "title": "Towards National-Scale Ecological Applications: Harmonised Framework for LiDAR Point Cloud Processing for Vegetation Metrics Calculation",
    "authors": [
      {
        "name": "Eveli Sisas",
        "orcid": "0009-0002-5032-249X"
      },
      {
        "name": "Holger Virro",
        "orcid": "0000-0001-6110-5453"
      },
      {
        "name": "Wai Tik Chan",
        "orcid": "0009-0005-3779-139X"
      },
      {
        "name": "Alexander Kmoch",
        "orcid": "0000-0003-4386-4450"
      },
      {
        "name": "Aveliina Helm",
        "orcid": "0000-0003-2338-4564"
      },
      {
        "name": "Evelyn Uuemaa",
        "orcid": "0000-0002-0782-6740"
      }
    ],
    "reference": "https://doi.org/10.5194/agile-giss-7-18-2026",
    "abstract": {
      "text": "<jats:p>Abstract. 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. 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 2019–2024 acquisition campaigns of the Estonian national ALS archive, comprising approximately 132 000 LAZ files from leaf-off and leaf-on surveys. 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.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.  Reproducibility review available at: https://doi.org/10.17605/OSF.IO/75pwn<\/jats:p>",
      "source": "CrossRef"
    },
    "openalex": "https://openalex.org/W7164197613"
  },
  "codecheck": {
    "codecheckers": [
      {
        "name": "Frank O. Ostermann",
        "orcid": "0000-0002-9317-8291"
      }
    ],
    "check_time": "2026-03-31 12:00:00",
    "repository": "github::reproducible-agile/reviews-2026|reports/017",
    "report": "https://doi.org/10.17605/OSF.IO/75PWN",
    "type": "conference",
    "venue": "AGILEGIS",
    "summary": "Full reproduction of a sample data set. A complete reproduction was infeasible as part of the review due to the computational environment required for the national-scale workflow, but the successful reproduction of a sample data set demonstrates the overall reproducibility of the study.\n",
    "manifest": [
      {
        "file": "repro_rev_2026_17_notebook.html",
        "comment": "Notebook reproducing the sample-data LiDAR vegetation-metrics workflow"
      }
    ]
  }
}