{
  "abstract": "Forest managers need timely stand-level information to support operational planning, particularly in mixed-species, structurally heterogeneous forests facing climate-related disturbance. Model-based estimation combines sparse field data with remotely sensed predictors to estimate growing stock volume (GSV) for small areas. While uncrewed aerial vehicle laser scanning (ULS) offers flexible, high-resolution LiDAR acquisition, its advantages over conventional airborne laser scanning (ALS) remain unclear. We compared publicly available ALS and newly acquired ULS data using Bayesian change-of-support models to estimate GSV in a mixed-species forest in north-eastern Germany. We evaluated distributional LiDAR metrics and spatial random effects. ULS consistently outperformed ALS, achieving cross-validated RMSPEs of 68.5 m^3/ha and 79.5 m3/ha, respectively-a 13.8 % reduction in prediction error. Distributional metrics improved ULS models more strongly, reducing RMSPE by up to 10.1 %; spatial effects provided only minor gains at substantially higher computational cost. ULS also produced lower uncertainty in latent stand-mean GSV estimates. The ULS advantage may reflect both finer-scale canopy information and closer temporal alignment with field measurements. Timely, information rich LiDAR may therefore be more valuable for stand-level GSV estimation than increasingly complex spatial models. Temporally matched ALS-ULS comparisons are needed to isolate platform effects.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06476",
  "author_total": 7,
  "authors": [
    {
      "name": "Arthur Spitta"
    },
    {
      "name": "Andrew O. Finley"
    },
    {
      "name": "Christoph Gollob"
    },
    {
      "name": "Ralf Kraßnitzer"
    },
    {
      "name": "Tim Ritter"
    },
    {
      "name": "Andreas Tockner"
    },
    {
      "name": "Arne Nothdurft"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6jfy174dz3twf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06476",
  "fulltext_url": null,
  "id": "6jfy174dz3twf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06476",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2305|Remote Sensing and LiDAR Applications"
    ]
  ],
  "openalex": "W7220670781",
  "pdf_url": "https://arxiv.org/pdf/2610.06476",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6jfy174dz3twf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:48Z",
      "native_id": "2610.06476",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06476"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory",
  "title_text": "Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:48Z",
  "url": "https://research.draconitic.com/paper/6jfy174dz3twf",
  "venue": "arXiv",
  "versions": []
}