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Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory — arXiv:2610.06476

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.06476
License (as archived): CC0-1.0
Archived: 2026-10-06T09:41:18.582Z
SHA-256 of the archived text: 7b0185a2352413893c15183fd31bec852ecd20ddb1a8aa3eec6bfac0f3bf9c4a
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How to cite

Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory — arXiv:2610.06476 — arXiv (en), https://arxiv.org/abs/2610.06476 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0afff85cd2fa (Metraipolis archive dc_0afff85cd2fa, sha256 7b0185a2352413893c15183fd31bec852ecd20ddb1a8aa3eec6bfac0f3bf9c4a)

BibTeX

@misc{dc0afff85cd2fa,
  title = {Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory — arXiv:2610.06476},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.06476},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0afff85cd2fa; sha256 7b0185a2352413893c15183fd31bec852ecd20ddb1a8aa3eec6bfac0f3bf9c4a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.06476. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Forest managers require timely stand-level information for operational planning. Model-based estimation combines sparse field samples with remotely sensed auxiliary data to estimate growing stock volume (GSV) for small-area units. This is particularly relevant in mixed-species and structurally heterogeneous forests, where timely structural information can support management decisions under climate change and associated disturbance pressures. Uncrewed aerial vehicle laser scanning (ULS) provides flexible access to high-resolution LiDAR data, but its benefits over conventional airborne laser scanning (ALS) for model-based inference remain insufficiently understood. We compared public ALS and new ULS data using Bayesian change-of-support models to estimate GSV in a mixed-species forest in north-eastern Germany. We assessed the effects of distributional LiDAR metrics and spatial random effects. ULS models consistently outperformed ALS models, with cross-validated root mean squared prediction errors (RMSPEs) of 68.5 m^3/ha and 79.5 m^3/ha , respectively, a 13.8 % reduction in prediction error. Distributional metrics benefited ULS models more than ALS models, reducing RMSPE by up to 10.1 %, whereas spatial effects yielded minor improvements at greater computational cost. ULS models also exhibited lower stand-level predictive uncertainty for latent stand-mean GSV. This advantage may partly reflect closer temporal alignment with field data and finer-scale predictor information. These findings suggest that timely, information-rich LiDAR data may be more beneficial for stand-level GSV estimation than increasingly complex spatial model structures. ULS is promising where timely acquisition and high-resolution canopy characterization are operationally feasible. Controlled comparisons with temporally matched ALS and ULS data are needed to distinguish platform effects from temporal mismatch.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.AP, nuovi lavori del giorno).
Categorie arXiv: Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2610.06476 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T08:24:44.224Z dalla pagina https://arxiv.org/list/stat.AP/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).