{
  "abstract": "Key message: The integration of national forest inventory data with spaceborne remote sensing data from the Global Ecosystem Dynamics Investigation (GEDI) improves the precision of forest productivity estimates in small domains via area-level Fay-Herriot models. Despite sparse spatial coverage, GEDI data enhance the reliability of localized estimates at finer spatial and temporal resolutions, thus supporting improved forest management and decision-making. Context: Forest productivity is highly sensitive to environmental changes, necessitating reliable, localized estimation for effective forest management. National forest inventories typically use design-based estimators that are unbiased but imprecise at such fine scales due to inadequate sample size. In such circumstances, model-based small area estimation provides approaches for improving estimate precision by leveraging auxiliary data. The recent availability of GEDI LiDAR data offers a promising auxiliary source to support such estimation. Aims: This study evaluated the suitability of GEDI data as auxiliary information in a Fay-Herriot model and compared estimates of forest productivity to traditional design-based direct estimators in localized domains. We also evaluated changes in basal area production and volume production (increment + ingrowth) over a 4-year period. Methods The study, conducted in the Région Bourgogne-Franche-Comté, Eastern France (45,000 km 2 ), used public forest data from the French National Forest Inventory and GEDI L2A metrics from 2019 to 2022. With area-level Fay-Herriot models, we estimated five forest attributes (quadratic mean diameter, basal area, volume, basal area production, and volume production) at two hierarchical levels of forest management. Results GEDI-augmented Fay-Herriot models achieved higher relative efficiency for forest attribute estimates than direct estimates, particularly in domains with limited field data. Precision gains exceeded 50% across all domains, with basal area production showing a slight temporal decrease that mostly remained statistically consistent with the temporally aggregated baseline, demonstrating FH’s capability to capture minor interannual dynamics within a generally stable multi-year trend. Conclusion: Our results highlight GEDI’s utility in addressing the limitations of national forest inventories to produce reliable estimates in heterogeneous landscapes where field observations are scarce. Future work should explore fusing GEDI with complementary data sources to expand spatial coverage and enable more continuous temporal tracking of forest dynamics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Irene Chiagozielam Onwunji"
    },
    {
      "name": "Cedric Vega"
    },
    {
      "name": "Sylvie Durrieu"
    },
    {
      "name": "Lauri Korhonen"
    },
    {
      "name": "Timo Tokola"
    },
    {
      "name": "Alexander Massey"
    },
    {
      "name": "Thomas Cordonnier"
    },
    {
      "name": "Olivier Bouriaud"
    },
    {
      "name": "Nikola Besic"
    },
    {
      "name": "Selim Behloul"
    },
    {
      "name": "Jean-Pierre Renaud"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/46f054gzrmftc.bib",
  "cited_by": 0,
  "doi": "10.1186/s13595-026-01358-2",
  "fulltext_url": null,
  "id": "46f054gzrmftc",
  "issns": [
    "1286-4560",
    "1297-966X"
  ],
  "landing_url": "https://hal.science/hal-05769618v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sde"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sde.ie"
    ],
    [
      "hal",
      "1.sdv.sa"
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    [
      "hal",
      "2.sdv.sa.sf"
    ],
    [
      "openalex",
      "2305|Remote Sensing and LiDAR Applications"
    ]
  ],
  "openalex": "W7214740447",
  "pdf_url": "https://hal.science/hal-05769618/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "Springer Nature (since 2011)/EDP Science (until 2010)",
  "ris": "https://research.draconitic.com/paper/46f054gzrmftc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "hal-05769618",
      "source": "hal",
      "url": "https://hal.science/hal-05769618v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    }
  ],
  "title": "Leveraging GEDI and NFI to inform on forest productivity at the level of the management units",
  "title_text": "Leveraging GEDI and NFI to inform on forest productivity at the level of the management units",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/46f054gzrmftc",
  "venue": "Annals of Forest Science",
  "versions": []
}