{
  "abstract": "Satellite-based aboveground biomass mapping typically relies on validation data from field-based inventory plots or other high resolution ground truthing, but acquiring validation data is challenging. In the United States, the Forest Inventory and Analysis (FIA) program is a common source of field validation data for biomass mapping, but currently the US Forest Service is not processing Material Transfer Agreements (MTA) that share exact plot coordinates due to reductions in the federal workforce. We developed a workflow that circumvents this limitation, by using individual tree-level data and stand density information from the FIA database to model “hypothetical plots” of uniform canopy structure that can be downscaled to 10 m Sentinel pixels to approximate aboveground biomass given a pixel’s canopy height and vegetation class, predicted from a separate canopy and vegetation modeling workflow. This approach is readily transferable to other regions globally where precise geolocation of validation data is not available.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Erik Nati-Johnson"
    },
    {
      "name": "Jodi Brandt"
    },
    {
      "name": "Trevor Caughlin"
    },
    {
      "name": "Rose A Graves"
    },
    {
      "name": "Anand Roopsind"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5pqwn4tgeh9pp.bib",
  "cited_by": 0,
  "doi": "10.31223/x5cf9q",
  "fulltext_url": null,
  "id": "5pqwn4tgeh9pp",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15442/",
  "language": null,
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Environmental Monitoring"
    ],
    [
      "bepress",
      "Natural Resources and Conservation"
    ],
    [
      "bepress",
      "aboveground biomass"
    ],
    [
      "bepress",
      "allometric modeling"
    ],
    [
      "bepress",
      "remote sensing"
    ],
    [
      "openalex",
      "2305|Remote Sensing and LiDAR Applications"
    ]
  ],
  "openalex": "W7221105370",
  "pdf_url": "https://eartharxiv.org/repository/object/15442/download/26775/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5pqwn4tgeh9pp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:42Z",
      "native_id": "15442",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15442/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Natural Resources and Conservation",
      "subfield": "ecology"
    },
    {
      "field": "earth",
      "origin": "bepress:Environmental Monitoring",
      "subfield": "environmental-science"
    }
  ],
  "title": "Workflow for estimating above ground biomass from satellite imagery with non-geolocated ground validation data",
  "title_text": "Workflow for estimating above ground biomass from satellite imagery with non-geolocated ground validation data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:42Z",
  "url": "https://research.draconitic.com/paper/5pqwn4tgeh9pp",
  "venue": "EarthArXiv",
  "versions": []
}