{
  "abstract": "Accurate quantification of forest biomass and carbon stocks is essential for understanding terrestrial carbon dynamics, climate change mitigation, and improving forest monitoring and greenhouse gas reporting. However, biomass estimation approaches vary considerably in accuracy, scalability, cost, and uncertainty, creating challenges for selecting appropriate methods across ecosystems and spatial scales. This systematic review synthesizes recent advances in forest biomass and carbon stock estimation and evaluates the strengths, limitations, and future directions of major methodological approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, 147 peer-reviewed studies published between 2000 and 2025 were reviewed. The studies encompassed field-based measurements, allometric modelling, remote sensing, machine learning, carbon stock models, and integrated multi-source frameworks. The evidence reveals a clear transition from conventional field inventories toward multi-source approaches that integrate ground observations with optical, radar, and Light Detection and Ranging data, advanced modelling, and artificial intelligence. Field measurements and locally calibrated allometric equations remain indispensable for model development and validation, while remote sensing and machine learning enhance spatial coverage and predictive capability. However, estimation accuracy remains strongly influenced by vegetation structure, environmental conditions, data quality, and model transferability. The review demonstrates that no single approach is universally optimal across all ecosystem conditions. Reliable biomass and carbon stock assessments require integrated frameworks that balance accuracy, scalability, cost, and uncertainty through the complementary use of field observations, remote sensing, and advanced modelling techniques. Future research should prioritize expanding biomass reference datasets in underrepresented regions, strengthening model validation and uncertainty quantification, and developing transparent, ecosystem-specific estimation frameworks. Such advances will improve forest carbon accounting, climate mitigation, and evidence-based forest management.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Natural Resource Management, College of Agriculture and Environmental Science, Arsi University, Asella, Ethiopia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Annissa Muhammed Ahmedin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/80zr13ry7v0hd.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pclm.0000935",
  "fulltext_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000935",
  "id": "80zr13ry7v0hd",
  "issns": [
    "2767-3200"
  ],
  "landing_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000935",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Remote Sensing and LiDAR Applications"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Ecological metrics/Biomass"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Ecosystems"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Ecosystems/Forests"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Forest ecology"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Ecological metrics/Biomass"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Ecosystems"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Ecosystems/Forests"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Forest ecology"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Terrestrial environments/Forests"
    ],
    [
      "plos",
      "/Engineering and technology/Environmental engineering/Carbon sequestration"
    ],
    [
      "plos",
      "/Engineering and technology/Remote sensing"
    ],
    [
      "plos",
      "/Engineering and technology/Remote sensing/Lidar"
    ]
  ],
  "openalex": "W7214124166",
  "pdf_url": "https://journals.plos.org/climate/article/file?id=10.1371/journal.pclm.0000935&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/80zr13ry7v0hd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pclm.0000935",
      "source": "plos",
      "url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000935"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "A systematic review of biomass and carbon stock estimation approaches: Methods, uncertainties, and emerging opportunities",
  "title_text": "A systematic review of biomass and carbon stock estimation approaches: Methods, uncertainties, and emerging opportunities",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/80zr13ry7v0hd",
  "venue": "PLOS Climate",
  "versions": []
}