{
  "abstract": "We develop the mathematical foundations of a framework that attributes a disturbance agent, namely wildfire, harvest, insect defoliation, windthrow, or drought, to each disturbance event detected in standardized satellite time-series change metrics. The attribution task is posed as a statistical decision problem on a feature space built from spectral-temporal descriptors, object geometry, terrain, and climate. Within this formulation we prove the Bayes optimality of the posterior-maximizing rule and of its cost-sensitive counterpart, a finite-sample generalization bound for the empirical-risk minimizer over a finite class, the variance-reduction identity that governs ensemble estimators together with a bias-variance decomposition, an ordering between the Gini and entropy impurities, the convexity and second-order optimality conditions of gradient-boosted trees, the gradient identities of the softmax and focal objectives, the efficiency, symmetry, and null-player properties of the Shapley attribution used for explanation, the order preservation of temperature scaling, and the distribution-free lower and upper coverage bounds of the marginal and class-conditional conformal predictors used for uncertainty. We further prove an unbiasedness and variance result for the design-based accuracy estimator and a decomposition of the cross-region transfer gap, together with an estimable form of that decomposition. Many of these results are specializations of established statistical-learning theory to the attribution setting; the contribution lies in their unified formulation on a standardized metric product, in the explicit statement of the assumptions under which each guarantee holds, and in a small number of new corollaries. The paper is theoretical: all results are established by proof and illustrated with synthetic numerical examples, no empirical forest-disturbance dataset is analysed, and the empirical transferability, calibration, and accuracy of an implemented system remain to be evaluated.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Khaleel Ahmad"
    },
    {
      "name": "Walid Abdelfattah"
    },
    {
      "name": "Malick Sallah"
    },
    {
      "name": "Jawaher Saud Alrashood"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4q6ytz439zc0z.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-72550-7",
  "fulltext_url": null,
  "id": "4q6ytz439zc0z",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-72550-7",
  "language": null,
  "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": [
    [
      "openalex",
      "2214|Remote-Sensing Image Classification"
    ]
  ],
  "openalex": "W7221517886",
  "pdf_url": "https://www.nature.com/articles/s41598-026-72550-7_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4q6ytz439zc0z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.1038/s41598-026-72550-7",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-72550-7"
    }
  ],
  "subjects": [],
  "title": "A statistical learning framework for explainable forest disturbance-agent attribution with provable guarantees and theoretical foundations for spatial transfer",
  "title_text": "A statistical learning framework for explainable forest disturbance-agent attribution with provable guarantees and theoretical foundations for spatial transfer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/4q6ytz439zc0z",
  "venue": "Scientific Reports",
  "versions": []
}