{
  "abstract": "Mapping dynamic riverine habitats presents a particular challenge, often requiring high resolution bathymetric measurement and advanced sediment transport and hydraulic computational methods. To assess satellite-based screening under these constraints, this study links three analysis steps in sequence: satellite image classification, change detection against a fixed time-stacked baseline, and comparison against hydraulic-model output, then extends the analysis to direct prediction of hydraulically defined aquatic mesohabitats. This study evaluates two open-source workflows for mapping surface-water change and fluvial mesohabitats from optical satellite data: a cluster-assisted pipeline and a class-balanced supervised random forest. Increasingly complex targets were tested: (i) inundation dynamics, defined as surface water versus land within the river corridor, and (ii) direct prediction of eight hydraulically derived mesohabitat classes. Using PlanetScope (~3 m), Sentinel-2 (10–20 m), and Landsat-9 (30 m) imagery, binary inundation mapping was reliable for both workflows (mean balanced accuracy 0.810 cluster-assisted and 0.823 random forest across seven date-matched hydraulic conditions), whereas direct eight-class mesohabitat classification was not (mean leave-one-date-out accuracy 0.100 and 0.149, respectively, against a 0.125 random-guess expectation). Cluster-assisted surface-water area was positively associated with both Wamego discharge and hydraulic surface-water area (Spearman ρ = 0.893, p = 0.012), while the random-forest associations were weak (ρ = −0.214, p = 0.662). These results suggest that lightweight, open workflows may support screening of surface-water change, while also highlighting that direct image-based mesohabitat prediction remains experimental at this site. Future gains will likely require incorporating texture and morphological context, and integrating hydraulic predictors. Research Domain: Remote Sensing, Machine Learning, Ecosystem Modeling.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Marine Sciences, University of Puerto Rico-Mayagüez, PO Box 9000, Mayagüez, PR, United States of America; SeaMount Geospatial Labs, Brooklyn, NY, United States of America",
    "US Army Corps of Engineers, Engineering Research and Development Center, Environmental Laboratory, Albuquerque, NM, United States of America; Corresponding author."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Elvin Cordero"
    },
    {
      "affs": [
        1
      ],
      "name": "Aubrey Harris"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/42ww0h0zev0j7.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ecoinf.2026.104110",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1574954126005182",
  "id": "42ww0h0zev0j7",
  "issns": [
    "1574-9541",
    "1878-0512"
  ],
  "landing_url": "https://doi.org/10.1016/j.ecoinf.2026.104110",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH540-549.5"
    ],
    [
      "lcc",
      "T58.5-58.64"
    ],
    [
      "openalex",
      "2303|Hydrology and Sediment Transport Processes"
    ]
  ],
  "openalex": "W7223028694",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/42ww0h0zev0j7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:18Z",
      "native_id": "30e280a9ecb34e0b9713ba89ac464e36",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ecoinf.2026.104110"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Hydrology and Sediment Transport Processes",
      "subfield": "ecology"
    }
  ],
  "title": "Satellite-based surface water change detection and mesohabitat classification at Big Blue—Kansas Rivers confluence",
  "title_text": "Satellite-based surface water change detection and mesohabitat classification at Big Blue—Kansas Rivers confluence",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:18Z",
  "url": "https://research.draconitic.com/paper/42ww0h0zev0j7",
  "venue": "Ecological Informatics",
  "versions": []
}