Abstract

Submerged aquatic vegetation (SAV) is a sensitive indicator of shallow-water condition, but operational mapping has relied on multispectral, red-edge, or near-infrared sensing, leaving low-cost consumer RGB drones largely unused for the task. We present an RGB-only detector for SAV. It treats SAV as local darkening rather than a greenness signal, and sets the detectable patch scale with two label-free mechanisms, a ground-sample-distance (GSD)-anchored minimum mapping unit that rescales with altitude and a CFAR-inspired scale criterion N·(s/σ)2 ≥ τ driven by per-scene clutter σ. Across four densely annotated scenes most SAV is small, with 73–98% of labelled patches falling below the 0.43 m2 that a conventional fixed lower limit on pixel number retains at 4 cm GSD. Anchoring the minimum unit correctly raises pixel recall from 0.52 to 0.71 with no false positives inside the annotated open-water rectangles, and reaches a cross-scene SAV-versus-open-water AUC of 0.969, whereas an excess-green index performs near chance (AUC 0.438). Consumer RGB drones can therefore map binary SAV presence and extent within a darkness × scale detectability envelope, recovering the small, fragmented stands that coarser sensors miss, while cover fraction, biomass, species identity and the faintest SAV remain out of reach. The reference standard throughout is expert photointerpretation of 4 cm imagery, so what is mapped is image-interpreted SAV-like dark patches rather than field-confirmed SAV, and all results come from one lake system, one camera and one flight altitude.

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