Floating Forests: Citizen-Science Kelp Cover from Landsat Imagery

Quarterly aggregated kelp cover data from the Floating Forests citizen science project. The dataset contains consensus-classified polygons of surface-canopy forming kelp, primarily giant kelp (Macrocystis pyrifera), derived from Landsat satellite imagery. Each image was classified by up to fifteen citizen scientists, with polygons tagged by the minimum number of users who identified kelp.

Use Cases

• Mapping the spatial distribution of kelp forests based on citizen-science consensus polygons.
• Analyzing changes in kelp cover over time using quarterly aggregated Landsat data.
• Validating automated kelp detection algorithms against expert-matched citizen science classifications.

Strengths

• Data is derived from a citizen-science consensus method shown to match closely with expert classifications.
• Uses Landsat satellite imagery with a global two-week repeat cycle and 30-meter resolution.
• Each image tile was classified a minimum of four times, with potential for up to fifteen classifications.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Floating Forests project (floatingforests.org) via the Australian Ocean Data Network.

Collection Method

Citizen scientists classified manipulated Landsat satellite image tiles to identify kelp, with results aggregated into consensus polygons.

Freshness

Last updated 2026-06-05 07:42:44.888990; freshness should be verified.