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Refuge mapping compares manual and machine-learning approaches to track phragmites
Summary
Researchers compared manual photo-interpretation maps and random-forest models across Bear River Refuge units and found models perform well for dense Phragmites but less reliably for treated stands and small fragments; high-resolution imagery and robust training data are critical for scaling.
Forestry, Fire and State Lands staff and collaborators presented a mapping and modeling pilot for Phragmites on lower Bear River Refuge units (6–10) that compared manual photo-interpretation to automated random-forest models using multiple imagery sources.
Pete Goodwin described a multi-tiered approach using high-resolution WorldView imagery (collected 07/20/2023), Planet (≈3 m) and Sentinel (10 m). The team supplied training data from manual maps, converted to pixel-level samples, and used Google Earth Engine to develop and test…
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