Headwater streams transport nutrients, sediment, and mineral-rich groundwater downstream. In High Mountain Asia (HMA), headwater streams also funnel glacier and snow melt to sustain continuous water supply for the downstream region. These channels remain poorly mapped because of their inaccessibility and because they are smaller than the resolution of Landsat (30 m) and Sentinel-2 (10 m). In this study, we assessed the ability of 3 m resolution PlanetScope imagery to detect the proglacial headwaters downstream of all high-altitude glaciers larger than 5 km2 in HMA. We created 3000 manually labeled image tiles to train and evaluate computer vision (CV) against techniques common in the hydrologic remote sensing literature, specifically normalized difference water index (NDWI) thresholding and random forests (RF). Results indicate that CV best detects the headwater streams with textgreater0.60 F1-scores, nearly 0.20 points higher than RF and 0.45 points higher than thresholding. We also assessed how errors in CV propagate to derived hydrologic information, exemplified by the biogeochemically critical measurement of stream surface area. We found that CV classifications produced surface areas with 0.98 R2, 0.01 km2 MAE, and 0.02 km2 RMSE against manually labeled surface areas. We also observed the best CV performance during the spring season with 30% more skillful classification performance than in summer and fall. Our results prove the ability of PlanetScope imagery to detect and map headwater streams accurately and at scale, and that classification errors stemming from the imagery or the CV methods do not greatly impair our ability to quantify stream surface area meaningful for biogeochemical exchange and hydrology studies.
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