Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)
May 31, 2023
Image library of (1) tile-drained landscapes and (2) tile-drain types that will be used for a machine-learning model workflow that identifies (1) tile-drained landscapes and (2) differentiates two types of tile-drained areas visible in satellite imagery. These images were sourced from WorldView and Quickbird satellite imagery (copyright DigitalGlobe) and cropped to features of interest. Imagery has a ground resolution of 0.34 - 0.65 m.
Citation Information
Publication Year | 2023 |
---|---|
Title | Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains) |
DOI | 10.5066/P9KSZ382 |
Authors | Tanja N Williamson, Dayle J Hoefling |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Ohio-Kentucky-Indiana Water Science Center |
Rights | This work is marked with CC0 1.0 Universal |
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Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery
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Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery
In the mid-1800s, tile-drains were installed in poorly-drained soils of topographic lows as water management to protect cropland during wet conditions; consequently, estimations of tile-drain location have been based on soil series. Most tile drains are in the Midwest, however each state has farms with tile and tile-drain density has increased in the last decade. Where tile drains quickly remove w
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Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality a
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Research Hydrologist
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Dayle Jordan Hoefling
Physical Scientist
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