Harder, P.

Helgason, W.

Pomeroy, J.

English

eCommons@USASK

Unmanned aerial vehicle remote sensing of snow: improving snowmelt prediction

University of Saskatchewan Research Archive

Unmanned aerial vehicle remote sensing of snow: improving snowmelt predictionPhillip Harder1, Warren Helgason1,2, John Pomeroy11Centre for Hydrology, 2Civil and Geological EngineeringUniversity of SaskatchewanWhy do we want to improve snow melt predictions?• Snowmelt is often the most important hydrologic event in on the Prairies• snow melt = runoff + infiltration• agriculturally significant• for every 25mm of water over a base requirement wheat yield increased by 3.5-6 bu/acre• Hydrologic models struggle with representing thin, patchy snowcover• Snow is traditionally quantified with snow surveys Research Gap• To improve the understanding of prairie snowmelt high spatio-temporal resolution data of snow depth and extent is needed• Remote sensing struggles to capture snow depth and water equivalent• Snow covered area can be measured, but:• Satellite imagery cannot observe:• Small scale heterogeneity• Rapid changes during melt• Repeat aerial imagery costs are prohibitiveUnmanned Aerial Vehicles• Come in all shapes and sizes• Can carry variety of sensors• commonly RGB camera• NIR• thermal• ……….• Deploy on demand• Can fly under cloud cover• Subject to regulations which are still evolvingcourtesy smalluavcoalition.comcourtesy nationalgeographic.comObjectivesTo assess the utility of UnmannedAerial Vehicles (UAVs) to quantifyand improve the prediction ofPrairie snowmeltSensefly Ebee RTK• Autonomous platform• Can cover a quarter section in <40 minutes• Images are geotagged at an accuracy of <2.5cm Structure from Motion (SfM)• SfM resolves 3D structure from 2D images• Produces a point cloud from the identification and triangulation of common features from multiple images• Georeferenced orthomosaics and Digital Surface Models (DSMs) are generated if :• camera locations are known• locations of features in images are known Site• Wheat field near Rosthern, SK• Short (15cm) and tall (35cm) standing wheat stubble • Flat terrain with significant blowing snow redistributionSnow Covered AreaSnow DepthAccuracy of Snow Depth Estimation• Accuracy assessed w.r.t. manual snow depth observations• Vertical accuracy with SfM is theoretically 2x the horizontal resolution• Actual RMSE < 15cm• Signal-to-Nosie ratio suggests only peak snow depths can be reliably measuredModelling Snowmelt• Three Source Area Model (3SOM): representssnowmelt of a heterogeneous surface (snow,vegetation and bare ground)• Inputs include:• Pre-melt snow depth standard deviation and mean • Peak snow density • Vegetation distribution • Hourly meteorological data• 3SOM assessed against UAV derived SCA andsnow survey observed snow water equivalent(SWE)• Tall stubble confounds assessment of modelledSCA and SWEConclusions• UAVs can:• Quantify snow-covered area in presence of exposed vegetation at a resolution previously impossible • Measure snow depth distribution prior to melt• UAVs cannot:• Measure the spatial variability of snowmelt in shallow snow with exposed vegetation• UAV derived snow depth properties can be used to improve the modeling of snow melt• 3SOM modelling identifies a deficiency with respect to tall stubble representationWhy does this matter to a soils and crops crowd?• Snowmelt water is crucial for crop production• Previously unable to obtain high resolution spatially distributed snow depth dataThe challenge:• Can the spatial distribution of snow and snowmelt be related to variables relevant to crop growth?• Could this information be leveraged in precision ag systems to improve crop production?

https://harvest.usask.ca/bitstream/10388/8827/1/P.%20Harder%20et%20al.%2c%202016.pdf

Unmanned aerial vehicle remote sensing of snow: improving snowmelt prediction

Abstract

Similar works

Full text

Available Versions

eCommons@USASK

University of Saskatchewan Research Archive