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Unmanned aerial vehicles and structure from motion technique

Presentation from the 2017 American Water Resources Conference

Joby Czarnecki

on 8 January 2018

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Transcript of Unmanned aerial vehicles and structure from motion technique

Point sampling tool in QGIS - compare DSM to GPS points in cross sections
Future Directions
Immediate Goals
Focus on smaller areas
More collection in heavily vegetated areas
More traditional survey
Accuracy versus vegetation cover and structure
Accuracy versus slope
Funding Sources and Contact Information
1. Determine the
of low-cost, off-the-shelf unmanned aerial vehicles to perform SfM analysis

2. Determine the
of this data for decision making by water resource managers

3. Produce a method and
best practices
that end users could adopt fairly easily, affordably, and quickly
Data Processing
ala Proud Mary
Structure from Motion
Unmanned aerial vehicles
structure from motion
techniques and their use in protecting
surface water

Synergistic Effort
Vegetation survey
Cross sectional survey
Water quality monitoring
Hydraulic modeling
Hydrologic modeling
Landscape evolution modeling
Rough: AgiSoft, a proprietary software for UAV data
Complete control over processing options
Unlimited images
Relatively inexpensive for educational license
Joby M. Prince Czarnecki*
, Lee A. Hathcock, John J. Ramirez-Avila, Anna C. Linhoss, and TIM J. Schauwecker;
Mississippi State University

Structure from Motion creates 3D structure from a combination of multiple viewing angles

Structure from Motion is called "
image parallax
" in the remote sensing parlance
is a displacement or difference in the apparent position of an object viewed along two different lines of sight, and is measured by the angle or semi-angle of inclination between those two lines
SfM generally has higher resolution and a lower collection cost

SfM images can be collected with low cost UAVs
But.... because it's based on "the view" trees and other vegetation can limit utility

And.... it doesn't work very well on uniform scenes
Catalpa Creek
Main drainage channel for campus runoff
Traverses from urban to agroecosystem land cover
Land uses include production and research facilities for:
Impaired under the Clean Water Act
Now a 319(h) designated priority watershed
MSU campus
MSU research facilities
Image Data Collection
Previous UAV: DJI Phantom 4
Current UAV: DJI Inspire v2
Flight lines N-S and E-W at 80% overlap
Monthly monitoring of a 750m reach of the main channel
Approximately 0.5 in resolution at capture
Field Data Collection
Not all press is good press....
...although this just confirms how


erosion can be
Nice and easy: DroneDeploy, a commercial service for UAV data
Data turnaround in < 24 hours
Sharable weblink
$99/month fee
Limited to 1000 images per model
Verdict: For simple site characterization, an easy option with reasonable output
Learning curve
Computationally intensive
Hard to share outputs
Verdict: More control, but more taxing; however, necessary for research applications
Joby Czarnecki
Assistant Research Professor
Geosystems Research Institute
Funding acknowledgments:
National Institute of Food and Agriculture, USDA, Hatch funds
The Mississippi Agricultural and Forestry Experiment Station
Mississippi Water Resources Research Institute, under USGS 104B
AgiSoft Processing and Settings
“High” for camera alignment
GCPs utilized for improving camera alignment accuracy
UAS GPS accuracy is relatively low accuracy
GCPs collected using survey-grade GNSS unit
Dense cloud generated at two levels
Medium – ¼ resolution of mosaic
Ultra high – full resolution
Orthomosaic generated based on produced DSM
DSM generated based on dense cloud
Started with 20 GCPs placed, but..... many were washed away from erosion
Active zones challenging
Full flight can take upwards of a week to generate a dense cloud
Ultra high cloud takes some time to process, often more than a week per dataset
Lessons Learned
Monitoring through vegetation is difficult, as RGB cameras are passive sensors
Erosion can happen underneath the vegetation, and the change will be undetectable

Supplement camera with active sensor (e.g., LIDAR, TLS) that will penetrate vegetation
More GCPs to increase accuracy
Smaller study area to reduce processing time
More images in high vegetation areas and more overlap
Additional Suggestions
Farther Ahead
Monitor sediment transport and fate
Precision placement of best management practices
Cut Fill in ArcGIS - compare sequential DSM to look for loss and deposition
COSI-CORR through ENVI/AgiSoft - different approach to comparing DSM (perhaps)
Integrate with SFMToolkit (perhaps)
Bigger dots - less GPS accuracy
Full transcript