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Assessing soil erosion with unmanned aeria vehicles

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2018 SWCS International Annual Conference
July 29-August 1, 2018
Albuquerque Convention Center

Published in: Environment
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Assessing soil erosion with unmanned aeria vehicles

  1. 1. ASSESSING SOIL EROSION WITH UNMANNED AERIAL VEHICLES FOR PRECISION CONSERVATION Joby Czarnecki, Anna Linhoss, Lee Hathcock, John Ramirez Avila, and Timothy Schauwecker Mississippi State University
  2. 2. THE STORY OF CATALPA CREEK Catalpa Creek is the main drainage channel for Mississippi State University, which is located at the headwaters Catalpa Creek flows through two of MSU’s research facilities The Watershed is impaired for sediments, pathogens, and nutrients Faculty and state agency personnel successfully applied for 319(h) status for the
  3. 3. IN OTHER WORDS…. Our University has world class researchers working in conservation, but our research facilities are disconnected from their expertise
  4. 4. RESEARCH ACTIVITIES WITHIN THE WATERSHED Surveying  Vegetation survey  Cross-sectional survey Monitoring  Water quality  Flow rates Modeling  Hydraulic modeling  Hydrologic modeling  Landscape evolution modeling Designing  New best management practices  New methodologies for research
  5. 5. STUDENT ENGAGEMENT WITHIN THE WATERSHED In 2 years of the project:  37 undergraduate and 7 graduate students involved in research  Students from 9 academic departments in 4 colleges and 2 universities, engaged with faculty members from 5 academic departments in 3 colleges and 1 research center  60 students utilizing the watershed for class projects
  6. 6. STRUCTURE FROM MOTION Structure from Motion (SfM) creates 3D structure from a combination of multiple viewing angles Structure from Motion is called "image parallax" in the remote sensing parlance Unmanned aerial vehicles (UAV) are flown with high levels of overlap between successive flight lines to create a series of stereopairs Parallax - 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.
  7. 7. SFM VERSUS LIDAR SFM Passive remote sensing process Can be performed with RGB cameras on low-cost UAVs Does not penetrate vegetation well, if at all Performs poorly on uniform scenes LIDAR Active remote sensing process Requires a LIDAR sensor, as well as additional hardware for the UAV With enough returns, can penetrate some vegetation Can be used on uniform scenes
  8. 8. APPLICATION OF SFM TO SOIL EROSION 1. Determine the limitations to the technology and the accuracy obtainable 2. Assess usefulness in popular models 3. Determine the suitability of this data for decision making by water resource managers 4. Produce a method and best practices that end users could adopt fairly easily, affordably, and quickly
  9. 9. MATERIALS AND METHODS Materials 1. COTS UAV systems from DJI 2. AgiSoft image processing software 3. DroneDeploy and Pix4DModel cloud-based image processing 4. Ground control points and calibration structures 5. Survey grade GPS unit 6. Traditional survey equipment Methods 1. Conduct UAV mission with high overlap between flight lines 2. Process image data with desktop and cloud-based software 3. Collect ground truth data 4. Evaluate agreement in elevation values • Between SfM surfaces produced with varying techniques • Between SfM surfaces and ground truth
  10. 10. LIMITATIONS AND LESSONS LEARNED – GCP
  11. 11. LIMITATIONS AND LESSONS LEARNED – GROUND TRUTH Increased size of dots coincides with decreased precision of GPS instrument (as reported by the instrument under vertical dilution of precision). Small dots with blue, pink, or red are acceptable.
  12. 12. LIMITATIONS AND LESSONS LEARNED – FLIGHT Generally ± 1m difference on average between elevations for surfaces processed with short paths or long paths only. Disagreement between surfaces highest in areas with vegetation present, with brown indicating higher elevation in short paths and blue representing higher elevation in the long path. Long - Short
  13. 13. LIMITATIONS AND LESSONS LEARNED – PROCESSING Yellow areas indicate negligible differences in elevation. Brown (lower density values dominate) and blue (higher density values dominate) areas indicate disagreement of up to 3 meters between resolutions indicated in each image. Ultra - High High - Med Ultra - Med
  14. 14. LIMITATIONS AND LESSONS LEARNED – SOFTWARE Proprietary software Pros:  Complete control over processing options  Unlimited images  Relatively inexpensive for educational license Cons:  Learning curve  Computationally intensive  Hard to share outputs Verdict:  More control, but more taxing  Necessary for research applications Cloud-based service Pros:  Data turnaround in < 24 hours  Sharable weblink Cons:  Accuracy?  Monthly fee (albeit low)  Limited to 1000 images per model  Desktop install for analysis  Elevated license required for downloading DEM Verdict:  Good for simple site characterization  An easy option with reasonable visual output *DroneDeploy, Pix4DModel
  15. 15. CONCLUDING REMARKS Promising findings:  This project demonstrated UAV images are capable of providing spatially- explicit, fine- to medium-temporal scale data.  When the need for high accuracy is secondary to identification of eroded areas and periodic review of landscape change in response to practice placement, UAV-based SfM surfaces represent a low-cost, rapid turn- around solution.  With a cloud-based, paid processing service, surfaces were generated within 24 hours. The service was simple to use and provided a sharable weblink to a 3D model. Cautions:  Every choice matters and will affect accuracy.  In some cases, the proprietary software that took more than a week to produce a SfM surface, which could be a consideration for high-temporal
  16. 16. FUTURE DIRECTIONS Explore glacier research for dealing with undercutting Incorporation of tracers Vegetative effects by type and percent cover
  17. 17. FUNDING SOURCES AND CONTACT INFORMATION Structure from Motion research in Catalpa Creek is supported by funding from:  National Institute of Food and Agriculture, USDA, Hatch funds  The Mississippi Agricultural and Forestry Experiment Station  Mississippi Water Resources Research Institute, under USGS 104B Joby Czarnecki Assistant Research Professor Geosystems Research Institute Joby.Czarnecki@msstate.edu 662-325-5972

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