69th SWCS International Annual Conference “Making Waves in Conservation: Our Life on Land and Its Impact on Water” July 27-30, 2014 Lombard, IL

1. Quantification of ephemeral
gully erosion with close-range
digital photogrammetry
K.R. Gesch, R.R. Wells, H.G. Momm,
S.M. Dabney, R.M. Cruse
July 28, 2014

2. Research team
1
Robert Wells Henrique Momm
Rick CruseSeth Dabney

3. Key ideas
1. Close-range digital photogrammetry
(CRDP) can be used to reconstruct
ephemeral gully morphology.
2. CRDP generates time-sequenced
physical data of channels that can
improve soil erosion models.
2

4. Soil erosion
Data: Montgomery, 2007 3
0.01
0.1
1
10
100
Soil production Geological Native
vegetation
Conventional
agriculture
Conservation
agriculture
Medianrate(Mgha-1y-1)
Erosion scenario

5. Soil erosion models
• Conservation planning
• Field-scale models
»RUSLE: Revised Universal Soil Loss
Equation 2
»WEPP: Water Erosion Prediction Project
hillslope model
Estimate only sheet & rill erosion
Flanagan et al., 1995; USDA-ARS, 2013 4

6. Ephemeral gully erosion
• Small channel (≤ 50 cm deep)
• Re-form in similar location
SSSA, 2008; Momm et al., 2012 5
S. Rasmussen

7. Ephemeral gully erosion
• Removes topsoil & nutrients and
decreases yields
• Interferes with farm operations
• Connects landscape drainage
network
6

8. Status
• Ephemeral gully erosion poses on-
farm and off-site problems
• Soil conservation works
• Conservation planning tools "ignore"
ephemeral gully erosion
7

9. Needs
• Field-scale soil erosion models that
accurately predict channel erosion
• Data to validate models
• Technique to supply data
8

10. Research goals
1. Establish method to produce data.
Close-range digital
photogrammetry
2. Apply CRDP to generate data for
validation of predictive models.
Digital elevation models
Cross-sections
9

11. Photogrammetry
10

12. CRDP: A hybrid technique
1. In-field measurement
»High detail
2. Remote sensing
»Non-contact
»Digital data
11

13. 12

14. Research site
Neal Smith National
Wildlife Refuge
• Experimental
watersheds
»Area: 0.5 to 3.2 ha
»Slope: 6.1 to 10%
Helmers et al., 2012 13

15. Field setup
14
R. Cruse

16. Field setup
15
H. Li

17. Field setup
16
H. Li

18. Photography
17
Wireless
router Camera
Reference
markers

19. Photography
18
H. Li
S. Lee

20. 19
Upstream Downstream

21. 3D model
20

22. Geo-referencing
21
Coordinate 1 X,Y,Z
Coordinate 2 X,Y,Z
Coordinate 3 X,Y,Z
Coordinate 4 X,Y,Z

23. 3D model…
22

24. …dense surface model
23
265.43 m
264.80

25. Point cloud
24
265.43 m
264.80

26. Time-sequence analysis
25
T0 T1
268.02
267.47
267.89
267.43

27. Time-sequence analysis
26
T0 T1

28. Cross-section
27

29. Elevation change
28

30. Output
Intermediate data Final products
1. GPS coordinates 1. Cross sections
2. Photographs Graphical
3. Point clouds Tabular
2. Volume change
Erosion
29

31. Field-scale erosion
• Morphometric channel evolution
• Estimate erosion to verify CRDP data
30

32. Field-scale erosion
31

33. Field-scale erosion
• Channel volume = 6.39 (± 0.20) m3
• Bulk density = 1.24 Mg m-3
• Field area = 0.73 ha
• Erosion = 10.87 (± 0.34) Mg ha-1
32

34. Quality: LiDAR
33
LiDAR CRDP

35. Quality: LiDAR
34
LiDAR CRDP

36. Quality: Uncertainty
35
A B

37. Quality: Uncertainty
36

38. Quality: Uncertainty
37
• 72 replications
• ∆volume precision = 0.0014 m3
• Area = 2.11 m2
• Average vertical precision = 0.65 mm
• Vertical change uncertainty = 1.3 mm

39. Benefits of CRDP
• Post-initialization, this technique
requires only 1 researcher
• Speed
»Data collection (photography)
»Data processing
• High data accuracy
38

40. Future research
• Tabular cross-section data
»Time-sequenced & geo-referenced
• Soil properties
• Topographic characteristics
• Rainfall & runoff measurements
Improve field-scale soil
conservation planning tools
39

41. Key ideas
1. Close-range digital photogrammetry
(CRDP) can be used to reconstruct
ephemeral gully morphology.
2. CRDP generates time-sequenced
physical data of channels that can
improve soil erosion models.
40

42. References
Flanagan, D.C., J.C. Ascough II, A.D. Nicks, M.A. Nearing, J.M. Laflen. 1995. Chapter 1: Overview of the
WEPP erosion prediction model. In USDA-Water Erosion Prediction Project Hillslope Profile and
Watershed Model Documentation, NSERL Report #10.
Helmers, M.J., X. Zhou, H. Asbjornsen, R. Kolka, M.D. Tomer, R.M. Cruse. 2012. Sediment removal by prairie
filter strips in row-cropped ephemeral watersheds. Journal of Environmental Quality, 41(5):1531-1539.
Momm, H.G., R.L. Bingner, R.R. Wells, D. Wilcox. 2012. AGNPS GIS-based tool for watershed-scale
identification and mapping of cropland potential ephemeral gullies. Applied Engineering in Agriculture.
28(1):17-29.
Montgomery, D.R. 2007. Soil erosion and agricultural sustainability. Proceedings of the National Academy of
Sciences, 104(33):13268-13272.
Soil Science Society of America (SSSA). 2008. Glossary of soil science terms. Soil Science Society of
America. Madison, WI.
USDA-Agricultural Research Service (ARS). 2013. Science documentation: Revised universal soil loss
equation version 2 (RUSLE2). Washington, D.C.
41

43. Acknowledgements
Robert Wells
Henrique Momm
Seth Dabney
Rick Cruse
Kevin Cole
Chris Witte, Matt Helmers, STRIPS
Pauline Drobney, Neal Smith NWR, USFWS
Gary Van Ryswyk
Hao Li
Scott Lee
Victoria Scott
Sarah Anderson
Anthony Miller
Iowa State University Department of Agronomy
USDA National Institute of Food and Agriculture
Grant number 2012-03654
42

44. Thank you.
Research team
Karl Gesch – kgesch@iastate.edu
Robert Wells – robert.wells@ars.usda.gov
Henrique Momm – henrique.momm@mtsu.edu
Seth Dabney – seth.dabney@ars.usda.gov
Rick Cruse – rmc@iastate.edu
43

45. 44

46. Calculations
45
Uncertainty Erosion

47. Quantification of ephemeral gully erosion with close-range digital photogrammetry
K.R. Gesch1, R.R. Wells2, H.G. Momm3, S.M. Dabney2, R.M. Cruse1
1Iowa State University, 2USDA-ARS, 3Middle Tennessee State University
Abstract
Soil erosion in agricultural landscapes poses a substantial challenge to conservationists. Soil erosion
estimation models are useful tools for conservation planning; however, commonly used models such
as the Revised Universal Soil Loss Equation 2 (RUSLE2) or the Water Erosion Prediction Project
(WEPP) Hillslope Model cannot predict soil erosion due to topographically concentrated runoff –
ephemeral gully (EG) erosion. While the physical processes of concentrated flow erosion that occur in
EG channels are similar to those of rill erosion, EG erosion differs because EG channels are larger
and locations are non-random. There is a critical need to improve the capability of models by
incorporating EG erosion. High-precision data of physical EG development is necessary in order to
calibrate new or improved models. This research seeks to augment current scientific knowledge of EG
erosion processes through the generation of time-sequenced high-precision digital elevation models
(DEMs) of EGs using a novel systematic and practical methodology based on geo-referenced close-
range digital photogrammetry (CRDP) technology. Photograph pairs collected throughout the year are
used to generate detailed sequences of channel DEMs at 5 mm resolution and cross-sections of EGs.
DEM post-processing determines volume difference between two time steps and EG cross-section
profiles. Measured changes in surface topography will be analyzed with reference to observed rainfall
and runoff. Preliminary results indicate that CRDP is an effective method for estimating EG
morphology and changes in EG volume over time. Coupling CRDP and DEM analyses with observed
rainfall data provides precise three-dimensional data of the time-evolution of EGs. This type of data will
be highly beneficial to existing erosion models such as RUSLE2 or WEPP or for the development of
new models that explicitly account for EG erosion. Improved data will enhance models and allow for
more effective conservation planning.
46