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July 29-1110-Brian Gelder

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2019 SWCS International Annual Conference
July 28-31, 2019
Pittsburgh, Pennsylvania

Publicado en: Medio ambiente
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July 29-1110-Brian Gelder

  1. 1. Remote Sensing of Crop Residue Conditions for the Daily Erosion Project Brian Gelder Iowa State University 1 High Resolution Estimates of Hydrologic Processes in the Midwestern USA
  2. 2. The Daily Erosion Project • Our mission – To help farmers, land managers, and the public better understand the dynamics and magnitude of runoff and soil erosion through daily estimation of these processes on agricultural areas and dissemination of the estimates via the web • • Our beginning – ISU Agronomy Dept. Endowment funding led to DEP • Township based estimates of precipitation, runoff, delivery, and soil moisture
  3. 3. • Modern spatial data and remote sensing makes it possible to generate high-resolution inputs for Water Erosion Prediction Project (WEPP) model – Use discrete flowpaths from LiDAR-derived DEM • Overlay on other appropriate data sources – Precipitation, soil properties, crop and tillage practices – Use a HUC12 watershed spatial framework with approximately 125 random flowpaths per HUC12 • 2,500 HUC12 watersheds in the current domain – Each approximately 100 km2 – Over 280,000 flowpaths modeled daily Daily Erosion Project
  4. 4. NEXRAD Precipitation LiDAR Elevation gSSURGO Soils Field-scale Land-use & Management Runoff Detachment Delivery Soil Moisture Water Erosion Prediction Project Mechanistic Model
  5. 5. • The WEPP mechanistic erosion model needs tillage information to simulate the residue burial and decomposition process – Less measurement intensive than providing temporal estimates of residue mass and surface coverage – Better guides residue mass and coverage estimates than extrapolating from last measurements • Tillage information is not readily available – High repeat sources do not have enough resolution – High resolution sources do not have enough repeat • We are attempting to estimate tillage practices from field mean residue cover remaining at planting Daily Erosion Project
  6. 6. DEP - Land Use & Management • Estimates are made for agricultural land parcels greater than 15 acres • Field boundaries are derived from 2008 available USDA FSA CLUs • Manually edited to reflect single crop boundary from a NASS Cropland Data Layer – IA, IL 2009 – KS, WI 2014 – MN, MO 2016
  7. 7. Each field polygon has • Majority land cover for previous 8 years – Example shows only 2010- 2015 imagery – Assigned a major rotation + DEP - Land Use & Management 2013 20112010 2012 20152014
  8. 8. USDA/ARS Agricultural Conservation Planning Framework (ACPF) and the Daily Erosion Project • The DEMs, crop rotation, and soils data used in the Daily Erosion Project are derived from the Agricultural Conservation Planning Framework (ACPF) database – Tomer et al., USDA/ARS National Laboratory for Agriculture and the Environment (NLAE) • ACPF database combines with the ACPF Toolbox to do field-level conservation planning – Saturated Buffers, Nutrient Removal Wetlands, WASCOBs, etc. • The ACPF (Midwestern USA only) is available at the website: • Summary results for ACPF containing 2017 CDL crop fraction after removing duplicates on border buffers – 3.598 million boundaries in 258 HUC8s – 1.767 million agricultural fields greater than 15 acres in size
  9. 9. • We attempt to estimate residue cover by observing reflectance in the visible and infrared • Residue has slightly different characteristics than soil or vegetation – Hyperspectral sensors can better target cellulose and lignin signals in SWIR but these lack areal coverage and repeatability needed Daily Erosion Project
  10. 10. • We thus use SWIR information from the Landsat TM, ETM+, and OLI sensors as well as Sentinel 2 sensors • The Normalized Difference Tillage Index (NDTI; next slide) or other custom SWIR ratios can correlate well with remaining residue cover/tillage intensity DEP Management – Residue Cover
  11. 11. Post Fall Residue Polygons NDTI = Band 5 – Band 7 Band 5 + Band 7 DEP Management – Residue Cover
  12. 12. • We analyze all Landsat and Sentinel 2 scenes for acceptable imagery – Filter out water, snow, clouds, and cloud shadows • This gives us the maximum amount of observations per year – Landsat every 7/9 days – Sentinel 2 every 5 days • Utilize Minimum NDTI value for the year DEP Management – Residue Cover
  13. 13. • This method requires gathering thousands of residue counts at ground sample points – Resulting in multiple groups using multiple methods of surveying, often as part of other duties • Minnesota soil residue cover estimates courtesy of Leif Olmanson and David Mulla at UM • Iowa data as part of Iowa Nutrient Research Center project and various residue survey projects • Nebraska data as part of NE NRCS estimation program • Kansas data as part of a KSU residue survey project • Estimates are then converted back to tillage Daily Erosion Project
  14. 14. • Minnesota Board of Water and Soil Resources is required by law to estimate sheet, rill, and wind erosion across the state every year • The Daily Erosion Project is helping generate these estimates for Minnesota BWSR after being given residue cover estimates for the state • The University of Minnesota has been collecting residue cover ground truth samples at thousands of locations across the state each year starting in 2016 Daily Erosion Project
  15. 15. Sentinel 2 Crop Residue May 5, 2016 Courtesy of Leif Olmanson, PhD
  16. 16. Sentinel 2 Crop Residue May 5, 2016 based on 2015 CDL Corn
  17. 17. Sentinel 2 Crop Residue May 5, 2016 based on 2015 CLDL Soybeans
  18. 18. Daily Erosion Project 2015 and 2016 Residue Cover was collected at locations across the state as part of an INRC grant. Analysis was done by photographic interpretation. Soil moisture conditions were favorable across the sampling domain and emergence was early in some fields.
  19. 19. Daily Erosion Project High NDVI (emerged crops) High NDVI (emerged crops)
  20. 20. Daily Erosion Project High NDVI (emerged crops) High NDVI (emerged crops)
  21. 21. Daily Erosion Project 2014 and 2017 Residue Cover was collected at locations across the state as part of work by watershed coordinators or other watershed projects. Analysis was done by visual estimation. Soil moisture conditions were wet in 2014, especially in potholes and normal in 2017. Emergence was also early in some fields.
  22. 22. Daily Erosion Project Ultra Low NDTI (likely ponded) Ultra Low NDTI (likely ponded)
  23. 23. Daily Erosion Project
  24. 24. DEP Residue Cover Future • Finish analyzing Iowa residue cover surveys – Remove high vegetation fraction fields from correlation analysis • NDVI threshold-based filtering – Remove inundated portions of fields from correlation analysis • The USGS Landsat team has recently provided a new product to automate this – Develop final residue cover/NDTI correlation curve – Automate high vegetation and inundation removal/correction – Generate • Analyze Nebraska and Kansas residue cover surveys • Investigate alternative methods to define the tillage index/residue cover relationship – Hyperspectral analysis
  25. 25. DEP Questions? • Acknowledgements: – ISU Agronomy Department Endowment – IDEP1, IDEP2, DEP – USDA Agricultural Research Service NLAE – IDEP1, IDEP2, DEP – USDA NRCS – Preliminary Management Work, ACPF Expansion – Environmental Defense Fund – Preliminary ACPF Work – Iowa DOT and Iowa Institute for Hydraulic Research (IIHR) – DEM Enforcement – North Central Regional Water Network – ACPF Expansion in Nebraska, Missouri, Wisconsin, and Minnesota DEP Web Pages
  26. 26. DEP Questions? • Acknowledgements: – Iowa Nutrient Research Center – Cover Crops/Residue Cover Mapping – Conservation Practice Mapping – Stacked Scenarios for Phosphorus – Union of Concerned Scientists – Erosion Scenarios – Minnesota BWSR and DNR – Minnesota DEM Enforcement and ACPF/DEP Expansion – Nebraska NRCS – Nebraska DEM Enforcement and ACPF/DEP Expansion – US Department of Housing and Urban Development FEMA – Flood Reduction Scenarios
  27. 27. DEP Sampling Scheme HUC12 Estimate – Mean of all flowpaths below (Each HUC12 approx. 10000 ha) HUC12 Sub-catchment (Stratified sample) (Each sub-catchment approx. 100 ha) (Approximately 125 flowpaths per HUC12) Flowpath (1 Random sample w/i sub-catchment) (Approximately 10-100 m long) (Only in dispersed flow)
  28. 28. HUC12s & Sub-catchments HUC 12 Boundary (approx. 10000 ha) Subcatchment Boundary (approx. 100 ha) Subcatchments are defined by the Peuker-Douglas constant drop stream Algorithm in TAU-DEM
  29. 29. Sub-catchments & Flowpaths Subcatchment Boundary Flowpath (Not modeled) Flowpath (Modeled) 1 random flowpath per subcatchment Must be on agricultural land Approximately 10-100 m long Only modeled in sheet/rill flow
  30. 30. What’s It Like In the Field? We model the yellow flowpath (sheet and rill) The black flowpath (concentrated flow) is currently not modeled
  31. 31. Flowpaths & Land Use FB070802030106 GenLU Corn/Soybeans C/S with Continuous Corn Pasture Conservation Rotation Mixed Agriculture Continuous Corn Extended Rotation Flood-prone Cropland Forest LT 15ac UnAssigned Water/wetland
  32. 32. Flowpath Parameterization Land Use Soils
  33. 33. DEP Hillslope Profiles • WEPP OFEs break at Land-use and Soils boundaries • Slope estimated for each OFE from DEM OFE 2 OFE 3 OFE 1 OFE 4
  34. 34. Sentinel 2 Crop Residue May 5, 2016 Courtesy of Leif Olmanson, PhD
  35. 35. Sentinel 2 Crop Residue May 5, 2016 With agroecoregions
  36. 36. Minnesota Tillage/Residue Combos Mulch Level Soybean Corn No-Till 0.25 0.70 Very High 0.15 0.45 High 0.10 0.30 Medium 0.05 0.15 Low 0.02 0.05 Moldboard 0.00 0.02