Brennan - Soil Survey Applications of LiDAR Data
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This document discusses the use of Light Detection and Ranging (LIDAR) and Interferometric Synthetic Aperture Radar (IFSAR) data to improve soil survey mapping in North Dakota. It provides examples of how high resolution digital elevation models from LIDAR and IFSAR can help identify landforms and landscapes, map soil types more accurately, and infer soil properties. The improved terrain data allows soil scientists to build more consistent mapping techniques and develop predictive models to guide their field work.
Brennan - Soil Survey Applications of LiDAR Data
- 1. Soil Survey Applications of LIDAR Improving the topographic Base Joe Brennan Northern Great Plains Region USDA-NRCS Soil Survey Staff
- 2. WAFFLE - Walsh County Forest River LIDAR - 2004 NRCS & Energy & Environmental Research Center High Intensity Soil Survey Projects 10m DEM - 2004 NRCS Soil Survey & North Dakota State University Central North Dakota Mapping Projects IFSAR - 2007 NRCS Soil Survey, University of North Dakota James Headwaters & Pipestem Watershed IFSAR - 2007 NRCS Red River Basin Mapping Initiative LIDAR - 2008 NRCS Partnership led by International Water Institute Eastern North Dakota IFSAR – 2008 NRCS Statewide NED 1/3 Arc DEM Coverage 10m DEM - 2009 State of North Dakota GIS Technical Team Improving topographic Base
- 3. SRTM (1 AS) Shuttle Radar Topographic Mission (Satellite) Intereferometric Radar 1 AS Available Thought Conterminous US 3AS Worldwide <60° N & S
- 4. USGS NED (1 AS) National Elevation Dataset – 30m Resolution Largely Based on Existing Hypsography Available Throughout North Dakota
- 5. USGS NED (1/3 AS) National Elevation Dataset – 10m Resolution Mostly Based on Existing Hypsography Available Throughout North Dakota
- 6. IFSAR Interferometric Synthetic Aperture Radar 5m Resolution, Sub Meter Vertical Accuracy Available Nationwide (Proprietary) Additional Data (Radar Image, Surface Model) Digital Surface Model (DSM) Digital Terrain Model (DTM)
- 7. LIDAR Light Detection & Ranging – 1 meter (Intensive Post Spacing) Meets FEMA Flood Plain Mapping Specifications Additional Products (Intensity Image, Classified Points LAS, First Return & Last Return)
- 8. Improving topographic Base 2009 2010 LIDAR Available - Blue IFSAR Available - Green LIDAR & IFSAR Coverage in the Eastern Dakotas (USDA-SCA, SCD, TSP)
- 9. SRTM 30m Digital Elevation Model USGS NED 30m Digital Elevation Model USGS NED 10m Digital Elevation Model IFSAR 5m DTM Slope in Hilly AND Rolling Terrain
- 10. NED 30m Digital Elevation Model NED 10m Digital Elevation Model 1m Terrain Model Bare-Earth LIDAR Nearly Level and Level Terrain
- 11. Why Now?: Improved Terrain Base Materials – LIDAR/IFSAR 1-5m DTM Existing Terrain Base Materials – USGS 10m DEMs LIDAR Landsape Position Identification - Prairie Pothole Region - Northeastern South Dakota Soil Survey ApplicationS OF LIDAR Terrain Depressions Terrain Depressions
- 12. IFSAR Landform Identification - Ice-Walled Lake Plain - Central North Dakota Soil Survey ApplicationS OF LIDAR
- 13. LIDAR Landform Identification - Beach Deposits - Glacial Lake Agassiz - North Dakota Soil Survey ApplicationS OF LIDAR
- 14. LIDAR Landform Identification – Ice-Drag Markings - Glacial Lake Agassiz - North Dakota Soil Survey ApplicationS OF LIDAR
- 15. LIDAR Derivatives – Surface Hydrology - Glacial Lake Agassiz – Minnesota, North Dakota Surface Drainage Flooding Frequency Soil Survey ApplicationS OF LIDAR
- 16. LIDAR Canopy Penetration – Floodplain Channel Complexity - Northeastern North Dakota Soil Survey ApplicationS OF LIDAR
- 17. IFSAR Analysis – Slope Length - Central North Dakota Soil Survey ApplicationS OF LIDAR
- 18. IFSAR – Building Consistent Mapping Techniques & Projecting Line Work Soil Survey ApplicationS OF LIDAR
- 19. LIDAR Soilscape Identification - Uplands – Till Plain – Northeastern North Dakota Soil Survey ApplicationS OF LIDAR
- 20. High Resolution DEMs – Building Consistent Mapping Techniques Soil Survey ApplicationS OF LIDAR
- 21. Why Now?: LIDAR Hydric Landscape Analysis - Northeastern South Dakota Wet Year Ortho Imagery (Fall 1997) Spectral Ortho Rectified Radar Image (IFSAR) or Intensity Image (LIDAR) Composite Hydric Rating Highest Probability Model for Potential Wetlands Define Inputs Guide Field Work Soil Survey ApplicationS OF LIDAR Soil Survey Hydric Rating Soils 5m DTM from LIDAR or IFSAR Terrain
- 22. LIDAR – Soil Landscape Covariates – Till Plain – Northeastern North Dakota Slope Gradient Slope Shape Wetness Depression Distance Local Relief Relative Position Soil Survey ApplicationS OF LIDAR Existing Knowledge & Documentation
- 23. LIDAR – Soil Series Inference – Till Plain – Northeastern North Dakota Series 1 Series 2 Soil Survey ApplicationS OF LIDAR
- 24. LIDAR – Applications of Inference Models – Till Plain – Northeastern North Dakota Inherent Soil Productivity Soil Survey ApplicationS OF LIDAR Organic Carbon Management Zones
- 25. LIDAR Soil Series Inference - Glacial Lake Agassiz - North Dakota Soil Survey ApplicationS OF LIDAR
- 26. Necessary Software, Processing and Capacity Artifacts Building Applications vs. Generating Products Building Necessary Analytical Skills in Workforce Imagination CHALLENGES Data Sources: USGS NED/CLICK, Fugro Horizons, Sanborn Thank You!
Editor's Notes
- #2: Joe Brennan Soil Data Quality Specialist for GIS in North Dakota and the Northern Great Plains MLRA Region. Topographic rendering of North Dakota demonstrates that North Dakota is most certainly not flat State with variable landscapes needs a multidimensional strategy to model the topography
- #3: Soil Landscape Modeling has been seen as the future of Soil Survey for the last ten years with SOLIM (Soil Land Inference Model) and susbequent projects. Field Soil Scientist have tacit knowledge of Soil Landscape relationships to the point that many can predict to a high level of accuracy where a soil series will occur within a mapunit that may contain 5-10 series. The only thing preventing us from making strides in this effort is the limited availability of datasets that accurately represent the landscape. Therefore we have been a part of multiple efforts in the last several years to improve the topographic base, which our State Conservationist has strongly supported. Not only for soil survey, but for other applications that will come to light as we further explore this data.
- #4: To demonstrate the importance of this data I went through an exercise in cross-sectioning the same landscape using all digital terrain data we have available to us. SRTM is interferometric radar data that creates a surface model not a terrain model.
- #5: Then the National Elevation Dataset 30m resolution creates a smooth terrain model, but coarser in resolution
- #6: And 10m resolution. These are both good products, but they are limited by spatial resolution and the intervals in the existing hypsography.
- #7: IFSAR is also a surface model, but is post-processed into a digital terrain model that is a fairly accurate representation of the surface in the right conditions
- #8: LIDAR is of course the gold standard having seemingly limitless applications
- #9: In the Eastern Dakotas and NW MN the data is becoming readiliy available. We have pursued IFSAR in areas of moderate relief recognizing that a statewide LIDAR collection is unlikely with the lower land-use intensity outside of the Red-River Basin. While LIDAR and IFSAR are very different products we are using them interchangably for soil survey. IFSAR data is a desirable product in canopy free conditions and luckly for us in North Dakota that is 99% of the state.
- #10: Slope in lower relief areas are better represented by remotely sensed terrain models LIDAR & IFSAR
- #11: Microrelief is best represented by LIDAR where cms of elevation change can make all the difference in soil formation
- #12: While the difference between these two oblique views are very subtle, when you get right down to it in modeling for such things a depressional landscapes these products make all the difference.
- #13: In older soil surveys less soils were used and often times certain unique features were glossed over, that may be seen as critical. Glaciolacustrine soils identified in soil survey, where we would expect them to also be mapped on ice-walled lake plains
- #14: The coarser beach ridges on Lake Agassiz are often times not ridges at all, but may only be a foot or so difference in elevation and sometimes not recognizable in photography
- #15: Ice drag markings in the Red River Valley need to be further investigated to see if there is a unique morphological environment
- #16: Patterns of surface drainage are very evident in LIDAR data. We see a lot of soils formerly mapped as depressional with surface drainage.
- #17: The complexity of a channel in alluvium is significant to soil formation and potential landuse and is most certainly not evident in leaf on imagery
- #18: Slope length is significant in conservation planning, it is not populated in many soil surveys, but maybe we can take a second look
- #19: We can better quantify which slopes are contained within delineations, to build consistent slope groups and consistent methods of interpreting slopes
- #22: Soils & Hydrology can both be infered in a great level of detail, to some degree through accurate depiction of the landscape.