2016 conservation track: automated river classification using gis delineated functional process zones by nicholas kotlinski
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The document describes an automated GIS tool called RESonate that is used to classify river systems into functional process zones (FPZs) based on hydrogeomorphic characteristics. The tool extracts over a dozen variables like elevation, slope, and width from geospatial datasets. It then uses these variables to generate sample points and calculate additional metrics. Statistical analysis is applied to cluster sample segments into distinct FPZ classes. The tool was tested on the Carson River where it identified 5 FPZ classes. The goal of the tool is to provide a consistent classification method that can enhance compatibility between river analyses and improve communication among scientists.
2016 conservation track: automated river classification using gis delineated functional process zones by nicholas kotlinski
- 1. Nicholas Kotlinski KU Aquatic Ecology Laboratory, Kansas Biological Survey GIS in the Rockies Conference: September 2016 Automated River Classification Using GIS-Delineated Functional Process Zones
- 2. ► The MACRO project focuses on the macrosystem ecology of river basins in temperate steppe regions of the United States and Mongolia ► The project received NSF support to sample rivers in 3 different ecoregions in each of 2 continents (18 rivers total) over 3 field seasons 2
- 3. Riverine Ecosystem Synthesis (RES) ► Theory of aquatic bio-complexity (Thorp et al. 2008) ► Ecosystem structure and function vary by hydrogeomorphic “patches” ► Hydrogeomorphic patches are repeatable and non-continuous ► At the reach-to-valley scale, these patches are referred to as Functional Process Zones (FPZs) 3
- 4. Goal: To provide a consistent and efficient way to extract hydrogeomorphic variables and define FPZs across different scales and river systems Why? A standard, self emerging classification method has the potential to enhance compatibility between analyses of river systems and improve communication among river scientists and managers Applications: ► Characterizing riverbed sediment patterns by FPZs (Collins et al., 2015) ► Classification framework used for selecting sampling sites for the Macroecological Riverine Synthesis (MACRO) project 4 Applying GIS to RES
- 5. RESonate Tool 5 ► A custom 6-step toolbox designed for ESRI ArcGIS that facilitates rapid, low cost riverine landscape characterization and FPZ classification ► RESonate automatically extracts ~13 hydrogeomorphic variables from readily available geospatial datasets and datasets derived from modeling procedures ► Tool overview and development process outlined in Williams et al., 2013
- 6. What are the inputs? 6 Layer Type Dataset (U.S. Examples) Hydrography NHD streamlines + ArcHydro segments Digital Elevation Model 10-m DEM (USGS) Precipitation PRISM (30-year mean annual precipitation) Geology USGS National Geology Map Floodplain Arc Hydro + Valley Floor Mapper output Microshed (Valley Tops) Arc Hydro catchment delineation output Aerial Photography USDA NAIP imagery / satellite imagery
- 7. What are the outputs? 7 ►11 variables can be extracted using automated analysis ►2 variable must be extracted manually
- 8. Processing: Arc Hydro & Floodplain Mapping 8 Arc Hydro - Standard Hydrologic Processing Sink Filled DEM Flow Direction Flow Accumulation Stream Channel Valley Floor Mapper (FLDPLN) - Models valley floor morphology Developed by Jude Kastens at the Kansas Applied Remote Sensing Program (KARS)
- 9. Processing: Microsheds & Channel Belt 9 ► Microsheds ► Dense catchment lines created with Arc Hydro ► Edited using the valley floor polygon ► Correspond to valley ridge tops, providing a valley width measurement ► Channel Belt ► A channel belt, or meander belt, is created by manually tracing lines connecting peaks in the meander bends of the river channel
- 10. RESonate: 1. Start Tool ► Input all prepared datasets ► Generates sampling points (SPs) based on user defined distance (e.g., 5km) ► Automatically creates master table and geodatabase for data storage ► SPs collect attribute information on: 1. Precipitation 2. Elevation 3. Geology 4. Down valley slope 10
- 11. RESonate: 2. Transects & 3. Calculate Widths ► Transects are generated at each sample point ► Transect length and streamline simplification inputs are defined by user ► Variables calculated using transects: 1. Whole valley width 2. Valley floor (floodplain) width 3. VW:VFW ratio 11
- 12. RESonate: 4. Side Slope, 5. Sinuosity & 6. Channel Belt 12
- 13. RESonate: Master Table 13 ► The primary product of RESonate is a data table containing the calculated hydrogeomorphic variables of each sample segment ► Values can then be exported into statistical software packages for further analysis
- 14. RESonate: Software & Workflow ► ESRI ArcMap 10.x ► ESRI Arc Hydro Plugin ► Valley Flood Mapper v1.0 (FLDPLN) ► Statistical Software (i.e., R, Minitab) 14 Estimated processing time from start (no data) to complete master table for a study basin of ~23,000 km2
- 15. Case Study: Statistical Analysis 15 ► Cluster analysis is used to identify groups of sample segments with similar hydrogeomorphic characteristics (i.e., FPZs) ► 6 Group solution used for Carson River (Great Basin, NV/CA) ► 5 FPZ “classes” ► 1 Reservoir/Water class (outlier)
- 16. Case Study: FPZ Mapping 16
- 17. Case Study: FPZ Typing & Fieldwork 17 High Energy Upland Open Upland High Energy Upland Constricted Lowland
- 18. Continued Research & Future Applications 18 Applications: Watershed Management ► Rapid and cost-effective assessment ► Scalable and question specific ► Ability to add other continuous datasets to code (i.e., land cover) ► Characterize difficult to study river basins (i.e., Mongolia) Reach-level Analysis ► LiDAR ► Fine scale metrics and clustering Continued Research: ► GIS analysis and sampling of selected Mongolian rivers (2017-2019) ► Site selection for Yellowstone/Bighorn river basin (2017) ► Riparian vegetation / land use variables ► Automating bankfull width and river complexity measurements (i.e., island/sand bar morphology)
- 19. User manual, downloads and more information: macrorivers.org Contact: kotlinic@ku.edu Nicholas Kotlinski GIS Technician & Senior Assistant Researcher Aquatic Ecology Laboratory Kansas Biological Survey University of Kansas Lawrence, KS Other Contributors: Bradley Williams Jude Kastens (KARS, KBS) Ellen D’Amico (EPA) Jacob Goering (KBS) Thank you. 19
- 20. 20 REFERENCES Collins, S. E., Thoms, M. C., & Flotemersch, J. E. (2015). Hydrogeomorphic zones characterize riverbed sediment patterns within a river network. River Systems, 21(4), 203-213. Thorp, J. H., Thoms, M. C., & Delong, M. D. (2010). The riverine ecosystem synthesis: toward conceptual cohesiveness in river science. Elsevier. Williams, B. S., D’Amico, E., Kastens, J. H., Thorp, J. H., Flotemersch, J. E., & Thoms, M. C. (2013). Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management. Environmental monitoring and assessment, 185(9), 7485-7499.
Editor's Notes
- #3: Explain the MACRO project and what it hopes to achieve…
- #4: Hydrogeomorphic structure is one of the central components of the MACRO project…
- #6: A Python script created by researchers at the Kansas Biological Survey’s Aquatic Ecology Lab in collaboration with the Environmental Protection Agency. 2D flood model, FLDPLN, used to determine valley morphology by systematically flooding river networks
- #9: The FLDPLN tool models valley flooding, based on the digital elevation model, ArcHydro defined flowlines, and a user defined “depth-to-flood” value This Floodplain Width variable characterizes the potential lateral movement of a river channel within a valley
- #10: The valley width and valley side slope are calculated from these microshed lines * The channel belt may not be appropriate for all rivers (i.e., dryland)
- #11: User defined sample distance