Sponsored by the
National Geospatial Intelligence Agency
and the National Science Foundation
In cooperation with Oglala Lakota College
Table 1 – Classification Parameters
Overview
Reproduction of the native Great Plains Cottonwood
(Populous deltoides) may be significantly declining within the
boundaries of the Pine Ridge Reservation in southwestern
South Dakota. Cottonwood is culturally significant to the
Lakota people, and is ecologically important to Great Plains
ecosystems.
Climate Data:
Max Temperature
Min Temperature
Total Precipitation
Precipitation Intensity
Humidity
Degree Growing Days
Curvature
Terrain Data:
Watershed Area
Mean Slope
Std Dev Slope
Elevation
Relief
Flow Length
Physical:
Rock 3-10 in.
#4 Sieve/Gravel
#10/Very Course Sand
#40/Course Sand
#200/Sand
Total Sand
Total Silt
Total Clay
kw or erodibility factor
Albedo
ksat (permeability)
% Organic Matter
Chemical:
Soil pH
ec (electrical conductivity)
cec7 (cation exchange rate)
SAR or Na ratio to Ca/Mg
Gypsum
CaCO3-Calcium Carbonate
Biological:
Range Productivity
Grain habitat
Grass habitat
Herb habitat
Shrub habitat
Hardwood habitat
Conifer habitat
Wetlands habitat
Water habitat
Sources of Uncertainty in our Model
1.Flow direction was derived from 10 meter DEM data.
2.Pour points were manipulated to form “tear drop” shaped
catchments within close proximity of other catchments.
Discussion
GIS appears to be an effective tool for watershed modeling in the
complex and varied terrain of the Pine Ridge Reservation. The
Strahler flow model in Figure 1 (colored lines) is very close to the
USGS 1:24,000 digital line graph of streams (black lines) for both
perennial and intermittent streams.
Oglala Lakota College has initiated a project to identify the
distribution of cottonwood and other woody riparian species
across the Pine Ridge reservation. The Great Riparian
Protection Project (GRIPP) incorporates GIS remote sensing,
dendrology and geomorphology. We will apply ArcGIS and
ERDAS Imagine software to analyze and model GIS remotely
sensed and field data to better understand the life history of
cottonwoods and other woody riparian species.
Watershed classification is a part of our larger study. We
hypothesize we can identify potential cottonwood recruitment
sites by integrating hydrologic models and available soils
data using ArcGIS. We have selected 15 - 20 physical,
chemical and habitatl parameters . These parameters will be
used to group small catchments on the Pine Ridge
reservation into broader physiographic regions.
Figure 1: shows the projection area of Medicine Root Creek’s confluence with the White River.
A watershed layer of the entire reservation is the projection base.
Parameters for Classification
We created continuous raster files of selected soils, and DEM
data that may affect vegetation distribution. First, we
downloaded and joined the SSURGO soil databases to soils
polygon shapefiles. Next, we generated and mosaiced rasters
of the parameters shown in Table 1. We displayed each of
the rasters to determine whether or not the raster would be
significant in our final physiographic classification (figure 2).
For example, rocks greater than 10 inches do not commonly
occur in Pine Ridge reservation soils and therefore this raster
was removed from our final parameter list.
Methodology
We generated 3,884 watersheds in our study area. First, we
generated flow direction and flow accumulation rasters from a
depressionless 10-m digital elevation model (DEM) with
Spatial Analyst and calibrated a final flow accumulation
model with 1:24,000 vector stream data. Next, we created a
Strahler stream order vector shapefile from the calibrated
rasters. The Strahler model allowed us to identify pourpoint
locations needed to model an initial watershed layer. We
iteratively added and manipulated the locations of the
pourpoints to generate a final watershed model that
resembled the “tear drop” shape of actual watersheds.
The classification rasters show sharp distinctions between
physiographic regions. For example, the percent sand raster (Figure
2) reveals the three major physiographic regions of the reservation;
White River Badlands, Keya Paha Tablelands, and Nebraska
Sandhills.
Future Work
1, Summarize the classification parameters for individual catchments
with zonal statistics in ArcMap.
2. Group the catchments on the reservation into 10 – 15
physiographic regions using an isomeans clustering algorithm
available in ArcGIS and ERDAS Imagine. Clusters form around nodes
(peaks) in the data and data that is most similar to a certain node is
grouped into a class.
Acknowledgements
Jim Sanovia for helping with creation of the initial watershed; deriving flow direction and a depressionless DEM, and 3 rd order pourpoint
manipulation.
Resources
Bulley, H. N., J. W. Merchant, D. B. Marx, J. C. Holz, and A. A. Holz. 2007. A GIS approach to watershed classification for Nebraska
reservoirs. Journal of the American Water Resources Association 43(3):607-612.
Environmental Systems Research Institute, Inc.2004. Arc GIS Desktop. Version 9.2. ESRI Inc.,Redlands, California
Tinant, C. T. (2007) [Great Riparian Protection Project]. Unpublished raw data.
USDA NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service), 2005. Soil Survey Geographic (SSURGO)
database. Bennet County, South Dakota. http://soildatamart.nrcs.usda.gov. Accessed October, 2007
USDA NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service), 2006. Soil Survey Geographic (SSURGO)
database. Jackson County, South Dakota. http://soildatamart.nrcs.usda.gov. Accessed October, 2007
USDA NRCS (U.S. Department of Agriculture, Natural Resources Conservation Service), 2006. Soil Survey Geographic (SSURGO)
database. Shannon County, South Dakota. http://soildatamart.nrcs.usda.gov. Accessed October, 2007
USGS (United States Geological Survey), 2006. National Elevation Dataset. Digital elevation model, Bennett County, South Dakota.
http://ned.usgs.gov/. Accessed October, 2007
USGS (United States Geological Survey), 2006. National Elevation Dataset. Digital elevation model, Jackson County, South Dakota.
http://ned.usgs.gov/. Accessed October, 2007
USGS (United States Geological Survey), 2006. National Elevation Dataset. Digital elevation model, Shannon County, South Dakota.
http://ned.usgs.gov/. Accessed October, 2007
Figure 2: a parameter raster for sand distribution.
USGS (United States Geological Survey), South Dakota Geological Survey. Digital line graph, Bennett County, South Dakota.
http://www.sdgs.usd.edu/data-access/index.html . Accessed October, 2007
USGS (United States Geological Survey), South Dakota Geological Survey. Digital line graph, Jackson County, South Dakota.
http://www.sdgs.usd.edu/data-access/index.html . Accessed October, 2007
USGS (United States Geological Survey), South Dakota Geological Survey. Digital line graph, Shannon County, South Dakota.
http://www.sdgs.usd.edu/data-access/index.html . Accessed October, 2007