GIS RS Habitat Modeling Approaches to
Identify Riparian Communities
on the Pine Ridge Reservation
* Charles Jason Tinant
Don Belile
Helene Gaddie
Devon Wilford
* Corresponding Author, Oglala Lakota College 490 Piya Wiconi
Road – Kyle, South Dakota
605-721-1435 (USA)
charlesjasontinant@gmail.com
Overview
• Populus deltoides are an
• early successional species
colonizing point bars;
• Recruitment is correlated with
floods.
Damming and river alteration
effects depend on channel type:
• For meandering wash load
streams (Missouri River) become
hardwood forests;
• For braided gravel streams (Platte
River) cottonwoods woodlands
extent increases.
White River Group
Medicine Root Creek
Arikaree Group
Porcupine Creek
Great Plains Riparian Protection Project
(GRIPP) Research Objectives
1) Understand
PRR woodlands
distribution and
demography;
3) Predict
woodlands
community type
using GIS
remote sensing
techniques.
Methodology – Using RS
to Identify Sites
Figures are courtesy
of Jim Sanovia
1. Unsupervised classification
of 2-m DOQ;
2. Pull out remotely sensed
“tree” layer;
3. Buffer streams 50-m from
center of stream;
4. Buffer roads 250-m from
roads;
5. Intersect and use output to
clip “tree” layer;
6. Draped 100-m grid and
randomly selected points.
Methodology - Fieldwork
Sampled 22 plots in 2007 and
26 plots in 2008;
• Estimated canopy cover at 4
community levels;
• Enumerated trees to species at 5
age classes.
- Measured
stream
morphology (2007 only)
•
13 cross-sections by Rosgen
Method.
White River Group
Medicine Root Creek
Analytical Approaches
Remotely Sensed
Landsat – 7
Geology
Physiographic
Regions
SSURGO + DEM
Multivariate
Approaches (DA +
Clustering)
• Distinguishes juniper from cottonwoods
• Identifies invasive Russian olive
• Cloud cover!!
• Doesn’t distinguish cottonwoods from
hardwoods
• Correctly identifies woodlands > 70%
• Scale of mapping overlooks features below
about 1:50,000 scale
• Correctly Identifies Woodlands > 80%
• Needs Additional Information (underlying
geology)
• Computationally complex process
• Misclassified watersheds
• Provides a context for understanding the
underlying abiotic and ecologic processes
• Lacks spatial context
Final
Habitat
Model
MaxEnt
Remotely Sensed Approach -Final
Classified Landsat - 7 Image
• Distinguishes juniper from cottonwoods
• Identifies invasive Russian olive
• Cloud cover!!
• Doesn’t distinguish cottonwoods from
hardwoods
• Computationally simple process
• Geology for Pine Ridge Reservation
has a need for stratigraphic revision
• Correctly Identifies Woodlands ~ 70%
Physiographic Regions Logic Model - ArcGIS
Pourpoint
shapefile
10-m DEM
Shannon
10-m DEM
Jackson
Mosaic DEM
10-m DEM
Bennett
Project to UTM
Zone 13
Mosiac Rasters
SSURGO
database
MUKEY
Flat file
database
Select
Hydrologic
Properties
Tie to MUKEY
Depressionless
DEM
Streamflow
Model
Strahler
Model
Apply Sink and Fill Flow Direction
Functions
Flow Accumulation
Set Null Functions
SSURGO
shapefiles
Join database
to SSURGO
shapefile by
MUKEY
Strahler
Model
Add
pourpoints and
Iterate
Hydrologic
Hydrologic
Properties
Hydrologic
Properties
Hydrologic
shapefile
Properties
31 - Hydrologic
shapefile
Properties
shapefile
Properties
shapefile
Rasters
Hydrologic
Properties
Select
Hydrologic
Properties
Tie to MUKEY
Mosaic DEM
Apply Zonal
Statistics
(Mean, Std.
Dev, Max, Min)
Rasters
Spatial Analyst
(Slope,
Curvature)
Watershed
Model
Terrain
Rasters
Physiographic Regions Logic Model - Erdas Imagine
Hydrologic
Hydrologic
Properties
Hydrologic
Properties
Hydrologic
shapefile
Properties
31 - Hydrologic
shapefile
Properties
shapefile
Properties
shapefile
Rasters
Hydrologic Properties
Stack - 31 Layers
PCA Stack
15 Layers
Import into Imagine
Layer Stack
PCA to reduce
dimensionality
Physiographic Regions Model
– Based on USGS
Nomenclature (when possible)
•Sand Hills
•Eolian Sands
•Fertile Lands
•Tablelands
•Foothills
•Escarpment
•Badlands
•Alluvial
•River Breaks
Intermediate
Classification
9 - 14 classes
Initial Classification
20 classes
Recode Results
Overlay
Geology
Shapefile
DOQ
DEM
Isomeans
Clustering
Mask Mixed Classes
• Correctly Identifies Woodlands > 80%
• Aa class needs additional information on bedrock geology
• Computationally complex process
• Misclassified watersheds
Multivariate Approach – Clustering Dendrogram
Cottonwood
Willow
Woodlands
Active Point Bars
Russian Olive
Woodlands
Boxelder
Green Ash
American Elm
Unconfined Channels
High Peak Flows
Juniper
Woodlands
Foot slopes
Confined Channels
Narrow Flood Plains
Microhabitat Niches by Geologic Unit
White River Group and Pierre
Shale – Plains cottonwoods
and willows species: erodible
sediments with sparse
vegetation, unconfined flood
plains, high peak flows,
frequent channel migration
Arikaree Formation - Green Ash,
Boxelder, American Elm:
cohesive sediments, mixedgrass prairie uplands,
confined flood plains,
attenuated peak flows, stable
channels
Maximum Entropy Model
• Uses ascii rasters and sample locations in csv format as
model inputs;
– Used 30m ascii rasters in UTM14 prepared using ArcGIS Spatial
Analyst;
• Model calculates omission rate, sensitivity, marginal and
correlated response curves, model variable contributions and
a jackknife test of model variable importance;
• The following slides are results from MaxEnt model runs
analyzing 28 variables from SSURGO soils data;
– SSURGO quality for Shannan, Jackson, and Bennett counties (last
updated in 1960s) has an effect on the quality of the model results;
• The final model will incorporate SSURGO data, geology data,
gridded precipitation data, classified Landsat imagery, and
NVDI data.
Cottonwood/Willow Prediction using SSURGO Soils
Variables
Variable Percent Contribution
dem
ec
kw
grass
slope
gypsum
water
silt
albedo
sar
om
caco3
shrub
ksat
hardwood
conifer
29.3
23
17.3
9.9
7.4
3.9
2.8
1.8
1.3
0.7
0.6
0.6
0.5
0.4
0.3
0.1
Cottonwood/Willow Prediction using SSURGO Soils Variables
Conclusions
• Cottonwoods and hardwoods species on the Pine Ridge
reservation are end-members distributed along a
disturbance gradient;
• The disturbance gradient corresponds with geomorphic
response to precipitation events, which can be predicted
by bedrock geology;
• Landscape level variables accurately predict riparian
community type on the Pine Ridge Reservation;
• MaxEnt software predicts riparian community occurrence
at a finer level of spatial detail than other landscape or
watershed level analyses.
Acknowledgements
• Funded by:
• National Geospatial Agency
• NSF Tribal College and University Program (TCUP)
• Project is supported by:
• OLC Math and Science Department:
– Hannan LaGarry, Al Eastman, Chris Lee, Kyle White, Elvin
Returns, Michael DuBray, Dylan Brave, Michael Thompson,
Beau White, Jeremy Phelps, Landon Lupe (SDSU), Jim
Sanovia (SDSMT)
• MaxEnt reference:
– Maximum Entropy Modeling of Species Geographic
Distributions – Phillips, Anderson, and Shapire, Ecological
Modeling ,Vol 190, 2006.