gis rs habitat modeling approaches to identify riparian communities on the pine ridge reservation *...
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GIS RS Habitat Modeling Approaches to Identify Riparian Communities on the Pine Ridge Reservation
* Charles Jason TinantDon Belile
Helene GaddieDevon Wilford
* Corresponding Author, Oglala Lakota College 490 Piya Wiconi Road – Kyle, South Dakota
605-721-1435 (USA)charlesjasontinant@gmail.com
• 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.
Overview
Medicine Root Creek
Porcupine Creek
White River Group
Arikaree Group
1) Understand PRR woodlands distribution and demography;
3) Predict woodlands community type using GIS remote sensing techniques.
Great Plains Riparian Protection Project (GRIPP) Research Objectives
Figures are courtesy of Jim Sanovia
Methodology – Using RS to Identify Sites
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 - FieldworkSampled 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
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
10-m DEMShannon
Mosaic DEM
10-m DEMBennett
10-m DEMJackson
Project to UTM Zone 13Mosiac Rasters
Depressionless DEM
WatershedModel
StrahlerModel
Apply Sink and Fill Functions
Streamflow Model
Flow DirectionFlow AccumulationSet Null Functions
Pourpointshapefile
Add pourpoints and Iterate
SSURGOdatabase
MUKEYFlat filedatabase
Select Hydrologic PropertiesTie to MUKEY
SSURGOshapefiles
Join database to SSURGO shapefile by MUKEY
Select Hydrologic PropertiesTie to MUKEY
HydrologicProperties
HydrologicPropertiesshapefile
HydrologicPropertiesshapefile
HydrologicPropertiesshapefile
HydrologicPropertiesshapefile
31 - HydrologicPropertiesRasters
Apply Zonal Statistics(Mean, Std. Dev, Max, Min)
Mosaic DEM
StrahlerModel
RastersTerrain RastersSpatial Analyst
(Slope, Curvature)
Physiographic Regions Logic Model - Erdas Imagine
Hydrologic PropertiesStack - 31 Layers
GeologyShapefile
HydrologicPropertiesshapefile
HydrologicPropertiesshapefile
HydrologicPropertiesshapefile
HydrologicPropertiesshapefile
31 - HydrologicPropertiesRasters
Import into ImagineLayer Stack
PCA Stack 15 Layers
• Sand Hills• Eolian Sands• Fertile Lands• Tablelands• Foothills
• Escarpment• Badlands• Alluvial
• River Breaks
PCA to reduce dimensionality
Initial Classification20 classes
Intermediate Classification9 - 14 classes
Isomeans ClusteringRecode Results
Overlay
Mask Mixed ClassesDEMDOQ
Physiographic Regions Model – Based on USGS
Nomenclature (when possible)
• Correctly Identifies Woodlands > 80%• Aa class needs additional information on bedrock geology• Computationally complex process• Misclassified watersheds
Multivariate Approach – Clustering Dendrogram
Unconfined Channels High Peak Flows
Confined ChannelsNarrow Flood Plains
Foot slopes
Active Point Bars
CottonwoodWillow
WoodlandsRussian Olive
Woodlands
JuniperWoodlandsBoxelder
Green Ash American Elm
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, mixed-grass prairie uplands, confined flood plains, attenuated peak flows, stable channels
Microhabitat Niches by Geologic Unit
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 29.3ec 23kw 17.3grass 9.9slope 7.4gypsum 3.9water 2.8silt 1.8albedo 1.3sar 0.7om 0.6caco3 0.6shrub 0.5ksat 0.4hardwood 0.3conifer 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.
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