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.