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GIS RS Habitat Modeling Approaches to Identify Riparian Communities on the Pine Ridge Reservation * Charles Jason Tinant Don Belile Helene Gaddie Devon.

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Presentation on theme: "GIS RS Habitat Modeling Approaches to Identify Riparian Communities on the Pine Ridge Reservation * Charles Jason Tinant Don Belile Helene Gaddie Devon."— Presentation transcript:

1 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 (USA)

2 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

3 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

4 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.

5 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

6 Analytical Approaches Distinguishes juniper from cottonwoods Identifies invasive Russian olive Cloud cover!! Doesn’t distinguish cottonwoods from hardwoods Remotely Sensed Landsat – 7 Correctly Identifies Woodlands > 80% Needs Additional Information (underlying geology) Computationally complex process Misclassified watersheds Physiographic Regions SSURGO + DEM Correctly identifies woodlands > 70% Scale of mapping overlooks features below about 1:50,000 scale Geology Provides a context for understanding the underlying abiotic and ecologic processes Lacks spatial context Multivariate Approaches (DA + Clustering) Final Habitat Model MaxEnt

7 Remotely Sensed Approach -Final Classified Landsat - 7 Image Distinguishes juniper from cottonwoods Identifies invasive Russian olive Cloud cover!! Doesn’t distinguish cottonwoods from hardwoods

8 Computationally simple process Geology for Pine Ridge Reservation has a need for stratigraphic revision Correctly Identifies Woodlands ~ 70%

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10 Physiographic Regions Logic Model - ArcGIS 10-m DEM Shannon Mosaic DEM 10-m DEM Bennett 10-m DEM Jackson Project to UTM Zone 13 Mosiac Rasters Depressionless DEM Depressionless DEM Watershed Model Strahler Model Apply Sink and Fill Functions Streamflow Model Flow Direction Flow Accumulation Set Null Functions Pourpoint shapefile Add pourpoints and Iterate SSURGO database MUKEY Flat file database Select Hydrologic Properties Tie to MUKEY SSURGO shapefiles SSURGO shapefiles Join database to SSURGO shapefile by MUKEY Select Hydrologic Properties Tie to MUKEY Hydrologic Properties Hydrologic Properties shapefile Hydrologic Properties shapefile Hydrologic Properties shapefile Hydrologic Properties shapefile 31 - Hydrologic Properties Rasters Apply Zonal Statistics (Mean, Std. Dev, Max, Min) Mosaic DEM Strahler Model Rasters Terrain Rasters Spatial Analyst (Slope, Curvature)

11 Physiographic Regions Logic Model - Erdas Imagine Hydrologic Properties Stack - 31 Layers Hydrologic Properties Stack - 31 Layers Geology Shapefile Hydrologic Properties shapefile Hydrologic Properties shapefile Hydrologic Properties shapefile Hydrologic Properties shapefile 31 - Hydrologic Properties Rasters Import into Imagine Layer Stack PCA Stack 15 Layers Sand Hills Eolian Sands Fertile Lands Tablelands Foothills Escarpment Badlands Alluvial River Breaks PCA to reduce dimensionality Initial Classification 20 classes Intermediate Classification classes Isomeans Clustering Recode Results Overlay Mask Mixed Classes DEMDOQ Physiographic Regions Model – Based on USGS Nomenclature (when possible)

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13 Correctly Identifies Woodlands > 80% Aa class needs additional information on bedrock geology Computationally complex process Misclassified watersheds

14 Multivariate Approach – Clustering Dendrogram Unconfined Channels High Peak Flows Confined Channels Narrow Flood Plains Foot slopes Active Point Bars Cottonwood Willow Woodlands Russian Olive Woodlands Juniper Woodlands Boxelder Green Ash American Elm

15 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

16 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.

17 Cottonwood/Willow Prediction using SSURGO Soils Variables Variable Percent Contribution dem29.3 ec23 kw17.3 grass9.9 slope7.4 gypsum3.9 water2.8 silt1.8 albedo1.3 sar0.7 om0.6 caco30.6 shrub0.5 ksat0.4 hardwood0.3 conifer0.1

18 Cottonwood/Willow Prediction using SSURGO Soils Variables

19 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.

20 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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