Presentation on theme: "GIS RS Habitat Modeling Approaches to Identify Riparian Communities"— Presentation transcript:
1GIS RS Habitat Modeling Approaches to Identify Riparian Communities on the Pine Ridge Reservation* Charles Jason TinantDon BelileHelene GaddieDevon Wilford* Corresponding Author, Oglala Lakota College 490 Piya Wiconi Road – Kyle, South Dakota(USA)
2Overview Populus deltoides are an White River GroupMedicine Root CreekPopulus deltoides are anearly 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.Arikaree GroupRecruit 3 – 4 Native American STEM undergraduates to work as research internsEnhance environmental science courses at OLC by incorporating GRIPP research into 2 – 4 classes over the next four semestersDisseminate GRIPP results to K-12 students and undecided undergraduate students on the Pine Ridge reservation through participation in 4 – 6 outreach activities in the next two years.Measure the canopy cover and geomorphology at 60 sampling unitsEvaluate the health of woodlands by enumerating stand composition and age class of woody riparian species for at least 500 subplotsIdentify geomorphic parameters significant to native woody riparian species recruitmentCreate a predictive map of woody riparian species on the Pine Ridge Reservation at an 80% level of accuracyPorcupine Creek
3Great Plains Riparian Protection Project (GRIPP) Research Objectives 1) Understand PRR woodlands distribution and demography;3) Predict woodlands community type using GIS remote sensing techniques.
4Methodology – Using RS to Identify Sites Unsupervised classification of 2-m DOQ;Pull out remotely sensed “tree” layer;Buffer streams 50-m from center of stream;Buffer roads 250-m from roads;Intersect and use output to clip “tree” layer;Draped 100-m grid and randomly selected points.Figures are courtesyof Jim Sanovia
5Methodology - 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 GroupMedicine Root Creek
6Analytical Approaches Distinguishes juniper from cottonwoodsIdentifies invasive Russian oliveCloud cover!!Doesn’t distinguish cottonwoods from hardwoodsRemotely SensedLandsat – 7Correctly Identifies Woodlands > 80%Needs Additional Information (underlying geology)Computationally complex processMisclassified watershedsPhysiographic RegionsSSURGO + DEMCorrectly identifies woodlands > 70%Scale of mapping overlooks features below about 1:50,000 scaleGeologyProvides a context for understanding the underlying abiotic and ecologic processesLacks spatial contextMultivariate Approaches (DA + Clustering)Final Habitat ModelMaxEnt
13Correctly Identifies Woodlands > 80% Aa class needs additional information on bedrock geologyComputationally complex processMisclassified watersheds
14Unconfined Channels High Peak Flows Multivariate Approach – Clustering DendrogramUnconfined Channels High Peak FlowsConfined ChannelsNarrow Flood PlainsFoot slopesActive Point BarsCottonwoodWillowWoodlandsRussian OliveWoodlandsJuniperWoodlandsBoxelderGreen AshAmerican Elm
15Microhabitat 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 migrationArikaree Formation - Green Ash, Boxelder, American Elm: cohesive sediments, mixed-grass prairie uplands, confined flood plains, attenuated peak flows, stable channels
16Maximum Entropy ModelUses 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.
18Cottonwood/Willow Prediction using SSURGO Soils Variables
19ConclusionsCottonwoods 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.
20Acknowledgements 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.