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Using Geographic Information Systems in Predicting Reference Communities for Landscape Scale Restoration by ESRA OZDENEROL, PhD University of Memphis Department.

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Presentation on theme: "Using Geographic Information Systems in Predicting Reference Communities for Landscape Scale Restoration by ESRA OZDENEROL, PhD University of Memphis Department."— Presentation transcript:

1 Using Geographic Information Systems in Predicting Reference Communities for Landscape Scale Restoration by ESRA OZDENEROL, PhD University of Memphis Department of Earth Sciences

2 A nonprofit, community-based organization that exists to help communities restore, manage and learn about their natural environment through volunteer involvement.

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4 Oak Savanna

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7 The Vision …

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13 BIG RIVERS PARTNERSHIP PROJECT AREA

14 Collaborators: Cynthia Lane, Ph.D. Greg Noe, Ph.D. Bart Richardson

15 Methods: 1. Land Cover Classification data 2. Environmental data 3. Data categorized 4. Statistical Analyses 5. Predictive Model 6. Filters

16 1. LAND COVER CLASSIFICATION DATA MLCCS Hierarchical Classification System:  Cultural or Natural/Semi-natural  Five level system beginning with vegetation type or dominant cover type  % impervious estimated for cultural cover types  Modifiers for adding information for specific polygons

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18 2. ENVIRONMENTAL DATA Data obtained for each HQN and Restorable site (polygon):  Soil Texture and Drainage  Slope and Aspect  Shade

19  Soil Drainage and Texture  USDA-NRCS, Official Soil Series description  Soil characteristics commonly affecting the establishment and persistence of perennial native vegetation  Predominant drainage and texture in upper horizon Soil

20 Soils Drainage: Drainage class: 1. = Excessively drained, Somewhat Excessively drained 2. = Well drained, Moderately well drained 3. = Somewhat Poorly Drained, Poorly drained, Very poorly drained Drainage class diversity

21 Soil Texture: S = Sand L = Loam O = Organic Texture class diversity

22  U.S.G.S. 30 meter digital elevation model  Converted to grid format using ArcView Spatial Analyst  Slope - mean and standard deviation for each site Slope and Aspect:

23 Aspect:  Mean aspect & angular dispersion (aspect variability)  Mean aspect converted to sine and cosine using circular statistics

24  Shade layer generated using DEM and ArcView Spatial Analyst  Hottest day and time of day modeled Shade:

25  High Quality Native Community  Disturbed  Unsuitable  Unknown  Restorable 3. SITES CATEGORIZED

26 Disturbed = Disturbed = soils classified as “urban lands”, “udorthents”, and “gravel pits”; >75% impervious cover Unknown = Unknown = no soils data or aspect Unsuitable = Unsuitable = wetlands; >90% impervious

27 High Quality Native Community# sites  Oak Forest (mesic, dry) 228  Maple Basswood Forest 44  Aspen Forest (temporarily flooded) 14  Floodplain Forest (silver maple) 212  Lowland Hardwood Forest 48  White Pine Hardwood Forest 2  Oak Woodland Brushland 120  Mesic Prairie 8  Dry Prairie (barrens, bedrock bluff, sand gravel) 58  Wet Meadow (shrub) 11  Dry Oak Savanna (sand gravel) 12  Mesic Oak Savanna 23

28 Restorable Cover Type # polys #ha  Sparse trees + turf/grassland  Agricultural crops  Turf/grassland  Deciduous trees  Boxelder/Green ash forest  Mixed woodland, disturbed  Mixed coniferous & deciduous  Coniferous trees

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30 3. STATISTICAL ANALYSES 1. Tested relationship between High Quality Native Communities and environmental characteristics 2. Applied results of analysis to Restorable polygons to predict target community

31 3. STATISTICAL ANALYSES  Factor analysis  Linear discriminant function analysis

32 High Quality Native Community  Oak Forest (mesic, dry)  Maple Basswood Forest  Aspen Forest (temporarily flooded)  Floodplain Forest (silver maple)  Lowland Hardwood Forest  White Pine Hardwood Forest  Oak Woodland Brushland  Mesic Prairie  Dry Prairie (barrens, bedrock bluff, sand gravel)  Wet Meadow (shrub)  Dry Oak Savanna (sand gravel)  Mesic Oak Savanna 21 full, 12 aggregated

33 RESULTS – Full Analysis  All environmental variables significantly different (Wilks’ Lamda, P<.00001)  94.8% of variation explained  6 discriminant functions statistically significant  9 communities reliably predicted >50%

34 RESULTS – Aggregated Analysis  All environmental variables significantly different (Wilks’ Lamda, P<.00001), except shade  98.3% of variation explained  6 discriminant functions statistically significant  6 communities reliably predicted >50%

35 Predicted Native Communities

36 Undifferentiable communities:  Oak forest  Maple basswood forest  Oak woodland brushland  Mesic prairie  Aspen forest  Lowland hardwood forest

37 3. FILTERS  Cost, Ease of restoration  Rare native community  Landscape – Patch size & Connectivity

38 Cost Filter:  Ease of Conversion  Patch size (polygon size)  % impervious surface  Access (slope)

39 Conversion matrix:

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41 Patch Size & Connectivity Filter:  Straight line allocation  Existing native communities used as targets  Undifferentiable sites converted to nearest native community

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44 Prioritize restoration sites:  Target rare community for restoration  Communities reliably predicted from full analysis

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46 SUMMARY:  9 full, 6 aggregated communities reliably predicted  Target refined using cost and landscape filters  Method can be used to prioritize sites based on project goals

47 Acknowledgements:  Legislative Commission on Minnesota Resources  Mississippi National River and Recreation Area  Minnesota Department of Natural Resources, Conservation Partners Grant


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