Using Geographic Information Using Geographic Information Systems in Predicting Reference Systems in Predicting Reference

Communities for Landscape Scale Communities for Landscape Scale RestorationRestoration

bybyESRA OZDENEROL, PhD

University of MemphisDepartment of Earth Sciences

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

Oak SavannaOak Savanna

The Vision …The Vision …

BIG RIVERS PARTNERSHIP PROJECT AREABIG RIVERS PARTNERSHIP PROJECT AREA

Collaborators:Collaborators:

Cynthia Lane, Ph.D.

Greg Noe, Ph.D.

Bart Richardson

Methods:Methods:

1. Land Cover Classification data

2. Environmental data

3. Data categorized

4. Statistical Analyses

5. Predictive Model

6. Filters

1. LAND COVER CLASSIFICATION DATA 1. LAND COVER CLASSIFICATION DATA MLCCSMLCCS

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

2. ENVIRONMENTAL DATA2. ENVIRONMENTAL DATA

Data obtained for each HQN and Restorable site (polygon):

Soil Texture and Drainage

Slope and Aspect

Shade

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

SoilSoil

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

Soil Texture:

S = Sand

L = Loam

O = Organic

Texture class diversity

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: Slope and Aspect:

Aspect: Aspect: Mean aspect & angular dispersion

(aspect variability) Mean aspect converted to sine

and cosine using circular statistics

Shade layer generated using DEM and ArcView Spatial Analyst

Hottest day and time of day modeled

Shade:Shade:

High Quality Native Community

Disturbed

Unsuitable

Unknown

Restorable

3. SITES CATEGORIZED3. SITES CATEGORIZED

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

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

Restorable Cover Type # polys #ha Sparse trees + turf/grassland 647 3563

Agricultural crops 142 1835

Turf/grassland 481 1556

Deciduous trees 278 1437

Boxelder/Green ash forest 263 690

Mixed woodland, disturbed 187 397

Mixed coniferous & deciduous 28 147

Coniferous trees 55 144

3. STATISTICAL ANALYSES3. 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

3. STATISTICAL ANALYSES3. STATISTICAL ANALYSES

Factor analysis

Linear discriminant function analysis

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

RESULTS – RESULTS – Full AnalysisFull 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%

RESULTS – RESULTS – Aggregated AnalysisAggregated 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%

Predicted Native CommunitiesPredicted Native Communities

Undifferentiable communities:Undifferentiable communities:

Oak forest

Maple basswood forest

Oak woodland brushland

Mesic prairie

Aspen forest

Lowland hardwood forest

3. FILTERS3. FILTERS

Cost, Ease of restoration

Rare native community

Landscape – Patch size & Connectivity

Cost Filter:Cost Filter:

Ease of Conversion

Patch size (polygon size)

% impervious surface

Access (slope)

NATIVE COMMUNITYMesic prairie

Oak woodland/

brush Oak

Forest Maple-

basswood

boxelder/green ash forest 3 4 2 2coniferous trees 4 4 3 3cropland 1 2 3 3sparse trees+grassland 3 2 4 4turf 1 2 3 3

Conversion matrix:Conversion matrix:

Patch Size & Connectivity Patch Size & Connectivity Filter:Filter:

Straight line allocation

Existing native communities used as targets

Undifferentiable sites converted to nearest native community

Prioritize restoration sites:Prioritize restoration sites:

Target rare community for restoration

Communities reliably predicted from full analysis

SUMMARY: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

Acknowledgements:Acknowledgements:

Legislative Commission on Minnesota Resources

Mississippi National River and Recreation Area

Minnesota Department of Natural Resources, Conservation Partners Grant