jeremy erickson, lucinda b. johnson, terry brown, valerie brady, natural resources research...
TRANSCRIPT
Developing a GIS based wetland restoration prioritization tool
for Minnesota
Jeremy Erickson, Lucinda B. Johnson, Terry Brown, Valerie Brady,
Natural Resources Research Institute, University of MN Duluth
Outline
Project objectives Background
• Available restorable wetland inventories (RWIs)• Supplementing RWIs
Project overview
Objectives
Prioritize areas where wetland restoration will result in the improvement of water quality (N and P) and habitat.
Identify areas that will most likely result in high quality wetlands that will be self-sustaining into the future.
What this tool is NOT…
A site-specific model to identify individual wetlands for restoration;
Does not replace: • Wildlife Habitat Evaluation Procedure • Water quality assessments• Local knowledge• Soil loss equations
How this tool could be used… To identify stressed areas that would benefit from wetland
restoration
To identify areas with the greatest chance for successful restoration
To recognize areas where current wetlands should be protected or restored
To allow managers and researchers see what types of broad conditions wetlands are being restored in.
MNBWSR- GIS analysis
Ducks Unlimited- photo interpretation
Incomplete areas- CTI/SSURGO method
MN Restorable Wetland Inventory
Restorable Wetland Delineation (CTI/SSURGO method)
Required data: Compound topographic index (CTI): a wetness index
estimated from slope and flow accumulation (estimation of soil moisture content). Requires a DEM.
CTI = ln (As / (tan(beta))
where As = (flow accumulation + 1 ) *(pixel area m2)
beta = slope expressed in radians.
SSURGO drainage data
National Wetlands Inventory (NWI) coverage
CTI >10.5Poorly or very poorly drained soils
NWI RWI
ESRI: http://arcscripts.esri.com/details.asp?dbid=11863)
Statewide RWI using CTI/SSURGO method
Covers entire state
Can be easily adjusted stricter RWI estimates
• CTI threshold• Higher resolution
DEM Can supplement
areas without RWIs
Developing the decision tool:overview and vision
Web based tool
Utilizes readily available GIS data layers
Definitions Decision Layer- one of three primary groups of data
which will form the basis of our model, e.g., Stress, Viability, Benefits.
Focus Area- distinct ecosystem services that are affected by wetland restoration, e.g. water quality in the form of N and P inputs and habitat.
Data Layer- thematic layers representing distinct spatial data inputs, e.g., Land use.
Class- distinct classification units for a given data layer, e.g., row crops, high density development.
Wetland restoration decision layers (data layer summary)
ViabilityFactors that predict the success (or failure) of restoration
StressFactors that predict the success (or failure) of restoration
BenefitsEnvironmental services that will be enhanced by restoration
ConditionEnvironmental data that acts as a potential modifier to the final output
Viability
Stressor
Benefits
Final output
Condition
ViabilityFactors that predict the success (or failure) of restoration
Topography (CTI)
Soil type
Network position
Ownership
Stress Land use
• Open development• Low density development• Medium density development• High density development• Pasture• Row crops
Twin Cities
Distance to Roads
Population Distance to Feedlots (MPCA)
Factors that predict the success (or failure) of restoration
Benefits
Environmental Benefits Index
Environmental services that will be enhanced by restoration
Soil erosion risk
Water quality risk
Wildlife habitat quality• Sites of biodiversity• Species of greatest conservation need• Bird potential habitat• Weighted habitat protection level
ConditionEnvironmental data that acts as a potential modifier to the final output
MPCA IBI data
MPCA Impaired waters designation (TMDL)
Biological, habitat, and water quality surveys
Surrounding landscape (buffers)
Google or Bing maps
Restorable wetlands inventories
Ownership
Network location
Topography
Viability score
Summarizing at the 30 m pixel level Watershed
boundary
Political boundary
Soil type
Weighting Expert panel
• Comprised of wetland, hydrology, GIS, and landscape experts
• Survey Monkey (http://www.surveymonkey.com ) N and P Habitat
• Weighting discussion• Additional data layer discussion
Literature review
Variable class
Unknown
All hydric
Partially hydric
1
2
3
4
43
21
Each pixel is assigned a score based on class weight
Data Layer
Soil type
Not hydric
Categorical layer weighting
High stress Gradual stress reduction
No stress
High stress Gradual stress reduction
No stress
Maximum effect threshold
No effect threshold
Continuous data: example 1
10050
Population tracts
150
Pixel population normalized
x’ = (x-xmin)/(xmax-xmin)
x’ = (100-50)/(150-50)
x’ = 0.5
Continuous data: example 2
Unknown
All hydric
Partially hydric
1
2
3
4
43
21
Soil typeNot hydric
Network
Ownership
Network
Ownership
Topography
Habitat
Water quality (N or P)
Viability
Soil type
Topography
Soil type
Land cover
Roads
Land cover
Roads
Population
Habitat
Water quality(N or P)
Stress
Feedlots
2
Population
Feedlots
Habitat suitability
Water quality
Soil erosion
Terrestrial value Habitat
Water quality(N or P)
Benefit
Final output
Condition
Class Data layers Focus areas Decision layers
2
1
1 1
1
Spatial tool schematic
2
1
1
1
Scenario A: low stress/high viability
Carver County
Low stress areas
High viability Restorable wetland locations
Scenario B: high stress
Locate highly stressed areas
Less concern about viability
Locate restorable wetlands
Carver CountyBluff Creek
Questions…..
Contact: Jeremy EricksonNatural Resources Research InstituteUniversity of Minnesota [email protected]