Michigan Tech Research Institute (MTRI) Michigan Technological University 3600 Green Court, Suite 100 Ann Arbor, MI 48105

(734) 913-6840 – Phone (734) 913-6880 – Fax www.mtri.org

Great Lakes Coastal Wetland Mapping

Dr. Laura Bourgeau-Chavez, Mary Ellen Miller, Sarah Endres, Michael Battaglia, Zach Laubach, and Phyllis Higman

Multi-Season Passive and

Active Satellite Imagery

Synthesis: Fusion of moderate resolution (10-30 m)

satellite remote sensing from optical and

Synthetic Aperture RADAR (SAR) sensors

• Landsat TM optical-IR (30m) / thermal

(120m resampled to 30m)

• PALSAR L-band HH and HV

polarizations (20m resolution)

Goal: • Identify and classify coastal wetlands and adjacent land use for the entire

coastal Great Lakes basin (an area of approximately 92,000 square

kilometers) with contemporary (2007-2011) satellite imagery of moderate

resolution (10 – 30 meters) from multiple sources.

• Follow the recommended approach of the Great Lakes Coastal Wetlands

Consortium to provide a comprehensive regional baseline map suitable for

coastal wetland assessment and management by agencies at the local,

tribal, state and federal levels.

Background: Long-term monitoring of Great Lakes coastal wetlands is currently accomplished using

SOLEC (State of the Lakes Ecosystem Conference) or GLEI (Great Lakes Environmental

Indicators) indicators. The weakest element of these management tools is their reliance

on old, incomplete and static landscape-scale data. This severely impacts the

monitoring system’s ability to detect the extent and effect from two of the most

significant coastal wetland stressors; development and invasive plant species. Our map

will be the first comprehensive wetland delineation of the bi-national coastal Great

Lakes, and will include adjacent land use and at least two invasive plant species: Typha

spp. and Phragmites australis.

Current Mapping Status

Field

Data

Data Sheets: Physical Measurements

Summary: o Map Validation: Roughly 20% of the data have been reserved for validation.

The validation data will be used to calculate user’s and

producer’s accuracy - overall accuracy goals are 80% and

individual wetland class accuracies above 70%.

o Overall Basin Accuracy • Lake Michigan= 84%

• Lake Erie= 79%

• Lake Ontario= 82%

• Lake Huron= 78%

o Website:

http://geodjango.mtri.org/coastal-

wetlands/

Random Forests: • Is a machine learning algorithm that

works by creating many decision

trees from random samples of the

input training data.

• Highly trained image interpreters

select the training data by

delineating polygons around known

Land Use \ Land Cover (LULC)

areas on the seasonal imagery

stacks (Landsat and PALSAR (left).

• Random Forests then creates a

forest of decision trees (500) using

a different random subset of training

data for each tree.

• Each decision tree is than used to

predict LULC for the entire scene

and individual pixels are placed in

the class selected by the majority of

trees.

Great Lakes Basin:

10km Coastal Map

Extent

Quality Control Checked Field Data

Image

Interpretation

Methods

Dr. Laura Bourgeau-Chavez MTRI

lchavez@mtu.edu (734) 913-6873

Dr. Mary Ellen Miller MTRI

memiller@mtu.edu (734) 994-7221

Michael Battaglia MTRI

mjbattag@mtu.edu (734) 994-7230

Sarah Endres MTRI

slendres@mtu.edu (734) 994-7231