Thematic Workshop on Standardization and Exchange of Land Use and Cover Information Wednesday, April 27, 2005 Chicago, Illinois

Agenda Introductions (Names, Organization, LU/LC experiences) Facilities, Breaks, Lunch Arrangements Background –Workshop Goals and Objectives –RDX Conferences / Workshops –USEPA/EC Conference Call –CSO Coastal Managers Workshop –Great Lakes Regional Collaboration –LU/LC Mapping Program Inventory Morning Presentations Afternoon Group Discussions Expected Accomplishments

Challenges 1.Clearly define mapping requirements as a function of application needs 2.Discriminate between land cover and land use mapping emphasis 3.Assess scale / resolution needs 4.Assess classification details required 5.Anticipate hurdles for land use / cover exchange

Afternoon Group Discussions Classification Schemes 1. What are the most important classification categories? 2. What are the preferred characteristics of a basin-wide classifications scheme? Acquisition Requirements 1. What is the preferred spatial resolution/scale of land use or land cover polygons or grid cells? 2. What frequency of data collection is needed? Exchange Opportunities 1. What program activities need land use and land cover data to be shared between jurisdictions? 2. What barriers need to be overcome to allow for exchange of data between jurisdictions?

NOAA C-CAP Program Land cover and land cover change analyses were conducted for the entire U.S. coastal zone of the Great Lakes using Landsat Thematic Mapper and Landsat Enhanced Thematic Mapper satellite imagery. All states were completed in fall 2002, except Michigan. Michigan was completed in fall 2003.

NOAA C-CAP Program Environmental Characterization – Lake St. Clair Habitat Restoration and Conservation Project Great Lakes Commission and NOAA CSC to characterize the coastal habitats of Lake St. Clair. As part of the project, land cover and land cover change analyses were conducted for the Canadian side of the Lake St. Clair area.

MRLC – NLCD 29 classes of land cover data derived from the imagery, ancillary data, and derivatives normalized imagery for three time periods per path/row; Ancillary data, including a 30 m Digital Elevation Model (DEM) derived into slope, aspect and slope position; Per-pixel estimates of percent imperviousness and percent tree canopy;

MRLC – NLCD Water 11 Open Water 12 Perennial Ice/Snow Developed 21 Low Intensity Residential 22 High Intensity Residential 23 Commercial/Industrial/Transportation Barren 31 Bare Rock/Sand/Clay 32 Quarries/Strip Mines/Gravel Pits 33 Transitional Forested Upland 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest Shrubland 51 Shrubland Non-natural Woody 61 Orchards/Vineyards/Other Herbaceous Upland 71 Grasslands/Herbaceous Herbaceous Planted/Cultivated 81 Pasture/Hay 82 Row Crops 83 Small Grains 84 Fallow 85 Urban/Recreational Grasses Wetlands 91 Woody Wetlands 92 Emergent Herbaceous Wetlands Land Cover Classification System Key - Rev. July 20, 1999

MRLC Imagery data referenced to the National Albers Equal map projection; imagery re-sampled using cubic convolution to 30m pixels, and; all 8 TM bands processed (including thermal and pan bands) for Landsat 7 data Available - Sensor Reflectance Dataset and Terrain Corrected

NRCan - CCRS Landsat 7 Orthorectified Imagery –the orthoimage data set is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and accuracy of 30 metres or better in the South and 50 metres or better in the North for a 90% level of confidence. Available at

Michigan DNR – MIRIS – Current Use Inventory 82 Counties covered by 1978 Land Use Mapping project Referenced to Michigan Georef Coordinate System 1:24,000 scale Based on photo- interpretation of color aerial photography 54 detailed land use categories Intermittent updates for selected counties for specific projects and by regional planning agencies

Wisconsin DNR - WISCLAND The WISCLAND Land Cover data set is a raster representation of vegetation/land cover for the state of Wisconsin. The source data were acquired from the nationwide MRLC from The data should be used at a scale of at least 1:40,000