Forest Stewardship Spatial Analysis Project Illinois Methodology March, 2007 Project Summary Administered by the U.S. Forest Service and implemented by State forestry agencies, the Forest Stewardship Program encourages private forest land owners to manage their lands using professionally prepared Forest Stewardship plans. These plans consider all associated forest-related resources to meet landowner objectives including – but not limited to – timber, wildlife, recreation, water, and aesthetics. The Forest Stewardship Program’s Spatial Analysis Project (SAP) is a GIS-based process through which participating states can identify those lands with the greatest potential for Stewardship Program benefits, that is, those lands most suitable for inclusion in the Program. Private land program and GIS staff from the four states involved in the pilot SAP effort (Connecticut, Maryland, Massachusetts and Missouri), along with Forest Service Stewardship Program and GIS staff selected 12 factors which help identify the “Stewardship potential” of a given piece of land. Existing, readily available data layers were selected to address each factor and an overlay analysis based on 30-meter by 30meter grid cells was then conducted to identify suitability. Factors Defining Stewardship Potential and Datalayer Selection Although states were encouraged to use additional layers to address any state-specific issues or concerns, it was determined that the core 12 would suffice for the State of Illinois. The factors were differentiated into two groups: resource potentials and resource threats. Resource potential factors: Riparian Zones Priority Watersheds Forest Patch Size Threatened and Endangered Species Habitat Public Drinking Water Supply Sources Private Forest Lands Proximity to Public Lands Wetlands Topographic Slope Resource threat factors: Forest Health Risk Development Threat Wildfire Risk Certain lands within any state are considered to be ineligible for inclusion in the Forest Stewardship Program based on land use/land cover. These land use/land cover classes include open water, urban, and publicly owned lands. A mask was created to exclude these lands from the area of analysis. Each state determined the relative importance of each of the 12 factors based on their state-specific conditions. This determined the influence, or weight, each layer would have in the analysis model. The 12 layers were then combined in a GIS-based overlay analysis which took into consideration the weight for each factor. The final product was a composite data layer representing the Forest Stewardship Program potential of a given piece of land. A “natural breaks” classification algorithm was used to break the values into high, medium and low classes. The resulting dataset is shown below. Map of Forest Stewardship Program potential in the State of Illinois. The 12 Core Data Layers Riparian Corridors: This layer is intended to place importance on river and stream corridors where buffers of forest or vegetative cover can have a positive or restorative effect on water quality and riverine ecosystems. The riparian corridors dataset was created by buffering perennial rivers and streams by 300' on each side of the feature and intermittent streams by 100' on each side of the feature. Note: Artificial river paths (channel paths through ponds and lakes) were removed from the dataset prior to buffering. Data source: “Rivers” feature class from the ESRI U.S. Rivers and Streams in the United States dataset; (1:24,000 scale). Priority Watersheds: This data layer is intended to place emphasis on landscapes that impact long-term watershed function. Priority watersheds can be: those that are impaired or deforested, but could be measurably improved through planning and active management, or those that are currently productive, but somehow threatened. Data source: Watershed priorities for Illinois DNR as defined and identified in the Clean Water Action Plan – Unified Watershed Assessment and Watershed Restoration Priorities for Illinois (1998). http://www.epa.state.il.us/water/unified-watershedassessment/index.html Wetlands: This data layer is intended to identify wetlands where planning and management can achieve a higher degree of protection for purposes including water quality and wildlife habitat. Data source: Digital National Wetlands Inventory (NWI) data originally compiled at 1:24,000 scale by the U.S. Fish and Wildlife Service. NWI classes FO (forested) and SS (scrub/shrub) were used. Public Water Supply Areas: This data layer is intended to place emphasis on areas of watersheds that drain onto public drinking water supply intake points. Data source: Watersheds of community public water supply inchannel reservoirs in Illinois. Sally McConkey, sally@uiuc.edu Phone: 217-333-5482 Private Forest: This data layer is intended to place emphasis on eligible private lands with existing forest cover. Five NLCD classes were used to create a "forest" layer: deciduous forest, coniferous forest, mixed forest & woodland, shrubland, and woody wetlands (NLCD classes 41, 42, 43, 51, 91). The analysis mask eliminates public land from consideration. Data source: National Land Cover Database (NLCD 1992). Forest Patches: This data layer is intended to emphasize forest patches of ecologically and/or economically viable size (50 ac.). This layer consists of NLCD classes 41, 42, 43, 51, and 91 erased with buffered road corridors. Buffer widths are based on estimated travel speeds (65 mph roads were buffered 100' on each side, 30-55 mph roads were buffered 50'). Note: This layer includes private and public forests so that private forest land that adjoins public forest, and thus is part of a large, contiguous patch of forestland, is not eliminated from consideration. This ensures that when public lands are removed with the analysis mask, private forests less than 50 acres in size will still get the additional weighting associated with this layer even if they combine with public forest to form a patch larger than 50 acres. Data source: Roads data were extracted from the StreetMap USA data provided in the ESRI Data & Maps media kit on the ESRI Data & Maps/StreetMap USA DVD. Proximity to Protected Land: This data layer is intended to place emphasis on areas in close proximity to lands that are considered to be permanently protected (and managed) and thus contribute to a viably large, interconnected forest landscape. This layer is based on the assumption that public lands are in a permanently protected status, and is intended to include private lands in a permanently protected status (easements, or other). Lands within a ½ mile buffer around protected lands are included in this layer. Data source: Illinois Natural History Survey's 1:100,000 scale Illinois Gap Analysis Stewardship layer, version 4.0, December 2003. For SAP, owner codes 7000 (private) and 8000 (water) are excluded. Tari Tweddale GIS/Remote Sensing Specialist Illinois Natural History Survey tweicher@uiuc.edu 217-265-0583 Threatened & Endangered Species: This data layer is intended to identify areas that provide habitat for threatened and endangered species. This data set depicts the locations of endangered and threatened species habitat, unique natural communities, and other significant natural resources in Illinois, as reported to the Natural Heritage Database (INHD) Program. Data source: Illinois Natural Heritage Database, Illinois Department of Natural Resources Tara Gibbs Kieninger, Database Admin. ORC - Illinois Natural Heritage Database Illinois Department of Natural Resources tkieninger@dnrmail.state.il.us 217.782.2685 Topographic Slope: This layer is intended to serve as a proxy for both forest timber or fiber productivity potential and as an indicator of operability. Slope was chosen as it is nationally available proxy for timber or fiber productivity because of its relationship to ease and feasibility for forest harvesting operations. Selected all areas within the state where slopes are > 5% (mostly agricultural land, where it is forested there is less erosion concern), and < 40% (inoperable with a skidder). Data source: National Elevation Dataset (USGS) 30-meter Digital Elevation Model (DEM), 1:24,000 scale. Forest Health: This data layer is intended to place importance on areas where silvicultural treatments can address significant risks to forest health. The definition of risk is as follows: “The potential that 25% or more of the standing live volume of trees greater than 1'' in diameter will die over the next 15 years, including background mortality”. The 1 km Forest Health Risk data set was resampled to 30 meter and then erased with a layer comprised of the inverse of the forest layer developed for the Forest Patches layer to confine the forest health risk to only forested areas. Data source: National Insect and Disease Risk Map (NIDRM), USDA Forest Service Forest Health Technology Enterprise Team (FHTET). Wildfire Assessment: This data layer is intended to emphasize areas where planning and management are likely to reduce a relatively high risk of wildfire. The Northeastern Area Wildland - Urban Interface Fire Analysis considers the risk of fire (potential for ignition, based on 1990 pop. density), fire hazard (potential to burn, based on fuels and topography), and capability to protect the resources (distribution of volunteer fire departments, by ZIP code). Wildfire risk is classed into High, Moderately high, Moderate, and Low. All areas where risk was determined to be “High” or “Moderately high” were selected to form the basis for this data layer. Data source: The Northeastern Area Wildland - Urban Interface Fire Analysis: Northeastern Area, State and Private Forestry. Risk of Development: This data layer is intended to emphasize areas that are projected to experience increased housing development in the next 30 years. Increased management of private forests can improve the likelihood that these lands will remain forested and continue to provide forest values such as timber, wildlife habitat, and water quality. This layer is particularly important in the wildland-urban interface. Based on updated housing density data (v4) produced by Dr. David Theobald for the “Forests on the Edge” study. These data were developed by subtracting public lands and water from 2000 Census block data and then calculating acres per house. For SAP, 2030 projections were subtracted from 2000 density to determine areas under pressure from development. Data Source: Housing Density Projections (Dr. David Theobald, Colorado State University). Analysis Extent Analysis Mask: The analysis mask defines the area within the analysis extent that will be considered when performing an operation or function. For the Illinois SAP analysis this includes all areas that are not urban/developed, public ownership, or open water. Data sources: USGS’s NLCD (1992) classes: • open water (11) • developed land (21, 22, 23) • barren land (31, 32) IL GAP Stewardship data (public ownership classes) Datalayer Weighting The Illinois Forestry Development Council members attending the January 17, 2007 meeting in Champaign, IL voted on the relative importance of the above 12 data layers. They were asked to assign numerical ranks to each layer in order of decreasing importance, and the average score for each data layer was then converted to a percentage to determine the amount of influence, or weight, each layer would have in the analysis model. Following the example of the state of Missouri, the averages of the ranking values were subtracted from 12 in order to obtain inverse ranks. Each inverse rank was divided by the sum of all inverse ranks, in order to obtain a decimal rank directly proportional to the value of the assigned numerical ranks. Each of the decimal rank values were multiplied by 100. The results are shown below. Layer Weight (%) Private Forest 15.32 Riparian Corridors 12.73 Forest Patches 11.31 Wetlands 9.60 Priority Watersheds 9.09 Development Pressure 8.59 T&E Species 6.97 Drinking Water Supply 6.87 Proximity to Public Lands 6.67 Forest Health 6.46 Topographic Slope 5.45 Fire Risk Percent (%) influence by layer. .91 GIS Analysis Each of the 12 layers used in the analysis had a grid cell size of 30 meters, and they were all snapped to the mask layer. Every cell in each of the 12 layers were coded 0 or 1, where cells not representing the layer in question were 0, and all cells that did represent the layer were 1. For example, in the slope layer, all pixels with a slope >5% and <40% were reclassed to 1, all other pixels were reclassed to 0. Each raster layer was then multiplied by its weight so that each pixel took the value of its weighting. To reduce computation time, actual weights were multiplied by 10,000 so that all weights were integers. The raster layers were all reclassified to exhibit either the value of their decimal ranking * 10,000, or zero in all cells. These binary layers were summed using the Raster Calculator to create the composite SAP layer. The next step was to remove lands ineligible for Stewardship, using the Analysis Mask shown above. To do this, the analysis mask was recoded to values of NoData and 1, with NoData areas ineligible for stewardship. This raster was then multiplied with the Stewardship Potential raster, with the result being that NoData areas in the Analysis Mask became NoData pixels in the Stewardship Potential raster. Once we had a stewardship potential mask for areas eligible for stewardship, we used the Natural Breaks classifier in ArcMap to classify Stewardship Potential into Low, Medium, and High. The ranges are shown below. The raster was then reclassified the so that 0 = no potential, 1=low potential, 2=medium potential, and 3=high potential. Digitizing Existing Stewardship Plans The second part of the SAP is the creation of a shapefile showing existing stewardship plans in each state. The State of Illinois has committed to completing this part of the SAP process in FY 2008.