An example application in GIS Modeling Presentation and hands-on exercise materials prepared by Joseph K. Berry Keck Scholar in Geosciences, University of Denver – Special Faculty in Natural Resources, Colorado State University Principal, Berry & Associates // Spatial Information Systems — Website: Forest Availability and Accessibility Vast regions of the Rocky Mountains are under attack by mountain pine beetles and a blanket of brown is covering millions of acres. Is there something we can do to contain the spread and hasten the regenerative cycle? One suggestion is to remove the dead wood to speed forest health and convert it to useful products— but where and how much harvesting is appropriate? Availability = fn (roads, forests, ownership, legal constraints) Accessibility = fn (terrain, water, housing density, visual exposure) …identify the best Landing Sites and characterize their Timbersheds …for more information, see the online book Beyond Mapping III, Topic 29, Spatial Modeling in Natural Resources

Mountain Pine Beetle Devastation (Situation) Vast regions of the Rocky Mountains are under attack by mountain pine beetles. Feller Buncher SkidderWood Chipper …but where and how much harvesting is environmentally, economically and socially appropriate? One possible action to help contain the spread of the beetles and improve forest health is to remove the dead wood…

Forested areas are first assessed for Availability considering ownership and sensitive area designation… Sensitive AreasOwnershipRoadsForests Forests and Roads SlopeWater Houses …and then characterized by Relative Access considering intervening terrain factors of steepness and stream buffers, plus human factors of housing density and visual exposure to roads and houses. Intervening Considerations Assessing Forest Availability and Access (Fundamental considerations)

Forest Access Model A map of Slope is used to establish relative and absolute barriers for operating mechanized harvesting equipment. Maps of Ownership, Water, and Sensitive Areas are used to establish additional absolute barriers based on legal constraints. Critical “ Map Variables” determining Accessible Forests locations Forest Access Model (Flowchart) Forests 0 = not forested 1= forested Forest Accessibility 0 =road to 120+ units away Roads 1 = road 0 = no road Accumulation Cost Surface Relative Accessibility 0 = road location to 120+ units away Discrete Cost Surface Unavailable Ownerships = 0 Ownership Unavailable Sensitive = 0 Sensitive Areas Unavailable Water Buffer = 0 Water Unavailable Too Steep = 0 else assigned 1 Slope Relative Steepness 1 = best : 9 = worst Slope Basic Access Model Preference 0 = no-go 1 = best : 9 = worst Processing Flowchart “Effective Proximity”

40 Reach ft Simulation of different “reach scenarios” provides information on variations in wood supply from reaching deeper into the forest at increasingly higher access costs. The inset on the right shows the forested areas that are much more easily accessed (40/120=.33 as far). Note the elimination of available forested locations (yellow to red) that are deemed effectively “too far” from roads. Basic Model Results (Effective proximity) 120 Reach Not Available Not Forested Accessible Forests by Effective Proximity Relative access values for all of the available forested locations with warmer tones indicating a long harvesting reach into the woods. Effectively Too Far Far

Forests 0 = not forested 1= forested Forest Accessibility 0 =road to 120+ units away Extended Model Extended Model …willing to reach farther into areas with low visual exposure and housing density Multiplicative Weight …willing to reach farther into areas with low visual exposure and housing density Multiplicative Weight Avg Weight 1 = High :.7 = Medium :.5 = Low Average Housing Density 0 = no houses to 66+ houses Houses Scan HD Adjust 1 = >50 High.7 = 20 to 50.5 = 0 to 20 Low HD Adjust 1 = >50 High.7 = 20 to 50.5 = 0 to 20 Low Renumber Extended Forest Access Model (Flowchart) Roads 1 = road 0 = no road Accumulation Cost Surface Relative Accessibility 0 = road location to 120+ units away Discrete Cost Surface Unavailable Ownerships = 0 Ownership Unavailable Sensitive = 0 Sensitive Areas Unavailable Water Buffer = 0 Water Unavailable Too Steep = 0 else assigned 1 Slope Relative Steepness 1 = best : 9 = worst Slope Basic Access Model (Physical and Legal concerns) Preference 0 = no-go 1 = best : 9 = worst Visual Exposure 0 = not seen to 344+ seen Radiate VE Adjust 1 = >75 High.7 = 20 to 75.5 = 0 to 20 Low VE Adjust 1 = >75 High.7 = 20 to 75.5 = 0 to 20 Low Renumber Elevation Extended Access Model (Human Concerns) Roads Houses Compute Viewer Locations Basic Extended

Basic Model Results on the left-side indicates that the farthest away location is 116 effective distance units considering physical and legal barriers to access. Comparison of Basic and Extended Model Results Max = 116 Extended Model Results on the right-side indicates that the farthest away location is 76 effective distance units when considering the preference to harvest in areas of low visual exposure and housing density. Max = 76

Accessible Forest Vegetation Type Composite 2) Region-Wide Overlay …there are 374 acres of accessible forest in Watershed 3 Accessible Forest Data Map Watersheds Template Map Characterizing Accessible Forest Areas (Watershed area & timber types) …there are 964 acres of accessible Lodgepole Pine 3) Location-Specific Overlay Compute Renumber Accessible Forest 1) Create a Binary Map of Accessible Forests Relative Access

Identifying Candidate Landing Sites (Gently sloped forest roads) Stream_buffer Protected Unavailable Forests 1) COMPUTE Roads times Forests times Unavailable times Protected times Stream_buffer for Forest_roads Roads Forest_Roads The Forest_Roads map identifies road locations passing through available forest areas. 1 Slope Forest_Roads 0 increasing steepness 65 2) SCAN Slope Average within 1 square around Forest_roads for Forest_roads_avgSlope Roads_avgSlope 2 3) RENUMBER Forest_roads_avgSlope assign 1 to.01 thru 15 assign 0 to 15 thru 200 for Landing_candidates The Landing_candidates map identifies road locations with gentle to moderate terrain (0-15% slope) within a 100 foot reach (~one acre) of a road that is accessible to available forested areas. Landing_candidates 3

Landing_clumps (#13, 159 paths) (#9, 407 paths) (#6, 155 paths) (#4, 56 paths) (#2, 256 paths) (#5, 58 paths) (#15, 785 paths) 9 9) CLUMP Potential_landings Diagonally AT 1 FOR Landing_clumps Locating the Best Landing Sites (High optimal path density) 6 OptimalPath_density 6) DRAIN Forests over Landing_accumulated_cost for OptimalPath_density 8) COMPUTE High_pathDensity Times Landing_candidates FOR Potential_landings 8 Landing _candidates Potential_Landings 7) RENUMBER OptimalPath_density assign 0 to 0 thru 40 assign 1 to 40 thru 1000 for High_pathDensity 7 High_pathDensity Discrete_cost 4) COMPUTE Discrete_cost Times Forests for Dcost_forests 4 Dcost forests 5) SPREAD Landing_candidates to 200 THRU Dcost _forests Simply for Accum_proximity The Accum_proximity map identifies the forest areas accessible from the Landing_Candidates as a continuous surface of effective distance. 5 Accum_proximity Landing_candidates The Landing_clumps map uniquely identifies the locations with the most accessible forests optimally connected Services the largest number of accessible forest locations Services the largest number of accessible forest locations

11) SPREAD Landings_best to 200 thru D_cost_forest simply for Landings_Accessible_forest 11 Landings_Accessible_forest Identifying “ Timbersheds ” of the Best Landing Sites 10) RENUMBER Landing_clumps assign 0 to 1 assign 0 to 3 assign 0 to 7 thru 8 assign 0 to 10 thru 12 assign 0 to 14 assign 0 to 16 thru 50 for Landings_best 10 Landings_best (#13, 159 paths) (#9, 407 paths) (#6, 155 paths) (#4, 56 paths) (#2, 256 paths) (#5, 58 paths) (Landing #15, 785 paths) 12) RENUMBER Landings_Accessible_forest ASSIGNING -4 TO 80 THRU 200 FOR Timbersheds Timbersheds #13 #9 #6 #4 #2 #5 Timbershed #15 The Timbersheds map identifies all of the accessible forest locations that are “optimally” skidded to each of the Landing sites. Timbershed #15 740cells *.222ac/cell = 164 acres …considering a practical reach of 80 effective cell lengths

…enabling technology used in spatial reasoning, dialog and decision-making— GPS/GIS/RS Geotechnology is one of the three "mega technologies" for the 21st century and promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications (U.S. Department of Labor) Modeling involves analysis of spatial relationships and patterns (Continuous/Numerical) Prescriptive Modeling Why and So What Global Positioning System (location and navigation) Remote Sensing (measure and classify) Geographic Information Systems (map and analyze) The Spatial Triad (Nanotechnology) Geotechnology (Biotechnology) Mapping involves precise placement of physical features and inventories (Discrete/Graphic) Descriptive Mapping Where is What Map Analysis …provides “tools” for investigating spatial patterns and relationships

Geotechnology Geotechnology – one of three “mega-technologies” for the 21 st Century Global Positioning System (Location and navigation) Remote Sensing (Measure and classify) Geographic Information Systems Geographic Information Systems (Map and analyze) 70s Computer Mapping (Automated cartography) 80s Spatial Database Management (Mapping and geo-query) Map Analysis 90s Map Analysis (Spatial relationships and patterns) 00s Multimedia Mapping (Spatial relationships and patterns) A Logical Framework for Map Analysis …for more information see Spatial Analysis Spatial Analysis (Geographic context) Reclassify (single map layer; no new spatial information) Overlay (coincidence of two or more map layers; new spatial information) Proximity (simple/effective distance and connectivity; new spatial information) Neighbors (roving window summaries of local vicinity; new spatial information) Spatial Statistics Spatial Statistics (Numeric context) Surface Modeling (point data to continuous spatial distributions) Spatial Data Mining (interrelationships within and among maps) Map Analysis Toolbox