Presentation by Joseph K. Berry W.M. Keck Scholar in Geosciences, University of Denver Adjunct Faculty in Natural Resources, Colorado State University Principal, Berry & Associates // Spatial Information Systems — Website: GIS in Forestry and Natural Resources: Current Analytical Capabilities and Future Directions Presentation for Faculty Delegation from Wondo Genet College of Natural Resources, Hawassa University, Ethiopia Warner College of Natural Resources, Colorado State University — Friday, April 11, 2014 Presentation by Joseph K. Berry Materials posted online at This presentation discusses underlying concepts, capabilities, considerations and challenges facing natural resources faculty in moving beyond mapping and instilling the ability to “think with maps” in their students.

Modeling involves analysis of spatial patterns and relationships (map analysis/modeling) Prescriptive Modeling Mapping involves precise placement (delineation) of physical features (graphical inventory) Descriptive Mapping 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) Why So What and What If Global Positioning System (location and navigation) Geographic Information Systems (map and analyze) is Where What Remote Sensing (measure and classify) GPS/GIS/RS The Spatial Triad Computer Mapping (70s) Spatial Database Management (80s) Map Analysis (90s) Multimedia Mapping (00s) (Berry) Analytical ToolTechnological Tool Geotechnology as a Mega-Technology

Elevation (Surface Gradient) Map Stack of Grid Map Layers A Grid Map Laye r consists of a matrix of numbers with a value indicating the characteristic or condition at each grid cell location– geo-registered Map Stack. Spatial Analysis and Statistics use “map-ematical” operations to analyze mapped data for a better understand of geographic patterns and relationships. …pictures later (graphics). Pine Layer draped over 3D Elevation Surface Grid-based Data Organization (Numerical Context) (Berry) Contour Lines Vector (discrete) 3D Surface (stair steps) Interval Polygons Elevation Contour Interval ’ Grid Raster (continuous) 3D Surface (gradient) Modern digital maps are organized sets of numbers first (data)… Vector Map …collection of irregular spatial objects Points Lines Polygons Raster Surface …collection of regular grid cells

Click on… Zoom Pan RotateDisplay Shading Manager Continuous, regular grid cells (objects) Points, Lines, Polygons and Surfaces : --, --, --, 2438, --, --, --, : Grid Table Map Analysis …calculate a slope map and drape on the elevation surface Grid Analytics… Example of Grid-based Organization (Raster, Continuous) (Berry)

Elevation Surface Inclination of a fitted plane to a location and its eight surrounding elevation values Total number of the steepest downhill paths flowing into each location Flow (28,46) = 451 Paths Flow map draped on Elevation Flow map Slope (47,64) = 33.23% Slope map draped on Elevation Slope map % (Berry) Calculating Slope and Flow (Map Analysis)

Erosion Potential Flowmap Slopemap Erosion_potentialFlow/Slope Slope_classes Flow_classes Reclassify Overlay Reclassify Protective Buffers Simple Buffer StreamsSimple Buffer Erosion_potential But all buffer-feet are not the same… (slope/flow Erosion_potential) …reach farther in areas of high erosion potential (Berry) Deriving Erosion Potential & Buffers

Erosion_potential Streams Erosion Buffers Distance Distance away from the streams is a function of the erosion potential (Flow/Slope Class) with intervening heavy flow and steep slopes computed as effectively closer than simple distance— “as the crow walks” Effective Distance Buffer (Variable-width) (digital slide show VBuff2)VBuff2 Effective Erosion Distance CloseFar Heavy/Steep (far from stream) Light/Gentle (close) Simple Buffer Calculating Effective Distance (variable-width buffers) (Berry)

Manual Map Overlay (Binary) Ranking Overlay (Binary Sum) Rating Overlay (Rating Average) Generating maps of animal habitat… (digital slide show Hugag2)Hugag2 The Hugag is a curious beast with strong preferences for terrain configuration:  Prefers low elevations (severe nose bleeds at higher altitudes)  Prefers gentle slopes (fear of falling over and unable to get up)  Prefers southerly aspects (a place in the sun) Evaluating Habitat Suitability (Binary, Ranking and Rating Models) (Berry)

Covertype Water Mask 0= No, 1= Yes Habitat Rating 0= No, 1 to 9 Good Constraint Map Combined Map Habitat Rating Bad 1 to 9 Good (Times 1) (1) Interpreted Maps Gentle Slopes Slope Preference Bad 1 to 9 Good Aspect Preference Bad 1 to 9 Good Elevation Preference Bad 1 to 9 Good Southerly Aspects Lower Elevations Derived Maps Slope Aspect Base Maps Elevation FactJudgment CalibrateAlgorithmWeight Reclassify Overlay …while Reclassify and Overlay operations aren’t very exciting, they are frequently used Conveying Suitability Model Logic (Flowchart of Model Logic) (Berry)

Script of analytic operation sequencing on Map Variables Evaluating Habitat Suitability (hands-on example of a Rating Model) (Berry) Grid-based Map Analysis software Rating Model -- "calibrate" preferences on a scale of 1= poor through 9= excellent RENUMBER Slopemap ASSIGNING 9 TO 0 THRU 3 ASSIGNING 7 TO 3 THRU 4 ASSIGNING 5 TO 4 THRU 6 ASSIGNING 3 TO 6 THRU 10 ASSIGNING 1 TO 10 THRU 100 FOR S_Pref2 RENUMBER Aspectmap ASSIGNING 1 TO 1 THRU 9 ASSIGNING 5 TO 3 ASSIGNING 6 TO 7 ASSIGNING 9 TO 4 THRU 6 ASSIGNING 5 TO 9 FOR A_Pref2 RENUMBER Elevation ASSIGNING 9 TO 0 THRU 2350 ASSIGNING 7 TO 2350 THRU 2375 ASSIGNING 5 TO 2375 THRU 2425 ASSIGNING 3 TO 2425 THRU 2450 ASSIGNING 1 TO 2450 THRU FOR E_Pref2...calculate total and average "score“ COMPUTE S_Pref2 Plus A_Pref2 Plus E_Pref2 FOR Total_Suitable COMPUTE Total_Suitable Dividedby 3 FOR Avg_Suitable Model Solution

Evaluating Habitat Suitability (Berry) Habitat Rating Bad 1 to 9 Good Gentle Slopes Slope Preference Bad 1 to 9 Good Aspect Preference Bad 1 to 9 Good Elevation Preference Bad 1 to 9 Good Southerly Aspects Lower Elevations Slope Aspect Elevation Additional criteria can be added… Near Forests Forest Preference Bad 1 to 9 Good Forest Proximity Forests — Hugags would prefer to be in/near forested areas Near Water Water Preference Bad 1 to 9 Good Water Proximity Water — Hugags would prefer to be near water — Hugags are 10 times more concerned with slope, forest and water criteria than aspect and elevation (Times 10) (10) (1)(1) (1)(1)

Future Directions:  Social Acceptability as 3 rd filter/rail The Softer Side of GIS (The NR Experience) (Berry) Increasing Social Science & Public Involvement 1970s2010s Inter-disciplinary Science Team Table “-ists”“-ologists” “Where is What” Podium Historically Ecosystem Sustainability and Economic Viability have dominated Natural Resources discussion, policy and management. But Social Acceptability has become the critical third filter needed for successful decision-making. Spatial Reasoning, Dialog and Consensus Building

GIS Evolution Map Analysis (1990s) Computer Mapping (1970s) Spatial dB Mgt (1980s) The Early Years Contemporary GIS GeoWeb (2000s) GIS Development Cycle (…where we’ve been) But keep in mind — …NR is about doing the right thing at the right place and at the right time …it identifies and responds to variability within a landscape …it augments indigenous knowledge (not a replacement) —these are expressions of GIS as an “ Analytical Tool ” …but today’s digital map expression involves radically different technologies with extremely high expectations that are moving Natural Resources Management beyond mapping to analysis of spatial patterns and relationships– will science and education lead or simply follow geospatial technology? Mapping focus Data/Structure focus Analysis focus …about every decade (Berry) Our paper map legacy focused on the precise placement of physical features primarily for navigation and inventory… The lion’s share of recent growth has been GIS’s ever expanding capabilities as a “ Technical Tool ” …corralling vast amounts of spatial data and providing near instantaneous access to remote sensing images, GPS navigation, interactive maps, records management, geo-queries and awesome displays.

GIS Evolution The Early Years Revisit Analytics (2020s) GIS Development Cycle (…where we’re heading) Map Analysis (1990s) Computer Mapping (1970s) Spatial dB Mgt (1980s) The Early Years Contemporary GIS GeoWeb (2000s) Revisit Geo-reference (2010s) Future Directions Mapping focus Data/Structure focus Analysis focus …about every decade Cube (6 squares) 3D Solid (X,Y,Z Data) Pentagonal Dodecahedral (12 pentagons) Hexagon (6 sides) Future Directions Square (4 sides) 2D Planar (X,Y Data) Cartesian Coordinates (Berry)

Grid-based Map Data (geo-registered matrix of map values) Latitude/Longitude Grid (140mi grid cell size) Coordinate of first grid cell is 90 0 N 0 0 E Analysis Frame (grid “cells”) …the bottom line is that… All spatial topology is inherent in the grid. Conceptual Spreadsheet (73 x 144) #Rows= 73 #Columns= 144 = 10,512 grid cells …each grid cell is about 140mi x 140mi 18,735mi …from Lat/Lon “ crosshairs to grid cells ” that contain map values indicating characteristics or conditions at each location Lat/Lon The easiest way to conceptualize a grid map is as an Excel spreadsheet with each cell in the table corresponding to a Lat/Lon grid space (location) and each value in a cell representing the characteristic or condition (information) of a mapped variable occurring at that location. …maximum Lat/Lon decimal degree resolution is a four-inch square anywhere in the world The Latitude/Longitude grid forms a continuous surface for geographic referencing where each grid cell represents a given portion of the earth’ surface. (Berry) Lat/Lon serves as a Universal dB Key for joining data tables based on location

(Berry) So What’s the Point? 3) Grid-based map analysis and modeling involving Spatial Analysis and Spatial Statistics is in large part, simply extensions of traditional mathematics and statistics. 1)Current GIS education in NR for the most part insists that non-GIS students interested in understanding map analysis and modeling must be tracked into general GIS courses that are designed for GIS specialists, and that the material presented primarily focus on commercial GIS software mechanics that GIS-specialists need to know to function in the workplace. 4) The recognition by the GIS community that quantitative analysis of maps is a reality and the recognition by the STEM community that spatial relationships exist and are quantifiable should be the glue that binds the two perspectives. 2) However, solutions to complex spatial problems need to engage “domain expertise” in GIS– outreach to other disciplines to establish spatial reasoning skills needed for effective solutions that integrate a multitude of disciplinary and general public perspectives. Handout Online Presentation Materials and References Joseph K. Berry — Handout, PowerPoint and Online References Handout, PowerPoint and Online References …also see online Beyond Mapping Compilation Series …also see online Beyond Mapping Compilation Series Handout, PowerPoint and Online References Handout, PowerPoint and Online References …also see online Beyond Mapping Compilation Series …also see online Beyond Mapping Compilation Series

Where to Go from Here (13 slides with presenter notes) The future of GIS in forestry is moving from a “down the hall and to the right” specialist’s role providing mapped data, to a broader and more active role providing spatial information through analysis and modeling that directly interacts with research, policy formation, planning and management decisions. The shifting emphasis from data–centric tools for mensuration (Where is What) to application-specific constructs of prescriptive mapping (Why, So What and What If) infuses consideration of geographic patterns and relationships within problem-solving contexts. The paradigm shift replaces spatially-aggregated tools and models that assume uniform or random distribution of typical conditions in geographic space, with spatial reasoning and analytical procedures that capitalize on the variation within and among map variables. The result is a “map-ematical” structure that enables resource professionals from various disciplines to better understand and communicate complex spatial interplay of edaphic, topographic, biological, ecological, environmental, economic and social considerations. This presentation describes a comprehensive framework for grid-based map analysis and modeling concepts and procedures as direct spatial extensions of traditional mathematics and statistics enabling individuals with minimal or no GIS background to develop spatial reasoning skills—thinking with maps. The keynote address was video recorded and available for live streaming. However in the live feed, the graphics are poor. You are encouraged to download the companion PowerPoint from the InnovativeGIS link noted above and view it in a separate side-by-side window while advancing the slides with the presenter. (39 minutes) SpatialSTEM