Future Directions of GIS in Forestry: Extending Grid-based Map Analysis and Geo-Web Capabilities Joseph K. Berry David Buckley

(Nanotechnology) Geotechnology (Biotechnology) 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) Global Positioning System (location and navigation) Remote Sensing (measure and classify) Geographic Information Systems (map and analyze) GPS/GIS/RS The Spatial Triad Mapping involves precise placement (delineation) of physical features (Graphical Inventory) Descriptive Mapping is Where What Why So What and What If Modeling involves analysis of spatial relationships and patterns (Numerical Analysis) Prescriptive Modeling

Interpreting The Trailing “S” (historical setting) ScienceSystems SpecialistSolutions GIS …four main perspectives of the trailing “S” GISystem s — At the birth of the discipline, the “S” unequivocally stood for Systems focusing on hardware, software and dataware with little or no reference to people or uses GISpecialists — The idea that the trailing “S” defines Specialists took hold in the 1990s as the result of two major forces, uniqueness and utility Data-focus GIScience — recognition of a more in-depth discipline has evolved the “practitioner” role (what does it take to keep a GIS alive and how can it be used?) into a more “theoretical” role (how does GIS work, how could it be improved and what else could it do?) GISolutions — early GIS solutions focused on mapping and geo-query that primarily automated existing business practices; the new focus seems to be on entirely new GIS applications from iPhone crowdsourcing to Google Earth visualizations to advanced map-ematical models predicting wildfire behavior, customer propensity and optimal routing Application-focus

History/Evolution of Map Analysis Geotechnology – one of the three “mega-technologies” for the 21 st Century (Nanotechnology and Biotechnology) Global Positioning System (Location and Navigation) Remote Sensing (Measure and Classify) Geographic Information Systems (Map and Analyze) 70s Computer Mapping (Automated Cartography) 80s Spatial Database Management (Mapping and Geo-query) 90s Map Analysis (Investigates Spatial Relationships and Patterns) 00s Enterprise GIS (Centralized Repositories with Distributed GIS Capabilities) 10s Geo-web Applications (Integration/Interaction of GIS, Visualization, Social Media) Spatial Analysis (Geographical 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 (Numerical context) Surface Modeling (point data to continuous spatial distributions) Spatial Data Mining (interrelationships within and among map layers) Map Analysis

Mapped Data Analysis Evolution (Revolution) Traditional Statistics Mean, StDev (Normal Curve) Central Tendency Typical Response (scalar) Mean= 22.4 ppm StDev= 15.5 Traditional GIS Points, Lines, Polygons Discrete Spatial Objects Mapping and Geo-query Forest Inventory (Map) Spatial Analysis Cells, Surfaces Continuous Geographic Space Contextual Spatial Relationships Emergency Response (Surface) Spatial Statistics Map of Variance (gradient) Spatial Distribution Numerical Spatial Relationships Spatial Distribution (Surface)

Emergency Response (Off-road e911) …a “stepped” accumulation surface analysis (on- and off-road travel-time) considering Truck, ATV and Hiking travel throughout a project area Estimated response time in minutes Increasing Travel-Time from HQ HQ (start) Step 1) Drive truck on the roads… Truck travel “friction” …Step 2) offload and drive ATV off-road… ATV travel “friction” HQ (start) Hiking travel “friction” …Step 3) hike in slopes >40% …farthest away by truck, ATV and hiking is 96.0 min HQ (start) Response Surface (click for animation)

Timber Biomass Access (Availability and Access) Forests and Roads Forested areas are first assessed for Availability considering ownership and sensitive areas… Intervening Considerations …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. Effective Proximity …simulation of different “reach scenarios” provides information on variations in wood supply from reaching deeper into the forest at increasingly higher access costs. Unavailable Non-Forest or Inaccessible Economically Undesirable

Identifying “Timbersheds” (Economic Harvesting Access) #13 #9 #6 #4 #2 #5 Timbershed #15 A Timbershed map identifies all of the accessible forest locations that are “optimally” skidded to each of the proposed Landing sites. Economic and operational conditions within each timbershed are generated to assist harvest planning. …considering a practical reach of 80 effective cell lengths Timbershed #15 740cells *.222ac/cell = 164 acres Landing is the lowest point with all other available/accessible/desirable forested locations identified with increasing harvesting costs Timbershed “ridge” is economically equidistant Low Points

Characterizing Visual Exposure (Visual Connectivity) A Viewshed map is like a search light rotating at a viewer location and identifying each illuminated map location as “seen”— concentric rings of increasing distance carrying the “tangent to be beat” (rise/run). A Visual Exposure Density surface identifies how many times (count) each map location is seen from a set of viewer locations— (simple sum). 621 road cells …270/621= 43% of the entire road network is visually connected A Weighted Visual Exposure Density surface is where different road types are weighted by the relative number of cars per unit of time— (weighted sum). …weighted visual exposure—max12,592 “relative” times seen Visual Exposure (multiple viewers)

Mapped Data Analysis Evolution (Revolution) Traditional Statistics Mean, StDev (Normal Curve) Central Tendency Typical Response (scalar) Mean= 22.4 ppm StDev= 15.5 Traditional GIS Points, Lines, Polygons Discrete Spatial Objects Mapping and Geo-query Forest Inventory (Map) Spatial Analysis Cells, Surfaces Continuous Geographic Space Contextual Spatial Relationships Emergency Response (Surface) Spatial Statistics Map of Variance (gradient) Spatial Distribution Numerical Spatial Relationships Spatial Distribution (Surface)

Thematic Mapping vs. Map Analysis 22.0 Spatially Generalized 40.7 …<50 so not a problem Spatially Detailed Adjacent Parcels High Pocket Discovery of problem sub- area… Thematic Mapping graphically links generalized statistics to discrete spatial objects (Points, Lines, Polygons) — non-spatial analysis (GeoExploration) Discrete Spatial Object Average = 22.0 StDev = 18.7 Thematic Mapping Data Space Standard Normal Curve (Numeric Distribution) X, Y, Value Point Sampled Data Continuous Spatial Distribution Map Analysis Geographic Space Map Analysis map-ematically relates patterns within and among continuous spatial distributions (Map Surfaces) — spatial analysis and statistics (GeoScience) (Geographic Distribution) “Maps are numbers first, pictures later”

Comparing Maps Apples (Rosales) Oranges (Sapindales) Elevation (raw data) Slope (raw data) …the absolute difference between the SNV normalized Elevation and Slope maps indicates that the two maps are fairly similar– 50% of the map area is.52 StDev or less SNV = ((mapValue - Mean) / Stdev) * 100 SNV “Mixed Fruit” Scale Standard Normal Variable (SNV) Normalized (SNV) G#1 R#1 R#2 R#3 Compare by subtracting the two SNV maps and then taking the absolute value to generate a map of the relative difference between the two maps at every map location G#1, R#1= |0| G#1, R#2= |-100| G#1, R#3= |-300|

Correlating Maps r = =.432 aggregated Correlation Coefficient equation …where x = Elevation value, y = Slope value and n = number of value pairs … 625 small data tables within 5 cell reach = 81 paired values for localized summary “Roving Window” =.562 localized Spatially Localized Correlation Scalar Value – single value represents the aggregated non-spatial relationship between two map surfaces Map Variable – continuous quantitative surface represents the localized spatial relationship between two map surfaces Slope (Percent) Elevation (Feet) … one large data table with 25rows x 25 columns = 625 paired values for aggregated summary “Point- by-Point” Spatially Aggregated Correlation Y slope = 38% X elev = 2,063 feet Column= 17 Row= 10

Spatial Data Mining (The Big Picture) Mapped data that exhibits high spatial dependency create strong prediction functions. As in traditional statistical analysis, spatial relationships can be used to predict outcomes …the difference is that spatial statistics predicts WHERE responses will be high or low. …making sense out of a map stack— …the “secret” is geographic stratification and use of CART, Induction or Neural Network spatial data mining technology, not traditional multivariate statistics

Geotechnology’s Future Direct ions (Evolution to Revolution) Geotechnology’s “Mega Status” depends more on how we innovatively apply the technology in new ways, than on cost savings and data dissemination efficiency— …with an emphasis on Spatial Reasoning, Modeling and Communication of “solutions” within decision-making contexts (Application-centric) over inventory Geo-query and Display (Data-centric) Map Analysis Where is What Why, So What and What If… The “Future Directions” of GIS in forestry seem to be responding to three primary forces— Dominant GIS Forces – Dominant GIS Forces (Alternative Geographic Referencing, Universal Spatial Key) Dominant Human Forces – Dominant Human Forces (The “-ists” and the “-ologists”, The Softer Side of GIS) Dominant Geo-web Forces – Dominant Geo-web Forces (Mobile, Social Media, Cloud)

Internet Mapping (IV -2000s) Spatial dB Mgt (II -1980s) Contemporary GIS GIS Modeling (III -1990s) Computer Mapping (Decade I -1970s) The Early Years A Peek at the Bleeding Edge (2010’s and Beyond) Mapping focus Data/Structure focus Analysis focus Cyclical Nature of GIS Development Revisit Analytics (VI -2020s) Future Directions Geo-web Applications & Revisit Geo-referencing (V -2010s) …but those who live by the Crystal Ball are bound to eat ground glass. Evan Vlachos

Dominant GIS Force #1) Alternative Geographic Referencing Consistent distances and adjacency to surrounding grid elements Consistent distances and adjacency to surrounding grid elements Inconsistent distances and adjacency to surrounding grid elements (Orthogonal and Diagonal) Inconsistent distances and adjacency to surrounding grid elements (Orthogonal and Diagonal) Tightly Clustered Groupings Continuously Nested Grid Elements Hexagonal Grid (6 facets) Hexagon Dodecahedral Grid (12 facets) Dodecahedral Cubic Grid (26 facets) Square Grid (8 facets) 2D Grid Element (Planimetric) Square 3D Grid Element (Volumetric) Cube Cartesian Coordinate System Square Cube

Dominant GIS Force #2) Universal Spatial Key (grid space as key) …that form a complex Address Code (x,y,z) for spatial reference of any record in a database that can be used to join any other spatially referenced table– Spatially-enabled Universal Key WHERE is WHAT Entire 3D volume containing the earth is pre- partitioned into small Grid Elements using basic geometry equations… 100km, 10km, …1m UTM gridlines Planimetric Volumetric

Dominant Human Force #1) The “-ists” and the “-ologists” Together the “-ists” and the “-ologists” frame and develop the Solution for an application. Application Space Geotechnology’s Core …understand the “ tools ” that can be used to display, query and analyze spatial data Data focus Technology Experts “-ists” The “-ists” …understand the “ science ” behind spatial relationships that can be used for decision-making Information focus Domain Experts “-ologists” The “-ologists” — and — Solution Space

Dominant Human Force #1, continued) A Significantly Larger GIS Tent “Policy Makers” “Stakeholders” Knowledge/ Perceptions (interrelationships of relevant facts) Wisdom/Opinions and Values (actionable knowledge) Decision Makers utilize the Solution under Stakeholder, Policy & Public auspices. “Decision Makers” Data (all facts) Application Space Geotechnology’s Core Technology Experts “-ists” Domain Experts “-ologists” Solution Space Information (facts within a context)

Dominant Human Force #2) The Softer Side of GIS (the NR Experience) Historically Economic Viability and Ecosystem Sustainability have dominated Natural Resources discussion, policy and management. Podium Experts and Professionals as decision-makers/managers Communication/Infusion of Perceptions, Opinions and Values Banquet Table Public Involvement Increasing Social Science & Public Involvement 1970s2010s Inter-disciplinary Science Team Table Analysis of Data and Information Spatial Reasoning, Dialog and Consensus Building Future Directions:  Social Acceptability as 3 rd filter …but Social Acceptability has become the critical third filter needed for successful decision-making.

History/Evolution of Geo-web Applications Geotechnology – one of the three “mega-technologies” for the 21 st Century (Nanotechnology and Biotechnology) Global Positioning System (Location and Navigation) Remote Sensing (Measure and Classify) Geographic Information Systems (Map and Analyze) 70s Computer Mapping (Automated Cartography) 80s Spatial Database Management (Mapping and Geo-query) 90s Map Analysis (Investigates Spatial Relationships and Patterns) 00s Enterprise GIS (Centralized Repositories with Distributed GIS Capabilities) 10s Geo-web Applications (Integration/Interaction of GIS, Visualization, Social Media) Web Mapping (from ArcIMS / MapServer …. to ArcGIS Server) Geoprocessing Services (in addition to map services, data services, etc.) Client Side Analysis (in the browser!) Web Mobile Apps (native versus web mobile – browser, smartphone, tablets Cloud Apps (cloud GIS deployment) Geo-web Applications Today, 3:30 p.m. – 4:00 p.m.

Online Presentation Materials and References Joseph K. Berry — David Buckley — Handout, PowerPoint and Online References Handout, PowerPoint and Online References …also see online book Beyond Mapping II I …also see online book Beyond Mapping II I Handout, PowerPoint and Online References Handout, PowerPoint and Online References …also see online book Beyond Mapping II I …also see online book Beyond Mapping II I