Transcript Future Directions of GIS in Forestry: Extending Grid

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)
Geographic Information
Systems (map and analyze)
Global Positioning
System (location and navigation)
Remote Sensing
(measure and classify)
GPS/GIS/RS
The Spatial Triad
is
Mapping involves
precise placement
(delineation) of
physical features
(Graphical Inventory)
Where
What
Descriptive
Mapping
Prescriptive
Modeling
Why
So What
and
What If
Modeling
involves
analysis of spatial
relationships and
patterns
(Numerical Analysis)
Interpreting The Trailing “S” (historical setting)
GISystems — 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
GIS
…four main perspectives of the trailing “S”
Systems
Science
Data-focus
Application-focus
Specialist
Solutions
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
History/Evolution of Map Analysis
Geotechnology – one of the three “mega-technologies” for the 21st 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)
Mapped Data Analysis Evolution
Traditional GIS
Forest
Inventory
(Map)
• Points, Lines, Polygons
• Discrete Spatial Objects
• Mapping and Geo-query
Traditional Statistics
Mean= 22.4 ppm
StDev= 15.5
• Mean, StDev (Normal Curve)
• Central Tendency
• Typical Response (scalar)
(Revolution)
Spatial Analysis
Emergency
Response
(Surface)
• Cells, Surfaces
• Continuous Geographic Space
• Contextual Spatial Relationships
Spatial Statistics
Spatial
Distribution
(Surface)
• Map of Variance (gradient)
• Spatial Distribution
• Numerical Spatial Relationships
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
Hiking travel “friction”
HQ (start)
Step 1) Drive
truck on the roads…
…Step 2) offload and
drive ATV off-road…
HQ (start)
…Step 3) hike
in
slopes >40%
…farthest away by
truck, ATV and
hiking is 96.0 min
HQ (start)
Truck travel “friction”
ATV travel “friction”
Increasing Travel-Time from HQ
Estimated response time in minutes
Response Surface
(click for animation)
Timber Biomass Access (Availability and Access)
Forested areas are first assessed for Availability considering ownership and sensitive areas…
Forests and
Roads
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
Non-Forest or
Inaccessible
Unavailable
Economically
Undesirable
…simulation of different “reach
scenarios” provides information
on variations in wood supply from
reaching deeper into the forest at
increasingly higher access costs.
Identifying “Timbersheds” (Economic Harvesting Access)
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.
#2
#5
Timbershed
“ridge” is
economically
equidistant
…considering a
practical reach
of 80 effective
cell lengths
#4
#6
#9
Low Points
#13
Timbershed #15
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
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).
Visual Exposure
(multiple viewers)
Density surface
Visual
Exposure Density
identifies how many times
(count) each map location
is seen from a set of
viewer locations—
(simple sum).
surface is where different
road types are weighted by
the relative number of cars
per unit of time—
(weighted sum).
A Visual
Exposure
A Weighted
621 road cells
…270/621= 43% of the
entire road network is
visually connected
…weighted visual
exposure—max12,592
“relative” times seen
Mapped Data Analysis Evolution (Revolution)
Traditional GIS
Forest
Inventory
(Map)
• Points, Lines, Polygons
• Discrete Spatial Objects
• Mapping and Geo-query
Traditional Statistics
Mean= 22.4 ppm
StDev= 15.5
• Mean, StDev (Normal Curve)
• Central Tendency
• Typical Response (scalar)
Spatial Analysis
Emergency
Response
(Surface)
• Cells, Surfaces
• Continuous Geographic Space
• Contextual Spatial Relationships
Spatial Statistics
Spatial
Distribution
(Surface)
• Map of Variance (gradient)
• Spatial Distribution
• Numerical Spatial Relationships
Thematic Mapping vs. Map Analysis
Thematic Mapping
graphically links generalized statistics to discrete spatial objects
(Points, Lines, Polygons) — non-spatial analysis (GeoExploration)
“Maps are numbers first,
pictures later”
X, Y, Value
Thematic Mapping
Map Analysis
Data Space
Geographic Space
Standard Normal Curve
Point
Sampled
Data
(Numeric Distribution)
Average = 22.0
StDev = 18.7
40.7 …<50 so not a problem
Discrete
Spatial Object
22.0
Map Analysis
(Geographic Distribution)
Continuous
Spatial Distribution
Spatially
Generalized
Spatially
Detailed
Discovery of
problem subarea…
High Pocket
Adjacent
Parcels
map-ematically relates patterns within and among continuous spatial distributions
(Map Surfaces) — spatial analysis and statistics (GeoScience)
Elevation
(raw data)
Comparing Maps
Slope
Standard Normal Variable (SNV)
(raw data)
Apples
Oranges
(Rosales)
(Sapindales)
SNV “Mixed Fruit” Scale
Normalized (SNV)
SNV = ((mapValue - Mean) / Stdev) * 100
Normalized (SNV)
G#1, R#1= |0|
G#1, R#2= |-100|
G#1, R#3= |-300|
G#1
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
…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
R#2
R#1
R#3
Correlating Maps
Spatially
Aggregated
Correlation
Spatially
Localized
Correlation
“Roving Window”
Column= 17
Row= 10
Elevation
(Feet)
Slope
(Percent)
Correlation Coefficient equation
…where x = Elevation value, y = Slope value
and n = number of value pairs
Xelev = 2,063 feet
Yslope = 38%
…625 small data tables within 5 cell reach =
81 paired values for localized summary
“Point- by-Point”
…one large data table
with 25rows x 25 columns =
625 paired values for aggregated summary
r=
= .562
= .432
localized
aggregated
Scalar Value –
Map Variable –
single value represents the
aggregated non-spatial
relationship between two
map surfaces
continuous quantitative
surface represents the
localized spatial
relationship between two
map surfaces
Spatial Data Mining (The Big Picture)
…making sense out of a map stack—
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.
…the “secret” is geographic stratification and use of CART, Induction or Neural Network
spatial data mining technology, not traditional multivariate statistics
Geotechnology’s Future Directions
Geotechnology’s “Mega
(Evolution to Revolution)
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)
Where
Map Analysis
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 (Alternative Geographic Referencing, Universal Spatial Key)
– Dominant Human Forces (The “-ists” and the “-ologists”, The Softer Side of GIS)
– Dominant Geo-web Forces (Mobile, Social Media, Cloud)
A Peek at the Bleeding Edge (2010’s and Beyond)
Revisit Analytics
(VI -2020s)
Future Directions
Internet Mapping
(IV -2000s)
Geo-web Applications
& Revisit Geo-referencing (V -2010s)
Contemporary GIS
Spatial dB Mgt (II -1980s)
GIS Modeling (III -1990s)
Cyclical Nature of GIS Development
Mapping focus
Data/Structure focus
Analysis focus
The Early Years
Computer Mapping
(Decade I -1970s)
…but those who live by the
Crystal Ball are bound to
eat ground glass.
Evan Vlachos
Dominant GIS Force #1)
Alternative Geographic Referencing
Tightly Clustered Groupings
Continuously Nested Grid Elements
Hexagonal
Grid
Hexagon
Consistent
Dodecahedral
distances and adjacency
to surrounding grid elements
(6 facets)
(12 facets)
Inconsistent
Square Grid
Cubic Grid
distances and adjacency to surrounding grid elements
(8 facets)
Dodecahedral
Grid
(26 facets)
(Orthogonal and Diagonal)
Cartesian Coordinate System
Square
Square
2D Grid Element (Planimetric)
Cube
Cube
3D Grid Element (Volumetric)
Dominant GIS Force #2)
Planimetric
Universal Spatial Key (grid space as key)
100km, 10km,
…1m UTM
gridlines
Volumetric
Entire 3D volume containing the earth is prepartitioned into small Grid Elements using
basic geometry equations…
WHERE is WHAT
…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
Dominant Human Force #1)
The “-ists” and the “-ologists”
Together the “-ists” and the “-ologists” frame and develop the Solution for an application.
The “-ists”
The “-ologists”
— and —
…understand the “tools” that can
be used to display, query and
analyze spatial data
…understand the “science”
behind spatial relationships that
can be used for decision-making
Data focus
Information focus
Application Space
Geotechnology’s Core
“-ists”
Technology
Experts
Solution
Space
“-ologists”
Domain
Experts
Dominant Human Force #1, continued)
A Significantly Larger GIS Tent
Wisdom/Opinions
and Values
Knowledge/
Perceptions
“Policy Makers”
(actionable knowledge)
(interrelationships of
relevant facts)
“Stakeholders”
“Decision Makers”
Decision Makers utilize the Solution under Stakeholder, Policy & Public auspices.
Application Space
Geotechnology’s Core
“-ists”
Technology
Experts
Solution
Space
“-ologists”
Domain
Experts
Data
Information
(all facts)
(facts within a context)
Dominant Human Force #2)
The Softer Side of GIS (the NR Experience)
Future Directions:
Spatial Reasoning, Dialog and Consensus Building
 Social Acceptability as 3rd filter
Historically Economic Viability and Ecosystem Sustainability
have dominated Natural Resources discussion, policy and management.
Podium
…but Social Acceptability has become the
critical third filter needed for successful decision-making.
Team Table
Analysis of
Data and Information
1970s
Public Involvement
Inter-disciplinary Science
Experts and Professionals
as decision-makers/managers
Banquet Table
Communication/Infusion of
Perceptions, Opinions and Values
Increasing Social Science & Public Involvement
2010s
History/Evolution of Geo-web Applications
Geotechnology – one of the three “mega-technologies” for the 21st 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)
Today, 3:30 p.m. – 4:00 p.m.
Online Presentation Materials and References
www.innovativegis.com/basis/Papers/Other/Esri_Forestry2011
Handout, PowerPoint and Online References
…also see www.innovativegis.com/basis, online book Beyond Mapping III
Joseph K. Berry — www.innovativegis.com
David Buckley — www.dtswildfire.com