Raster models in GIS

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Raster models in GIS

• What is GIS modeling
• Why GIS modeling
• Raster models
• Binary models
• Index models
• Regression models

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What is a GIS model?

• Its spatially explicit!
• Abstraction and simplification of reality
• Often used to identify locations that meet
  specific criteria
• Can be used to infer an unknown quality or
  quantity using relationships with known or
  measurable quantities or qualities
• Can be used to generate new data

Predicted Mountain Bluebird habitat in Idaho
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Why GIS modeling?

• Simplification of reality
• Increases the understanding of a situation or
  system
• Provides useful guidance
• Predicting the future
• Extrapolation of information to other areas
• Evaluations of scenarios
• Explain trends

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Applications in Natural Resources

• Predicting future conditions
• Predicting impact of alternative management
  actions
• Landuse planning
• Site selection
• Risk assessment - Identify areas of possible
  concern

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Raster data structure

• Pixels!
• Resolution is expressed in terms of pixel size.
  30m X 30m for a USGS DEM
• Best for representing continuous gradients (e.g.
  elevations, image brightness values etc.)
• Can represent continuous or categorical
  (thematic) information
• Not as precise as the vector model for
  calculating area and length
• Slivers as a result of data overlay is less of
  a problem in raster data compared to vector data

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Binary models

• Represent presence or absence of a phenomena as 1
  or 0 respectively
• Categorical and very simple
• Often used as components in more complex models
• Uses include habitat models and site selection
  models

Craig Mountain Slope Green lt 20 degrees Yellow
- gt 20 degrees
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Raster Index Models

• Calculates an index value for each pixel and
  creates a ranked map.
• Weighted linear combinations is a common method
• The importance of each factor is evaluated
  against each other.
• Commonly the data for each criteria is
  standardized (scaled to an interval between 0 and
  1)

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Raster regression models

• Are based on linear or logistic regression
• Variables are entered as grid (raster) cell
  values and outputs are rendered as grids
• This is a regression model based estimate of
  foliar biomass (Kg/ha) from lidar canopy height
  data
• Equation
• FB 0.05TB
• TB 5.5 0.0385(CH)2
• Where CH is Canopy Height
• and TB is Total Biomass

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Modeling Process

• 0. Define objectives and purpose
• State assumptions
• Identify model variables
• Locate GIS data representing the model variables
  at the desired scale
• Implement the model
• Evaluate model results

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Example Coeur dAlene Salamander

• Define objectives and purpose
• To create a model for potential habitat for the
  Coeur dAlene Salamander
• 2. State assumptions
• This model will be developed at a 30 m scale for
  the state of Idaho. Species specific information
  from adjacent states apply to Idaho.
• 3. Identify model variables
• Rangemaps, elevation, vegetation, distance to
  water

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4. Locate GIS data
Criteria for Coeur dAlene salamander habitat,
Idaho GAP
Predicted to occur in

• Northern Idaho

• lt 90m from water

• lt 1525m elevation

• Mesic forest and riparian

Idaho Gap Analysis Project 2001
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5. Final Habitat Model
Leah Ramsay
Coeur dAlene Salamander Final WHR Model
Idaho Gap Analysis Project 2001
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6. Model evaluation
Present
Model
Present Absent
Correct Present (CP)
Omission (OM)
Actual
Absent Present
Correct Absent (CA)
Commission (CO)
Commission CO / (CP CO)
Omission OM / (CP OM)
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Raster Calculator in Spatial Analyst
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Risk Models
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Hazard and Risk

• Hazard
• A source of potential danger or adverse
  condition.
• A natural event is a hazard when it has the
• potential to harm people or property.
• Hazard Identification
• The process of identifying hazards that threaten
  an area.
• Hazard Mitigation
• Sustained actions taken to reduce or eliminate
  long-term risk from hazards and their effects.

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Risk

• Risk
• The estimated impact that a hazard would have on
  people, services, facilities, and structures in a
  community the likelihood of a hazard event
  resulting in an adverse condition that causes
  injury or damage.
• (hazard and risk definitions after FEMA 386-2)

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Risk of what?
Risk of ignition? Risk of fast spread? Risk of
high fire severity? Risk to structures?
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Risk of fast fire spread Northwest Management,
Moscow, ID

• Xeric cover types
• South west aspects
• Ramp of yellow to red on a slope gradient

• Latah County Plan
• The risk rating presented here serves to
  identify where certain constant variables are
  present that aid in identifying where fires
  typically spread the fastest across the
  landscape.

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Fuel Moisture

• Concepts
• Wet things dont burn
• Small things dry more quickly than big things
• Fire start with small fuels
• Fire spread is the fire starting over and over
  again
• Dead fuels
• 1 hour less than ¼ diameter
• 10 hour ¼ to 1 diameter
• 100 hour 1 to 3 diameter
• 1000 hour 3 to 8 diameter

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Fuel Model

• A way to put fuel into categories according to
  how it burns
• There are several fuel model systems in use for
  wildland fire
• Fire behavior software uses the Fire Behavior
  Prediction System models
• Most models of wildland fire fuels initially
  classify fuels as grass, shrub, timber, or slash

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Fuel Model

• Considerations
• Fuel load
• Fuel moisture
• Ratio of surface area to volume
• Depth of the fuel bed
• Horizontal/vertical orientation

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Fuel models (Anderson, 1982)
FM 1 Short Grass
FM 2 Open Timber Grass Understory
FM 5 Short Brush
FM 8 Closed Short Needle Conifer
FM 9 Closed Long Needle Conifer
FM 10 Closed Timber Heavy DWD
FM 11 Light Logging Slash
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BEHAVE outputs Rate of spread
Rate of Spread for Fuelmodels at 5 mph wind
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BEHAVE outputs Flame length
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BEHAVE outputs Fireline intensity