Raster models in GIS - PowerPoint PPT Presentation

About This Presentation
Title:

Raster models in GIS

Description:

GIS models are commonly used for site selection, landuse planning and habitat modeling. These applications are all based on multi-layer selection in GIS. – PowerPoint PPT presentation

Number of Views:314
Avg rating:3.0/5.0
Slides: 29
Provided by: fore54
Category:

less

Transcript and Presenter's Notes

Title: Raster models in GIS


1
Raster models in GIS
  • What is GIS modeling
  • Why GIS modeling
  • Raster models
  • Binary models
  • Index models
  • Regression models

2
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
3
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

4
Applications in Natural Resources
  • Predicting future conditions
  • Predicting impact of alternative management
    actions
  • Landuse planning
  • Site selection
  • Risk assessment - Identify areas of possible
    concern

5
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

6
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
7
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)

8
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

9
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

10
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

11
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
12
5. Final Habitat Model
Leah Ramsay
Coeur dAlene Salamander Final WHR Model
Idaho Gap Analysis Project 2001
13
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)
14
Raster Calculator in Spatial Analyst
15
Risk Models
16
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.

17
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)

18
Risk of what?
Risk of ignition? Risk of fast spread? Risk of
high fire severity? Risk to structures?
19
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.

20
(No Transcript)
21
(No Transcript)
22
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

23
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

24
Fuel Model
  • Considerations
  • Fuel load
  • Fuel moisture
  • Ratio of surface area to volume
  • Depth of the fuel bed
  • Horizontal/vertical orientation

25
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
26
BEHAVE outputs Rate of spread
Rate of Spread for Fuelmodels at 5 mph wind
27
BEHAVE outputs Flame length
28
BEHAVE outputs Fireline intensity
Write a Comment
User Comments (0)
About PowerShow.com