raster models in gis what is gis modelingwhat is gis modeling why gis modeling why gis modeling...

Raster models in GIS

•What is GIS modeling What is GIS modeling

• Why GIS modelingWhy GIS modeling

• Raster modelsRaster models

• Binary modelsBinary models

• Index modelsIndex models

• Regression modelsRegression models

What is a GIS model?

• It’s spatially explicit! It’s spatially explicit!

• Abstraction and simplification of Abstraction and simplification of realityreality

• Often used to identify locations that Often used to identify locations that meet specific criteriameet specific criteria

• Can be used to infer an unknown Can be used to infer an unknown quality or quantity using relationships quality or quantity using relationships with known or measurable quantities or with known or measurable quantities or qualities qualities

• Can be used to generate new dataCan be used to generate new data

Predicted Mountain Bluebird habitat in Idaho

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

• Predicting future conditions

• Predicting impact of alternative management actions

• Landuse planning• Site selection

• Risk assessment - Identify areas of possible concern

Applications in Natural Resources

Raster data structure•Pixels! Pixels!

• Resolution is expressed in terms of pixel Resolution is expressed in terms of pixel size. 30m X 30m for a USGS DEM size. 30m X 30m for a USGS DEM

• Best for representing continuous Best for representing continuous gradients (e.g. elevations, image gradients (e.g. elevations, image brightness values etc.)brightness values etc.)

• Can represent continuous or categorical Can represent continuous or categorical (thematic) information(thematic) information

• Not as precise as the vector model for Not as precise as the vector model for calculating area and lengthcalculating area and length

• ‘ ‘Slivers’ as a result of data overlay is Slivers’ as a result of data overlay is less of a problem in raster data compared less of a problem in raster data compared to vector datato vector data

Binary models•Represent presence or absence of a Represent presence or absence of a phenomena as 1 or 0 respectively phenomena as 1 or 0 respectively

• Categorical and very simple Categorical and very simple

• Often used as components in more Often used as components in more complex modelscomplex models

• Uses include habitat models and Uses include habitat models and site selection modelssite selection models

Craig Mountain Slope

Green – < 20 degrees

Yellow - > 20 degrees

Raster Index Models

•Calculates an index value for each Calculates an index value for each pixel and creates a ranked map.pixel and creates a ranked map.

• Weighted linear combinations is a Weighted linear combinations is a common methodcommon method

• The importance of each factor is The importance of each factor is evaluated against each other.evaluated against each other.

• Commonly the data for each Commonly the data for each criteria is standardized (scaled to an criteria is standardized (scaled to an interval between 0 and 1)interval between 0 and 1)

Raster regression models•Are based on linear or logistic Are based on linear or logistic regressionregression

• Variables are entered as grid Variables are entered as grid (raster) cell values and outputs are (raster) cell values and outputs are rendered as grids rendered as grids

• This is a regression model based This is a regression model based estimate of foliar biomass (Kg/ha) estimate of foliar biomass (Kg/ha) from lidar canopy height datafrom lidar canopy height data

Equation: Equation:

FB = 0.05*TBFB = 0.05*TB

TB = 5.5 + 0.0385*(CH)TB = 5.5 + 0.0385*(CH)2 2

Where: CH is Canopy Height Where: CH is Canopy Height

and TB is Total Biomassand TB is Total Biomass

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

Example: Coeur d’Alene Salamander

1. Define objectives and purposeTo create a model for potential habitat for the Coeur d’Alene Salamander

2. State assumptionsThis 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 variablesRangemaps, elevation, vegetation, distance to water

Idaho Gap Analysis Project 2001

Criteria for Coeur d’Alene

salamander habitat, Idaho

GAPPredicted to occur in…

Northern Idaho< 90m from water< 1525m elevationMesic forest and

riparian

4. Locate GIS data

Coeur d’Alene Salamander

Final WHR Model

Idaho Gap Analysis Project 2001

Leah Ramsay

5. Final Habitat Model

6. Model evaluation

% Omission =OM / (CP + OM)

% Commission = CO / (CP + CO)

PresentModel

Act

ual Correct

Present(CP)

Correct Absent

(CA)

Omission

(OM)

Commission

(CO)

Present Absent

Ab

sent

Pre

sen

t

Raster Calculator in Spatial Analyst

Risk Models

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.

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)

Risk of ignition?Risk of fast spread?Risk of high fire severity?Risk to structures?

Risk of what?

Risk of fast fire spreadNorthwest 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.”

Fuel Moisture

• Concepts Wet things don’t burn

Small things dry more quickly than big thingsFire start with small fuelsFire 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

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

Fuel Model

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

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

BEHAVE outputs: Rate of spread

Rate of Spread for Fuelmodels at 5 mph wind

BEHAVE outputs: Flame length

Flame length for Fuel models at 5 mph winds

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 10 20 30 40

Slope (degrees)

Fla

me

len

gth

(m

ete

rs)

FuelM 1

FuelM 2

FuelM 5

FuelM 8

FuelM 9

FuelM 10

FuelM 11

BEHAVE outputs: Fireline intensity