The Coeur d'Alene Tribe is learning the remote sensing methodology developed by LANDFIRE, and will be attempting to apply the methods to higher resolution 1-meter imagery.

Intially, we will learn the process of LANDFIRE using 30 meter Landsat Data to get acquainted with the remote sensing methodology.

LandFire Methodology

Issues•Is the process worth investing in to get useable tools to help us make betterdecisions, not necessarily fight fires, but suppress, or treat on tribal lands???thoughts

•Landfire methodology is intended for a National Scale project – but methods could beused at a finer scale for local use.

•Number of plots is scarce for Tribal Organizations, as well as expertise to produce theselayers.

LandFire Methodology

EROS Data Center-Sioux Falls, SD

In a recent visit to the USGS EROS Data Center (EDC), the Coeur d’Alene Tribe GIS team was exposed to and taught the remote sensing methodologies used in producing LANDFIRE preliminary products.

As part of the overall Landfire process, the technical team at the USGS EROS Data Center (EDC) has the task of developing near current (circa 2001) vegetation and vegetation structure datasets.

Canopy Height Vegetation

Landfire MethodologyThe figure below illustrates the data inputs, processes, and end products produced for the LANDFIRE project by EDC. In this flow chart three primary “blocks” are used to identify the major components within the data development process.

Landfire MethodologyBasically the remote sensing methods utilized by EDC use field data (FIA, CFI, FIREMON) to modelcertain attributes such as current vegetation, height, and canopy cover. A wide variety of other data such as satellite imagery, climate, soils, and elevation are incorporated into the model.

After pre-processing, the input data are utilized in two modeling packages as shown in the modeling block of the previous diagram. The See5 model software is a data mining, decision tree package that is used for developing discrete variable output (vegetation classes). The Cubist software is used to develop regression trees used for continuous variable output (i.e. percent canopy, average tree height).

See5 modeling software Cubist modeling Software

CDA field data

Regression Tree Output

OverviewClassification Tree – C5/See5•Predicts categorical variables like Land cover, vegetation, etc.

Regression Tree – Cubist•Predicts continuous variables like Canopy cover, height, etc.

•Both require the generation of two files –

*.data file –extracted values from Input layers

*.names file- points to where all the input layers are located

•Collect training points (CFI, FIREMON)

•Develop a classification tree model (aka decision tree, or d-tree) via See5, Cubist

•Apply the model spatially to create a map

Major Steps in Developing a Spatial Classification (map) using C5

List of what we used in models:

Three dates of LandSat 7 data

Leaf on, Leaf off, Spring dates

One of the most time consuming tasks is to get all your input data ready for processing.

•Dem

•Slope

•Aspect

•Soils (Silc dataset)

•B9 – Brightness of all three dates

•NDVI – Vegetation Indices for all three dates

•Reflectance – Reflectance for all three dates

•Tassel Cap – for all three dates

See5/Cubist Demo

Preliminary Results:

Herbaceous Height Classification

Forest Canopy Cover Classification

Herbaceous VegetationClassification

Final Vegetation Classification

Tree: Height > 10m, Canopy > 40%

Tree: Height > 10m, Canopy <= 40%

Tree: Height <= 10m, Canopy > 40%

Tree: Height <= 10m, Canopy <= 40%

Shrub: Height > 1m, Canopy > 40%

Shrub: Height > 1m, Canopy <= 40%

Shrub: Height <= 1m, Canopy > 40%

Shrub: Height <= 1m, Canopy <= 40%

Herbaceous: Height > 0.2m, Canopy > 40%

Herbaceous: Height > 0.2m, Canopy <= 40%

Herbaceous: Height <= 0.2m, Canopy > 40%

Herbaceous: Height <= 0.2m, Canopy <= 40%

Utah Existing Structural Stages

FireLab processingBased on fire behavior expertise and knowledge they assign one of the 13 fuel models (Anderson 82) based on the layers that EROS provides.

They have about 10 different ways to derive a fuels layer?

This process is relatively new process for everyone including the Firelab, EROS, and it is not an exact science.

The Firelab also processes different deliverables for the Landfire program,

but since the CDA project is primarily focused on producing input layers for Farsite, we

Didn’t learn how to produce the full suite of Landfire products.

They did however share a program called Fuelcalc, which is a very simpleLooking program that calculates certain attributes needed for modeling Farsite Inputs like Crown Bulk Density, Canopy base height, Canopy stand height, etc.

Existing vegetation

Potential vegetation

Fuels

Structure class

Topography/Edaphic

Climate

Ecophysiological

Potential vegetation mapping

Existing vegetation mapping

LandscapeSimulation

Fuel and fuel loading models

FIREHARM

Fuels

Fire Danger

ConditionClass

Historical fire regime

What we are interested in mapping

They did however share a program called Fuelcalc, which is a very simple

Looking program that calculates certain attributes needed for modeling Farsite

Inputs like Crown Bulk Density, Canopy base height, Canopy stand height, etc.

Canopy Base Ht

Canopy Cover

Mapped Deliverables

(Fuels)

lCanopy Height

CrownBulk

Density

Fuel Calc/Farsite Demo

Where do we go from here???

• From what you have experienced this week, is this something Tribal organizations are interested in, is it useful??

•There is no standard for applying this methodology to a localized scale, so we would like other tribal agencies to help in creating a standardfor producing what we need in the field.

• If this is not a feasible way to approach mapping fuels, what other ways can we help manage fuels?? Suggestions??

•If interested, the CDA tribe GIS program is willing to share what we have learned and support other tribal folks in mapping fuels. As well asestablishing a “Tribal Network” to tailor this technology to better meet Tribal needs.

Any Questions? Contact Info:

James TwoteethGIS Analyst

jtwoteeth@cdatribe-nsn.gov