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 better decisions, not necessarily fight fires, but suppress, or treat on tribal lands???thoughts Landfire methodology is intended for a National Scale project – but methods could be used at a finer scale for local use. Number of plots is scarce for Tribal Organizations, as well as expertise to produce these layers.

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 Methodology The 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 Methodology Basically the remote sensing methods utilized by EDC use field data (FIA, CFI, FIREMON) to model certain 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

Overview Classification 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 Vegetation Classification Final Vegetation Classification

Tree: Height > 10m, Canopy > 40% Tree: Height > 10m, Canopy <= 40% Tree: Height 40% Tree: Height <= 10m, Canopy <= 40% Shrub: Height > 1m, Canopy > 40% Shrub: Height > 1m, Canopy <= 40% Shrub: Height 40% Shrub: Height <= 1m, Canopy <= 40% Herbaceous: Height > 0.2m, Canopy > 40% Herbaceous: Height > 0.2m, Canopy <= 40% Herbaceous: Height 40% Herbaceous: Height <= 0.2m, Canopy <= 40% Utah Existing Structural Stages

FireLab processing Based 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 simple Looking 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 Landscape Simulation Fuel and fuel loading models FIREHARM Fuels Fire Danger Condition Class 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) l Canopy Height Crown Bulk 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 standard for 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 as establishing a “Tribal Network” to tailor this technology to better meet Tribal needs.

Any Questions? Contact Info: James Twoteeth GIS Analyst