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Project ID: 01-1-4-15

Year: 2001

Date Started: 08/16/2001

Date Completed: 09/30/2006

Title: Mapping Horizontal and Vertical Distribution of Fuel by Fusing High-Resolution Hyperspectral and Polarimetric Data

Project Proposal Abstract: Current fuel maps have limited use for predicting fire behavior, analyzing fire hazard, or developing fuel management strategies. Thus a critical need exists for cost-effective remote sensing methodologies that render accurate, efficient fuel maps for landscape to regional scales. New remote sensing techniques are needed to accurately map wildland fuels according to specific vegetation types and the horizontal and vertical position of biomass, two factors that dramatically affect the intensity and spread of fires. The fusion of optical (especially hyperspectral) data with synthetic aperture radar (SAR) data has the potential to provide this much needed information. These two data types have complimentary strengths, enabling the creation of high- resolution maps describing the vertical and horizontal distribution of fuels by plant community or species. The proposed three-year study will develop unique algorithms for geo-referencing optical data, fusing it with terrain corrected SAR data, and then modeling that data with GIS. The project will integrate intensive field research with lab-based analysis of representative, available remote sensing data sets and GIS data layers. One deliverable from this project is to develop a precise process that makes this analysis more easily accessible to the user community. Because the study area--the northern section of the Greater Yellowstone Ecosystem--provides an excellent proxy for much of the West, the methods developed in the study are expected to have wide applicability. A second deliverable will be the development of a decision matrix table for helping fire managers determine the cost-effectiveness, resolution, and timeliness tradeoffs between selecting remotely sensed data of varying levels of spatial and spectral resolutions. A third deliverable is the creation of immediately usable, high-resolution fuel maps of the study area. These maps will represent the best fuel management information derivable from remotely sensed data to date.

Principal Investigator: Don Despain

Agency/Organization: USGS-Geological Survey

Branch or Dept: NOROCK-Northern Rocky Mountain Science Center

Other Project Collaborators




Branch or Dept

Co-Principal Investigator

Richard Aspinall

Montana State University

Department of Earth Sciences

Co-Principal Investigator

Robert Crabtree

Yellowstone Ecological Research Center

Co-Principal Investigator

Kerry Halligan

Yellowstone Ecological Research Center

Co-Principal Investigator

Sassan Saatchi

NASA-National Aeronautics & Space Administration

Jet Propulsion Laboratory


Tom Boatner

BLM-Bureau of Land Management

Montana-Dakotas State Office


Bill Breedlove

Forest Service

WO-Washington Office


Julie Neff-Shea

Forest Service

Gallatin National Forest


Phil Perkins

NPS-National Park Service

Yellowstone National Park


Dave Sisk

Forest Service

Bighorn NF-Medicine Wheel/Paintrock District


John Varley

NPS-National Park Service

Yellowstone National Park

Federal Cooperator

Don Despain

USGS-Geological Survey

NOROCK-Northern Rocky Mountain Science Center

Project Locations

Fire Science Exchange Network


There are no project locations identified for this project.

Project Deliverables

Final Report view or print

("Results presented in JFSP Final Reports may not have been peer-reviewed and should be interpreted as tentative until published in a peer-reviewed source.")

  ID Type Title
    2400 Journal Article Scientific papers will be prepared and published documenting the project's methods and results.
    548 Dataset (including spatial) Field-validated, high resolution fuel maps.
    549 Computer Model/Software/Algorithm Field-validated algorithms and processes for creating fuel maps at scales suitable for operational fuel and fire management programs.
  go to website 551 Website Field data, classifications and maps associated with the project will be made available for viewing and downloading from websites.
  go to website 5415 Computer Model/Software/Algorithm Decision matrix table capable of assisting wildland managers in determining the cost versus utility of selecting remotely sensed data of high and low levels of spatial and spectral resolution.

Supporting Documents

The following supporting documents are available for this project.

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