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Estimation of Forest Fuel Load from Radar Remote SensingUnderstanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar imagery acquired over a large area of the Yellowstone National Park (YNP) by the AIRSAR sensor, to estimate the distribution of forest biomass and canopy fuel loads. Semi-empirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, canopy fuel weight, canopy bulk density, and foliage moisture content. These estimates when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy, and when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R(sup 2) = 85 for the canopy fuel weight, R(sup 2)=.84 for canopy bulk density and R(sup 2) = 0.78 for the foliage biomass.
Document ID
20070035993
Acquisition Source
Stennis Space Center
Document Type
Reprint (Version printed in journal)
Authors
Saatchi, Sassan
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Despain, Don G.
(Montana State Univ. Bozeman, MT, United States)
Halligan, Kerry
(California Univ. Santa Barbara, CA, United States)
Crabtree, Robert
(Yellowstone Ecological Research Center Bozeman, MT, United States)
Date Acquired
August 24, 2013
Publication Date
June 1, 2007
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Volume: 45
Issue: 6
Subject Category
Earth Resources And Remote Sensing
Funding Number(s)
CONTRACT_GRANT: NNS06AA23G
Distribution Limits
Public
Copyright
Other

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