Agriculture Reference
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fuel dynamics, we can then develop standardized sampling methods that describe
fuels at their appropriate scales for quantifying reference conditions and select
biophysical layers that represent those ecological processes that most influence fuel
dynamics (Chap. 8). Spatial fuels databases containing all collected geo-referenced
field data that is appropriately scaled to each fuel component can then be created
so that spatially explicit fuels data can be accessible to everyone. Comprehensive,
robust, and flexible fuel classifications can then be developed from these data
(Chap. 7) that incorporate and account for the high variability in their design (Keane
2013 ). Categories in these new classifications can then be mapped using a fusion of
the technologies mentioned here and any new technologies developed in the future.
A new approach to fuels mapping is needed for enlightened fire management.
References
Abell CA (1937) Rate of spread and resistance to control data for Region 7 fuel types and their
application to determine strength and speed of attack needed. Preliminary Report. U.S.
Department of Agriculture, Forest Service, Asheville, pp 7
Agee JK, Skinner CN (2005) Basic principles of forest fuel reduction treatments. For Ecol Manage
211:83-96
Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters
using LIDAR data. Remote Sens Environ 94(4):441-449
Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest
Service Intermountain Research Station, General Technical Report INT-122 Ogden, Utah,
USA, pp 22
Anderson M, Bourgeron P, Bryer MT, Crawford R, Engelking L, Faber-Langendoen D, Gallyoun
M, Goodin K, Grossman DH, Landaal S, Metzler K, Patterson KD, Pyne M, Reid M, Sneddon
L, Weakley AS (1998) International classification of ecological communities: terrestrial vege-
tation of the United States. The National Vegetation Classification System: list of types, vol. 2.
The Nature Conservancy, Arlington, Virginia, pp 445
Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role
of remote sensing. For Ecol Manage 256:1239-1252
Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Re-
mote Sens Environ 64(3):234-253
Ball GL, Guertin DP (1992) Advances in fire spread simulation. In: Proceedings on the third for-
est service remote sensing applications conference—Protecting natural resources with remote
sensing. Tucson, Arizona USA, April 9-13 1992. American Society of Photogrammery and
Remote Sensing, 5410 Grosvenor lane, Bethesda, Maryland USA, pp 241-249
Banks WG, Frayer HC (1966) Rate of forest fire spread and resistance to control in the fuel types
of the eastern region. U.S. Department of Agriculture, Forest Service, Washington, DC. Fire
Control Notes 27(2):10-13.
Barrett SW, Arno SF (1991) Classifying fire regimes and defining their topographic controls in the
Selway-Bitterroot Wilderness. In: 11th Conference on Fire and Forest Meteorology, Missoula,
MT, 1991, pp 233-245
Belfort W (1988) Controlled-scale aerial sampling photography: development and implications for
multiresource inventory. J For 86(11):21-28
Bergen KM, Dobson MC (1999) Integration of remotely sensed radar imagery in modeling and
mapping of forest biomass and net primary production. Ecol Model 122:257-274
Brandis K, Jacobson C (2003) Estimation of vegetative fuel loads using Landsat TM imagery in
New South Wales, Australia. Internat J Wildland Fire 12:185-194
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