Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Michael J. Falkowski; Paul E. Gessler; Penelope Morgan; Andrew T. Hudak; Alistair M.S. Smith

doi:10.1016/j.foreco.2005.06.013

Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Michael J. Falkowski, Paul E. Gessler, Penelope Morgan, Andrew T. Hudak, Alistair M.S. Smith

Forest Resources

Research output: Contribution to journal › Article › peer-review

128 Scopus citations

Abstract

Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite sensor, and gradient modeling, for mapping fuel layers for fire behavior modeling with FARSITE and FLAMMAP. Empirical models, based upon field data and spectral information from an ASTER image, were employed to test the efficacy of ASTER for mapping and characterizing crown closure and crown bulk density. Surface fuel models (National Forest Fire Laboratory (NFFL) 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage. The final surface fuel model layer had an overall accuracy of 0.632 (K _HAT = 0.536). Results for the canopy fuel empirical models developed here suggest that vegetation indices incorporating visible wavelengths (i.e. the green red vegetation index (GRVI)) are suitable for predicting crown closure and crown bulk density (r² = 0.76. and 0.46, respectively).

Original language	English (US)
Pages (from-to)	129-146
Number of pages	18
Journal	Forest Ecology and Management
Volume	217
Issue number	2-3
DOIs	https://doi.org/10.1016/j.foreco.2005.06.013
State	Published - Oct 10 2005

Bibliographical note

Funding Information:
This research was supported in part by funds provided by the Rocky Mountain Research Station, Forest Service, US Department of Agriculture. The authors also acknowledge partial funding for this work from the following additional sources: the NASA Synergy program, the USDA Forest Service Rocky Mountain Research Station Missoula Fire Sciences Laboratory (RJVA-11222048-140), and a grant (NS-7327) from NASA's Earth Science Applications Division as part of the Food and Fiber Applications of Remote Sensing (FFARS) program managed by the John C. Stennis Space Center.

Keywords

ASTER
Crown bulk density
Crown closure
Forest fire
Fuel model
Fuels
Gradient modeling
Remote sensing

Access

10.1016/j.foreco.2005.06.013

OpenUrl availability

Full text

Cite this

@article{6b43b0a2206a47b9b2e4c86c5871e50e,

title = "Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling",

abstract = "Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite sensor, and gradient modeling, for mapping fuel layers for fire behavior modeling with FARSITE and FLAMMAP. Empirical models, based upon field data and spectral information from an ASTER image, were employed to test the efficacy of ASTER for mapping and characterizing crown closure and crown bulk density. Surface fuel models (National Forest Fire Laboratory (NFFL) 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage. The final surface fuel model layer had an overall accuracy of 0.632 (K HAT = 0.536). Results for the canopy fuel empirical models developed here suggest that vegetation indices incorporating visible wavelengths (i.e. the green red vegetation index (GRVI)) are suitable for predicting crown closure and crown bulk density (r2 = 0.76. and 0.46, respectively).",

keywords = "ASTER, Crown bulk density, Crown closure, Forest fire, Fuel model, Fuels, Gradient modeling, Remote sensing",

author = "Falkowski, {Michael J.} and Gessler, {Paul E.} and Penelope Morgan and Hudak, {Andrew T.} and Smith, {Alistair M.S.}",

note = "Funding Information: This research was supported in part by funds provided by the Rocky Mountain Research Station, Forest Service, US Department of Agriculture. The authors also acknowledge partial funding for this work from the following additional sources: the NASA Synergy program, the USDA Forest Service Rocky Mountain Research Station Missoula Fire Sciences Laboratory (RJVA-11222048-140), and a grant (NS-7327) from NASA's Earth Science Applications Division as part of the Food and Fiber Applications of Remote Sensing (FFARS) program managed by the John C. Stennis Space Center.",

year = "2005",

month = oct,

day = "10",

doi = "10.1016/j.foreco.2005.06.013",

language = "English (US)",

volume = "217",

pages = "129--146",

journal = "Forest Ecology and Management",

issn = "0378-1127",

publisher = "Elsevier",

number = "2-3",

}

TY - JOUR

T1 - Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

AU - Falkowski, Michael J.

AU - Gessler, Paul E.

AU - Morgan, Penelope

AU - Hudak, Andrew T.

AU - Smith, Alistair M.S.

N1 - Funding Information: This research was supported in part by funds provided by the Rocky Mountain Research Station, Forest Service, US Department of Agriculture. The authors also acknowledge partial funding for this work from the following additional sources: the NASA Synergy program, the USDA Forest Service Rocky Mountain Research Station Missoula Fire Sciences Laboratory (RJVA-11222048-140), and a grant (NS-7327) from NASA's Earth Science Applications Division as part of the Food and Fiber Applications of Remote Sensing (FFARS) program managed by the John C. Stennis Space Center.

PY - 2005/10/10

Y1 - 2005/10/10

N2 - Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite sensor, and gradient modeling, for mapping fuel layers for fire behavior modeling with FARSITE and FLAMMAP. Empirical models, based upon field data and spectral information from an ASTER image, were employed to test the efficacy of ASTER for mapping and characterizing crown closure and crown bulk density. Surface fuel models (National Forest Fire Laboratory (NFFL) 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage. The final surface fuel model layer had an overall accuracy of 0.632 (K HAT = 0.536). Results for the canopy fuel empirical models developed here suggest that vegetation indices incorporating visible wavelengths (i.e. the green red vegetation index (GRVI)) are suitable for predicting crown closure and crown bulk density (r2 = 0.76. and 0.46, respectively).

AB - Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite sensor, and gradient modeling, for mapping fuel layers for fire behavior modeling with FARSITE and FLAMMAP. Empirical models, based upon field data and spectral information from an ASTER image, were employed to test the efficacy of ASTER for mapping and characterizing crown closure and crown bulk density. Surface fuel models (National Forest Fire Laboratory (NFFL) 1-13) were mapped using a classification tree based upon three gradient layers; potential vegetation type, cover type, and structural stage. The final surface fuel model layer had an overall accuracy of 0.632 (K HAT = 0.536). Results for the canopy fuel empirical models developed here suggest that vegetation indices incorporating visible wavelengths (i.e. the green red vegetation index (GRVI)) are suitable for predicting crown closure and crown bulk density (r2 = 0.76. and 0.46, respectively).

KW - ASTER

KW - Crown bulk density

KW - Crown closure

KW - Forest fire

KW - Fuel model

KW - Fuels

KW - Gradient modeling

KW - Remote sensing

UR - http://www.scopus.com/inward/record.url?scp=26444446056&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=26444446056&partnerID=8YFLogxK

U2 - 10.1016/j.foreco.2005.06.013

DO - 10.1016/j.foreco.2005.06.013

M3 - Article

AN - SCOPUS:26444446056

SN - 0378-1127

VL - 217

SP - 129

EP - 146

JO - Forest Ecology and Management

JF - Forest Ecology and Management

IS - 2-3

ER -

Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Abstract

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this