Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Farahmand, Alireza, Stavros, E. Natasha, Reager, John T., and Behrangi, Ali, 2020. Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States. Remote Sensing, 12(8):1252, doi:10.3390/rs12081252.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2020RemS...12.1252F,
author = {{Farahmand}, Alireza and {Stavros}, E. Natasha and {Reager}, John T. and {Behrangi}, Ali},
title = "{Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States}",
journal = {Remote Sensing},
keywords = {wildfire danger, vapor pressure deficit (VPD), soil moisture, enhanced vegetation index, prediction, GRACE, AIRS, MODIS},
year = 2020,
month = apr,
volume = {12},
number = {8},
eid = {1252},
pages = {1252},
abstract = "{Wildfire danger assessment is essential for operational allocation of
fire management resources; with longer lead prediction, the more
efficiently can resources be allocated regionally. Traditional
studies focus on meteorological forecasts and fire danger index
models (e.g., National Fire Danger Rating
System{\textemdash}NFDRS) for predicting fire danger.
Meteorological forecasts, however, lose accuracy beyond
\raisebox{-0.5ex}\textasciitilde10 days; as such, there is no
quantifiable method for predicting fire danger beyond 10 days.
While some recent studies have statistically related hydrologic
parameters and past wildfire area burned or occurrence to fire,
no study has used these parameters to develop a monthly
spatially distributed predictive model in the contiguous United
States. Thus, the objective of this study is to introduce Fire
Danger from Earth Observations (FDEO), which uses satellite data
over the contiguous United States (CONUS) to enable two-month
lead time prediction of wildfire danger, a sufficient lead time
for planning purposes and relocating resources. In this study,
we use satellite observations of land cover type, vapor pressure
deficit, surface soil moisture, and the enhanced vegetation
index, together with the United States Forest Service (USFS)
verified and validated fire database (FPA) to develop spatially
gridded probabilistic predictions of fire danger, defined as
expected area burned as a deviation from ``normal''. The results
show that the model predicts spatial patterns of fire danger
with 52\% overall accuracy over the 2004-2013 record, and up to
75\% overall accuracy during the fire season. Overall accuracy
is defined as number of pixels with correctly predicted fire
probability classes divided by the total number of the studied
pixels. This overall accuracy is the first quantified result of
two-month lead prediction of fire danger and demonstrates the
potential utility of using diverse observational data sets for
use in operational fire management resource allocation in the
CONUS.}",
doi = {10.3390/rs12081252},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020RemS...12.1252F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Generated by
bib2html_grace.pl
(written by Patrick Riley
modified for this page by Volker Klemann) on
Thu Apr 10, 2025 10:40:58
GRACE-FO
Thu Apr 10, F. Flechtner