Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data

Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here...

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Autores: Nolan, Rachael H., Resco de Dios, Víctor, Boer, Matthias M., Caccamo, Gabriele, Goulden, Michael L., Bradstock, Ross A.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/59346
Acceso en línea:https://doi.org/10.1016/j.rse.2015.12.010
http://hdl.handle.net/10459.1/59346
Access Level:acceso abierto
Palabra clave:Remote sensing
Land surface temperature
MODIS
Wildfire
Teledetecció
Canvis climàtics
Climatic changes
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spelling Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather dataNolan, Rachael H.Resco de Dios, VíctorBoer, Matthias M.Caccamo, GabrieleGoulden, Michael L.Bradstock, Ross A.Remote sensingLand surface temperatureMODISWildfireTeledeteccióCanvis climàticsRemote sensingClimatic changesSpatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404 mm year− 1) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE < 1.16 kPa and 1.62 kPa respectively). D calculated with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was under-predicted in areas with low leaf area index. Both D from remotely sensed data and gridded weather station data were good predictors of the moisture content of dead suspended fuels at validation sites, with mean absolute errors less than 3.9% and 6.0% respectively. The occurrence of data gaps in remotely sensed time series presents an obstacle to this approach, and assimilated or extrapolated meteorological observations may offer better continuity.We would like to thank R. Gibson, M. Chick, D. Spencer, S. Khanal, A. Boer-Cueva, L. Serrano-Grijalva, D. Bridgman, and C. Beattie for their in-valuable assistance in collecting fuel moisture data. This project was funded by Victorian Department of Environment, Land, Water and Plan-ning via the Bushfire Cooperative Research Centre, grants from the US Department of Energy and the National Aeronautics and Space Administration to MLG, the Education Investment Fund, the Hawkesbury Institute for the Environment and a Ramón y Cajal Fellowship to VRD (RYC-2012-10970). MODIS data products are courtesy of the online Data Pool at the NASA Land Processed Distributed Archive Centre (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South DakotaElsevier2017201720162017info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.rse.2015.12.010http://hdl.handle.net/10459.1/59346http://hdl.handle.net/10459.1/59346reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1016/j.rse.2015.12.010Remote Sensing of Environment, 2016, vol. 174, p. 100-108cc-by-nc-nd, (c) Elsevier, 2015info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:recercat.cat:10459.1/593462026-05-29T05:05:01Z
dc.title.none.fl_str_mv Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
title Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
spellingShingle Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
Nolan, Rachael H.
Remote sensing
Land surface temperature
MODIS
Wildfire
Teledetecció
Canvis climàtics
Remote sensing
Climatic changes
title_short Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
title_full Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
title_fullStr Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
title_full_unstemmed Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
title_sort Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data
dc.creator.none.fl_str_mv Nolan, Rachael H.
Resco de Dios, Víctor
Boer, Matthias M.
Caccamo, Gabriele
Goulden, Michael L.
Bradstock, Ross A.
author Nolan, Rachael H.
author_facet Nolan, Rachael H.
Resco de Dios, Víctor
Boer, Matthias M.
Caccamo, Gabriele
Goulden, Michael L.
Bradstock, Ross A.
author_role author
author2 Resco de Dios, Víctor
Boer, Matthias M.
Caccamo, Gabriele
Goulden, Michael L.
Bradstock, Ross A.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Remote sensing
Land surface temperature
MODIS
Wildfire
Teledetecció
Canvis climàtics
Remote sensing
Climatic changes
topic Remote sensing
Land surface temperature
MODIS
Wildfire
Teledetecció
Canvis climàtics
Remote sensing
Climatic changes
description Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404 mm year− 1) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE < 1.16 kPa and 1.62 kPa respectively). D calculated with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was under-predicted in areas with low leaf area index. Both D from remotely sensed data and gridded weather station data were good predictors of the moisture content of dead suspended fuels at validation sites, with mean absolute errors less than 3.9% and 6.0% respectively. The occurrence of data gaps in remotely sensed time series presents an obstacle to this approach, and assimilated or extrapolated meteorological observations may offer better continuity.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.rse.2015.12.010
http://hdl.handle.net/10459.1/59346
http://hdl.handle.net/10459.1/59346
url https://doi.org/10.1016/j.rse.2015.12.010
http://hdl.handle.net/10459.1/59346
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1016/j.rse.2015.12.010
Remote Sensing of Environment, 2016, vol. 174, p. 100-108
dc.rights.none.fl_str_mv cc-by-nc-nd, (c) Elsevier, 2015
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv cc-by-nc-nd, (c) Elsevier, 2015
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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