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International Journal of Remote Sensing Volume 28, 2007 - Issue 22
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Letter

Stability, normalization and accuracy of MODIS‐derived estimates of live fuel moisture for southern California chaparral

D. Stow Department of Geography, San Diego State University, San Diego, CA 92182‐4493Correspondence[email protected]
&
M. Niphadkar Department of Geography, San Diego State University, San Diego, CA 92182‐4493
Pages 5175-5182 | Received 05 Jun 2007, Accepted 03 Aug 2007, Published online: 23 Oct 2007

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Gengke Lai, Xingwen Quan, Marta Yebra & Binbin He. (2022) Model-driven estimation of closed and open shrublands live fuel moisture content. GIScience & Remote Sensing 59:1, pages 1837-1856.
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Articles from other publishers (12)

Nastassia Barber, Ernesto Alvarado, Van R. Kane, William E. Mell & L. Monika Moskal. (2021) Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors. Sensors 21:19, pages 6350.
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José M. Costa-Saura, Ángel Balaguer-Beser, Luis A. Ruiz, Josep E. Pardo-Pascual & José L. Soriano-Sancho. (2021) Empirical Models for Spatio-Temporal Live Fuel Moisture Content Estimation in Mixed Mediterranean Vegetation Areas Using Sentinel-2 Indices and Meteorological Data. Remote Sensing 13:18, pages 3726.
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Liujun Zhu, Geoffrey I. Webb, Marta Yebra, Gianluca Scortechini, Lynn Miller & François Petitjean. (2021) Live fuel moisture content estimation from MODIS: A deep learning approach. ISPRS Journal of Photogrammetry and Remote Sensing 179, pages 81-91.
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Mariano García, David Riaño, Marta Yebra, Javier Salas, Adrián Cardil, Santiago Monedero, Joaquín Ramirez, M. Pilar Martín, Lara Vilar, John Gajardo & Susan Ustin. (2020) A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models. Remote Sensing 12:11, pages 1714.
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Shenyue Jia, Seung Hee Kim, Son V. Nghiem, Wonhee Cho & Menas C. Kafatos. (2018) Estimating Live Fuel Moisture in Southern California Using Remote Sensing Vegetation Water Content Proxies. Estimating Live Fuel Moisture in Southern California Using Remote Sensing Vegetation Water Content Proxies.
Rosa Lasaponara, Angelo Aromando, Gianfranco Cardettini & Monica Proto. 2018. Computational Science and Its Applications – ICCSA 2018. Computational Science and Its Applications – ICCSA 2018 375 388 .
Hamed Adab, Kasturi Devi Kanniah & Jason Beringer. (2016) Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data. Remote Sensing 8:11, pages 961.
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R. H. Nolan, M. M. Boer, V. Resco de Dios, G. Caccamo & R. A. Bradstock. (2016) Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia. Geophysical Research Letters 43:9, pages 4229-4238.
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Rachael H. Nolan, Víctor Resco de Dios, Matthias M. Boer, Gabriele Caccamo, Michael L. Goulden & Ross A. Bradstock. (2016) Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data. Remote Sensing of Environment 174, pages 100-108.
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Marta Yebra, Philip E. Dennison, Emilio Chuvieco, David Riaño, Philip Zylstra, E. Raymond HuntJr.Jr., F. Mark Danson, Yi Qi & Sara Jurdao. (2013) A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sensing of Environment 136, pages 455-468.
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G. Caccamo, L. A. Chisholm, R. A. Bradstock, M. L. Puotinen & B. G. Pippen. (2012) Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data. International Journal of Wildland Fire 21:3, pages 257.
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Stephen Yool. (2009) Broad-Scale Monitoring of Live Fuel Moisture. Geography Compass 3:5, pages 1703-1716.
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