@article {bnh-6000, title = {Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications}, journal = {Scientific Data}, volume = {6}, year = {2019}, month = {08/2019}, abstract = {

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. the primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

}, keywords = {database, Emergency management, land management, Natural disasters, Wildfire spread}, doi = {https://doi.org/10.1038/s41597-019-0164-9}, url = {https://www.nature.com/articles/s41597-019-0164-9.epdf?author_access_token=HISJcfE-VovHPab3al2NwNRgN0jAjWel9jnR3ZoTv0OARKV_7w7xO9p9PGwHd2zKbrs5f-VkYE5AC2lYTydBxaTKy0JaWSgXKUWz0U-fruuzViNrn1JJFl8mARAjGudmQfIcQsd98fM0zv-fk4bXxA\%3D\%3D}, author = {Marta Yebra and Gianluca Scortechini and Abdulbaset Badi and Maria Eugenia Beget and Matthias M. Boer and Ross Bradstock and Emilio Chuvieco and F. Mark Danson and Philip Dennison and Victor Resco de Dios and Carlos M. Di Bella and Greg Forsyth and Philip Frost and Mariano Garcia and Abdelaziz Hamdi and Binbin He and Matt Jolly and Tineke Kraaij and Pillar Martin and Florent Mouillot and Glenn J Newnham and Rachael Nolan and Grazia Pellizzaro and Yi Qi and Xingwen Quan and David Ria{\~n}o and Dar Roberts and Momadou Sow and Susan Ustin} } @article {bnh-2532, title = {Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data}, journal = {Remote Sensing of Environment}, volume = {174}, year = {2016}, month = {03/2016}, chapter = {100}, abstract = {

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{\textendash}1404\ mm\ year-\ 1) and leaf area index (0.1{\textendash}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).\ Dcalculated 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.

}, url = {http://www.sciencedirect.com/science/article/pii/S0034425715302315}, author = {Rachael Nolan and Victor Resco de Dios and Matthias M. Boer and Gabriele Caccamo and Michael L Goulden and Ross Bradstock} }