@article {bnh-6974, title = {A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models}, journal = {Remote Sensing}, volume = {12}, year = {2020}, month = {05/2020}, abstract = {

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000{\textendash}2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat -5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21\% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

}, keywords = {data normalization, fire behavior simulator, fire danger, fire propagation, Landsat-5 TM, live fuel moisture content}, doi = {https://doi.org/10.3390/rs12111714}, url = {https://www.mdpi.com/2072-4292/12/11/1714}, author = {Mariano Garcia and David Ria{\~n}o and Marta Yebra and Javier Salas and Adrian Cardil and Santiago Monedero and Joaquin Ramirez and Pillar Martin and Lara Vilar and John Gajardo and Susan Ustin} } @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} }