@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-5388, title = {The Vegetation Structure Perpendicular Index (VSPI): A forest condition index for wildlife predictions}, journal = {Remote Sensing of Environment}, volume = {224}, year = {2019}, month = {04/2019}, chapter = {167}, abstract = {

Wildfires are a major\ natural hazard, causing substantial damage to infrastructure as well as being a risk to lives and homes. An understanding of their progression and behaviour is necessary to reduce risks and to develop operational management strategies in the event of an active fire. Many empirical fire-spread models have been developed to predict the spread and overall behaviour of a wildfire, based on a range of parameters such as weather and fuel conditions. However, these parameters may not be available with sufficient accuracy or spatiotemporal resolution to provide reliable fire spread predictions. Fuel condition data include variables such as vegetation quantity, structure and moisture content and, in the event of previous wildfires, the burn severity and stage of ecosystem recovery. In this study, an index called the\ Vegetation Structure\ Perpendicular Index (VSPI) is introduced. The VSPI utilises the short-wave infrared reflectance in bands centred at 1.6 and 2.2 μm, essentially representing the amount and structure of the vegetation{\textquoteright}s woody biomass (as opposed to the photosynthetic activity and moisture content). The VSPI is quantified as the divergence from a linear regression between the two bands in a time series and represents vegetation disturbance and recovery more reliably than indices such as the Normalised Burn Ratio (NBR) and\ Normalised Difference Vegetation Index\ (NDVI). The VSPI index generally shows minor inter-annual variability and stronger post-wildfire detection of disturbance over a longer period than NBR and\ NDVI. The index is developed and applied to major wildfire events within eucalypt forests throughout southern Australia to estimate both burn severity and time to recovery. The VSPI can provide an improved information layer for fire risk evaluation and operational predictions of wildfire behaviour.

}, keywords = {Forest condition, Landsat, Vegetation recovery, Wildfire spread}, doi = {https://doi.org/10.1016/j.rse.2019.02.004}, url = {https://www.sciencedirect.com/science/article/pii/S0034425719300586?dgcid=coauthor}, author = {Andrea Massetti and Christoph R{\"u}diger and Marta Yebra and James Hilton} }