@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-6881, title = {Satellite Remote Sensing Contributions to Wildland Fire Science and Management}, journal = {Current Forestry Reports}, year = {2020}, month = {04/2020}, abstract = {

Purpose

This paper reviews the most recent literature related to the use of remote sensing (RS) data in wildland fire management.

Recent Findings

Studies dealing with pre-fire assessment, active fire detection, and fire effect monitoring are reviewed in this paper. The analysis follows the different fire management categories: fire prevention, detection, and post-fire assessment. Extracting the main trends from each of these temporal sections, recent RS literature shows growing support of the combined use of different sensors, particularly optical and radar data and lidar and optical passive images. Dedicated fire sensors have been developed in the last years, but still, most fire products are derived from sensors that were designed for other purposes. Therefore, the needs of fire managers are not always met, both in terms of spatial and temporal scales, favouring global over local scales because of the spatial resolution of existing sensors. Lidar use on fuel types and post-fire regeneration is more local, and mostly not operational, but future satellite lidar systems may help to obtain operational products. Regional and global scales are also combined in the last years, emphasizing the needs of using upscaling and merging methods to reduce uncertainties of global products. Validation is indicated as a critical phase of any new RS-based product. It should be based on the independent reference information acquired from statistically derived samples.

Summary

The main challenges of using RS for fire management rely on the need to improve the integration of sensors and methods to meet user requirements, uncertainty characterization of products, and greater efforts on statistical validation approaches.

}, keywords = {fire detection, management, remote sensing, Wildfire}, doi = {https://doi.org/10.1007/s40725-020-00116-5}, url = {https://link.springer.com/article/10.1007/s40725-020-00116-5?wt_mc=socialmedia.twitter.1.SEM.ArticleAuthorOnlineFirst}, author = {Emilio Chuvieco and Inmaculada Aguado and Javier Salas and Mariano Garcia and Marta Yebra and Patricia Oliva} }