Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Variables selected for LFMC estimation. Variable names as per Table 2. Suffix _r indicates the relative version (on separate lines) of the variable.
Source publication
Live fuel moisture content (LFMC) influences many fire-related aspects, including flammability, ignition, and combustion. In addition, fire spread models are highly sensitive to LFMC values. Despite its importance, LFMC estimation is still elusive due to its dependence on plant species traits, local conditions, and weather patterns. Although LFMC m...
Context in source publication
Context 1
... most important radar variables for LFMC estimation were the VH polarization and the SPAN acquired during both ascending and descending passes ( Figure S4). After removing the highly correlated radar variables, nine were kept for modeling (Table 4). Among the Sentinel-2 SIs, the most important variable was the VARI. ...Similar publications
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snowpack properties are changing, and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku...
Citations
... Regarding previous studies using Sentinel-2 data, our results are similar to those obtained in [25,40]. However, the models in [25] were only applied to a monospecific shrubland site, and the models in [40] used time series acquired from active (Sentinel-1) and passive (Sentinel-2) sensors. ...
... Regarding previous studies using Sentinel-2 data, our results are similar to those obtained in [25,40]. However, the models in [25] were only applied to a monospecific shrubland site, and the models in [40] used time series acquired from active (Sentinel-1) and passive (Sentinel-2) sensors. In [25], it is stated that Sentinel-2 may be a good alternative to MODIS if daily estimations are not a priority but where higher spatial resolution is needed (e.g., patchy vegetation areas). ...
This paper presents empirical models developed through stepwise multiple linear regression to estimate the live fuel moisture content (LFMC) in a Mediterranean area. The models are based on LFMC data measured in 50 field plots, considering four groups with similar bioclimatic characteristics and vegetation types (trees and shrubs). We also applied a species-specific LFMC model for Rosmarinus officinalis in plots with this dominant species. Spectral indices extracted from Sentinel-2 images and their averages over the study time period in each plot with a spatial resolution of 10 m were used as predictors, together with interpolated meteorological, topographic, and seasonal variables. The models achieved adjusted R2 values ranging between 52.1% and 74.4%. Spatial and temporal variations of LFMC in shrub areas were represented on a map. The results highlight the feasibility of developing satellite-derived LFMC operational empirical models in areas with various vegetation types and taking into account bioclimatic zones. The adjustment of data through GAM (generalized additive models) is also addressed in this study. The different error metrics obtained reflect that these models provided a better fit (most adjusted R2 values ranged between 65% and 74.1%) than the linear models, due to GAMs being more versatile and suitable for addressing complex problems such as LFMC behavior.
... Remote sensing remains an efficient approach that can provide regular information about vegetation parameters widely. Previous studies emphasize the satellite imagery capabilities from different characteristics for estimating AGB (Amuyou et al., 2022;Laurin et al., 2018;Lu et al., 2016;Nuthammachot et al., 2022;Pandit et al., 2018) and LFMC (Costa-Saura et al., 2021;Fan et al., 2018; Rao et al., 2020;Sow et al., 2013;Tanase et al., 2022;Yebra et al., 2013;Zhu et al., 2021). However, in-situ measurements are crucial and necessary to validate models and products (Lu et al., 2016;Yebra et al., 2013). ...
... Costa-Saura et al. (2021) also found a global correlation of 0.70 and RMSE 8.13% using Sentinel-2 data merged with meteorological variables (temperature and wind speed) derived from weather stations for grassy areas in the eastern part of Spain, demonstrating that the LFMC variability is mainly seasonal. Using the RF approach, Tanase et al. (2022) reveal results combining active and passive sensors and ancillary data, i. e., land cover and topography, showing an r 2 of 0.62. Some studies (Costa-Saura et al., 2021;Cunill Camprubí et al., 2022;Yebra et al., 2013) suggest that variables such as temperature, wind speed and soil moisture, as well as an optical and radar data combination, can improve the fuel content moisture estimation. ...
... Microwave wavelengths have been presented as an alternative data source to overcome such limitations [128,129]. Being sensitive to moisture of certain vegetation parts (e.g., crown, stems), different Synthetic Aperture Radar (SAR) bands from sensors, such as the RADARSAT-1 and most recently the Sentinel-1, have been used to reliably estimate FMC in various environments [130,131]. Current advances in satellite technology are expected to introduce data from new sensors, such as the upcoming Meteosat Third Generation (MTG)-Flexible Combined Imager (FCI), Biomass SAR and OzFuel [132], providing new insights and additional possibilities for LFMC retrieval. ...
This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.
