Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest.

The moisture content of live fuels is an important determinant of forest flammability. Current approaches for modelling live fuel moisture content typically focus on the use of drought indices. However, these have mixed success partly because of species-specific differences in drought responses. Here we seek to understand the physiological mechanisms driving changes in live fuel moisture content, and to investigate the potential for incorporating plant physiological traits into live fuel moisture models. We measured the dynamics of leaf moisture content, access to water resources (through stable isotope analyses) and physiological traits (including leaf water potential, stomatal conductance, and cellular osmotic and elastic adjustments) across a fire season in a Mediterranean mixed forest in Catalonia, NE Spain. We found that differences in both seasonal variation and minimum values of live fuel moisture content were a function of access to water resources and plant physiological traits. Specifically, those species with the lowest minimum moisture content and largest seasonal variation in moisture (Cistus albidus: 49–137% and Rosmarinus officinalis: 47–144%) were most reliant on shallow soil water and had the lowest values of predawn leaf water potential. Species with the smallest variation in live fuel moisture content (Pinus nigra: 96–116% and Quercus ilex: 56–91%) exhibited isohydric behaviour (little variation in midday leaf water potential, and relatively tight regulation of stomata in response to soil drying). Of the traits measured, predawn leaf water potential provided the strongest predictor of live fuel moisture content (R2 = 0.63, AIC = 249), outperforming two commonly used drought indices (both with R2 = 0.49, AIC = 258). This is the first study to explicitly link fuel moisture with plant physiology and our findings demonstrate the potential and importance of incorporating ecophysiological plant traits to investigating seasonal changes in fuel moisture and, more broadly, forest flammability.This study was made possible thanks to the collaboration of and the staff from the Natural Park of Poblet, P Sopeña, and the technical staff from MedForLab. This study was funded by the Spanish Government (RYC-2012-10970, AGL2015-69151-R). R. H. Nolan was supported with funding from the New South Wales Office of Environment and Heritage, via the Bushfire Risk Management Research Hub. We benefitted from critical comments from J Voltas, JM Moreno and L Serrano and instrument loans from R Savín

Estimating Live Fuel Moisture in Southern California Using Remote Sensing Vegetation Water Content Proxies

Wildfires are a major ecological disturbance in Southern California and often lead to great destruction along the Wildland-Urban Interface. Live fuel moisture has been used as an important indicator of wildfire risk in measurements of vegetation water content. However, the limited field measurements of live fuel moisture in both time and space have affected the accuracy of wildfire risk estimations. Traditional estimation of live fuel moisture using remote sensing data was based on vegetation indices, indirect proxies of vegetation water content and subject to influence from weather conditions. In this study, we investigated the feasibility of estimating live fuel moisture using vegetation indices, Soil Moisture Active Passive L-band soil moisture data and the modeled vegetation water content using a non-linear model based on VIs and the stem factor associated with remote sensing moisture data products. The stem factor describes the peak amount of water residing in stems of plants and varies by land cover. We also compared the outcomes from regression models and recurrent neural network using the same independent variables. We found the modeled vegetation water content outperformed vegetation indices and the L-band soil moisture observations, suggesting a non-linear relationship between live fuel moisture and the remotely sensed vegetation signatures. We discuss our results which will improve the predictability of live fuel moisture

Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

Southern California possesses a Mediterranean climate having semi-arid to arid characteristics and contains shrubland areas at high risk to wildfire. To assess wildfire danger, fire agencies have been monitoring the moisture of vegetation, called live fuel moisture (LFM), using field-based sampling. Unfortunately, spatial and temporal resolution of live fuel moisture data are significantly limited because sampling is labor intensive. Remote sensing satellite data has been used to monitor vegetation moisture content and health of shrublands. Therefore, a potential approach to overcome the limitations of manual measurements of live fuel moisture is to use vegetation indices (VIs) derived from satellite data. The objective of this study is to understand the link between vegetation indices derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both Terra and Aqua satellites and in-situ live fuel moisture data. In this study, five vegetation indices were calculated using 6 bands of MODIS data within the visible and infrared spectrum collectively with the focus on the three best performing: enhanced vegetation index (EVI), normalized difference water index (NDWI), and visible atmospherically resistant index (VARI). Six sites with multi-year live fuel moisture data collection type were each represented with one pixel of MODIS data with a 500m by 500m spatial resolution covering the time period of February 2000 through December 2017 acquired aboard Terra and June 2002 through December 2017 acquired aboard Aqua. Linear regression was then applied to measure the coefficient of determination (R2) between the vegetation indices and live fuel moisture data. The results show a great variance of R2 between the sites as well as a variance of best performing VI. The two strongest coefficients of determination, R2=0.74 and R2=0.72, were calculated at one site for enhanced vegetation index vs. live fuel moisture over a 15-year time period of data collected on Aqua and a 17-year time period of data collected on Terra respectively. The relationship was also affected by annual climate conditions including precipitation. Our results indicate that the satellite data reasonably well-represents the live fuel moisture with higher temporal resolutions over a large area. Utilizing the remote sensing data in wildfire danger assessment will support fire agencies by saving resources for collecting ground data and providing better dataset in both time and space. This will also be beneficial for land management and planning, stakeholders and local governments

Potential impact of climate change on length of ignition danger season in Mediterranean shrubland of North Sardinia

The main aim of this work is to identify useful tools to forecast impacts of expected climate change on live fuel moisture content (Live FMC) in Mediterranean shrublands

Monitoring live fuel moisture using soil moisture and remote sensing proxies

Live fuel moisture (LFM) is an important fuel property controlling fuel ignition and fire propagation. LFM varies seasonally, and is controlled by precipitation, soil moisture, evapotranspiration, and plant physiology. LFM is typically sampled manually in the field, which leads to sparse measurements in space and time. Use of LFM proxies could reduce the need for field sampling while potentially improving spatial and temporal sampling density. This study compares soil moisture and remote sensing data to field-sampled LFM for Gambel oak (Quercus gambelii Nutt) and big sagebrush (Artemisia tridentata Nutt) in northern Utah. Bivariate linear regression models were constructed between LFM and four independent variables. Soil moisture was more strongly correlated with LFM than remote sensing measurements, and produced the lowest mean absolute error (MAE) in predicted LFM values at most of the sites. When sites were pooled, canopy water content (CWC) had stronger correlations with LFM than normalized difference vegetation index (NDVI) or normalized difference water index (NDWI). MAE values for all proxies were frequently above 20 % LFM at individual sites. Despite this relatively large error, remote sensing and soil moisture data may still be useful for improving understanding of spatial and temporal trends in LFM

A live fuel moisture climatology in California

In this study, observations of live fuel moisture content (LFMC) for predominantly sampled fuels in six distinct regions of California were examined from 2000 to 2021. To gather the necessary data, an open-access database called the Fuel Moisture Repository (FMR), was developed. By harnessing the extensive data aggregation and query capabilities of the FMR, which draws upon the National Fuel Moisture Database, valuable insights into the live fuel moisture seasonality were obtained. Specifically, our analysis revealed a distinct downtrend in LFMC across all regions, with the exception of the two Northernmost regions. The uptrends of LFMC seen in those regions are insignificant to the general downtrend seen across all of the regions. Although the regions do not share the same trends over the temporal span of the study, from 2017 to 2021, all the regions experienced a downtrend two times more severe than the general 22-year downtrend. Further analysis of the fuel types in each of the six regions, revealed significant variability in LFMC across different fuel types and regions. To understand potential drivers of this variability, the relationship between LFMC and drought conditions was investigated. This analysis found that LFMC fluctuations were closely linked to water deficits. However, the drought conditions varied across the examined regions, contributing to extreme LFMC variability. Notably, during prolonged drought periods of 2 or more years, fuels adapted to their environment by stabilizing or even increasing their maximum and minimum moisture values, contrary to the expected continual decrease. These LFMC trends have been found to correlate to wildfire activity and the specific LFMC threshold of 79% has been proposed as trigger of an increased likelihood of large fires. By analyzing the LFMC and fire activity data in each region, we found that more optimal local thresholds can be defined, highlighting the spatial variability of the fire response to the LFMC. This work expands on existing literature regarding the connections between drought and LFMC, as well as fire activity and LFMC. The study presents a 22-year dataset of LFMC spanning the entirety of California and analyses the LFMC trends in California that haven’t been rigorously studied before

Electrical Responses of Pinus halepensis Mill. as an Indicator of Wildfire Risk in Mediterranean Forests by Complementing Live Fuel Moisture

[EN] Pinus halepensis forests, as Mediterranean-type ecosystems, are subject to high levels of wildfire risk in times of drought, with meteorological conditions of water stress and very high temperatures, mainly in summer. Considering the difficulty of knowing the phenological state of this species, the objective of this research was to evaluate the possibility of implementing the electrical responses (voltage and short-circuit current) as a variable in fire risk management models, compared to live fuel moisture. On the one hand, the obtained results demonstrate non-significant differences between the moisture content of the different fractions of the living branches (base and half of the branch and live fuel), even in times of drought with hydric stress and very high temperatures. Live fuel moisture of Pinus halepensis does not show significant seasonal variations under the influence of extreme fire risk factors. For this reason, it should be complemented with other variables for fire risk management models. On the other hand, the differences registered in the electrical signal show oscillations with significant variations, which are strongly correlated with the periods of extremely favourable meteorological conditions for wildfires. So, the voltages measured show ranges that correspond with great accuracy to the FWI. Voltage variation is dependent on the hydraulic dynamic plant behaviour and a result of the physiological response of pine trees to abiotic stress of drought. It is an easy-to-measure electrical parameter as well as a very reliable indicator with a high correlation with wildfire risk. Thus, electrical responses could add more knowledge about the phenological state of the trees in dependence on stress climatic conditions, allowing integration of these variables in the preventive wildfire modelling and managementZapata, R.; Oliver Villanueva, JV.; Lemus Zúñiga, LG.; Mateo Pla, MÁ.; Luzuriaga, JE. (2022). Electrical Responses of Pinus halepensis Mill. as an Indicator of Wildfire Risk in Mediterranean Forests by Complementing Live Fuel Moisture. Forests. 13(8):1-19. https://doi.org/10.3390/f1308118911913

Estimating live fuel moisture content in Oklahoma plants

Live fuel moisture content (LFM) is an important variable in fire danger rating systems. LFM collection is time and resource intensive and plant water relations vary within and between species. Consequently, the best approach for estimating LFM is unknown. Few studies have investigated LFM in the state of Oklahoma, and current estimates of LFM have not been validated. The objectives of this study were to evaluate the use of environmental and remote sensing proxies for estimating LFM in Oklahoma plants. I found that LFM can be accurately estimated using either hyperspectral leaf-level reflectance or environmental proxies. My analysis of several remote sensing vegetation indices identified the Water Index and VIgreen as the best suited indices for approximating LFM. Using functional group, photoperiod, vapor pressure deficit, and rainfall I was able to estimate LFM in Oklahoma plant communities. In addition to these findings, I identified a need to reevaluate current methods for estimating LFM. By advancing our understanding of LFM and how best to predict it, my results can be used in fire danger rating systems to protect lives and preserve natural resources

LIVE FUEL MOISTURE CONTENT AND IGNITION PROBABILITY IN THE IBERIAN PENINSULAR TERRITORY OF SPAIN

This paper presents an operational algorithm to produce Live Fuel Moisture Content (LFMC) at national scale from MODIS data. The algorithm is based on the inversion of Radiative Transfer Models (RTM) that estimate moisture content based on different simulation scenarios. In addition, logistic regression models were calibrated to convert the derived LFMC values into Ignition Probability (IP) maps. The areas under the curve obtained by the Receiver Operating Characteristic (ROC) plot method provided by the models were close to 0.6. Several statistical analyses were performed in order to ascertain whether the variables proposed to be included in the fire danger model were significantly related to forest fires. A non parametric U-Mann-Withney test confirmed significant differences between fire and non-fire pixels (p<0.001). Fire pixels occurred at significantly lower LFMC values than the non-fire pixels