Fuel Moisture Research Articles

Quantifying Forest Litter Fuel Moisture Content with Terrestrial Laser Scanning

Electromagnetic radiation at 1550 nm is highly absorbed by water and offers a novel way to collect fuel moisture data, along with 3D structures of wildland fuels/vegetation, using lidar. Two terrestrial laser scanning (TLS) units (FARO s350 (phase shift, PS) and RIEGL vz-2000 (time of flight, TOF)) were assessed in a series of laboratory experiments to determine if lidar can be used to estimate the moisture content of dead forest litter. Samples consisted of two control materials, the angle and position of which could be manipulated (pine boards and cheesecloth), and four single-species forest litter types (Douglas-fir needles, ponderosa pine needles, longleaf pine needles, and southern red oak leaves). Sixteen sample trays of each material were soaked overnight, then allowed to air dry with scanning taking place at 1 h, 2 h, 4 h, 8 h, 12 h, and then in 12 h increments until the samples reached equilibrium moisture content with the ambient relative humidity. The samples were then oven-dried for a final scanning and weighing. The spectral reflectance values of each material were also recorded over the same drying intervals using a field spectrometer. There was a strong correlation between the intensity and standard deviation of intensity per sample tray and the moisture content of the dead leaf litter. A multiple linear regression model with a break at 100% gravimetric moisture content produced the best model with R2 values as high as 0.97. This strong relationship was observed with both the TOF and PS lidar units. At fuel moisture contents greater than 100% gravimetric water content, the correlation between the pulse intensity values recorded by both scanners and the fuel moisture content was the strongest. The relationship deteriorated with distance, with the TOF scanner maintaining a stronger relationship at distance than the PS scanner. Our results demonstrate that lidar can be used to detect and quantify fuel moisture across a range of forest litter types. Based on our findings, lidar may be used to quantify fuel moisture levels in near real-time and could be used to create spatial maps of wildland fuel moisture content.

Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach

Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale fire outbreaks. This study aims to optimize IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM)—a processed-based model integrating biophysical and human impacts—to South Korea for projecting the pattern and scale of future forest fires. The developments performed in this study include: (1) the optimization of probability algorithms in FLAM based on the national GIS data downscaled to 1 km2 with additional factors introduced for national specific modeling; (2) the improvement of soil moisture computation by adjusting the Fine Fuel Moisture Code (FFMC) to represent vegetation feedbacks by fitting soil moisture to daily remote sensing data; and (3) projection of future forest fire frequency and burned area. Our results show that optimization has considerably improved the modeling of seasonal patterns of forest fire frequency. Pearson’s correlation coefficient between monthly predictions and observations from national statistics over 2016–2022 was improved from 0.171 in the non-optimized to 0.893 in the optimized FLAM. These findings imply that FLAM’s main algorithms for interpreting biophysical and human impacts on forest fire at a global scale are only applicable to South Korea after the optimization of all modules, and climate change is the main driver of the recent increases in forest fires. Projections for forest fire were produced for four periods until 2100 based on the forest management plan, which included three management scenarios (current, ideal, and overprotection). Ideal management led to a reduction of 60–70% of both fire frequency and burned area compared to the overprotection scenario. This study should be followed by research for developing adaptation strategies corresponding to the projected risks of future forest fires.

Critical fire weather conditions during active fire spread days in Canada

A spread day is defined as a day in which fires grow a substantial amount of area; such days usually occur during high or extreme fire weather conditions. The identification and prediction of a spread day based on fire weather conditions could help both our understanding of fire regimes as well as forecasting and managing fires operationally. This study explores the relationships between fire weather and spread days in the forested areas of Canada by spatially and temporally matching a daily fire growth database to a daily gridded fire weather database that spans from 2001 to 2019. By examining the correlations between spread day fire weather conditions and location, conifer coverage (%), and elevation, we found that a spread day happens under less severe fire weather conditions as latitude increases for the entire study area and as conifer coverage increases within non-mountainous study areas. In the western mountain areas, however, with increasing conifer coverage more severe fire weather conditions are required for a spread day to occur. Using two modeling approaches, we were able to identify spread day indicators (generalized additive model) and to predict the occurrence of spread days (semi-binomial regression model) by Canadian Ecozones both annually and seasonally. Overall, Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI), and Fire Weather Index (FWI) performed the best in all models built for spread day identification and prediction but varied depending on the conditions mentioned above. FFMC was the most consistent across all spatial and temporal scales.

Assessing the Effects of Fuel Moisture Content on the 2018 Megafires in California

In 2018, the megafire episodes on record occurred in California, causing a large number of civilian deaths and damages. As an important part of the “fire environment triangle,” the fuel moisture content (FMC) of both live (LFMC) and dead (DFMC) vegetation were broadly accepted as the important drivers of wildfire ignition and spread, but their effects on the 2018 megafires in California were less explored. Here, we explored and compared the effects of LFMC and DFMC on the 2018 megafire in California, allowing for highlighting the role of different types of FMC in megafire risk assessment. The LFMC was collected from the global LFMC product. We used three indices obtained from the Canadian Forest Service Fire Weather Index Rating System as a proxy of DFMC products, including the fine fuel moisture code, duff moisture code (DMC), and drought code. We analyzed the long-term series (2001–2018) of these four indices in California to test whether these indices were indicative of the occurrence of the megafire, and which of the index was the most powerful driving the 2018 megafires. The results showed that all these indices were correlated with the fires in California. The LFMC showed the highest correlation with the fire occurrence between 2001 and 2018, whereas the DMC performed a major role in driving the 2018 megafire in California. This study presented the insights that the LFMC and DMC should be carefully considered in future operational fire risk assessments for megafire prescription, suppression, and response.

Estimation of Live Fuel Moisture Content From Multiple Sources of Remotely Sensed Data

Estimation of Live Fuel Moisture Content From Multiple Sources of Remotely Sensed Data

Remotely sensed dry matter productivity and soil moisture content as potential predictors of arid rangeland wildfires: A case study of Kgalagadi District, Botswana

Fire is a critical tool for managing rangeland ecosystems; however, the increasing wildfire occurrence poses a considerable danger to rangeland ecosystem continuity. Predicting fire occurrence and mapping wildfire danger is critical in managing highly flammable rangelands. To identify potential remotely sensed variables for wildfire prediction, this study employed a Random Forest (RF) classifier using selected environmental variables to assess their possible use for wildfire prediction in Kgalagadi District, Botswana. The study used 107,883 active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor from 2015 to 2021. Datasets of remotely sensed Dry Matter Productivity (DMP), Soil Moisture (SM), Land Surface Temperature (LST), Live Fuel Moisture Content (LFMC), and Dead Fuel Moisture Content (DMFC) were analysed in ArcMap 10.7 Esri©. The RF model developed gave an Out of Bag (OOB) error of 9.91% and an overall accuracy of 90.15% for classifying fires and non-fire points using the test dataset. The results also showed a Kappa coefficient of 0.803, with 88.25% and 91.76% producer and user accuracies, respectively, for classifying fire points. The DMP was the most significant variable with Mean Decrease Accuracy (MDA)= 1,055.20 and Mean Decrease Gini (MDG)= 9.328.62), followed by SM (MDA= 828.39 and MDG= 15,745). The LFMC and DMFC were found to be weak in detecting fires. It is recommended that field studies be carried out in the study area to calibrate these to improve their contribution to accurate fire prediction, as most literature shows that they are significant in fire prediction.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought.

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.

Adjustment of the Grass Fuel Moisture Code for Grasslands in Southern Brazil

Grasslands are one of the vegetation types most widely affected by wildfires in southern Brazil. It is a fire-dependent ecosystem and it is necessary to know the hourly fuel moisture variation for its management. The objective of this work is to fit Grass Fuel Moisture Code (GFMC) models to estimate the moisture content for the grassland of the State Park of Vila Velha, Paraná, Brazil. Data sampling to determine the hourly moisture content was performed during the winter of 2018 and divided into two campaigns of five days with stable weather conditions. Destructive samples were taken out for the sorption tests on climatic chambers to obtain the equilibrium moisture content and the time lag values. The fitted equilibrium moisture and time lag models were evaluated by residual distribution analysis, mean absolute error (MAE), root mean square error (RSME) and coefficient of determination (R2). The fitted model performed better than the original GFMC model due to the obtained MAE, RSME and R2 values. The results showed that the fitted GFMC model is better to predict the fine fuel moisture for the region.

Regional Variability and Driving Forces behind Forest Fires in Sweden

Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA, and are now an increasing threat in boreal Europe, where recent fire events in 2014 and 2018 drew attention to Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas, and to link fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database of 1 × 1 km2 homogenous grids, where monthly burned areas were derived from the MODIS FireCCI51 dataset. The database consists of various socio-economic, topographic-, forest-, and weather-related remote sensing products. To include new factors in the IIASA’s FLAM model, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden’s geographical diversity, fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and observed burned areas were well captured by the model. The result accuracy differs with respect to zone; the area under the curve (AUC) was 0.875 and 0.94 for zones with few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and their variability across Sweden provide valuable information to understand the reasons behind forest fires. The Fine Fuel Moisture Code, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other boreal regions and thus is highly policy-relevant. Visualization of our results is available in the Google Earth Engine Application.

Characterizing Live Fuel Moisture Content from Active and Passive Sensors in a Mediterranean Environment

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 mapping from active synthetic aperture radar has increased over the past years, their utility for LFMC estimation needs further analysis to include additional areas characterized by different vegetation species and fire regimes. This study extended the current knowledge using medium spatial resolution (20 m) time series acquired by active (Sentinel-1) and passive (Sentinel-2) sensors. Our results show that optical-based LFMC estimation may achieve acceptable accuracy (R2 = 0.55, MAE = 15.1%, RMSE = 19.7%) at moderate (20 m) spatial resolution. When ancillary information (e.g., vegetation cover) was added, LFMC estimation improved (R2 = 0.63, MAE = 13.4%). Contrary to other studies, incorporating Sentinel-1 radar data did not provide for improved LFMC estimates, while the use of SAR data alone resulted in increased estimation errors (R2 = 0.28, MAE = 19%, RMSE = 25%). For increased fire risk scenarios (LFMC < 120%), estimation errors improved (MAE = 9.1%, RMSE = 11.8%), suggesting that direct LFMC retrieval from satellite data may be achieved with high temporal and spatial detail.

Assessment of lake-level fluctuation as an indicator of fire activity in boreal Canada

Fuel moisture content is a highly variable property that dictates the ignition and combustion potentials of wildfire fuels. The Canadian Fire Weather Index (FWI) System—a subset of the established Canadian Forest Fire Danger Rating System—relies solely on weather observations to track fuel moisture and compute indices of fire potential. Ancillary environmental measures, such as lake levels in non-regulated lake systems, may integrate climatic and landscape factors that better reflect environmental moisture deficits and thereby complement the FWI System. Accordingly, we investigated whether inter-annual variation in lake levels has potential to serve as an indicator of regional fire activity using a quasi-binomial regression approach. This study combines annual measurements of fire activity, lake level, and other climatic variables from the Boreal Shield and Boreal Plain ecozones of Canada from 1980 to 2019 to determine whether variation in regional area burned can be explained by lake-level fluctuation, and whether this explanatory power differs by ecozone. Results indicated that lake-level fluctuation is a significant predictor of regional area burned for many lakes (28–48%). It often performs as well as or better than traditional fire-weather metrics (such as temperature and precipitation), and this relationship is stronger on the Boreal Shield compared to the Boreal Plain. Lake-level fluctuation provides information on fuel availability unaccounted for by traditional fire-weather metrics, suggesting that this variable could be added to the suite of environmental indicators of regional fire activity in Canada.

Retrieval of Live Fuel Moisture Content Based on Multi-Source Remote Sensing Data and Ensemble Deep Learning Model

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R2 = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method.

The Fuel Moisture Index Based on Understorey Hygrochron iButton Humidity and Temperature Measurements Reliably Predicts Fine Fuel Moisture Content in Tasmanian Eucalyptus Forests

Fine fuel moisture content (FFMC) is a key determinant of wildfire occurrence, behaviour, and pyrogeographic patterns. Accurate determination of FFMC is laborious, hence managers and ecologists have devised a range of empirical and mechanistic measures for FFMC. These FFMC measures, however, have received limited field validation against field-based gravimetric fuel moisture measurements. Using statistical modelling, we evaluate the use of the relationship between gravimetric FFMC and the Fuel Moisture Index (FMI), based on Hygrochron iButton humidity and temperature dataloggers. We do this in Tasmanian wet and dry Eucalyptus forests subjected to strongly contrasting disturbance histories and, hence, percentage of canopy cover. We show that 24 h average FMI based on data from Hygrochron iButtons 0.75 m above the forest floor provides reliable estimates of gravimetric litter fuel moisture (c. 1 h fuels) that are strongly correlated with near surface gravimetric fuel moisture sticks (c. 10 h fuels). We conclude FMI based on Hygrochron iButton data provides ecologists with an economic and effective method to retrospectively measure landscape patterns in fuel moisture in Tasmanian forests.

Estimation of moisture in live fuels in the mediterranean: Linear regressions and random forests

The live fuel moisture content is an important factor in estimating the risk of forest fires and their rate of spread. However, due to a lack of research, the FMC values in the Mediterranean region of Andalusia, Spain, must be obtained by sample collection. This study is therefore the first to provide tools for estimating the moisture content of the most widespread plant species in Andalusia. First, samples were collected to estimate the moisture content of the plants; these data were collected from May 2007 to the present. Each species has its own range of moistures that depend on the time of year and the physiological state in which they are found. Secondly, an extensive database was obtained for each day of sample collection from the nearest weather station with free access. The statistics are performed at 12 solar hours on the day of sample collection and 24 h before collection, and then at 7 days, 14 days, 1 month, 3 months and 6 months before the day of collection. Finally, this database was statistically analyzed in two ways: Multiple linear regressions and random forest for each species. The predictive capacity of random forest is superior (R2 > 0.89) to that obtained in linear regression (R2 < 0.86). The highest root mean square error obtained in the case of the random forest is 0.74479 while in the linear regressions it was 1.29184. Consequently, uncertainty regarding fire behavior in the case of forest fires is reduced.

The Performance of ECMWF Ensemble Prediction System for European Extreme Fires: Portugal/Monchique in 2018

At the beginning of August 2018, Portugal experienced a severe heat episode over a few days that consequently increased the probability of wildfire events. Due to the advection of an anomalous very hot and dry air mass, severe fire-prone meteorological conditions were forecasted mainly over southern Portugal, in the Monchique region. Together with the significant fuel amount accumulated since the last extreme wildfire in August 2003, all the unfavorable conditions were set to drive a severe fire over this region. The Monchique fire started on 3 August 2018, being very hard to suppress and lasting for seven days, with a burnt area of 27,000 ha. Regarding the need to have operational early warning tools, this work aims to evaluate the reliability of fire probabilistic products, up to 72 h ahead, together with the use of fire radiative power products, as support tools in fire monitoring and resource activities. To accomplish this goal, we used the fire probabilistic products of the Ensemble Prediction System, provided by the Copernicus Atmosphere Monitoring Service. Among available fire danger rating systems, the Fire Weather Index and the Fine Fuels Moisture Code of the Canadian Forest Fire Weather Index System were selected to assess the meteorological fire danger. The assessment of the fire intensity was based on the Fire Radiative Energy released, considering the Fire Radiative Power, delivered in near real-time, by EUMETSAT Land Surface Analysis Satellite Applications Facility. The exceptional fire danger over southern Portugal that favors the ignition of the Monchique fire and its severity was essential driven by two important factors: (i) the anomalous fire weather danger, before and during the event; (ii) the accumulated fuel amount, since the last severe event occurred in 2003, over the region. Results show that the selected fire probabilistic products described the meteorological fire danger observed well, and the LSA-SAF products revealed the huge amount of fire energy emitted, in line with the difficulties faced by authorities to suppress the Monchique fire.

A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

• Live Fuel Moisture Content (LFMC) is an essential constraint of wildfire activity. • LFMC estimated from weather/vegetation indices lacks species-specific predictions. • We use a water balance model to predict species-specific LFMC from water potential. • Our model shows an improvement in predictive performance over existing approaches. • Our approach can be implemented within large-scale fire danger forecast systems. Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines the availability of existing live fuel to burn. Currently, LFMC is monitored through spectral vegetation indices or inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into species-specific LFMC variation and they are limited in the ability to forecast LFMC under altered future climates. Here, we developed a semi-mechanistic model to predict daily variation in LFMC across woody species from different functional types by adjusting a soil water balance model which estimates predawn leaf water potential (Ψ pd ). Our overarching goal was to balance the trade-off between biological realism, which enhances model applicability, and parameterization complexity, which may limit its value within operational settings. After calibration, model predictions were validated against a dataset comprising 1659 LFMC observations across peninsular Spain, belonging to different functional types and from contrasting climates. The overall goodness of fit for our model ( R 2 = 0.5) was better than that obtained by an existing models based on drought indices ( R 2 = 0.3) or spectral vegetation indices ( R 2 = 0.1). We observed the best predictive performance for seeding shrubs ( R 2 = 0.6) followed by trees ( R 2 = 0.5) and resprouting shrubs ( R 2 = 0.4). Through its relatively simple parameterization, the approach developed here may pave the way for a new generation of process-based models that can be used for operational purposes within fire risk mitigation scenarios.

Forecasting Live Fuel Moisture of Adenostema fasciculatum and Its Relationship to Regional Wildfire Dynamics across Southern California Shrublands

In seasonally dry environments, the amount of water held in living plant tissue—live fuel moisture (LFM)—is central to vegetation flammability. LFM-driven changes in wildfire size and frequency are particularly important throughout southern California shrublands, which typically produce intense, rapidly spreading wildfires. However, the relationship between spatiotemporal variation in LFM and resulting long-term regional patterns in wildfire size and frequency within these shrublands is less understood. In this study, we demonstrated a novel method for forecasting the LFM of a critical fuel component throughout southern California chaparral, Adenostema fasciculatum (chamise) using gridded climate data. We then leveraged these forecasts to evaluate the historical relationships of LFM to wildfire size and frequency across chamise-dominant California shrublands. We determined that chamise LFM is strongly associated with fire extent, size, and frequency throughout southern California shrublands, and that LFM–wildfire relationships exhibit different thresholds across three distinct LFM domains. Additionally, the cumulative burned area and number of fires increased dramatically when LFM fell below 62%. These results demonstrate that LFM mediates multiple aspects of regional wildfire dynamics, and can be predicted with sufficient accuracy to capture these dynamics. Furthermore, we identified three distinct LFM ‘domains’ that were characterized by different frequencies of ignition and spread. These domains are broadly consistent with the management thresholds currently used in identifying periods of fire danger.

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

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 management.

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

Remotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC). However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, we explored the potential of random forests (RF) to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. We built RF models (LFMCRF) using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. We used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). LFMCRF was calibrated with samples collected between 2000 and 2014 and validated with samples from 2015 to 2019, with overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively, which were lower than current approaches based on radiative transfer models (RMSE ~74–78%). We used our approach to generate a public database with weekly LFMC maps across the Mediterranean basin.

Relationship between Forest Fuel Moisture Contents and Weather Factors During the Forest Fires Danger Season in Chuncheon, Gangwon

The amount of moisture contained in fallen leaves in the forest is closely related to the possibility of the ignition and spread of a forest fire. This moisture content is an important fundamental component of the forest fire danger rating system, as well as an essential parameter in forest fire management. This study examined the relationship between the moisture content of fuel in the forests and weather information obtained from HOBO data loggers installed in forests during the forest fire seasons of 2018 and 2020. The moisture content of the forest fuel was divided into two slopes (south and north), and the fallen leaves used were broad-leaved (Prunus serrulate and Quercus dentata) and coniferous (Pinus densiflora and Pinus koraiensis). The temperature (mean, minimum, and maximum) and relative humidity (mean and minimum) data obtained from the weather measuring devices installed in the forests were used to correlate weather factors with fuel moisture content. During the spring forest fire period, the temperature (mean, minimum, and maximum) and relative humidity (mean and minimum) of each slope and tree species were significant, while the average temperature and relative humidity were significant during the autumn. According to the correlations between the fuel moisture content and weather, factors with high significance were selected, and multiple regression analyses of the slope and species were found to be significant at 1 percent. The results of this study are expected to be used as primary data for calculating the fuel index of the forest fire forecasting system by accounting for the effects of weather factors on the moisture content of the fuel.