Fuel Moisture Research Articles

An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment

Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-hours fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management.

Stand composition and development stage affect fuel characteristics of quaking aspen forests in UT, USA

In western North America, quaking aspen stands (Populus tremuloides Michx.) have predominantly been described as low flammability, “fireproof” forests, but the specific relationship between aspen stand composition, fuel characteristics, and potential fire behavior is not fully understood. We investigated surface and canopy fuel characteristics in 80 aspen stands in Utah, U.S. that spanned gradients of tree species composition from aspen to conifer dominance and stand development from early to late stages. We quantified fuel type and load, measured fuel moisture content in representative stands across two summer seasons, and modeled flame lengths in each stand. Fuel type and load varied greatly across stands, though late development, conifer-dominated stands had significantly higher (~2-5 times) fine dead woody and litter load and significantly lower (~2-5 times) live understory herbaceous load compared to pure aspen stands. Fuel moisture content did not vary by stand type. Modeled flame lengths were lowest in pure aspen stands, and flame lengths increased linearly with decreasing aspen composition, suggesting that potential surface fire behavior increases as a seral aspen stand progresses through succession to conifer dominance.

Simulation of Fire Occurrence Based on Historical Data in Future Climate Scenarios and Its Practical Verification

Forest fire is one of the dominant disturbances in the forests of Heilongjiang Province, China, and is one of the most rapid response predictors that indicate the impact of climate change on forests. This study calculated the Canadian FWI (Fire Weather Index) and its components from meteorological record over past years, and a linear model was built from the monthly mean FWI and monthly fire numbers. The significance test showed that fire numbers and FWI had a very pronounced correlation, and monthly mean FWI was suitable for predicting the monthly fire numbers in this region. Then FWI and its components were calculated from the SRES (IPCC Special Report on Emission Scenarios) A2 and B2 climatic scenarios, and the linear model was rebuilt to be suitable for the climatic scenarios. The results indicated that fire numbers would increase by 2.98–129.97% and −2.86–103.30% in the A2 and B2 climatic scenarios during 2020–2090, respectively. The monthly variation tendency of the FWI components is similar in the A2 and B2 climatic scenarios. The increasing fire risk is uneven across months in these two climatic scenarios. The monthly analysis showed that the FFMC (Fine Fuel Moisture Code) would increase dramatically in summer, and the decreasing precipitation in summer would contribute greatly to this tendency. The FWI would increase rapidly from the spring fire season to the autumn fire season, and the FWI would have the most rapid increase in speed in the spring fire season. DMC (Duff Moisture Code) and DC (Drought Code) have relatively balanced rates of increasing from spring to autumn. The change in the FWI in this region is uneven in space as well. In early 21st century, the FWI of the north of Heilongjiang Province would increase more rapidly than the south, whereas the FWI of the middle and south of Heilongjiang Province would gradually catch up with the increasing speed of the north from the middle of 21st century. The changes in the FWI across seasons and space would influence the fire management policy in this region, and the increasing fire numbers and variations in the FWI scross season and space suggest that suitable development of the management of fire sources and forest fuel should be conducted.

A national-scale sampled temperate fuel moisture database

Fuel moisture content (FMC) is important for the ignitability, behaviour and severity of wildfires. Understanding the drivers of FMC and its spatial and temporal variability can help us develop fuel moisture models and inform assessments of wildfire behaviour and danger. Here we present the first United Kingdom (UK) national-scale temperate FMC dataset of 8,057 samples of eighteen different fuel constituents collected across 58 sampling sites between 2021–2023. We sampled fuels across emerging fire-prone ecosystems in the UK across three studies: (1) UK-wide longer-term sampling characterising the spatio-temporal drivers of FMC; (2) landscape-scale measurement through the North Yorkshire Moors to investigate landscape-driven variability in FMC; (3) plot-scale intensive sampling in the West Midlands to quantify diurnal patterns and among-sampler variability in fuel measurements. This database addresses a global fuel moisture measurement gap within traditionally non-fire prone regions. The database will advance our understanding of temperate fuel moisture dynamics and forms a fundamental contribution towards the development of a fire danger rating system for traditionally non-fire prone regions such as the UK.

The Role of Field Measurements of Fine Dead Fuel Moisture Content in the Canadian Fire Weather Index System—A Study Case in the Central Region of Portugal

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content (mf) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of mf based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region.

Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction

Estimating live fuel moisture content (LFMC) is critical for assessing vegetation flammability and predicting potential fire behaviour, thus providing relevant information for wildfire prevention and management. Previous research has demonstrated that empirical modelling based on spectral data derived from remote sensing is useful for retrieving LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present study, we analysed the performance of empirical models based on Sentinel-2 spectral data for estimating LFMC in fire-prone shrubland dominated by Cistus ladanifer. We used LFMC data collected in the field between June 2021 and September 2022 in 27 plots in the region of Andalusia (southern Spain). The specific objectives of the study included (i) to test previous existing models fitted for the same shrubland species in a different study area in the region of Madrid (central Spain); (ii) to calibrate empirical models with the field data from the region of Andalusia, comparing the model performance with that of existing models; and (iii) to test the capacity of the best empirical models to predict decreases in LFMC to critical threshold values in historical wildfire events. The results showed that the empirical models derived from Sentinel-2 data provided accurate LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability throughout the year and with the MAE decreasing to 10% for the critical lower LFMC values (<100%). They also showed that previous models could be easily recalibrated for extrapolation to different geographical areas, yielding similar errors to the specific empirical models fitted in the study area in an independent validation. Finally, the results showed that decreases in LFMC in historical wildfire events were accurately predicted by the empirical models, with LFMC <80% in this fire-prone shrubland species.

A physics-based model of thermodynamically varying fuel moisture content for fire behavior prediction

Fuel moisture content (FMC) is a critical parameter in fire and plume behaviors, showing diurnal and spatial variations influenced by local meteorological conditions, soil characteristics, and fuel properties. In low-intensity fires, small-scale FMC variations intensify, leading to an amplification of their effects on fire physics. In an effort to capture these variations, this paper presents the development of a physics-based model that couples a thermodynamic-based FMC prediction model for dead fuels with the Fire Dynamics Simulator of the National Institute of Standards and Technology. The model accuracy is validated against several existing experimental data, showing improvements over the baseline model which uses the kinetic-based Arrhenius drying approach. A case study of flame propagation in a small fuel bed is also presented, indicating the improved performance of the new model and its novel capabilities in capturing complex processes of fuel drying and moisture flux exchanges between the fuel and ambient atmosphere.

Comparative Analysis of Machine Learning-Based Predictive Models for Fine Dead Fuel Moisture of Subtropical Forest in China

The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel on the forest surface is a critical challenge in forest fire management. Previous research on fine surface fuel moisture content has been mainly focused on coniferous forests in cold temperate zones, but there has been less attention given to understanding the fuel moisture dynamics in subtropical forests, which limits the development of regional forest fire warning models. Here, we consider the coupled influence of multiple meteorological, terrain, forest stand, and other characteristic factors on the fine dead fuel moisture content within the subtropical evergreen broadleaved forest region of southern China. The ability of five machine learning algorithms to predict the moisture content of fine dead fuel on the forest surface is assessed, and the key factors affecting the model accuracy are identified. Results show that when a single meteorological factor is used as a forecasting model, its forecasting accuracy is less than that of the combined model with multiple characteristic factors. However, the prediction accuracy of the model is improved after the addition of forest stand factors and terrain factors. The model prediction ability is the best for the combination of all feature factors including meteorology, forest stand, and terrain. The overall prediction accuracy of the model is ordered as follows: random forest > extreme gradient boosting > support vector machine > stepwise linear regression > k-nearest neighbor. Canopy density in forest stand factors, slope position and altitude in terrain factors, and average relative air humidity and light intensity in the previous 15 days are the key meteorological factors affecting the prediction accuracy of fuel moisture content. Our results provide scientific guidance and support for understanding the variability of forest surface fuel moisture content and improved regional forest fire warnings.

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

A global fuel characteristic model and dataset for wildfire prediction

Abstract. Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurrence and propagation. Fuel load and fuel moisture content are essential variables for forecasting fire occurrence, and whilst existing operational systems incorporate dead fuel moisture content, both live fuel moisture content and fuel load are either approximated or neglected. We propose a mid-complexity model combining data driven and analytical methods to predict fuel characteristics. The model can be integrated into earth system models to provide real-time forecasts and climate records taking advantage of meteorological variables, land surface modelling, and satellite observations. Fuel load and moisture is partitioned into live and dead fuels, including both wood and foliage components. As an example, we have generated a 10-year dataset which is well correlated with independent data and largely explains observed fire activity globally. While dead fuel moisture correlates highest with fire activity, live fuel moisture and load are shown to potentially enhance prediction skill. The use of observation data to inform a dynamical model is a crucial first step toward disentangling the contributing factors of fuel and weather to understand fire evolution globally. This dataset, with high spatiotemporal resolution (∼9 km, daily), is the first of its kind and will be regularly updated.

Vegetation moisture estimation in the Western United States using radiometer-radar-lidar synergy

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.

Influence of Wooden Compartment’s Fuel Moisture Content on Time to Flashover: An Experimental and Numerical Study

Time to flashover is an important fire safety parameter. The present study investigated the effects of fuel moisture content on the time to flashover, crucial in fire safety analysis. Experiments and simulations of an ISO 9750-1 room model at 1/8 scale were performed by varying the wooden compartment boundaries’ moisture content between 5% and 16%. The results showed a linear increase in time to flashover with fuel moisture content. An empirical model to predict the time to flashover according to the moisture content was developed. The experiments showed that increasing the moisture from 6.5% to 14.4% prolonged the flashover time from 4.6 min to 8.75 min. These experimental results are consistent with computational fluid dynamics (CFD) modeling using Fire Dynamics Simulator (FDS), which also depicts a corresponding increase in the time to flashover. These findings demonstrate the critical role of fuel moisture content in fire safety analysis. The results suggest that a 1/8-scale model can be utilized for cost-effective and easily manageable education and demonstration purposes. This includes helping fire brigades and fire academy students comprehend the significance of fuel moisture content in compartment fire development. Since the FDS modeling is not restricted to a 1/8 scale, the presented results are promising regarding CFD modeling of time to flashover in full-scale compartments.

Examining the Transferability of Remote-Sensing-Based Models of Live Fuel Moisture Content for Predicting Wildfire Characteristics

Examining the Transferability of Remote-Sensing-Based Models of Live Fuel Moisture Content for Predicting Wildfire Characteristics

Economic feasibility of retrofitting blowdown heat recovery system using steam dryer to lignite-fired power plant for reduced fuel consumption

Blowdown is responsible for heat losses in boiler systems. The main objective of this paper is to propose a system to recover blowdown heat using steam dryer. The system consists of a flash tank, a steam dryer, and an air preheater. The flash tank is used to produce flash steam that is supplied to the steam dryer. Fuel moisture is removed in the steam dryer, resulting in fuel with lower moisture content and exhaust vapor. Exhaust vapor is supplied to the air preheater to increase air temperature before combustion. Due to lower fuel moisture content and higher air temperature, exhaust flue gas temperature can be decreased by adding air heater area. An analysis was performed to determine the payback period for the investment cost of the proposed heat recovery system. It was demonstrated that retrofitting a power plant with this system led to increased energy efficiency of the power plant. A case study of 250-MW existing power plant operating at the boiler pressure of 12.34 MPa was considered. Simulation results indicate that the power plant retrofitted with this blowdown heat recovery system consumes 0.87% less fuel than the power plant without this system. The payback period or the investment in this system is 7 years.

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

AbstractPredicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

Investigating the Effect of Fuel Moisture and Atmospheric Instability on PyroCb Occurrence over Southeast Australia

The incidence of pyro-cumulonimbus (pyroCb) caused by extreme wildfires has increased markedly in Australia over the last several decades. This increase can be associated with a dangerous escalation of wildfire risk and severe stratospheric pollution events. Atmospheric and fuel conditions are important influences on pyroCb occurrence, but the exact causal relationships are still not well understood. We used the Continuous Haines Index (C-Haines) to represent atmospheric instability and the Fuel Moisture Index (FMI) to represent fuel moisture to provide better insight into the effects of atmospheric and fuel conditions on pyroCb occurrence over southeast Australia. C-Haines and FMI were related to the probability of pyroCb occurrence by employing a logistic regression on data gathered between 1980 and 2020. Emphasis is placed on investigating the independent effects and combined effects of FMI and C-Haines, as well as assessing their potential to predict whether a pyroCb develops over a fire. The main findings of this study are: (1) high C-Haines and low FMI values are representative of favorable conditions for pyroCb occurrence, but C-Haines can offset the effect of FMI—the addition of C-Haines to the logistic model muted the significance of FMI; (2) among the components of C-Haines, air temperature lapse rate (CA) is a better predictor of pyroCb occurrence than the dryness component (CB); (3) there are important regional differences in the effect of C-Haines and FMI on pyroCb occurrence, as they have better predictive potential in New South Wales than in Victoria.

Multi-scale investigation of factors influencing moisture thresholds for litter bed flammability

Fuel moisture is important to flammability. Vegetation communities vary in their moisture thresholds for ignition and fire spread. Different factors, operating at distinct spatial scales (litter vs. vegetation community) may be responsible for these variations in moisture thresholds. The relative importance of these factors at each scale remains unquantified. Our study sought to examine what factors influence moisture thresholds for flammability across two spatial scales (point vs. plot). Litter samples were collected repeatedly over one fire season (2020–21) from selected sites within temperate eucalypt forest along an aridity gradient in south-eastern Australia. Samples were reconstructed then burnt under controlled conditions. At the point-scale (0.05 m2), we quantified flammability as the probability of sustained ignition, flame spread rate and flaming duration. At the plot-scale (400 m2), we quantified flammability as the proportion of sustained ignitions. At the point-scale, moisture thresholds varied with leaf cover on the surface of the litter bed for ignition and leaf size for flame spread rate. At the plot-scale, vapour pressure deficit (VPD) was the best predictor of ignitability and moisture thresholds varied with aridity. Wetter parts of the landscape had a higher VPD threshold for ignition than more arid parts, meaning they were available to burn less often. The relationship between leaf cover and ignitability observed at the point-scale was overwhelmed by the effect of moisture at the plot-scale. Variations in ignitability between vegetation communities were driven by aridity-induced changes in canopy cover and its effect on litter moisture. Ignitability models based on VPD and aridity could be used to predict ignitability now and into the future, given anticipated increases in VPD under climate change.

PENENTUAN TINGKAT RESIKO KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN METODE INDEKS CUACA KEBAKARAN (FIRE WEATHER INDEX) DAN JUMLAH TITIK PANAS (HOTSPOT) DI KABUPATEN BANJAR PROVINSI KALIMANTAN SELATAN

Various indicators of hotspot occurrence as a cause of forest and land fires (karhutla) in Banjar District are still difficult to determine due to limited information. The analysis of FFMC (Fine Fuel Moisture Code) and DC (Drought Code) as well as the monitoring of the number of hotspots aims to determine the level of risk of forest and land fire hazards and can be an early picture of future forest and land fire disasters in the Banjar District of South Kalimantan Province. The data processing process to obtain the FFMC and DC values and their relationship with the number of hotspots is to calculate the FFMC and DC values of station observations and model observations through the Microsoft Excel Add-In (FWI Add-in) program. The two models will then be verified with a scatter plot and through the Pearson correlation test the relationship between the FFMC and DC of the ERA5 model and the number of hotspots can be found. As a result, the FFMC and DC (station observation and model) showed extreme risk levels for the 2014, 2015 and 2018 forest and land fires. Both models show a positive and linear relationship on the scatter plot. And in the Pearson correlation test, both variables between the FFMC and DC of the ERA5 model and the number of hotspots are moderately to strongly correlated. This condition indicates that an increase in the risk level of forest and land fires will be followed by a significant increase in the incidence of forest and land fires in the Banjar Regency area of South Kalimantan Province.