Dead Fuels 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.

Managing fire‐prone forests in a time of decreasing carbon carrying capacity

Changing climatic conditions are increasing overstory tree mortality in forests globally. This restructuring of the distribution of biomass is making already flammable forests more combustible, posing a major challenge for managing the transition to a lower biomass state. In western US dry conifer forests, tree density resulting from over a century of fire‐exclusion practices has increased the risk of high‐severity wildfire and susceptibility to climate‐driven mortality. Reducing dead fuel loads will require new approaches to mitigate risk to the remaining live trees by preparing forests to withstand future wildfire. Here, we used data from the Teakettle Experimental Forest in California to evaluate different prescribed fire burn frequencies and their impact on accumulated dead fuels after a 4‐year drought. Increasing burn frequency could reduce surface fuel build‐up but comes with additional challenges that will require creativity and experimentation to overcome.

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.

Effect of live/dead condition, moisture content and particle size on flammability of gorse (Ulex europaeus) measured with a cone calorimeter

Background Live fuel comprises a significant portion of the fuel consumed in forest and scrub crown fires. However, its flammability remains poorly understood. Although live fuel differs from dead fuel in moisture content, chemical composition, cellular structure and physiological characteristics, its higher moisture content masks the effect of other characteristics on its flammability. Aims The aim of the study was to delineate and assess the effects of live/dead condition, moisture content and particle size on flammability of gorse (Ulex europaeus L.). Methods Live and dead gorse material of three size classes (0–3, 3–6, and 6–10 mm in diameter) at six moisture contents (0, 10, 25, 50, 75 and 100%) was tested in a cone calorimeter to evaluate its flammability using new sample preparation and moisture conditioning techniques. Key results On average, live fuel ignited 21% slower, reached 11% higher peak heat release rate, and had a 12% shorter burn duration than dead fuel of the same moisture content. These differences were most pronounced in coarser material. Conclusions For gorse, fine dead fuels increase the likelihood of ignition, fine live fuels contribute to high burning intensities, and coarser live and dead fuels prolong combustion. Implications These findings highlight the need to account for flammability differences between live and dead fuels in fire behaviour models beyond those driven by variations in moisture content.

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.

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires.

Effect of climate change on the seasonal variation in photosynthetic and non-photosynthetic vegetation coverage in desert areas, Northwest China

Efficient prediction of the response of vegetation to future climate change requires an in depth understanding of the effect of climate change on the land cover by photosynthetic (fPV) and non-photosynthetic vegetation (fNPV) and its driving mechanism. Here, we aimed to estimate fPV and fNPV, and analyze the impacts of climate factors on their seasonal variability in the arid desert of northwestern China. First, optimal vegetation indices (VIs) were selected to estimate fPV and fNPV based on measured spectral reflectance, and the accuracy of estimated fPV and fNPV using Sentinel-2 images was evaluated with data from an unmannedaerialvehicle (UAV). Then, partial correlation, multi-correlation and time‐lag effect analyses were used to obtain the correlation coefficients and lag times between fPV, fNPV and climate change. The results showed that Normalized Difference Vegetation Index (NDVI) and Dead Fuel Index (DFI) were the optimal VIs to estimate fPV and fNPV in the desert, with the accuracy of 67 and 52 % for fPV and fNPV, respectively. The average fPV and fNPV were 8.15 and 9.26 % from March to November, their seasonal variability was consistent with plant phenology, and there was a decrease in fPV and fNPV from east to west with a decrease in precipitation. The variability in fPV was driven by precipitation while that of fNPV was dominated bytemperature. The time-lag of temperature and precipitation effect on fPV was 1.28 ± 1.28 and 1.61 ± 1.16 months, respectively, and on fNPV was 1.28 ± 1.2 and 1.19 ± 1.24 months, respectively. The time-lag of temperature-precipitation interaction on fPV and fNPV were 1.4 ± 1.22 and 1.2 ± 1.28 months, respectively. Accumulated precipitation had a positive effect on fPV and a negative effect on fNPV, while the impact of accumulated temperature was the opposite. We conclude that there were time-lag and cumulative effects of climate change on fPV and fNPV variability in desert areas. This study helps us better understand the climate-vegetation interactions and provides a scientific basis for desert vegetation management under climate change.

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.

Application of artificial intelligence methods to model the effect of grass curing level on spread rate of fires

Artificial intelligence (AI) enables new approaches to fire behaviour models of operational relevance, including prescribed burns. This is particularly important in modelling of processes that are poorly understood, such as live fuel's effect on fire propagation. The objective of this study was to apply AI algorithms to quantify the effect of the proportion of dead fuels in a senescing grassland, the curing level, on reducing the rate of fire spread relative to the fully cured condition. We applied three different machine learning (ML) models, regression trees, support vector regression (SVR) and Gene expression programming (GEP), two ensemble ML methods, Random Forest and GEP Forest, and non-linear regression analysis to an experimental fire dataset. Results show SVR and GEP as the best ML methods to model the curing level impact on fire spread. No differences in model fit were observed between the best ML methods and non-linear regression analysis.

Modeling fuel moisture dynamics under climate change in Spain’s forests

BackgroundCurrent assessments of the effects of climate change on future wildfire risk are based on either empirical approaches or fire weather indices. No study has yet used process-based models over national scales to understand how and where will increases in climate aridity affect the likelihood of fire activity through changes in the moisture content of live (LFMC) and of dead (DFMC) fuels. Here, we used process-based models to forecast changes in LFMC and DFMC under the 21st century climatic conditions projected from moderate and high greenhouse gas emission scenarios (RCP4.5 and RCP8.5). Predictions were performed across broad productivity gradients in peninsular Spain to understand how productivity mediates the effects of climate change on fuel moisture dynamics.ResultsLFMC and DFMC were predicted to decline under the climatic conditions projected for the coming decades. Increases in the annual frequency of days with fuel moisture content below wildfire occurrence thresholds were predicted to extend fire season lengths by 20 days under RCP4.5 and by 50 days under RCP8.5. The effects of climate change on LFMC and DFMC varied linearly and negatively with productivity (stronger fuel moisture decreases in least productive environments). Although we observed a significant mitigation effect from rising CO2 (via increases in water-use efficiency), it was not enough to offset LFMC declining trends induced by increased temperature and aridity.ConclusionsWe predicted that the warmer and more arid climatic conditions projected for the 21st century will lead to generalized declines in fuel moisture, lengthening fire seasons, and increasing wildfire danger. The use of process-based models to forecast LFMC dynamics allowed the consideration of plant species capabilities to buffer climate change impacts. Significant increases in the fire season length predicted in the most productive environments, currently with large fire return intervals, would pose an increase of fire danger in major Spanish carbon sinks. Finally, the CO2 mitigation effect would not be enough to offset climate change-driven declines in seasonal LFMC levels.

Modification and Comparison of Methods for Predicting the Moisture Content of Dead Fuel on the Surface of Quercus mongolica and Pinus sylvestris var. mongolica under Rainfall Conditions

The surface fine dead fuel moisture content (FFMC) is an important factor in predicting forest fire risk and is influenced by various meteorological factors. Many prediction methods rely on temperature and humidity as factors, resulting in poor model prediction accuracy under rainfall conditions. At the same time, there is an increasing number of methods based on machine learning, but there is still a lack of comparison with traditional models. Therefore, this paper selected the broad-leaved forest tree species Quercus mongolica and the coniferous forest species Pinus sylvestris var. mongolica in Northeast China. Taking surface dead fine fuel as the research object, we used indoor simulated rainfall experiments to explore the impact of rainfall on the surface dead fuel moisture content. The prediction model for surface dead fuel moisture content was modified by the direct estimation method. Finally, using field data, the direct estimation method and convolution neural network (CNN) model were used in the comparison. The rainfall simulation results showed that the indoor fuel moisture content had a logarithmic increasing trend. Rainfall and previous fuel moisture content had a significant impact on the fuel moisture content prediction model, and both the relational model and nonlinear model performed well in predicting fuel moisture content under indoor rainfall conditions. Under field conditions, humidity, temperature and rainfall played a significant role in fuel moisture content. Compared with the unmodified direct estimation method, the modified direct estimation method significantly improved the prediction accuracy and the goodness of fit (R2) increased from 0.85–0.94 to 0.94–0.96. Mean absolute error (MAE) decreased from 9.18–18.33% to 6.86–10.74%, and mean relative error (MRE) decreased from 3.97–17.18% to 3.53–14.48%. The modified direct estimation method has higher prediction accuracy compared with the convolutional neural network model; the R2 value was above 0.90, MAE was below 8.11%, and MRE was below 8.87%. The modified direct estimation method had the best prediction effect among them. This study has a certain reference value for the prediction model of surface fuel moisture content in post-rainfall fire risk assessment and is also of great significance for forest fire management in Northeast China.

Effects of Prescribed Burning on Surface Dead Fuel and Potential Fire Behavior in Pinus yunnanensis in Central Yunnan Province, China

Prescribed burning is a widely used fuel management employed technique to mitigate the risk of forest fires. The Pinus yunnanensis Franch. forest, which is frequently prone to forest fires in southwestern China, serves as a prime example for investigating the effects of prescribed burning on the flammability of surface dead fuel. This research aims to establish a scientific foundation for managing dead fuel in forests, as well as fire prevention and control strategies. Field data was collected from P. yunnanensis forests located in central Yunnan Province in 2021 and 2022. The study implemented a randomized complete block design with two blocks and three treatments: an unburned control (UB), one year after the prescribed burning (PB1a), and three years after the prescribed burning (PB3a). These treatments were evaluated based on three indices: surface dead-bed structure, physicochemical properties, and potential fire behavior parameters. To analyze the stand characteristics of the sample plots, a paired t-test was conducted. The results indicated no significant differences in the stand characteristics of P. yunnanensis following prescribed burning (p > 0.05). Prescribed burning led to a significant decrease in the average surface dead fuel load from 10.24 t/ha to 3.70 t/ha, representing a reduction of 63.87%. Additionally, the average fire−line intensity decreased from 454 kw/m to 190 kw/m, indicating a decrease of 58.15%. Despite prescribed burning, there were no significant changes observed in the physical and chemical properties of dead fuels (p > 0.05). However, the bed structure of dead fuels and fire behavior parameters exhibited a significant reduction compared with the control sample site. The findings of this study provide essential theoretical support for the scientific implementation of prescribed burning programs and the accurate evaluation of ecological and environmental effects post burning.

Prediction model of water content in surface dead fuel based on convolution neural network and meteoro-logical factors regression.

Water content of surface fuels is an important indicator of forest fire risk level and fire behavior, and the prediction model of which has a significant effect on fire risk prediction and management. Based on field meteorological factors of Quercus mongolica and Pinus sylvestris var. mongolica forests and water content data of dead fuels on the ground, we conducted the relative importance ranking of meteorological factors random forest and Pearson correlation analysis, and predicted water content of fuels using deeply learned convolutional neural network and meteorological factors regression. The results showed that water content of Q. mongolica fuels in the wild was significantly higher than that of P. sylvestris var. mongolica. The results of random forest showed that the factors that had significant effect on water content of fuel were humidity, temperature, rainfall, wind speed, and solar radiation, with the importance ranking from the largest to the smallest. Results of correlation analysis showed that temperature, humidity, and rainfall of current day had a significant impact on water content of fuels, and certain correlations were observed between meteorological factors. The prediction R2 of the convolutional neural network model for the surface fuel water content of Q. mongolica and P. sylvestris var. mongolica forest was 0.928 and 0.905, the mean absolute error (MAE) was 6.1% and 8.1%, and the mean relative error (MRE) was 8.9% and 4.2%, respectively. However, the R2, MAE, MRE of meteorological factors regression were 0.495 and 0.525, 30.5% and 39.5%, 52.1% and 32.6%, respectively. The precision of convolution neural network model was significantly higher than that of meteorological factors regression. Our results showed that the deeply learned convolutional neural network could provide some reference for the prediction of fuel water content in the future, and effectively support higher level forest fire management.

Quantitative assessment of fire occurrence Dead Fuel Index threshold and spatio-temporal variation in different grassland types of China-Mongolia border area

Quantitative assessment of fire occurrence Dead Fuel Index threshold and spatio-temporal variation in different grassland types of China-Mongolia border area

Contemporary wildfires are more severe compared to the historical reference period in western US dry conifer forests

Dry conifer forests in the western US historically experienced frequent fire prior to European American colonization. Mean fire return interval ranged from about 5–35 years, with the majority of fires burning at low-to-moderate severity. The arrival of European Americans initiated notable reductions in fire frequency through the cessation of Indigenous burning, livestock grazing, and fire suppression. These activities effectively excluded fire from most dry conifer forests, thereby allowing live and dead fuels to accumulate. There are concerns that this fuel accumulation is causing contemporary fires (since 1985) to burn more severely compared to pre-colonization time periods, thereby increasing potential for enduring fire-catalyzed conversions to non-forest. Here, we used satellite-derived fire severity data to characterize the prevalence of contemporary (1985–2020) stand-replacing fire in dry conifer forests within four broad ecoregions for all fires, non-wilderness fires, wilderness fires, and prescribed fires. Our results indicate, that as a proportion of area burned, contemporary fires experienced 2.9 to 13.6 times more stand-replacing fire (depending on the ecoregion) compared to the pre-colonization period. Non-wilderness areas exhibit somewhat higher prevalence of stand-replacing fire, relative to the historical fire regime, than wilderness areas (where logging is prohibited). The relatively small difference between non-wilderness and wilderness suggests that fuel accumulation resulting from fire exclusion has played a larger role than historical logging activities on the prevalence of contemporary stand-replacing fire. Prescribed fires do not exhibit a higher prevalence of stand-replacing fire compared to the historical fire regime. We conducted a parallel analysis in the Gila Wilderness in New Mexico, USA, is it is arguably the best example of an unrestrained fire regime (many fires are not suppressed) in western US dry conifer forests. The Gila Wilderness proportionally experienced only 1.8 times more stand-replacing fire compared to the historical reference period; in contrast, the southwest ecoregion experienced 5.9 times more stand-replacing fire. Fire will inevitably burn a given forest no matter how much money and personnel we commit to fire prevention and suppression, and because of the high prevalence of stand-replacing fire, the long-term persistence of dry conifer forests is jeopardized. Unless forest managers are provided the social license and enabling conditions to substantially accelerate efforts to restore conditions resistant to stand-replacing fire, we anticipate that the extent of dry conifer forests will continue to erode as fire-catalyzed conversions increasingly unfold across the western US.

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.

Microclimate and species composition shape the contribution of fuel moisture to positive fire-vegetation feedbacks

Ecosystems with positive fire-vegetation feedbacks are highly sensitive to extreme fire events, which are expected to increase in frequency with climate change. Understanding mechanisms maintaining flammability differences between alternative vegetation states is key to elucidate possible ecosystems shifts related to altered fire regimes. Here we assessed whether microclimatic differences between pyrophobic forests and pyrophilic shrublands in northwestern Patagonia can affect the fuel moisture content and partially explain their contrasting flammability. We measured dead and live fuel moisture content and recorded air temperature and relative humidity during one fire season in 10 sites corresponding to either forest or shrubland physiognomy. To disentangle effects from microclimate and species composition on live/dead fuel moisture content we used similar-composition fuels (wood sticks and single-species live leaves) and fuel mixtures (litter and a mixture of live leaves). We found that forests’ microclimate is cooler and moister than shrublands’, harbouring dead fuels with higher moisture content, irrespective of species composition. This suggests that dead fuel moisture content is contributing to make forests less flammable than shrublands. Live fuels tended to show higher moisture content under the forest microclimate, but only when single-species samples were considered. Contrarily, the species mixtures showed higher moisture content in shrublands, the most desiccant communities, suggesting that live fuel moisture content does not contribute to make shrublands more flammable.

Dry season forage assessment across senegalese rangelands using earth observation data

Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.

Fuel trait effects on flammability of native and invasive alien shrubs in coastal fynbos and thicket (Cape Floristic Region).

In June 2017, extreme fires along the southern Cape coast of South Africa burnt native fynbos and thicket vegetation and caused extensive damage to plantations and residential properties. Invasive alien plants (IAPs) occur commonly in the area and were thought to have changed the behaviour of these fires through their modification of fuel properties relative to that of native vegetation. This study experimentally compared various measures of flammability across groups of native and alien invasive shrub species in relation to their fuel traits. Live plant shoots of 30 species (10 species each of native fynbos, native thicket, and IAPs) were sampled to measure live fuel moisture, dry biomass, fuel bed porosity and the proportions of fine-, coarse- and dead fuels. These shoots were burnt experimentally, and flammability measured in terms of maximum temperature (combustibility), completeness of burn (consumability), and time-to-ignition (ignitability). Multiple regression models were used to assess the relationships between flammability responses and fuel traits, while the Kruskal-Wallis H test was used to establish if differences existed in flammability measures and fuel traits among the vegetation groups. Dry biomass significantly enhanced, while live fuel moisture significantly reduced, maximum temperature, whereas the proportion of fine fuels significantly increased completeness of burn. Unlike other similar studies, the proportion of dead fuels and fuel bed porosity were not retained by any of the models to account for variation in flammability. Species of fynbos and IAPs generally exhibited greater flammability in the form of higher completeness of burn and more rapid ignition than species of thicket. Little distinction in flammability and fuel traits could be made between species of fynbos and IAPs, except that fynbos species had a greater proportion of fine fuels. Thicket species had higher proportions of coarse fuels and greater dry biomass (~fuel loading) than species of fynbos and IAPs. Live fuel moisture did not differ among the vegetation groups, contrary to the literature often ascribing variation in flammability to fuel moisture differences. The fuel traits investigated only explained 21–53% of the variation in flammability and large variation was evident among species within vegetation groups suggesting that species-specific and in situ community-level investigations are warranted, particularly in regard fuel moisture and chemical contents.