Resources

With more frequent and intense fires expected under future climate conditions, it is important to understand the mechanisms that control flammability in Australian forests. We followed a systematic review approach to determine which physical traits make eucalypts leaves more or less flammable.

Fuel Moisture Content (FMC) is one of the primary drivers affecting fuel flammability that lead to fires. Satellite observations well-grounded with field data over the highly climatologically and ecologically diverse Australian region served to estimate FMC and flammability for the first time at a continental-scale.

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques.

Four leading scientists and engineers who work with smart sensors will headline a stimulating panel discussion at the next NSW Smart Sensing Network Women in Sensing event at the Australian National University (ANU).

Live Fuel Moisture Content (LFMC) is a critical variable in improving fire risk estimations, which varies widely among different vegetation types and ecosystems. However, many regions do not have an operational LFMC mapping system, as such tools are difficult to develop. This raises the question of transferability, namely the potential of using LFMC estimates generated by existing models in regions they were not calibrated for.

Live fuel moisture content (LFMC) is a crucial variable affecting fire ignition and spread. Satellite remote sensing has been effective in estimating LFMC over large spatial scales, but continuous sub-daily (e.g., every 10 mins to hourly during daylight) LFMC monitoring from space is yet to be accomplished.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density.

Scientists are trialling cost-effective sensors which will empower bushfire managers to efficiently monitor the moisture content of bushfire fuel in real time, therefore helping them to better identify prescribed burn opportunities and monitor fire risk.

New and diverse strategies are needed to achieve safe coexistence in an age of megafires. A redefinition of the wildfire management paradigm is central to the shift, placing greater emphasis on the adoption of high-tech solutions.

Australia does not have a central system for gathering and storing essential information about bush fires. State and territory governments, and even agencies within states, have different approaches. This worked fine when fires were smaller. But those in the 2019–20 season crossed multiple state borders.

The cutting-edge technology is a faster, cheaper and moreaccurate way to raise the alarm on lightning strikes or fl ashfl oods, experts say

Early detection of wildfires is critical, particularly on extreme fire risk days when fires can have catastrophic impacts. The earlier a fire is detected, the smaller it will be when fire crews arrive, and the greater the likelihood it can be successfully contained and suppressed.

Litter moisture content plays a critical role in fire danger rating systems and forest ecosystems. However, few models consider soil water content when predicting litter moisture, and to our knowledge, no model includes capillary fluxes.

Live fuel moisture content (FMC) is a key factor required to evaluate fire risk and its operative and accurate estimation is essential for allocating pre-fire resources as a part of fire prevention. This paper presents an operative and accurate procedure to estimate FMC though MODIS (moderate resolution imaging spectrometer) data and simulation models.

Wildfires are complex phenomena and can occur under a range of conditions, making it difficult to determine howdifferent drivers affect wildfire occurrence. For example, dense vegetation, dry litter, dry vegetation, high winds,and warm air all contribute to high fire likelihood.

This paper presents a method to estimate fuel moisture content (FMC) of Mediterranean
vegetation species from satellite images in the context of fire risk assessment.

A review of physical and chemical properties of fuels relevant for fire ignition and propagation is presented, along with different methods to estimate those properties, with special emphasis on satellite imagery. The discussion is more extended on estimating fuel moisture trends and fuel geometrical properties.

Live Fuel Moisture Content (LFMC) is a key determinant of landscape ignition potential, but quantitative estimates of its effects on wildfire are lacking. We present a causal interference framework to isolate the effect of LFMC from other drivers like fuel type, fuel amount, and meteorology.

SIX droid-sized sensors strategically located across the ACT are mapping the dangerous dry lightning strikes responsible for some of the country's biggest bushfires.

Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength.

Australia has experienced catastrophic bushfire conditions that exceed known firefighting technologies, leading to significant ecological, economic, health, and social costs. We need a novel approach that harnesses modern technologies and that is successful in reducing the risk of large-scale bushfires under extreme conditions.

One of the primary variables affecting ignition and spread of wildfire is fuel moisture content (FMC). Live
FMC (LFMC) is responsive to long term climate and plant adaptations to drought, requiring remote sensing
for monitoring of spatial and temporal variations in LFMC

Many fire management agencies aim to detect and suppress all ignitions within their
jurisdiction and may benefit from understanding the causes of year-to-year variation.

Algorithms for lightning strike investigation and ignition detection with an uncrewed aerial
vehicle (UAV) swarm are proposed.

Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions.

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate.

Live fuel moisture content is an important precondition for wildfire activity, yet it remains difficult to predict due to the dynamic interplay between atmospheric and hydrological conditions that determine a plant's access to, and loss of, water.

Leaf moisture content is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread.

The Bushfire Research Centre of Excellence is developing a novel, integrated, hi-tech solution to detect bushfires early with an approach that combines different remote sensing platforms including satellites, long endurance uncrewed vehicles (drones), cameras on fire towers and ground sensors in the Australian Capital Territory.

ANU researchers are using algorithms, drones and satellites to detect bushfires before they become natural disasters.

In the wake of storms of the near future, swarms of drones could replace helicopters and planes, providing emergency crews with more rapid and accurate data on the coming threats of lightning-sparked bushfires or flash floods heading for homes.

An early bushfire season that saw blazes burning in three states last week has forewarned the long, hot and dangerous summer fire season. We have ahead.

After three years of above-average rainfall in Australia, La Nina is thought to be waning, amid fears of a return to El Nino conditions.