The objective of this paper is to examine the sensitivity of fuel moisture to changes in temperature and precipitation and explore the implications under a future climate. We use the Canadian Forest Fire Weather Index System components to represent the moisture content of fine surface fuels (Fine Fuel Moisture Code, FFMC), upper forest floor (duff) layers (Duff Moisture Code, DMC) and deep organic soils (Drought Code, DC). We obtained weather data from 12 stations across Canada for the fire season during the 1971–2000 period and with these data we created a set of modified weather streams from the original data by varying the daily temperatures by 0 to +5 °C in increments of 1 °C and the daily precipitation from −40 to 40 % in increments of 10 %. The fuel moistures were calculated for all the temperature and precipitation combinations. When temperature increases we find that for every degree of warming, precipitation has to increase by more than 15 % for FFMC, about 10 % for DMC and about 5 % for DC to compensate for the drying caused by warmer temperatures. Also, we find in terms of the number of days equal to or above an FFMC of 91, a critical value for fire spread, that no increase in precipitation amount alone could compensate for a temperature increase of 1 °C. Results from three General Circulation Models (GCMs) and three emission scenarios suggest that this sensitivity to temperature increases will result in a future with drier fuels and a higher frequency of extreme fire weather days.

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

Byram’s fire intensity (IB,tot; kW m–1) is one the most important and widely accepted metrics for quantifying wildfire behaviour. Calculation of IB,tot requires measurement of fuel consumption, heat of combustion and rate of spread; existing methods for obtaining these measurements are either inexact or at times impossible to obtain in the field. This paper presents and evaluates a series of remote sensing methods for directly deriving radiative fire intensity (IB,rad; kW m–1) using the Fire Radiative Power (FRP) approach applied to thermal infrared imagery of spreading vegetation fires. Comparisons between the remote sensing data and ground-sampled measurements were used to evaluate the various estimates of IB,tot, and to determine the radiative fraction (radF) of a fire’s emitted energy. Results indicate that the IB,tot along an advancing flame front can be reasonably estimated (and agrees with traditional methods of estimation (R2 = 0.34–0.73)) from appropriately collected time-series of remote sensing imagery without the need for ground sampling or ancillary data. We further estimate that the radF of the fire’s emitted energy varies between 0.15 and 0.20 depending on the method of calculation, which is similar to previous estimates.

We develop a simulation model for prediction of forest canopy interception of upwelling fire radiated energy from sub-canopy smouldering vegetation fires. We apply this model spatially across the North American boreal forest in order to map minimum detectable sub-canopy smouldering fire size for three satellite fire detection systems (sensor and algorithm), broadly representative of the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea and Land Surface Temperature Radiometer (SLSTR) and Visible Infrared Imaging Radiometer Suite (VIIRS). We evaluate our results according to fire management requirements for “early detection” of wildland fires. In comparison to the historic fire archive (Canadian National Fire Database, 1980–2017), satellite data with a 1000 m pixel size used with an algorithm having a minimum MWIR channel BT elevation threshold of 5 and 3 K above background (e.g., MODIS or SLSTR) proves incapable of providing a sub-0.2 ha smouldering fire detection 70% and 45% of the time respectively, even assuming that the sensor overpassed the relevant location within the correct time window. By contrast, reducing the pixel area by an order of magnitude (e.g., 375 m pixels of VIIRS) and using a 3.5 K active fire detection threshold offers the potential for successfully detecting all fires when they are still below 0.2 ha. Our results represent a ‘theoretical best performance’ of remote sensing systems to detect sub-canopy smoldering fires early in their lifetime.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole‐system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre‐fire vegetation inventories, (2) active‐fire monitoring, (3) post‐fire assessment, and (4) multi‐scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co‐development of information products using cloud computing that benefits from open‐access Earth observation and other geospatial data layers.

The temperature and emissivity of forest fire flames play a key role in understanding fire behaviour, modelling fire spread and calculating fire parameters by means of active fire thermal remote sensing. Essential to many of these is the often-made assumption that vegetation fire flames behave as grey bodies in the infrared (IR). Although the emissivity of flames and its relationship to flame depth has been measured experimentally using thermal imagers working in the long-wave IR (LWIR, 8–12 µm), no published study has yet demonstrated relationships in the important mid-wave IR (MWIR, 3–5 µm) spectral region, nor conclusively demonstrated that assumptions about grey body behaviour across these two important IR atmospheric windows fit well with reality. Our study explores these issues using measurements of boreal forest fuels burned with flame depths ranging from 0.2 to 4.2 m. Observations of two stable black body sources made through the differing flame depths were used to explore flame spectral emissivities and their relationship to flame depth. We found essentially the same relationship between flame emissivity and flame depth for both spectral regions, (extinction coefficient K = 0.7 m–1), confirming that the grey body assumption for forest fire flames in the MWIR and LWIR atmospheric windows appears valid for the fire conditions encountered here.