Robert Kremens

Remote optical detection of biomass burning using a potassium emission signature

A remotely detectable signature for biomass burning that is specific to flaming combustion is found in the strong emission lines of potassium (K) at 766.5 nm and 769.9 nm. Ground level spectra of a test fire illustrate the high contrast signal provided by K emission. Image data collected at high altitude using the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and analysed for K emission vividly displays the fire fronts of a 1995 fire in Brazil. Sensors for K emission can use silicon detector technology for advantages in high sensitivity, low cost, wide area coverage and fine spatial resolution.

Radiant flux density, energy density and fuel consumption in mixed-oak forest surface fires

Closing the wildland fire heat budget involves characterising the heat source and energy dissipation across the range of variability in fuels and fire behaviour. Meeting this challenge will lay the foundation for predicting direct ecological effects of fires and fire–atmosphere coupling. In this paper, we focus on the relationships between the fire radiation field, as measured from the zenith, fuel consumption and the behaviour of spreading flame fronts. Experiments were conducted in 8 × 8-m outdoor plots using preconditioned wildland fuels characteristic of mixed-oak forests of the eastern United States. Using dual-band radiometers with a field of view of ~18.5 m2 at a height of 4.2 m, we found a near-linear increase in fire radiative energy density over a range of fuel consumption between 0.15 and 3.25 kg m–2. Using an integrated heat budget, we estimate that the fraction of total theoretical combustion energy density radiated from the plot averaged 0.17, the fraction of latent energy transported in the plume averaged 0.08, and the fraction accounted for by the combination of fire convective energy transport and soil heating averaged 0.72. Future work will require, at minimum, instantaneous and time-integrated estimates of energy transported by radiation, convection and soil heating across a range of fuels.

A Note on Dynamic Data Driven Wildfire Modeling

A proposed system for real-time modeling of wildfires is described. The system involves numerical weather and fire prediction, automated data acquisition from Internet sources, and input from aerial photographs and sensors. The system will be controlled by a non-Gaussian ensemble filter capable of assimilating out-of-order data. The computational model will run on remote supercomputers, with visualization on PDAs in the field connected to the Internet via a satellite.

A hybrid contextual approach to wildland fire detection using multispectral imagery

We propose a hybrid contextual fire detection algorithm for airborne and satellite thermal images. The proposed algorithm essentially treats fire pixels as anomalies in images and can be considered a special case of the more general clutter or background suppression problem. It utilizes the local background around a potential fire pixel and discriminates fire pixels based on the squared Mahalanobis distance in multispectral feature space. It also employs the normalized thermal index to identify background fire pixels that should be excluded from the calculation of the statistical properties of the local background. The use of the squared Mahalanobis distance naturally incorporates the covariance of the multispectral image into the decision and requires the setting of a single detection threshold. By contrast, previous contextual algorithms only incorporate the statistical properties of individual bands and require the manual setting of multiple thresholds. Compared with the latest Moderate Resolution Imaging Spectroradiometer fire product (version 4), our algorithm improves user accuracy and producer accuracy by 1.5% and 2.6% on average, respectively, and up to 28% for some images. In addition, the novel use of the squared Mahalanobis distance allows us to create fire probability images that are useful for fire propagation modeling. As an example, we demonstrate this use for the airborne data.

Towards a dynamic data driven application system for wildfire simulation

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed.

Autonomous field-deployable wildland fire sensors

An Autonomous Fire Detector (AFD) is a miniature electronic package combining position location capability [using the Global Positioning System (GPS)], communications (packet or voice-synthesized radio), and fire detection capability (thermal, gas, smoke detector) into an inexpensive, deployable package. The AFD can report fire-related parameters, like temperature, carbon monoxide concentration, or smoke levels via a radio link to firefighters located on the ground. These systems are designed to be inserted into the fire by spotter planes at a fire site or positioned by firefighters already on the ground. AFDs can also be used as early warning devices near critical assets in the urban–wildland interface. AFDs can now be made with commercial off-the-shelf components. Using modern micro-electronics, an AFD can operate for the duration of even the longest fire (weeks) using a simple dry battery pack, and can be designed to have a transmitting range of up to several kilometers with current low power radio communication technology. A receiver to capture the data stream from the AFD can be made as light, inexpensive and portable as the AFD itself. Inexpensive portable repeaters can be used to extend the range of the AFD and to coordinate many probes into an autonomous fire monitoring network.

Measurements relating fire radiative energy density and surface fuel consumption – RxCADRE 2011 and 2012

Small-scale experiments have demonstrated that fire radiative energy is linearly related to fuel combusted but such a relationship has not been shown at the landscape level of prescribed fires. This paper presents field and remotely sensed measures of pre-fire fuel loads, consumption, fire radiative energy density (FRED) and fire radiative power flux density (FRFD), from which FRED is integrated, across forested and non-forested RxCADRE 2011 and 2012 burn blocks. Airborne longwave infrared (LWIR) image time series were calibrated to FRFD and integrated to provide FRED. Surface fuel loads measured in clip sample plots were predicted across burn blocks from airborne lidar-derived metrics. Maps of surface fuels and FRED were corrected for occlusion of the radiometric signal by the overstorey canopy in the forested blocks, and FRED maps were further corrected for temporal and spatial undersampling of FRFD. Fuel consumption predicted from FRED derived from both airborne LWIR imagery and various ground validation sensors approached a linear relationship with observed fuel consumption, which matched our expectation. These field, airborne lidar and LWIR image datasets, both before and after calibrations and corrections have been applied, will be made publicly available from a permanent archive for further analysis and to facilitate fire modelling.

Measuring Radiant Emissions from Entire Prescribed Fires with Ground, Airborne and Satellite Sensors RxCADRE 2012

Characterising radiation from wildland fires is an important focus of fire science because radiation relates directly to the combustion process and can be measured across a wide range of spatial extents and resolutions. As part of a more comprehensive set of measurements collected during the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research (RxCADRE) field campaign, we used ground, airborne and spaceborne sensors to measure fire radiative power (FRP) from whole fires, applying different methods to small (2 ha) and large (>100 ha) burn blocks. For small blocks (n = 6), FRP estimated from an obliquely oriented long-wave infrared (LWIR) camera mounted on a boom lift were compared with FRP derived from combined data from tower-mounted radiometers and remotely piloted aircraft systems (RPAS). For large burn blocks (n = 3), satellite FRP measurements from the Moderate-resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors were compared with near-coincident FRP measurements derived from a LWIR imaging system aboard a piloted aircraft. We describe measurements and consider their strengths and weaknesses. Until quantitative sensors exist for small RPAS, their use in fire research will remain limited. For oblique, airborne and satellite sensors, further FRP measurement development is needed along with greater replication of coincident measurements, which we show to be feasible.

OMEGA Upgrade laser for direct-drive target experiments

Validation of the direct-drive approach to inertial confinement fusion requires the development of a 351-nm wavelength, 30-kJ, 50-TW laser system with flexible pulse shaping and irradiation uniformity approaching 1%. An upgrade of the existing OMEGA direct-drive facility at Rochester is planned to meet these objectives. In this article, we review the design rationale and specifications of the OMEGA Upgrade laser with particular emphasis on techniques planned to achieve the required degree of beam smoothing, temporal pulse shape, and beam-to-beam power balance.

Measurement of fuel ion temperatures in ICF implosions using current‐mode neutron time‐of‐flight detectors

In order to obtain useful fuel ion temperature measurements in inertial confinement fusion experiments using neutron time‐of‐flight techniques, detailed analysis of the data must be performed. We will present a data analysis technique to measure fuel ion temperature accurately, and a Monte Carlo model which uses known detector and target physics to simulate the current pulse produced by the detector. These simulations are compared with data taken for DT fuel implosions on the OMEGA laser system. Based on this model we will determine the neutron yields necessary to accurately assess the fuel ion temperature.