Anthony Vodacek

A wildland fire model with data assimilation

A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.

The Impact of Agricultural Best Management Practices on Downstream Systems: Soil Loss and Nutrient Chemistry and Flux to Conesus Lake, New York, USA

ABSTRACT Six small, predominantly agricultural (>70%) watersheds in the Conesus Lake catchment of New York State, USA, were selected to test the impact of Best Management Practices (BMPs) on mitigation of nonpoint nutrient sources and soil loss from farms to downstream aquatic systems. Over a 5-year period, intensive stream water monitoring and analysis of covariance provided estimates of marginal means of concentration and loading for each year weighted by covariate discharge. Significant reductions in total phosphorus, soluble reactive phosphorus, nitrate, total Kjeldahl nitrogen, and total suspended solids concentration and flux occurred by the second year and third year of implementation. At Graywood Gully, where Whole Farm Planning was practiced and a myriad of structural and cultural BMPs were introduced, we observed the greatest percent reduction (average = 55.8%) and the largest number of significant reductions in analytes (4 out of 5). Both structural and cultural BMPs were observed to have profound effects on nutrient and soil losses. Where fields were left fallow or planted in a vegetative type crop, reductions, especially in nitrate, were observed. Where structural implementation occurred, reductions in total fractions were particularly evident. Where both were applied, major reductions in nutrients and soil occurred. After 5 years of management, nonevent and event concentrations of total suspended solids in streams draining agricultural watersheds were not significantly different from those in a relatively “pristine/reference” watershed. This was not the case for nutrients.

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.

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 Dynamic Data Driven Wildland Fire Model

We present an overview of an ongoing project to build DDDAS to use all available data for a short term wildfire prediction. The project involves new data assimilation methods to inject data into a running simulation, a physics based model coupled with weather prediction, on-site data acquisition using sensors that can survive a passing fire, and on-line visualization using Google Earth.

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.

Demonstrating the validity of a wildfire DDDAS

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of weather and wildfire behavior from real-time weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code and apply an ensemble Kalman filter in time-space with a highly parallel implementation. In this paper, we discuss how we will demonstrate that our system works using a DDDAS testbed approach and data collected from an earlier fire.

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.

DDDAS approaches to wildland fire modeling and contaminant tracking

We report on two ongoing efforts to build dynamic data driven application systems (DDDAS) for (1) short-range forecasting of weather and wildfire behavior from real time weather data, images, and sensor streams, and (2) contaminant identification and tracking in water bodies. Both systems change their forecasts as new data is received. We use one long term running simulation that self corrects using out of order, imperfect sensor data. The DDDAS versions replace codes that were previously run using data only in initial conditions. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process