Automated extraction of fire line parameters from multispectral infrared images

Ononye, Ambrose E.; Vodacek, Anthony; Saber, Eli

doi:10.1016/j.rse.2006.09.029

Cited by 50 publications

(41 citation statements)

References 31 publications

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“…The RIT WASP sensor originally was developed for wildfire mapping research and has been a workhorse system for various phenomenology studies and for investigating the application of remote sensing to emergency response (Li et al, 2005 and2007;Ononye et al, 2007). The WASP sensor is composed of three Indigo Phoenix infrared imagers in the SWIR (1.0 to 1.7m), MWIR (3.0 to 5.0m), and LWIR (8.0 to 9.2m) spectral regions and a 11 mega-pixel Geospatial Systems, Inc. high-resolution color camera (McKeown et al, 2004;Arsenovic et al, 2009).…”

Section: Sensor System Overviewmentioning

confidence: 99%

Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination

Aardt¹,

McKeown²,

Faulring³

et al. 2011

photogramm eng remote sensing

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Section: Sensor System Overviewmentioning

confidence: 99%

Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination

Aardt¹,

McKeown²,

Faulring³

et al. 2011

photogramm eng remote sensing

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“…While real-time data are routinely available for weather forecasting systems, in a wildland fire the data collection is less straightforward. Available data include multi-spectrum infrared airborne photographs, processed to recover the fire region and to some extent the temperature, and radioed data streams from hardened sensors put in the fire path [54,73,74]. For overviews of the whole project including computer science aspects, data collection, and visualization, see [64,63,62] and [25].…”

Section: Introductionmentioning

confidence: 99%

A wildland fire model with data assimilation

Mandel

Bennethum

Beezley

et al. 2008

Mathematics and Computers in Simulation

132

101

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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.

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“…14,15 In addition to the imagery itself, the geographical delimitation of the perimeter will allow to identify two critical informations:…”

Section: Vd1 Day/night Fire Front Evolutionmentioning

confidence: 99%

An Architecture for the Seamless Integration of UAS Remote Sensing Missions

Pastor

Barrado

Chic

et al. 2009

AIAA Infotech@Aerospace Conference

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Unmanned Aerial Systems (UAS) are slowing becoming efficient platforms that can be applied in scientific/commercial remote sensing applications. UAS may offer interesting benefits in terms of cost, flexibility, endurance, etc. On the other side, the complexity of developing a full UAS-system is currently limiting its practical application. Currently, only large organizations like NASA or NOAA have enough budget and infrastructure to develop such applications.Nowadays, UAS technology offers feasible technical solutions for airframes, flight control, communications, and a wide set of multi-spectrum sensors. However, the generalized development of remote sensing applications are still limited by the absence of systems that support the development of the actual UAS sensing mission.This paper introduces a flexible and reusable hardware/software architecture designed to facilitate the development of UAS-based remote sensing applications. This flexibility is organized into an user-parameterizable UAS Service Abstraction Layer (USAL). The USAL defines a collection of standard services are their interrelations as a basic starting point for further development by users. Functionalities like enhanced flight-plans, a mission control engine, data storage, communications management, etc. are offered. Additional services can be included according to requirements but all existing services and inter-service communication infrastructure can be exploited and tailored to specific needs. The overall USAL architecture is demonstrated by means of a wild land fire remote sensing application currently being developed to support fire fighters in the Mediterranean area.

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Automated extraction of fire line parameters from multispectral infrared images

Cited by 50 publications

References 31 publications

Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination

Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination

A wildland fire model with data assimilation

An Architecture for the Seamless Integration of UAS Remote Sensing Missions

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