Satellite Remote Sensing Contributions to Wildland Fire Science and Management (original) (raw)
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Airborne Remote Sensing of Wildland Fires
2014
In wildland fire management, reliable fire intelligence is needed to direct suppression resources, maintain firefighter safety, predict fire behavior, mitigate fire effects in the environment, and justify and evaluate the effectiveness of fuel management. Fire intelligence needs to be synoptic, quantitative, consistent, and timely. Airborne remote sensing with specialized infrared radiometers is now providing an unprecedented level of information on fire behavior and effects. The temperature, radiant intensity, carbon and sensible heat fluxes, and fuel consumption associated with the flaming front of a wildland fire have been estimated by remotely measuring its radiance at short- and mid-wave infrared wavelengths. Measurements of upwelling long-wave or thermal-infrared radiation provide estimates primarily of ground-surface temperatures, even beneath flaming fronts, that reflect a local time course of energy release and fuel consumption. Characteristics of flames and hot ground can ...
Monitoring fire effects at landscape level is viable from remote sensing platforms providing repeatable and consistent measurements. Previous studies have estimated fire severity using optical and synthetic aperture radar (SAR) sensors, but to our knowledge, none have compared their effectiveness. Our study carried out such a comparison by using change detection indices computed from pre- and post-fire L-band space borne SAR datasets to estimate fire severity for seven fires located on three continents. Such indices were related to field estimated fire severity through empirical models, and their estimation accuracy was compared. Empirical models based on the joint use of optical and radar indices were also evaluated. The results showed that, optical based indices provided more accurate fire severity estimates. On average, overall accuracy increased from 61% (SAR) to 76% (optical) for high biomass forests. For low biomass forests (i.e., above ground biomass levels below the L-band saturation point), radar indices provided comparable results, with overall accuracy being only slightly lower when compared to optical indices (69% vs. 73%). The joint use of optical and radar indices decreased the estimation error and reduced misclassification of unburnt forest by 9% for eucalypt and 3% for coniferous forests. Additional keywords: Landsat, ALOS PALSAR, L-band, radar, accuracy assessment, radar-optical synergy, CBI