Comparing Fire Severity Models from Post-Fire and Pre/Post-Fire Differenced Imagery

Weber, Keith T.; Seefeldt, Steven S.; Moffet, Corey A.; Norton, Jill

doi:10.2747/1548-1603.45.4.392

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…Wrangle's key observational inputs included Landsat 8, MODIS, and AMSR-E. These data are used to develop fire intensity, fire severity, NDVI, fPAR, ET, and many other products of specific use to the wildfire community [3,4]. In addition to observational data, Wrangler automatically gathers several dozen other data products, including information on the fire site's vegetation cover and type, agroclimatic zone, environmental site potential, fire regime condition class, geology, hydrology, soils, historic fires, topography, and evapotranspiration.…”

Section: Wrangler Data Sourcesmentioning

confidence: 99%

NASA Wrangler: Automated cloud-based data assembly in the recover wildfire decision support system

Schnase

Carroll

Gill

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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NASA Wrangler is a loosely-coupled, event driven, highly parallel data aggregation service designed to take advantage of the elastic resource capabilities of cloud computing. Wrangler automatically collects Earth observational data, climate model outputs, derived remote sensing data products, and historic biophysical data for pre-, active-, and post-wildfire decision making. It is a core service of the RECOVER decision support system, which is providing rapid-response GIS analytic capabilities to state and local government agencies. Wrangler reduces to minutes the time needed to assemble and deliver crucial wildfire-related data.

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Section: Wrangler Data Sourcesmentioning

confidence: 99%

NASA Wrangler: Automated cloud-based data assembly in the recover wildfire decision support system

Schnase

Carroll

Gill

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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“…For example, incorporating Classification Tree Analysis (CTA) techniques and postfire field survey data, NBR along with various other band ratios was used to assess the severity of fire occurring in rangelands of Idaho (Weber et al 2008b). Furthermore, these reflectance indicators derived from remotely sensed data were widely used for fire studies in savannahs and semiarid environments (Smith et al 2005;Fisher et al 2006;Weber et al 2008a).…”

Section: Introductionmentioning

confidence: 99%

Assessing the susceptibility of semiarid rangelands to wildfires using Terra MODIS and Landsat Thematic Mapper data

Chen

Weber

Anderson

et al. 2011

Int. J. Wildland Fire

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In order to monitor wildfires at broad spatial scales and with frequent periodicity, satellite remote sensing techniques have been used in many studies. Rangeland susceptibility to wildfires closely relates to accumulated fuel load. The normalised difference vegetation index (NDVI) and fraction of photosynthetically active radiation (fPAR) are key variables used by many ecological models to estimate biomass and vegetation productivity. Subsequently, both NDVI and fPAR data have become an indirect means of deriving fuel load information. For these reasons, NDVI and fPAR, derived from the Moderate Resolution Imaging Spectroradiometer on-board Terra and Landsat Thematic Mapper imagery, were used to represent prefire vegetation changes in fuel load preceding the Millennial and Crystal Fires of 2000 and 2006 in the rangelands of south-east Idaho respectively. NDVI and fPAR change maps were calculated between active growth and late-summer senescence periods and compared with precipitation, temperature, forage biomass and percentage ground cover data. The results indicate that NDVI and fPAR value changes 2 years before the fire were greater than those 1 year before fire as an abundance of grasses existed 2 years before each wildfire based on field forage biomass sampling. NDVI and fPAR have direct implication for the assessment of prefire vegetation change. Therefore, rangeland susceptibility to wildfire may be estimated using NDVI and fPAR change analysis. Furthermore, fPAR change data may be included as an input source for early fire warning models, and may increase the accuracy and efficiency of fire and fuel load management in semiarid rangelands.

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Utility of satellite-derived burn severity to study short- and long-term effects of wildfire on streamflow at the basin scale

Moreno

Gourley

Pham

et al. 2020

Journal of Hydrology

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Comparing Fire Severity Models from Post-Fire and Pre/Post-Fire Differenced Imagery

Cited by 10 publications

References 30 publications

NASA Wrangler: Automated cloud-based data assembly in the recover wildfire decision support system

NASA Wrangler: Automated cloud-based data assembly in the recover wildfire decision support system

Assessing the susceptibility of semiarid rangelands to wildfires using Terra MODIS and Landsat Thematic Mapper data

Utility of satellite-derived burn severity to study short- and long-term effects of wildfire on streamflow at the basin scale

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