Relative suitability of indices derived from Landsat ETM+ and SPOT 5 for detecting fire severity in sagebrush steppe

Norton, Jill; Glenn, Nancy F.; Germino, Matt; Weber, Keith T.; Seefeldt, Steven S.

doi:10.1016/j.jag.2009.06.005

Cited by 34 publications

(23 citation statements)

References 39 publications

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“…While dNBR provides a measure of absolute change, RdNBR is adjusted to account for pre-fire conditions at each pixel. As such, RdNBR provides a more consistent measure of burn severity than dNBR when evaluating severity across broad regions and diverse vegetation (Miller et al 2009a, Norton et al 2009), including sites with low preburn biomass. As an indirect measure of biomass loss, RdNBR can be used to accurately identify stand-replacing fire in forests and woodlands (Miller and Thode 2007).…”

Section: Topography and Climate Are Likely Important Drivers Of Burn mentioning

confidence: 99%

“…More such studies are needed for longer-term assessment of fire effects. Keeley et al (2008) found that RdNBR accuracy is limited in shrublands where resprouting occurs, but RdNBR has been tested successfully in forests, woodlands, and sagebrush shrublands in California Thode 2007, Miller et al 2009a) and sagebrush steppe in Idaho (Norton et al 2009). Fourth, it is possible that the consistently higher performance of topographic variables in the Random Forest models is an artifact of the difference in spatial scale.…”

Section: Scope and Limitationsmentioning

confidence: 99%

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Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006

et al. 2011

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Fire is a keystone process in many ecosystems of western North America. Severe fires kill and consume large amounts of above‐ and belowground biomass and affect soils, resulting in long‐lasting consequences for vegetation, aquatic ecosystem productivity and diversity, and other ecosystem properties. We analyzed the occurrence of, and trends in, satellite‐derived burn severity across six ecoregions in the Southwest and Northwest regions of the United States from 1984 to 2006 using data from the Monitoring Trends in Burn Severity project. Using 1,024 fires from the Northwest (4,311,871 ha) and 497 fires from the Southwest (1,434,670 ha), we examined the relative influence of fine‐scale topography and coarse‐scale weather and climate on burn severity (the degree of change from before the fire to one year after) using the Random Forest machine learning algorithm. Together, topography, climate, and weather explained severe fire occurrence with classification accuracies ranging from 68% to 84%. Topographic variables were relatively more important predictors of severe fire occurrence than either climate or weather variables. Predictability of severe fire was consistently lower during years with widespread fires, suggesting that local control exerted by topography may be overwhelmed by regional climatic controls when fires burn in dry conditions. Annually, area burned severely was strongly correlated with area burned in all ecoregions (Pearson's correlation 0.86–0.97; p < 0.001), while the proportion of area burned severely was significantly correlated with area burned only in two ecoregions (p ≤ 0.037). During our short time series, only ecoregions in the Southwest showed evidence of a significant increase (p ≤ 0.036) in annual area burned and area burned severely, and annual proportion burned severely increased in just one of the three Southwest ecoregions. We suggest that predictive mapping of the potential for severe fire is possible, and will be improved with climate data at the scale of the topographic and Landsat‐derived burn severity data. Although severity is a value‐laden term implying negative ecosystem effects, we stress that severity can be objectively measured and recognize that high severity fire is an important ecological process within the historical range of variability in some ecosystems.

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Section: Topography and Climate Are Likely Important Drivers Of Burn mentioning

confidence: 99%

Section: Scope and Limitationsmentioning

confidence: 99%

Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006

et al. 2011

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“…25,26 Despite these challenges, an optical remote-sensing signal capable of characterization exists for semiarid shrublands, and monitoring is feasible. [26][27][28][29][30][31][32] Studies within the sagebrush ecosystem have demonstrated the ability for remote sensing to characterize more abrupt types of disturbance from fire 33,34 and human development 35,36 and gradual types of disturbance such as grazing 37 and climate change. 23 A comprehensive understanding of the relationship between remote-sensing change and gradual changes in sagebrush ecosystem components is still lacking; only a few studies have begun to explore that relationship.…”

Section: Introductionmentioning

confidence: 99%

Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields

Homer

Meyer

Aldridge

et al. 2013

J. Appl. Remote Sens

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Abstract. Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.

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“…This table was tested mainly in forested environments, and there are known differences in applying NBR to dryland ecosystems (Norton et al, 2009). Regardless, we used the below information for a relative measure of NBR between our study regions.…”

Section: Correlation Between the Fusion Product And Modis Asphalt Pixmentioning

confidence: 99%

Utilizing Satellite Fusion Methods to Assess Vegetation Phenology in a Semi-Arid Ecosystem

Gallagher¹

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leading to desertification, habitat loss, and the spread of invasive species. Modern public satellite imagery is unable to detect fine temporal and spatial changes that occur in drylands. These ecosystems can have rapid phenological changes, and the heterogeneity of the ground cover is unable to be identified at course pixel sizes (e.g. 250 m). We develop a system that uses data from multiple satellites to model finer data to detect phenology in a semi-arid ecosystem, a dryland ecosystem type.

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Relative suitability of indices derived from Landsat ETM+ and SPOT 5 for detecting fire severity in sagebrush steppe

Cited by 34 publications

References 39 publications

Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006

Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006

Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields

Utilizing Satellite Fusion Methods to Assess Vegetation Phenology in a Semi-Arid Ecosystem

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