The Relationship of Field Burn Severity Measures To Satellite-derived Burned Area Reflectance Classification (Barc) Maps

Preliminary results are presented from ongoing research on spatial variability of fire effects on soils and vegetation from the Black Mountain Two and Cooney Ridge wildfires, which burned in western Montana during the 2003 fire season. Extensive field fractional cover data were sampled to assess the efficacy of quantitative satellite image-derived indicators of burn severity. The objective of this study was to compare the field burn severity measures to the digital numbers used to produce Burned Area Reflectance Classification (BARC) maps. Canopy density was the field variable most highly correlated to BARC data derived from either SPOT Multispectral (XS) or Landsat Thematic Mapper (TM) imagery. Among the other field variables, old litter depth and duff depth correlated better with the satellite data than did old litter cover. Ash cover correlated most poorly. Old litter cover correlated better with the satellite data than did exposed mineral soil or rock cover, but combining the mineral soil and rock cover fractions into a single inorganic cover fraction improved the correlation to a comparable level. Most field variables, with the notable exception of ash, tended to vary more at low and moderate severity sites than at high severity sites. Semivariograms of the field variables revealed spatial autocorrelation across the spatial scales sampled (2 – 130 m), which the 20 m or 30 m resolution satellite imagery only weakly detected. Future analyses will be broadened to quantify burn severity characteristics in other forest types and to consider erosion processes, such as soil water infiltration following fire

The Relationship of Field Burn Severity Measures To Satellite-derived Burned Area Reflectance Classification (Barc) Maps

Preliminary results are presented from ongoing research on spatial variability of fire effects on soils and vegetation from the Black Mountain Two and Cooney Ridge wildfires, which burned in western Montana during the 2003 fire season. Extensive field fractional cover data were sampled to assess the efficacy of quantitative satellite image-derived indicators of burn severity. The objective of this study was to compare the field burn severity measures to the digital numbers used to produce Burned Area Reflectance Classification (BARC) maps. Canopy density was the field variable most highly correlated to BARC data derived from either SPOT Multispectral (XS) or Landsat Thematic Mapper (TM) imagery. Among the other field variables, old litter depth and duff depth correlated better with the satellite data than did old litter cover. Ash cover correlated most poorly. Old litter cover correlated better with the satellite data than did exposed mineral soil or rock cover, but combining the mineral soil and rock cover fractions into a single inorganic cover fraction improved the correlation to a comparable level. Most field variables, with the notable exception of ash, tended to vary more at low and moderate severity sites than at high severity sites. Semivariograms of the field variables revealed spatial autocorrelation across the spatial scales sampled (2 – 130 m), which the 20 m or 30 m resolution satellite imagery only weakly detected. Future analyses will be broadened to quantify burn severity characteristics in other forest types and to consider erosion processes, such as soil water infiltration following fire

Utility of Remotely Sensed Imagery for Assessing the Impact of Salvage Logging after Forest Fires

Remotely sensed imagery provides a useful tool for land managers to assess the extent and severity of post-wildfire salvage logging disturbance. This investigation uses high resolution QuickBird and National Agricultural Imagery Program (NAIP) imagery to map soil exposure after ground-based salvage operations. Three wildfires with varying post-fire salvage activities and variable ground truth data were used to evaluate the utility of remotely sensed imagery for disturbance classification. The Red Eagle Fire in northwestern Montana had intensive ground truthing with GPS-equipment logging equipment to map their travel paths, the Tripod Fire in north central Washington had ground truthed disturbance transects, and the School Fire in southeastern Washington had no salvage-specific ground truthing but pre-and post-salvage images were available. Spectral mixture analysis (SMA) and principle component analysis (PCA) were used to evaluate the imagery. Our results showed that soil exposure (disturbance) was measureable when pre-and post-salvage QuickBird images were compared at one site. At two of the sites, only post-salvage imagery was available, and the soil exposure correlated well to salvage logging equipment disturbance at one site. When ground disturbance transects were compared to NAIP imagery two years after the salvage operation, it was difficult to identify disturbance due to vegetation regrowth. These results indicate that soil exposure (ground disturbance) by salvage operation can be detected with remotely sensed imagery especially if the images are taken less than two years after the salvage operation

Field validation of Burned Area Reflectance Classification (BARC) products for post fire assessment

The USFS Remote Sensing Applications Center (RSAC) and the USGS EROS Data Center (EDC) produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Rehabilitation (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of soil burn severity, but photosynthetic and non-photosynthetic vegetation also influences the spectral properties of post-fire imagery. Our objective was to assess burn severity both remotely and on the ground at six 2003 wildfires. We analyzed fire effects data from 34 field sites located across the full range of burn severities observed at the Black Mountain Two, Cooney Ridge, Robert, and Wedge Canyon wildfires in western Montana and the Old and Simi wildfires in southern California. We generated Normalized Burn Ratio (NBR), delta Normalized Burn Ratio (dNBR), and Normalized Difference Vegetation Index (NDVI) indices from Landsat 5, SPOT 4, ASTER, MASTER and MODIS imagery. Pearson correlations between the 44 image and 79 field variables having an absolute value greater than 0.5 were judged meaningful and tabulated in overstory, understory, surface cover, and soil infiltration categories. Vegetation variables produced a higher proportion of meaningful correlations than did surface cover variables, and soil infiltration variables the lowest proportion of meaningful correlations. Soil properties had little measurable influence on NBR, dNBR or NDVI, particularly in low and moderate severity burn areas where unconsumed vegetation occludes background reflectance. BAER teams should consider BARC products much more indicative of post-fire vegetation condition than soil condition. Image acquisition date, in relation to time of field data collection and time since fire, appears to be more important than type of imagery or index used. We recommend preserving the raw NBR or dNBR values in an archived map product to enable remote monitoring of post-fire vegetation recovery. We further recommend that BAER teams rely on the continuous BARC-Adjustable (BARC-A) product (and assign their own severity thresholds as needed) more than the classified BARC product, which oversimplifies highly heterogeneous burn severity characteristics on the ground

Postfire soil burn severity mapping with hyperspectral image unmixing

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2=0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2=0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes

Review of Fuel Treatment Effectiveness in Forests and Rangelands and a Case Study From the 2007 Megafires in Central Idaho USA

This report provides managers with the current state of knowledge regarding the effectiveness of fuel treatments for mitigating severe wildfire effects. A literature review examines the effectiveness of fuel treatments that had been previously applied and were subsequently burned through by wildfire in forests and rangelands. A case study focuses on WUI fuel treatments that were burned in the 2007 East Zone and Cascade megafires in central Idaho. Both the literature review and case study results support a manager consensus that forest thinning followed by some form of slash removal is most effective for reducing subsequent wildfire severity

Remote sensing for prediction of 1-year post-fire ecosystem condition

Appropriate use of satellite data in predicting \u3e1 year post-fire effects requires remote measurement of surface properties that can be mechanistically related to ground measures of post-fire condition. The present study of burned ponderosa pine (Pinus ponderosa) forests in the Black Hills of South Dakota evaluates whether immediate fractional cover estimates of char, green vegetation and brown (non-photosynthetic) vegetation within a pixel are improved predictors of 1-year post-fire field measures, when compared with single-date and differenced Normalized Burn Ratio (NBR and dNBR) indices. The modeled estimate of immediate char fraction either equaled or outperformed all other immediate metrics in predicting 1-year post-fire effects. Brown cover fraction was a poor predictor of all effects (r2

Post-Fire Burn Severity and Vegetation Response Following Eight Large Wildfires Across The Western United States

Vegetation response and burn severity were examined following eight large wildfires that burned in 2003 and 2004: two wildfires in California chaparral, two each in dry and moist mixed-conifer forests in Montana, and two in boreal forests in interior Alaska. Our research objectives were: 1) to characterize one year post-fire vegetation recovery relative to initial fire effects on the soil surface that could potentially serve as indicators of vegetation response (and thus, ultimately longer term post-fire ecosystem recovery), and 2) to use a remotely-sensed indicator of burn severity to describe landscape patterns in fire effects. We correlated one-year post-fire plant species richness and percent canopy cover to burn severity and to soil surface cover immediately after the fires. For all eight wildfires, plant canopy cover and species richness were low and highly variable one year post-fire. We found a greater number of forbs when compared to other plant life forms, independent of burn severity. Plant cover was dominated by grasses in chaparral systems, by forbs in mixed-conifer forests, and by shrubs in boreal forests, similar to the unburned vegetation. Fine scale variability in post-fire effects on soils, the diversity of pre-fire vegetation, and the resilience of plants to fire likely explain the high variation observed in post-fire vegetation responses across sites and burn severities. On most low and moderate burn severity sites, \u3e30% of the soil surface was covered with organic material immediately post-fire, and one year later, the canopy cover of understory vegetation averaged 10% or more, suggesting low risk to post-fire erosion. In California chaparral and the two Montana mixed conifer sites, 5% or less of the area within the fire perimeter burned with high severity, while in Alaska, 58% was mapped as high burn severity; we think this is characteristic in Alaska, but uncharacteristic of chaparral fires, especially given the high proportion of non-native species post-fire in our chaparral sites. All fires had a mosaic of different burn severities (as indicated by delta Normalized Burn Ratio, dNBR) with highly variable patch size (mean 1.3 ha to 14.4 ha, range from \u3c1 ha to over 100,000 ha)

Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in Alaska’s boreal forest. We applied a linear spectral unmixing model with five end members representing green moss, non-photosynthetic moss, charred moss, ash and soil to reflectance data to produce fractional cover maps. Our study sites spanned low to moderately high burn severity, and both black and white spruce forest types; high cover of green or non-photosynthetic moss in the remotely sensed imagery indicated low consumption, whereas high cover of charred moss, ash or soil indicated higher consumption. Strong relationships (R2¼0.5 to 0.6) between green moss estimated from the imagery and both post-fire depth and percentage consumption suggest that potential burn severity may be predicted by a map of green (live) moss. Given that the depth of the post-fire forest floor is ecologically significant, the method of mapping the condition of the organic forest floor with hyperspectral imagery presented here may be a useful tool to assess the effect of future fires in the boreal region

Lessons Learned From Rapid Response Research on Wildland Fires

In recent years, more researchers are collecting data either on active wildfires or immediately after wildfire occurrence. Known as Rapid Response Research, this important undertaking provides real-time information, useful data, and improved tools for managers. Rapid Response Research can encompass fire ecology, burn severity, fire behavior, firefighter safety, emissions, erosion, vegetation response, remote sensing, and a multitude of various fire-related topics. By using this Rapid Response Research, we have the potential to link fire effects to conditions before, during, and after fires. This information is critical to building the next generation of tools for forecasting the consequences of fire and fuels management. In this way, Rapid Response Research products are also helping fire managers and local land managers make informed decisions about the ecological and social consequences of fire. At the same time, however, Rapid Response Researchers can complicate resource and personnel management for managers during critical emergency periods on wildfires. Researchers must therefore be constantly aware of the challenges of conducting research on active wildfires (see sidebar). They must understand and work closely with fire management organizations without compromising these managers’ primary tasks. Fire scientists and fire managers have long worked closely together, but if they are to successfully address today’s complex wildland fire challenges, they must now work together even more closely. Teams of research scientists and technicians have an increasing presence in today’s fire camps. Demands for information and accountability from the media and general public also peak during large fire incidents. The added safety and logistical requirements required for Rapid Response Research are justifiable only if the research data can be effectively collected—and we learn information that we cannot ascertain by any other means