Burned Area Emergency Response Research Articles

EVALUATION OF FIELD-BASED BURN INDICES FOR ASSESSING FOREST FIRE SEVERITY IN LUHANSK REGION, UKRAINE

Evaluation of forest fire severity is a basis of post-fire forest management. Remote sensing-based methods enable reliable delineation of fire perimeters, however, assessments of the degree of forest damage need to be verified and adjusted through field sampling. The forest damage assessment conducted in this study is useful for practitioners to understand and justify the design of clear cuts for restoration purposes. Thus, the aim of the study is to verify the different approaches to field assessment of forest fire severity. In this paper, the authors present a site-specific assessment of large wildfires in Luhansk oblast, Ukraine occurred in 2020 using field-based burn severity indices. The Composite Burn Index (CBI) and the Geometrically Structured Composite Burn Index (GeoCBI) were used to estimate the extent of forest damage. The Burned Area Emergency Response (BAER) methodology was also tested to assess the extent of soil damage. The authors used PlanetScope images to delineate perimeters of burned areas. These perimeters were overlaid over a forest inventory database to extract forest attributes and site characteristics for all forested and unforested areas affected by fires. Within the fire perimeters, the burned area was stratified into six strata to independently account for forest damage in diverse types of land cover. In total 73 test plots were proportionally distributed among different classes of land cover to assess fire severity using CBI, GeoCBI, and BAER approaches. It was found that the fire’s footprints covered 39,782 hectares. Among that area, 21.2% were forested lands. About 78% of burned forests were pine plantations. The highest fire intensity levels were estimated within pure pine plantations that were grown in very dry sites, while the lowest ones were associated with hardwoods forests in moisture site conditions. The average estimates of fire severity using the field-based indices varied within strata (CBI>GeoCBI) which could be an issue for assessing burn severity using remote sensing-based approaches. The authors also concluded that the BAER methodology contributed less to assessing the fire intensity because soil burn severity is not directly related to vegetation damage. This work creates a foundation for further assessment of fire severity using satellite imagery. As a result of this study, a spatial data set of sample plots was proposed that can facilitate calibrating approaches used to map fire severity in the region

RECOVER: An Automated, Cloud-Based Decision Support System for Post-Fire Rehabilitation Planning

Abstract. RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.

Rapid Response Tools and Datasets for Post-fire Erosion Modeling: Linking Remote Sensing and Process-based Hydrological Models to support Post-fire Remediation

Abstract. Post-fire flooding and erosion can pose a serious threat to life, property, and municipal water supplies. Increased peak flows and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern to both resource managers and the public. To respond to this threat, interdisciplinary Burned Area Emergency Response (BAER) Teams are formed to assess potential erosion and flood risks. These teams are under tight deadlines as remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making these decisions is a burn severity map derived from remote sensing data (typically Landsat) that reflects fire induced changes in vegetative cover and soil properties. Slope, soils, land cover, and climate are also important parameters that need to be considered when accessing risk. Many modeling tools and datasets have been developed to assist BAER teams, but process-based and spatially explicit empirical models are currently under-utilized compared to simpler, lumped models because they are both more difficult to set up and require spatially explicit inputs such as digital elevation models, soils, and land cover. We are working to facilitate the use of models by preparing spatial datasets within a web-based tool that rapidly modifies model inputs using burn severity maps derived from earth observation data. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.

BURN SEVERITY MAPPING IN AUSTRALIA 2009

Abstract. In 2009, the Victoria Department of Sustainability and Environment estimated approximately 430,000 hectares of Victoria Australia were burned by numerous bushfires. Burned Area Emergency Response (BAER) teams from the United States were deployed to Victoria to assist local fire managers. The U.S. Geological Survey Earth Resources Observation and Science Center (USGS/EROS) and U.S. Forest Service Remote Sensing Applications Center (USFS/RSAC) aided the support effort by providing satellite-derived "soil burn severity " maps for over 280,000 burned hectares. In the United States, BAER teams are assembled to make rapid assessments of burned lands to identify potential hazards to public health and property. An early step in the assessment process is the creation of a soil burn severity map used to identify hazard areas and prioritize treatment locations. These maps are developed primarily using Landsat satellite imagery and the differenced Normalized Burn Ratio (dNBR) algorithm.

An ArcGIS Toolkit to Assist in Using Digital Soil Data for BAER Erosion Hazard Ratings

After a wildfire, understanding a landscape's response to precipitation is essential. Land that has been stripped of its vegetation and soils that inhibit infiltration create a situation that can have disastrous effects, including severe erosion and landslides. Increased sediment in streams from soil erosion can result in fish kills and degraded water quality. Soil scientists play a key role in post-wildfire assessments and rehabilitation. The formal team assigned to assess post-fire landscape damage is known as a BAER (Burned Area Emergency Response) team. BAER teams must complete their assessment within 7 d of a fire's containment, and having soil survey data available makes the job much easier. One of the tasks for soil scientists on BAER teams is to assess the potential for erosion in areas damaged by the fire. The Smokey BEHR (BAER Erosion Hazard Rating) Toolkit for GIS isolates soil survey data within the fire perimeter and distills some of the most important soil attributes from SSURGO (Soil Survey Geographic) data into a readily usable format. Combined with burn severity information from a Burned Area Reflectance Classification (BARC) raster layer, areas with high erosion hazard can be quickly identified and targeted for treatment.

Using fuzzy C-means and local autocorrelation to cluster satellite-inferred burn severity classes

Burn severity classifications derived from multitemporal Landsat Thematic Mapper images and the Normalised Burn Ratio (NBR) are commonly used to assess the post-fire ecological effects of wildfires. Ongoing efforts to retrospectively map historical burn severity require defensible, objective methods of classifying continuous differenced Normalised Burn Ratio (dNBR) data where field data are often unavailable. For three fires, we compare three methods of classifying pre- and post-fire Landsat data: (1) dNBR classification using Composite Burn Index (CBI) field data to assign severity classes; (2) fuzzy C-means classification of a dNBR image; (3) local Getis–Ord statistic (Gi*) output applied to a dNBR image, classified using fuzzy C-means clustering. We then use a Kappa statistic to evaluate the agreement of severity classes assigned to a pixel with its corresponding CBI plot. For two of the three fires, the C-means clustering of the dNBR and the Gi* output performed as well or better than dNBR images classified using CBI data, with strong agreement for moderate- and high-severity classes. These results suggest that clustering of dNBR data may be a suitable approach for classifying burn severity data without field data. This method may also be useful as a tool for rapid post-fire assessments (e.g. Burned Area Emergency Response and Burned Area Reflectance Classification maps), where images must often be classified quickly and subjectively. Further analysis using additional field data and across different vegetation types will be necessary to better understand the importance of localised spatial variability in classifying burn severity data or other remote sensing change-detection analyses.

Emergency Post-Fire Rehabilitation Treatment Effects on Burned Area Ecology and Long-Term Restoration

The predicted continuation of strong drying and warming trends in the southwestern United States underlies the associated prediction of increased frequency, area, and severity of wildfires in the coming years. As a result, the management of wildfires and fire effects on public lands will continue to be a major land management priority for the foreseeable future. Following fire suppression, the first land management process to occur on burned public lands is the rapid assessment and emergency treatment recommendations provided by the Burned Area Emergency Response (BAER) team. These teams of specialists follow a dynamic protocol to make post-fire treatment decisions based on the best available information using a range of landscape assessment, predictive modeling, and informational tools in combination with their collective professional expertise. Because the mission of a BAER team is to assess burned landscape and determine if stabilization treatments are needed to protect valued resources from the immediate fire effects, the evaluation of treatment success generally does not include important longer term ecological effects of these treatments or the fates of the materials applied over the burned landscape. New tools and techniques that have been designed or modified for BAER team use are presented in conjunction with current post-fire treatment effectiveness monitoring and research. In addition, a case is made to monitor longer term treatment effects on recovering ecosystems and to make these findings available to BAER teams.

Post-Wildland Fire Desertification: Can Rehabilitation Treatments Make a Difference?

Desertification, caused by land degradation as opposed to the immediate creation of classical deserts, is of prime concern in the 21st century. As a result of human activities and climate change, the land loses its proper hydrologic function and biological productivity. Desertification affects 33 % of the earth’s surface and over a billion people. Fire-related desertification has a number of environmental, social, and economic consequences. The two key environmental consequences are soil erosion and non-native plant invasions. Erosion after wildland fires can be in the range of <1 Mg ha−1 to 370 Mg ha−1, depending on fire severity, degree of water repellency, slope, and post-fire rainfall events. Soil losses in the high end of that range definitely exceed soil loss tolerances and contribute to desertification. Non-native plants are typically ten times as abundant on landscapes burned by wildland fires than on unburned lands. Seeding has been used for many years as a prime Burned Area Emergency Response (BAER) treatment. Until recently, this seeding contributed to non-native plant invasions because fast-growing but non native plant seeds were used. The use of native plant seeds and sterile hybrids has reduced this problem somewhat. However, even certified weed-free seed lots have low percentages of non-native plant seeds. Recent use of wet and dry mulches have contributed to reduced postwildland fire erosion rates, but they are quite expensive. This paper examines post-wildland fire desertification and the capabilities of BAER treatments to deal with this growing problem.

Fire Severity Model Accuracy Using Short-Term, Rapid Assessment versus Long-Term, Anniversary Date Assessment

Fires are common in rangelands, and after a century of suppression, the potential exists for fires to burn with high intensity and severity. In addition, the ability of fires to affect long-term changes in rangelands is substantial; therefore, the assessment of fire severity following a burn is critical. These assessments are typically conducted following Burned Area Emergency Response team (or similar) protocols; the resulting data can be utilized to plan future land uses and remediation efforts. For the purpose of supplementing these procedures and exploring fire severity modeling of sagebrush steppe rangelands, we compared fire severity models developed using (1) short-term post-fire imagery (i.e., imagery collected within 30 days of the fire) with (2) long-term post-fire imagery (i.e., imagery collected on or about the one-year anniversary date of the fire). The models were developed using Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery as well as Shuttle Radar Topography Mission (SRTM) elevation data, as well as Classification Tree Analysis (CTA). The results indicate that while anniversary date imagery can be used to assess fire severity (overall accuracy ~90%), it is not as accurate as short-term imagery (overall accuracy ~97%). Furthermore, use of short-term imagery allows remediation strategies to be crafted and implemented shortly after the fire. Therefore, we suggest that rangeland fire severity is best modeled using CTA with short-term imagery and field-based fire severity observations.

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

Wildland fires are common in rangelands worldwide. The potential for high-severity fires to affect long-term changes in rangelands is considerable, and for this reason assessing fire severity shortly after the fire is critical. Such assessments are typically carried out following Burned Area Emergency Response teams or similar protocols. These data can then be used by land managers to plan remediation and future land uses. To complement these procedures and explore fire severity modeling of sagebrush steppe rangelands, we compared models developed using (1) post-fire imagery only with (2) differenced imagery (pre-fire minus post-fire imagery). All models were developed from Classification Tree Analysis (CTA) techniques using Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery and Shuttle Radar Topography Mission (SRTM) elevation data. The results indicate that both techniques produced similar fire severity models (model agreement = 98.5%) and that little improvement in overall accuracy was gained by using differenced imagery (0.5%). We suggest the use of CTA models developed using only the post-fire imagery. The analyses and techniques described in this paper provide land managers with tools to better justify their recommendations and decisions following wildland fires in sagebrush steppe ecosystems and provide remote sensing scientists with information that will potentially improve future modeling efforts.

Nonmarket Resource Valuation in the Postfire Environment

Abstract After the containment of large wildland fires, major onsite and downstream effects including lost soil productivity, watershed response, increased vulnerability to invasive weeds, and downstream sedimentation can cause threats to human life and property. Burned Area Emergency Response (BAER) teams are responsible for developing treatment plans to mitigate negative consequences associated with these postwildfire events. BAER teams are charged with demonstrating that proposed treatments are economically justified. When the resources at risk are market values this poses few challenges; however, when the resources at risk are nonmarket values serious challenges occur including limited supporting scientific research, a lack of economics expertise and training among BAER team members, and tight time frames for conducting assessments resulting in final assessments that may not be defensible. In this study we show a clear and reproducible valuation procedure termed implied minimum value that may be adopted by BAER teams to improve the economic justification of treatments proposed to protect nonmarket resource values.

The Relationship of Multispectral Satellite Imagery to Immediate Fire Effects

The Forest Service Remote Sensing Applications Center (RSAC) and the U.S. Geological Survey Earth Resources Observation and Science (EROS) Data Center produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Response (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of fire effects on soils, but green and nonphotosynthetic vegetation and other materials also affect the spectral properties of post-fire imagery. Our objective was to assess how well satellite image-derived burn severity indices relate to a suite of immediate post-fire effects measured on the ground. We measured or calculated fire effects variables at 418 plots, nested in 50 field sites, located across the full range of burn severities observed at the 2003 Black Mountain, Cooney Ridge, Robert, and Wedge Canyon wildfires in western Montana, the 2003 Old and Simi wildfires in southern California, and the 2004 Porcupine and Chicken wildfires in interior Alaska. We generated the Normalized Burn Ratio (NBR), differenced Normalized Burn Ratio (dNBR), Relative dNBR (RdNBR), Normalized Difference Vegetation Index (NDVI), and differenced NDVI (dNDVI) burn severity indices from Landsat 5 Thematic Mapper (TM) imagery across these eight wildfires. The NBR correlated best with the fire effects measures but insignificantly, meaning other indices could act as suitable substitutes. The overstory (trees in Montana and Alaska, shrubs in California) measures appear to correlate best to the image variables, followed by understory and surface cover measures. Exposed mineral soil and soil water repellency were poorly correlated with the image variables, while green vegetation was most highly correlated. The BARC maps are more indicative of post-fire vegetation condition than soil condition. We conclude that the NBR and dNBR, from which BARC maps of large wildfires in the United States are currently derived, are sound choices for rapid assessment of immediate post-fire burn severity across the three ecosystems sampled. Our future research will focus on spectral mixture analysis (SMA) because it acknowledges that pixel reflectance is fundamentally a mixture of charred, dead, green and nonphotosynthetic vegetation, soil, rock and ash materials that are highly variable at fine scales.

Predicting gully rejuvenation after wildfire using remotely sensed burn severity data

The loss of surface vegetation and reduced infiltration caused by wildfires can trigger gully rejuvenation, resulting in damage to downstream aquatic resources and risk to human life and property. We developed a spatially explicit metric of burn severity — the Burn Severity Distribution Index (BSDI) — and tested its ability to predict post-fire gully rejuvenation in 1st and 2nd order basins burned in the 2000 Valley Complex fires in the Sapphire Mountains of western Montana. The BSDI was derived from burn severity data interpreted from Landsat 7 satellite imagery using the Normalized Burn Ratio (NBR) method, and ranged from 0.0 for completely unburned basins to 4.0 for basins burned entirely at high severity. In July 2001 rainstorms with peak 30-minute intensities of up to 17 mm h − 1 triggered gully rejuvenation in 66 of the 171 basins examined. The frequency of gully rejuvenation was higher in basins with higher BSDI values, increasing from zero for basins with a BSDI less than 1.3 to 67% for basins with a BSDI greater than 3.0. Binary logistic regression indicated that BSDI was a more significant predictor of gully rejuvenation than basin morphometric variables. The absence of gully rejuvenation in several basins with a high BSDI was attributed to low gradient, dense riparian vegetation, or concentration of high burn severity at lower elevations in the basin. The presence of gully rejuvenation in several basins with a low BSDI was associated with false negative NBR classification errors in northwest aspects, and concentration of severe burn impacts in the drainage headslopes. BSDI is a useful metric for predicting gully rejuvenation after wildfire. The use of the BSDI in Burned Area Emergency Response team assessments could improve the planning, implementation, and monitoring of burned area recovery treatments.

Assessing burn severity and comparing soil water repellency, Hayman Fire, Colorado

An important element of evaluating a large wildfire is to assess its effects on the soil in order to predict the potential watershed response. After the 55 000 ha Hayman Fire on the Colorado Front Range, 24 soil and vegetation variables were measured to determine the key variables that could be used for a rapid field assessment of burn severity. The percentage of exposed mineral soil and litter cover proved to be the best predictors of burn severity in this environment. Two burn severity classifications, one from a statistical classification tree and the other a Burned Area Emergency Response (BAER) burn severity map, were compared with measured ‘ground truth’ burn severity at 183 plots and were 56% and 69% accurate, respectively. This study also compared water repellency measurements made with the water drop penetration time (WDPT) test and a mini-disk infiltrometer (MDI) test. At the soil surface, the moderate and highly burned sites had the strongest water repellency, yet were not significantly different from each other. Areas burned at moderate severity had 1·5 times more plots that were strongly water repellent at the surface than the areas burned at high severity. However, the high severity plots most likely had a deeper water repellent layer that was not detected with our surface tests. The WDPT and MDI values had an overall correlation of r = −0·64(p < 0·0001) and appeared to be compatible methods for assessing soil water repellency in the field. Both tests represent point measurements of a soil characteristic that has large spatial variability; hence, results from both tests reflect that variability, accounting for much of the remaining variance. The MDI is easier to use, takes about 1 min to assess a strongly water repellent soil and provides two indicators of water repellency: the time to start of infiltration and a relative infiltration rate. Copyright © 2005 John Wiley & Sons, Ltd.