Geology topics

Crystal A. Kolden

Publications and source records attributed to Crystal A. Kolden.

10 recordsLinked to original sources

USGS2021

Determination of burn severity models ranging from regional to continental scales for the conterminous United States

Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R 2 = 0.55, P < 0.001) for dNBR and poor (R 2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR R 2 ¯ "> R2¯ = 0.56 and 0.00–0.83 R 2 range; NBR R 2 ¯ "> R(bar) 2 = 0.43 and 0.00–0.82 R 2 range) and NVC (dNBR R 2 ¯ "> R(bar) 2 = 0.55 and 0.15–0.78 R 2 range; NBR R 2 ¯ "> R(bar) 2 = 0.41 and 0.00–0.79 R 2 range) were on average the same or better than the CONUS models for dNBR and NBR, with the strongest fit models exhibiting R 2 ≥ 0.70, whereas larger scale regional models R 2 ¯ "> R(bar) 2 ranged from 0.28 to 0.5. However, variation in accuracy among landcover types indicate that dNBR and NBR regression models could be used to effectively estimate CBI for future fires in certain regions, while for other regions models may require additional field observations or alternative spectral transformations. Our decision tree schema can be used to help users determine which scale is likely to produce the most accurate results using our models. The CBI regression models developed here, paired with the decision tree, provide users with a simple method to estimate burn severity in units of CBI for any fire within CONUS with moderate to high levels of confidence and provide a template for further development of models with new data going forward.

Conterminous United States↗

USGS2020

The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire

Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.

Scientific Data↗

USGS2020

Disturbance refugia within mosaics of forest fire, drought, and insect outbreaks

Disturbance refugia – locations that experience less severe or frequent disturbances than the surrounding landscape – provide a framework to highlight not only where and why these biological legacies persist as adjacent areas change but also the value of those legacies in sustaining biodiversity. Recent studies of disturbance refugia in forest ecosystems have focused primarily on fire, with a growing recognition of important applications to land management. Given the wide range of disturbance processes in forests, developing a broader understanding of disturbance refugia is important for scientists and land managers, particularly in the context of anthropogenic climate change. We illustrate the framework of disturbance refugia through the individual and interactive effects of three prominent forest disturbance agents: fire, drought, and insect outbreaks. We provide examples of disturbance refugia and related applications to natural resource management in western North America, demonstrate methods for characterizing refugia, identify research priorities, and discuss why a more comprehensive definition of disturbance refugia is relevant to conservation globally.

Frontiers in Ecology and the Environment↗

USGS2018

Fire behavior, weather, and burn severity of the 2007 anaktuvuk river tundra fire, North Slope, Alaska

In 2007, the Anaktuvuk River Fire (ARF) became the largest recorded tundra fire on the North Slope of Alaska. The ARF burned for nearly three months, consuming more than 100,000 ha. At its peak in early September, the ARF burned at a rate of 7000 ha d -1 . The conditions potentially responsible for this large tundra fire include modeled record high summer temperature and record low summer precipitation, a late-season high-pressure system located over the Beaufort Sea, extremely dry soil conditions throughout the summer, and sustained southerly winds during the period of vegetation senescence. Burn severity mapping revealed that more than 80% of the ARF burned at moderate to extreme severity, while the nearby Kuparuk River Fire remained small and burned at predominantly (80%) low severity. While this study provides information that may aid in the prediction of future large tundra fires in northern Alaska, the fact that three other tundra fires that occurred in 2007 combined to burn less than 1000 ha suggests site specific complexities associated with tundra fires on the North Slope, which may hamper the development of tundra fire forecasting models.

Arctic, Antarctic, and Alpine Research↗

USGS2018

Mapping wildfire burn severity in the Arctic Tundra from downsampled MODIS data

Wildfires are historically infrequent in the arctic tundra, but are projected to increase with climate warming. Fire effects on tundra ecosystems are poorly understood and difficult to quantify in a remote region where a short growing season severely limits ground data collection. Remote sensing has been widely utilized to characterize wildfire regimes, but primarily from the Landsat sensor, which has limited data acquisition in the Arctic. Here, coarse-resolution remotely sensed data are assessed as a means to quantify wildfire burn severity of the 2007 Anaktuvuk River Fire in Alaska, the largest tundra wildfire ever recorded on Alaska's North Slope. Data from Landsat Thematic Mapper (TM) and downsampled Moderate-resolution Imaging Spectroradiometer (MODIS) were processed to spectral indices and correlated to observed metrics of surface, subsurface, and comprehensive burn severity. Spectral indices were strongly correlated to surface severity (maximum R2 = 0.88) and slightly less strongly correlated to substrate severity. Downsampled MODIS data showed a decrease in severity one year post-fire, corroborating rapid vegetation regeneration observed on the burned site. These results indicate that widely-used spectral indices and downsampled coarse-resolution data provide a reasonable supplement to often-limited ground data collection for analysis and long-term monitoring of wildfire effects in arctic ecosystems.

Alaska↗

USGS2017

Landscape-scale quantification of fire-induced change in canopy cover following mountain pine beetle outbreak and timber harvest

Across the western United States, the three primary drivers of tree mortality and carbon balance are bark beetles, timber harvest, and wildfire. While these agents of forest change frequently overlap, uncertainty remains regarding their interactions and influence on specific subsequent fire effects such as change in canopy cover. Acquisition of pre- and post-fire Light Detection and Ranging (LiDAR) data on the 2012 Pole Creek Fire in central Oregon provided an opportunity to isolate and quantify fire effects coincident with specific agents of change. This study characterizes the influence of pre-fire mountain pine beetle (MPB; Dendroctonus ponderosae ) and timber harvest disturbances on LiDAR-estimated change in canopy cover. Observed canopy loss from fire was greater (higher severity) in areas experiencing pre-fire MPB (Δ 18.8%CC) than fire-only (Δ 11.1%CC). Additionally, increasing MPB intensity was directly related to greater canopy loss. Canopy loss was lower for all areas of pre-fire timber harvest (Δ 3.9%CC) than for fire-only, but among harvested areas, the greatest change was observed in the oldest treatments and the most intensive treatments [i.e., stand clearcut (Δ 5.0%CC) and combination of shelterwood establishment cuts and shelterwood removal cuts (Δ 7.7%CC)]. These results highlight the importance of accounting for and understanding the impact of pre-fire agents of change such as MPB and timber harvest on subsequent fire effects in land management planning. This work also demonstrates the utility of multi-temporal LiDAR as a tool for quantifying these landscape-scale interactions.

Oregon↗

USGS2017

Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r 2 = 0.86, p < 0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.

Oregon↗

USGS2014

Effects of wildfire on sea otter (Enhydra lutris) gene transcript profiles

Wildfires have been shown to impact terrestrial species over a range of temporal scales. Little is known, however, about the more subtle toxicological effects of wildfires, particularly in downstream marine or downwind locations from the wildfire perimeter. These down-current effects may be just as substantial as those effects within the perimeter. We used gene transcription technology, a sensitive indicator of immunological perturbation, to study the effects of the 2008 Basin Complex Fire on the California coast on a sentinel marine species, the sea otter ( Enhydra lutris ). We captured sea otters in 2008 (3 mo after the Basin Complex Fire was controlled) and 2009 (15 mo after the Basin Complex Fire was controlled) in the adjacent nearshore environment near Big Sur, California. Gene responses were distinctly different between Big Sur temporal groups, signifying detoxification of PAHs, possible associated response to potential malignant transformation, and suppression of immune function as the primary responses of sea otters to fire in 2008 compared to those captured in 2009. In general, gene transcription patterns in the 2008 sea otters were indicative of molecular reactions to organic exposure, malignant transformation, and decreased ability to respond to pathogens that seemed to consistent with short-term hydrocarbon exposure.

California↗

USGS2013

Carbon stocks of trees killed by bark beetles and wildfire in the western United States

Forests are major components of the carbon cycle, and disturbances are important influences of forest carbon. Our objective was to contribute to the understanding of forest carbon cycling by quantifying the amount of carbon in trees killed by two disturbance types, fires and bark beetles, in the western United States in recent decades. We combined existing spatial data sets of forest biomass, burn severity, and beetle-caused tree mortality to estimate the amount of aboveground and belowground carbon in killed trees across the region. We found that during 1984-2010, fires killed trees that contained 5-11 Tg C year -1 and during 1997-2010, beetles killed trees that contained 2-24 Tg C year -1 , with more trees killed since 2000 than in earlier periods. Over their periods of record, amounts of carbon in trees killed by fires and by beetle outbreaks were similar, and together these disturbances killed trees representing 9% of the total tree carbon in western forests, a similar amount to harvesting. Fires killed more trees in lower-elevation forest types such as Douglas-fir than higher-elevation forest types, whereas bark beetle outbreaks also killed trees in higher-elevation forest types such as lodgepole pine and Engelmann spruce. Over 15% of the carbon in lodgepole pine and spruce/fir forest types was in trees killed by beetle outbreaks; other forest types had 5-10% of the carbon in killed trees. Our results document the importance of these natural disturbances in the carbon budget of the western United States.

Environmental Research Letters↗

USGS2008

Using climate information for fuels management

Climate has come to the forefront of wildfire discussions in recent years as research contributes to the general understanding of how climate influences fuels availability to burn, the occurrence of severe fire weather conditions and other wildfire parameters. This understanding has crossed over into wildfire management applications through the creation of tools like climate forecasts for wildfire and drought indices, which are now widely used in wildfire suppression and mitigation planning. The overall question is how can climate information help fire managers meet management objectives? Climate underlies weather. For example, a number of days could be generally wet, but that may occur in the context of a two-year overall drought. Knowing the baseline climate is not only critical to preventing escaped prescribed fires, but also how it may affect fire behavior, fire effects and whether or not fire managers will meet their fuels management objectives. Thus, for fire managers to use prescribed and WFU fire safely and effectively, and to minimize the number of escaped fires and conversions to suppression, they need to understand how current climate conditions will impact the use of fire. One example is the need to use prescribed fire under set “burn windows”. Since meteorological conditions vary considerably from year to year for a given day, fire managers will be more successful in utilizing burn windows effectively if they understand those climate thresholds conducive to an increased number of safe burn windows, and are able to predict and take advantage of those burn windows. While climate and wildfire has been studied extensively, climate and fire use has not. The initial goal of this project was to assess how climate impacts prescribed fire use in a more general sense. After a preliminary informal survey in the spring of 2003, we determined that 1) there is insufficient data (less than 10 years) to conduct empirical correlative studies similar to those of the relationships between climate and wildfire (e.g., Swetnam and Betancourt 1990), and 2) prescribed fire policy has many regulations that potentially inhibited the use of climate information for decision-making. It was also determined that because fire use is a human decision, it would be more informative to ask fire managers themselves how climate impacts fire use through their decision-making processes, and whether or not they use climate information for prescribed fire. The first task for this project was to complete a regional survey of prescribed fire managers in California and Nevada. During the second phase of the project, additional prescribed fire managers were surveyed across the country. During the third year a second survey of WFU managers was completed. The goals of these inquiries were to determine: 1) If fire managers use climate information for fuels management; 2) The perspective fire managers have towards climate affecting fuels management; 3) Determine any obstacles that make it difficult to use climate information for fuels management; and 4) Determine climate information managers need to help them make better decisions for fire use.

Climate Ecosystem Fire Applications↗