Deconstructing the King megafire Coen, Janice L.; Stavros, E. Natasha; Fites-Kaufman, Josephine A.
Ecological applications,
09/2018, Volume:
28, Issue:
6
Journal Article
Peer reviewed
Open access
Hypotheses that megafires, very large, high-impact fires, are caused by either climate effects such as drought or fuel accumulation due to fire exclusion with accompanying changes to forest structure ...have long been alleged and guided policy, but their physical basis remains untested. Here, unique airborne observations and microscale simulations using a coupled weather–wildland-fire-behavior model allowed a recent megafire, the King Fire, to be deconstructed and the relative impacts of forest structure, fuel load, weather, and drought on fire size, behavior, and duration to be separated. Simulations reproduced observed details including the arrival at an inclined canyon, a 25-km run, and later slower growth and features. Analysis revealed that fire-induced winds that equaled or exceeded ambient winds and fine-scale airflow undetected by surface weather networks were primarily responsible for the fire’s rapid growth and size. Sensitivity tests varied fuel moisture and amount across wide ranges and showed that both drought and fuel accumulation effects were secondary, limited to sloped terrain where they compounded each other, and, in this case, unable to significantly impact the final extent. Compared to standard data, fuel models derived solely from remote sensing of vegetation type and forest structure improved simulated fire progression, notably in disturbed areas, and the distribution of burn severity. These results point to self-reinforcing internal dynamics rather than external forces as a means of generating this and possibly other outlier fire events. Hence, extreme fires need not arise from extreme fire environment conditions. Kinematic models used in operations do not capture fire-induced winds and dynamic feedbacks so can underestimate megafire events. The outcomes provided a nuanced view of weather, forest structure, fuel accumulation, and drought impacts on landscape-scale fire behavior—roles that can be misconstrued using correlational analyses between area burned and macroscale climate data or other exogenous factors. A practical outcome is that fuel treatments should be focused on sloped terrain, where factors multiply, for highest impact.
Most urban tree inventories depend on resource-intensive, field-based assessments, which are unevenly distributed in space and time. Recently, these inventories have been conducted using field ...inventories combined with airborne multispectral, hyperspectral, LiDAR, and spaceborne multispectral remote sensing. Significant advances have been made in urban tree GIS databases and remote sensing methods, which include delineating individual tree crowns, extracting tree species metrics, and employing classification techniques. Generally, remote sensing methods distinguish individual urban trees using either pixel-based or object-based methods, while image classification procedures are typically divided into parametric (e.g., regression-based classification, Bayesian, and principal component analysis) and non-parametric approaches such as machine learning (e.g., random forests support vector machines) and deep learning (e.g., convolutional neural networks). Our synthesis of the current state of science suggests sensors with the highest spatial (m), spectral (bands), and temporal (repeat time) resolutions result in the most accurate tree species identification. Combining airborne LiDAR/hyperspectral or airborne LiDAR/spaceborne high-resolution multispectral sensors yields the highest accuracy for the most diverse urban forests. An object-based non-parametric approach, like a fully convolutional neural network, scores higher in accuracy assessments than pixel-based parametric approaches. Future studies can leverage global/regional GIS field inventory databases to expand the scope of studies within and across multiple cities, utilizing LiDAR and spaceborne sensors.
Seasonal changes in the climatic potential for very large wildfires (VLWF ≥ 50,000 ac ~ 20,234 ha) across the western contiguous United States are projected over the 21st century using generalized ...linear models and downscaled climate projections for two representative concentration pathways (RCPs). Significant (p ≤ 0.05) increases in VLWF probability for climate of the mid-21st century (2031–2060) relative to contemporary climate are found, for both RCP 4.5 and 8.5. The largest differences are in the Eastern Great Basin, Northern Rockies, Pacific Northwest, Rocky Mountains, and Southwest. Changes in seasonality and frequency of VLWFs d7epend on changes in the future climate space. For example, flammability-limited areas such as the Pacific Northwest show that (with high model agreement) the frequency of weeks with VLWFs in a given year is 2–2.7 more likely. However, frequency of weeks with at least one VLWF in fuel-limited systems like the Western Great Basin is 1.3 times more likely (with low model agreement). Thus, areas where fire is directly associated with hot and dry climate, as opposed to experiencing lagged effects from previous years, experience more change in the likelihood of VLWF in future projections. The results provide a quantitative foundation for management to mitigate the effects of VLWFs.
A fundamental challenge in verifying urban CO2 emissions reductions is estimating the biological influence that can confound emission source attribution across heterogeneous and diverse landscapes. ...Recent work using atmospheric radiocarbon revealed a substantial seasonal influence of the managed urban biosphere on regional carbon budgets in the Los Angeles megacity, but lacked spatially explicit attribution of the diverse biological influences needed for flux quantification and decision making. New high-resolution maps of land cover (0.6 m) and irrigation (30 m) derived from optical and thermal sensors can simultaneously resolve landscape influences related to vegetation type (tree, grass, shrub), land use, and fragmentation needed to accurately quantify biological influences on CO2 exchange in complex urban environments. We integrate these maps with the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) to quantify spatial and seasonal variability in gross primary production (GPP) across urban and non-urban regions of Southern California Air Basin (SoCAB). Results show that land use and landscape fragmentation have a significant influence on urban GPP and canopy temperature within the water-limited Mediterranean SoCAB climate. Irrigated vegetation accounts for 31% of urban GPP, driven by turfgrass, and is more productive (1.7 vs 0.9 μmol m−2 s−1) and cooler (2.2 ± 0.5 K) than non-irrigated vegetation during hot dry summer months. Fragmented landscapes, representing mostly vegetated urban greenspaces, account for 50% of urban GPP. Cooling from irrigation alleviates strong warming along greenspace edges within 100 m of impervious surfaces, and increases GPP by a factor of two, compared to non-irrigated edges. Finally, we note that non-irrigated shrubs are typically more productive than non-irrigated trees and grass, and equally productive as irrigated vegetation. These results imply a potential water savings benefit of urban shrubs, but more work is needed to understand carbon vs water usage tradeoffs of managed vs unmanaged vegetation.
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•Satellite optical and thermal data show diverse landscape influences on urban GPP.•Irrigated vegetation is twice as productive as non-irrigated vegetation in summer.•Fragmented vegetation greenspaces show slight warming, and reduced GPP, along edges.•However, irrigating edge vegetation mitigates stress-driven GPP loss and warming.•Non-irrigated shrubs may offer high productivity water saving alternative.
Fire severity, the degree of environmental change caused by a fire, is traditionally assessed by broadband spectral indices, such as the differenced Normalized Burn Ratio (dNBR) from Landsat imagery. ...Here, we used an alternative indicator, the burned fraction derived from spectral mixture analysis (SMA), to evaluate and compare the performance for assessing fire severity of broadband and narrowband imaging spectroscopy (IS) data in the visible to shortwave infrared (VSWIR, 0.35–2.5μm). We used the band specifications of the broadband Operational Land Imager (OLI) and the narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). We integrated two techniques to account for endmember variability in the unmixing process, spectral weighting and iterative unmixing, in a model referred to as weighted multiple endmember SMA (wMESMA). Based on a separability index, we evaluated the separability between the different ground components, or endmembers, that comprise post-fire environments (char, green vegetation (GV), non-photosynthetic vegetation (NPV) and substrate). We found that the near infrared region (0.7–1.3μm) had the highest discriminatory power, followed by the shortwave infrared 2 (SWIR2, 2–2.4μm), SWIR1 (1.5–1.7μm) and visible (0.35–0.7μm) regions. Individual narrowbands did not substantially outperform individual broadbands, however, the higher data dimensionality of IS resulted in significantly improved post-fire fractional cover and burned fraction estimates compared to multispectral data. Multispectral data captured a fair amount of the variability in fire severity conditions as represented by the different fractional cover estimates of the endmembers in both a multispectral narrow- and broadband scenario, however, fractional cover estimates derived from IS data using all viable bands were significantly better. This demonstrated the benefits of IS over traditional multispectral data to assess fire severity and also showed that the additional information gain was the result of higher data dimensionality and not because of certain narrowbands capturing narrow spectral features. In addition, we found that the burned fraction derived from all viable AVIRIS bands over a fire in California, USA, was highly correlated with two field measures of fire severity (Geo Composite Burn Index: R2=0.86, and the percentage black trees and shrubs: R2=0.65). Formal quantification of potential improvements of IS over multispectral methods is important with the advent of upcoming spaceborne IS missions (e.g. the Environmental Mapping and Analysis Program and Hyperspectral Infrared Imager). Our analysis showed that IS data when combined with advanced analysis techniques significantly improved fire severity assessments. The improvements of using IS data require higher computational cost and advanced processing, thus multispectral data might still suit the needs of certain applications such as rapid fire damage assessments and global analysis of spatio-temporal fire severity patterns.
•The performance of narrow- and broadband data to assess fire severity was compared.•Multispectral data captured a large amount of the variability in fire severity.•Imaging spectroscopy (IS), however, consistently outperformed multispectral data.•Future spaceborne IS sensors (e.g. HyspIRI) will enable fire severity mapping.•Users may prefer IS or multispectral post-fire assessments depending on application.
Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional ...studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.
The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping ...over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome.
Very large wildfires can cause significant economic and environmental damage, including destruction of homes, adverse air quality, firefighting costs and even loss of life. We examine how climate is ...associated with very large wildland fires (VLWFs ≥50000 acres, or ~20234ha) in the western contiguous USA. We used composite records of climate and fire to investigate the spatial and temporal variability of VLWF–climatic relationships. Results showed quantifiable fire weather leading up and up to 3 weeks post VLWF discovery, thus providing predictors of the probability that VLWF occurrence in a given week. Models were created for eight National Interagency Fire Center Geographic Area Coordination Centers (GACCs). Accuracy was good (AUC>0.80) for all models, but significant fire weather predictors of VLWFs vary by GACC, suggesting that broad-scale ecological mechanisms associated with wildfires also vary across regions. These mechanisms are very similar to those found by previous analyses of annual area burned, but this analysis provides a means for anticipating VLWFs specifically and thereby the timing of substantial area burned within a given year, thus providing a quantifiable justification for proactive fire management practices to mitigate the risk and associated damage of VLWFs.
Remote sensing data are most useful if they are available with sufficient precision, accuracy, spatiotemporal and spectral sampling, as well as continuity across decades. The Landsat and Sentinel ...series, as well other satellites are currently covering significant parts of this observational trade space. It can be expected that growing demands and budget constraints will require new capabilities in orbit that can address as many observables as possible with a single instrument. Recent optical performance improvements of imaging spectrometers make them true alternatives to traditional multispectral imagers. However, they are much more adaptable to a wide range of Earth observation needs due to the combination of continuous high spectral sampling with spatial sampling consistent with previous sensors (e.g., Landsat). Unfortunately, there is a knowledge gap in demonstrating that imaging spectroscopy data can substitute for multi-spectral data while sustaining the long-term record. Thus, the objective of this analysis is to test the hypothesis that imaging spectroscopy data compare radiometrically with multi-spectral data to within 5%. Using a coincident Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight with over-passing Operational Land Imager (OLI) data on Landsat 8, we document a procedure for simulating OLI multi-spectral bands from AVIRIS data, evaluate influencing factors on the observed radiance, and assess the difference in top-of-atmosphere radiance as compared to OLI. The procedure for simulating OLI data include spectral convolution, accounting for the minimal atmospheric effects between the two sensors, and spatial resampling. The remaining differences between the simulated and the real OLI data result mainly from differences in sensor calibration, surface bi-directional reflectance, and spatial sampling. The median relative radiometric difference for each band ranges from −8.3% to 0.6%. After bias-correction to minimize potential calibration discrepancies, we find no more than a 1.2% relative difference. This analysis therefore successfully demonstrates that imaging spectrometer data can contribute to Landsat-type or other multi-spectral data records. It also shows that cross-calibration from a spectrometer to a radiometer can be easily performed as a result of the imaging spectrometer high spectral sampling and its ability to recreate multi-spectral response functions.
•After corrections there is no >1.2% mean radiometric difference by band.•Remaining differences are from spatial sampling and surface bi-directional effects.•Imaging spectrometer data can contribute to multi-spectral data records.•Cross-calibration from a spectrometer to a radiometer is relatively straightforward.
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