The “Zombie” Fires of 1942

This AFSC research brief takes a look at early Alaska fire history from the 1940s. The “Zombie” Fires of 1942 is a historical narrative of an exceptional fire event related to the Alaska Railroad, including an early description of a holdover fire burning over winter. 

View the Research Brief PDF here

Alaska Railroad Steam Engine ca. 1940s (State of Alaska photo archives).

EPSCoR Boreal Fires Team: Remote Sensing for AK Fire Season

This Fire Science Highlight is available as a standalone online and PDF publication: https://tinyurl.com/FSHJuly2020

Can remote sensing products help mitigate the loss of on-the-ground resources due to the COVID-19 pandemic?

Chris Waigl and the EPSCoR Boreal Fires Science team are rapidly developing new tools to aid with the fire season in Alaska. Products include, enhanced access to daily snow cover extent and fire danger maps, and highly focused fire-detection algorithms. The tools aim to provide data that can be integrated into existing systems facilitating direct applications for users, including fire operation managers.

Remote sensing of daily snow cover extent

Spring 2020 saw the introduction of a new daily snow cover extent mapping product for the state of Alaska. The source data from the NOAA National Ice Center is based on near-real time readings from the Interactive Multi-sensor Snow and Ice Mapping System (IMS). This satellite multi-sensor can differentiate between snow, ice, water, and snow-free ground with high levels of accuracy. The snow cover product is available seasonally for download as a vector file and as web-browser map with near-real time updates through the Alaska Interagency Coordination Center (AICC) mapping service, and year-round (with limited updates) from the Boreal
Fires Team. Inter-annual comparisons of snow cover (Figure 1) can be made by geographic zone or throughout the state. This snow classification data could potentially be improved by validation through Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infared Imaging Radiometer Suite (VIIRS) products, National Weather Service snow depth data, and citizen science projects that measure snow depth.

Figure 1. Inter-annual comparison of snow melt across Alaska and northwest Canada, 2016-2019. Green regions represent snow free areas. This animated comparison is just one way the data snow cover data can be visualized. The snow cover data can be obtained as a vector file, allowing for fine-scale pattern analysis within smaller geographic extents.

Improving access to spatial representations of Alaska Fire Danger Ratings

The Canadian Forest Fire Danger Rating System (CFFDRS) combines fire
occurrence prediction systems, fire weather indices, and fire behavior systems to establish a fire danger rating. MesoWest produces fire danger ratings from CFFDRS for Alaska. The Boreal Fires team helps make this data more accessible by processing the MesoWest GeoTIFFs into a format that can be more easily used for webmapping by AICC. These fire danger ratings are available on the AICC web-mapping service and also hosted by the Boreal Fires Team. The fire danger ratings, known as Spruce Adjective
Ratings, are grouped into low, moderate, high, very high, and extreme classes. These discrete groupings along with provincially specific parameters can create harsh differences in adjacent areas at province and international borders. CFFDRS has proven to have great application to Alaska. The Boreal Fires Team hopes that making the danger ratings more accessible will open the door for fine tuning the data to seamlessly fit Alaska, and lead to improved integration into fire behavior and analysis tools for the state.

Figure 2. Processing CFFDRS source data for Alaska creates an accessible spatial representation of Spruce Adjective Fire Danger Ratings. Fine tuning the application of the indices to Alaska could improve the interoperability of CFFDRS in Alaska.

VIFDAHL (VIIRS I-band Fire Detection Algorithm for High Latitudes)

VIIRS fire detection has shown to be invaluable for remote fire detection at high latitudes. VIFDAHL compliments VIIRS by subsetting high fire-danger areas and known fire locations. This information is particularly important for fire operations managers. Low-intensity detections have direct
application to spotting residual fire hazards, which can help with resource prioritization Having additional inputs for where fire is now, particularly
low-intensity detections, is helpful to identify ignition sources for fire behavior models.

Figure 3. VIFDAHL can provide up to two fire detections per day from satellite fly overs
providing valuable near-real time information. In this animation fire detections are shown
for the 2019 Shovel Creek Fire near Fairbanks. Some satellite flyovers produce no usable
information due to atmospheric interference such as clouds.

Using Citizen Science to Help Monitor Air Quality–A Poster

The Environmental Protection Agency (EPA) has just ~15 official air quality monitoring sites around the immense area of Alaska to monitor air pollutants that can affect human health.   Wildfire smoke, for example, produced about 60,000 tons of PM2.5 in 2018 (400,000 acres were burned –just a moderate fire season for Alaska!)  If data from lower quality private and academic air sensors (called “Purple Air”) could also be used, we could add an additional 100 monitoring sites to better understand and forecast air quality.  NASA ABoVE scientists Allison Baer and Tatiana Loboda from the University of Maryland compared EPA and Purple Air sensor data and came up with calibrations that correlate extremely well (coded T&RH—see example graphic below).  You can view their Interactive Poster at the 6th ABoVE Science Team meeting—this week (Jun 1-4): https://astm6-agu.ipostersessions.com/default.aspx?s=09-98-87-A0-E6-1A-FA-E4-79-58-CF-F8-B6-54-4B-79

SiteCorrPurpleair-Baer

Example correlation from one private air quality monitoring station in Fairbanks.

Spatiotemporal patterns of overwintering fire in Alaska

This Fire Science Highlight is available as a standalone PDF

Spatiotemporal patterns of overwintering fire in Alaska 

Rebecca Scholten and Sander Veraverbeke – Vrije Universiteit Amsterdam

What are holdover and overwintering fires?

Fires can appear to be out, but retain smoldering combustion deep in the fuelbed and flare up again when the weather favors flaming behavior and fire spread. This phenomenon occurs not unfrequently in boreal forests of North America, and presents a well-known challenge to firefighters. Over the last two decades, fire managers noted increasing occurrences where fires survive the cold and wet boreal winter months by smoldering, and re-emerged in the subsequent spring.

Scientists and managers seek better understanding of how these fires sustain during such unfavorable conditions. Fire managers have already started targeting locations where they expect fires to flare up again. However, they are missing detailed information on the environmental and climatic factors that facilitate these fires. This information is crucial to detect fires at an early stage and keep firefighting costs low. A research group at Vrije Universiteit Amsterdam is studying when and where these holdover fires emerge and how their occurrence is tied to specific geographic locations.

holdover3

 

Mapping overwintering fires from satellite data
Since 2005, fire managers reported data on 39 holdover fires that survived winter in Alaska. However, the location and emergence date of these fires were used in conjunction with satellite data to develop an algorithm for overwintering holdover detection. From satellite imagery, we can only observe fires that are large enough to generate a considerable amount of heat and burn a large enough area. Consequently, 32 out of 39 reported overwintering fires were too small (all smaller than 11 ha, 25 out of 32 smaller than 1 ha) to be detected from space. The location and emergence date of these small overwintering fires were used for the calibration of an algorithm focused on large overwintering fires. From the remaining seven large reported overwintering fires, our algorithm classified 6 out of 7 as overwintering fire. In addition, our approach revealed 9 large overwintering fires that were not reported by agencies between 2002 and 2018 in Alaska. A results paper is currently in preparation.

The spread rate of smoldering fires is known to be very low, and a smoldering fire would spread only between 100 and 250 m in an entire year (Rein, 2013). So, overwintered fires usually emerge within or close to the previous year fire (Fig.1) and can re-emerge with flaming behaviour as soon as favourable burning conditions appear in spring develop in to flaming forest fires before the major lightning-induced fire season. The onset of warm and dry conditions varies from year to year depending on the winter and spring temperatures and precipitation. These variables also shape the regional snowmelt day, which can be inferred from satellite observations. Indeed, our research indicates that holdover fires usually re-emerge within 50 days after the regional snowmelt. Overwintering fires are more likely to occur the year after a large fire
year (Fig. 2).

holdover1

 

Can we predict where overwintering may re-emerge?

It is not only important to know when these fires emerge, but also where. We therefore analyzed spatial drivers of the overwintering fires we detected. Our research indicates that holdover fires are facilitated in those regions of a fire perimeter that had burned deeper into the organic soil the year before. Deep burning is a characteristic of a high severity fire. We also observed that overwintering fires were more likely to emerge in lowland areas with black spruce-dominated forest. Overwintering fires thus have some temporal and spatial predictability. Monitoring the edges of fire perimeters from the preceding year in lowland forested peatlands early in the fire season, and especially after a year with large burned area, may prove beneficial to extinguish flare-ups from overwintering fires before they develop into a large flaming forest fire. This could be a cost-efficient strategy for fire management agencies. In addition, this would preserve terrestrial carbon by safeguarding it from combustion.

holdoverfig2

Figure 2: Years with a large burned area (grey bars) are more likely to generate
overwintering flare-ups (orange bars) than years with less burned area

References:

Rein, G. (2013). Smouldering Fires and Natural Fuels. In C. M. Belcher (Ed.), Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science (pp. 15–34). https://doi.org/10.1002/9781118529539

Turetsky, M. R., Benscoter, B., Page, S., Rein, G., Van Der Werf, G. R., & Watts, A. (2015). Global vulnerability of peatlands to fire and carbon loss. Nature Geoscience, 8(1), 11–14. https://doi.org/10.1038/ngeo2325
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Upgrading Satellite Mapping of Burn Severity

As discussed in the Feb. 7 Fire Science Highlight, burn severity in Alaska is best related to the amount of consumption of the forest floor—not the degree of tree canopy mortality as is in temperate pine and fir forest.  Yet the most commonly applied metric to map burn severity using satellite remote sensing does not correlate well with substrate burn severity.  The change in Normalized Burn Ratio (dNBR; Key and Benson 2003) is based on comparing a pre- and a post-fire image. However, NBR thresholds for severity differ from one fire to another and among different years: similar numbers don’t indicate the same severity levels (D. Chen et al. 2020).  And with tundra fires, sometimes it works, other times not.  This problem has dogged fire effects and ecology studies in Alaska for some time (see list of papers in Sean Parks November 2019 presentation) leading French et al. (2008) to conclude: “Satellite remote sensing of post-fire effects alone without proper field calibration should be avoided.”

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2008 Transect photo from Anaktuvuk River tundra fire (R. Jandt)

Recently, we’ve seen some promising new methods used to improve satellite remote sensing of burn severity in boreal forest.  Whitman et al. compared several indices including a relativized index that facilitated comparisons between different fires in Canada.  She told us about it at the Opportunities to Apply Remote Sensing in Boreal/Arctic Wildfire Management and Science Workshop in 2017—here’s her presentation if you missed it: Improving Remotely Sensed Multispectral Estimations of Burn Severity in Western Boreal Forests.  Loboda et al. ( 2020) found single images using just NIR (near-infrared) bands of Landsat did better than NBR in discriminating tundra fire severity.  Sean Parks is attempting to harness the power of Google Earth Engines and cloud-based computing to use multiple images to further define the ecological burn severity (Parks et al. 2019)—this work is kicking off at the University of Montana.  He also found that unusual aspects of some fires in Alaska (pre-existing beetle kill, short fire return interval) contribute to poor performance of the standard index (see his recorded November, 2019, Association of Fire Ecology meeting presentation HERE).  And Yaping Chen, from the University of Illinois, explored using indices based on Visible and NIR bands (which have a large archive of available imagery going back to the early 1970’s) to evaluate tundra fire severity.  Her paper (Y. Chen et al. 2020) points to a VNIR index called GEMI as a “robust surrogate to NBR in Arctic tundra ecosystems, capable of accurately estimating fire severity across fire seasons, tundra fires, ecoregions, and vegetation types.”  The fact that GEMI is not as influenced by different vegetation types as dNBR gives it a distinct advantage mapping tundra burn severity.

Being able to more accurately map burn severity levels from space would give ecologists a boost for understanding why fires sometimes induce radical changes in ecosystems while other times the system self-replaces in a very short span.  For example, Yaping Chen used GEMI to reconstruct burn severity on older tundra fires like the 1977 example below and tie it to thermokarst effects (like catastrophic lake drainage or ponding) resulting from the fires (poster presented at AGU meeting December 2019).  We look forward to more exciting products and tools coming from these research teams!

Y. Chen et al. 2020, Fig. 7

Reconstructed fire severity map of the 1977 OTZNNW 38 tundra fire computed with dGEMI using Landsat MSS imagery.

Citations:

Chen, Yaping; Lara, Mark J.; Hu, Feng Sheng. 2020. A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing 159:101-113.

Chen, Dong; Loboda, TV.; Hall, JV. 2020. A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing 159:63-77.

French, NHF.; Kasischke, ES.; Hall, RJ.; Murphy, KA.; Verbyla, DL.; Hoy, EE.; Allen, JL. 2008. Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results. International Journal of Wildland Fire 17(4): 443-462.

Key, Carl H.; Benson, NC. 2003. The normalized burn ratio (NBR): A Landsat TM radiometric measure of burn severity. US Geological Survey Northern Rocky Mountain Science Center.

Loboda, Tatiana V.; Hoy, EE.; Giglio, L; Kasischke, ES. 2011. Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm. International Journal of Wildland Fire 20(4):487-496.

Parks, SA.; Holsinger, LM.; Koontz, MJ.; Collins, L; Whitman, E; Parisien, MA; Loehman, RA.; Barnes, JL.; Bourdon, JF; Boucher, J; Boucher, Y; Caprio, AC.; Collingwood, A; Hall, RJ.; Park, J; Saperstein, LB.; Smetanka, C; Smith, RJ.; Soverel, NO. 2019. Giving ecological meaning to satellite-derived fire severity metrics across North American forests. Remote Sensing 11(14):1735.

Whitman, E, MA Parisien, DK Thompson, RJ Hall, RS Skakun, and MD Flannigan. 2018. Variability and drivers of burn severity in the northwestern Canadian boreal forest. Ecosphere 9(2):e02128. 10.1002/ecs2.2128

Stand Conversion or Back to Black Spruce? Key New Findings

In Alaska, we know that post-fire recovery of spruce forest generally takes one of two major pathways.  Fires in spruce forest burn with high intensity and it is typical for 90-100% of the standing trees to be killed in the fire, so trees primarily regenerate by seeds released from the cones—often preserved and dried in the dead snags with the heat of the fire.  What happens next is largely dependent on the amount of forest floor moss layers consumed in the blaze:  if much is consumed, maybe even leaving patches of mineral soil, we consider this a high severity fire, whereas if only a few centimeters of moss duff have been removed, we consider the fire severity to be low.  Black spruce

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Photo by USFS, PNW (2004).

readily re-establishes itself after low-to-moderate severity fires because its abundant and relatively large seeds can germinate and survive a few dry spells in the peat-like substrate of the remaining organic forest floor.  This is called “self-replacement”.  Other tree species, including white spruce and the deciduous trees prefer a more exposed mineral seed bed, which may offer more consistent moisture and nutrient availability. After higher severity fires, grass and fireweed are notable early on and followed by a period of shrub and deciduous tree seedlings and re-sprouts.  The dominance of the forest may then shift to aspen, birch and/or poplar for a period of years.  Ultimately, more shade-tolerant but slow-growing spruce will again dominate, but this may take 50-100 years.  This pattern of recovery is termed “relay succession”. The moisture available at the site and pre-fire species composition also influence recovery, as illustrated by Johnstone, Hollingsworth and Chapin in the Key for Predicting Postfire Successional Trajectories in Black Spruce that they prepared for managers in 2008 (below).

Now, here’s the punch line:  Alaska ecologists have long been asking themselves what percent of the time, over the whole landscape, does self-replacement vs. relay succession occur?  For many modeling efforts to date (LANDFIRE, for example) we had only our best guesses.  At a scientific meeting in December 2020 (American Geophysical Union) Jennifer Baltzer, a Canadian forest ecologist, related the findings of a large ecological study with over 1,538 burn study plots across Alaska and western Canada (1,140 of the plots were black spruce forest).  Her team demonstrated that about 62% of the time burned black spruce forest recovers by self-replacement (73% for all conifer forest types), while approximately 20% of the plots were headed for a relay type succession.  Another 20% or so of the plots were showing little forest regeneration (regeneration “failure”)—which seemed to be more likely after repeat burns in a fairly short time period. The exciting thing about this research is that it provides—for the first time–quantitative estimates for these competing post-fire pathways. The research is being reviewed for publication now–we’ll let you know!

 

Fire deficit increases the wildfire risk around communities in the Canadian boreal forest

The 8th International Fire Ecology and Management Congress, hosted by the Association for Fire Ecology had a remarkable special session “Fire in the Last Frontier: 21st Century Fire Patterns, Behavior, and Pyroecology of North American Boreal Forests and Tundra”. The Alaska Fire Science Consortium would like to highlight a few of these presentations.

Marc-André Parisien, of the Canadian Forest Service gave a presentation that addressed the effects of fire suppression on communities in boreal Canada.

Decades of fire suppression have resulted in a fire deficit around many communities in boreal Canada. Although human ignitions are 50 times more frequent within 5 km of a village, the percent of area burned within 30 years was 5-15% less around the village than in the surrounding Fire Management Zone.

“Suppression activities can offset the increased fire likelihood… until they don’t”

Since recently burned areas provide some moderation to new fire entry (especially under normal weather conditions), Parisien concludes fire suppression is increasing the risk around communities.

As an example, the 2016 Fort McMurray (top-right panel of figure) fire had only 2% recently burned-forests (pre-fire RBF) within 25 km. This is a much lower area of recently burned forests compared to an average of 42% in areas of the same fire regime zone (FRZ). After the explosive 2016 fire event, the forested area around town was 70% burned (post-fire RBF).

parisien

What’s the solution? Letting wildfire enter the WUI is risky business for managers, and prescribed fires create a lot of smoke and may be complicated by overlapping land ownership. One possibility is biomass utilization projects, which are being tapped by some communities in Canada and Alaska (Erni et al., 2017).

Want to learn more? The full presentation can be viewed here: https://www.frames.gov/catalog/60363

Citations:

Parisien, M-A., Q. Barber, K. Hirsch, C. Stockdale, S. Erni, X. Wang, D. Arseneault, and S. Parks. 2019. Fire deficit increases the wildfire risk around communities in the Canadian boreal forest. Lecture at the 8th International Fire Ecology and Management Congress.  (This research is being prepared for publication).

Erni, S., D. Arseneault, and M.-A. Parisien. 2018. Stand age influence on potential wildfire ignition and spread in the boreal forest of northeastern CanadaEcosystems 21, 1471–1486.

Erni, S., D. Arseneault, M.-A. Parisien, and Y. Begin. 2017. Spatial and temporal dimensions of fire activity in the fire‐prone eastern Canadian taiga. Global Change Biology 23:1152–1166. (Firescar study of reconstructed 300 years of fire activity in Quebec to examine relative effects of climate/ weather vs. forest age controls on fire activity. In younger stands, burn rate was lower for up to 50 years, depending on landscape).

Fire management adaptability in Alaska: as seen by the managers

Tait Rutherford and Courtney Shultz just published the results from the social science part of their Joint Fire Science Program (JFSP) funded study: Impacts of Climate and Management Options on Wildland Fire Fighting in Alaska—see full citation below. The paper seeks to understand strengths and weaknesses of the Alaska fire management process and how cooperating agencies are adapting to changes in the fire environment with warming climate. The data for the analysis came from 41 hour-long interviews with fire management decision-makers across Alaska, which were categorized and analyzed for common themes.

The authors note that “bridging” institutions can be “repurposed to meet new challenges” and can provide key assistance to more hierarchical federal and state agencies in adapting to new issues (including climate change). Examples of this in action at the national level were on display at the recent meeting of JFSP regional Fire Science Exchange Networks in Washington, DC. It was interesting how diverse the main business lines were in different regions. For example, Hawaii’s Pacific Fire Exchange focuses mainly on community protection and invasive species, several exchanges are deeply engaged in supporting training and workforce development to implement prescribed burns, and California Fire Science Consortium is gearing up efforts to help those already stricken by wildfire and looking into new closer working relationships with FEMA. Another example of “bridging” mentioned by several interviewees in Alaska was the Kenai Peninsula All-Lands All-Hands working group, which has been very instrumental in coordinating inter-agency fuelbreaks.

Rutherford, in summarizing manager’s views, notes that some challenges are enduring (like WUI protection) but a few emerging issues are also highlighted. For example, regarding subsistence use opportunities, participants indicated that the maintenance of wildlife habitat will require both using fire and fire suppression to support a diversity of age classes and forest cover types on the landscape. There is a growing recognition of the need for enhanced policy and management tools to support “point protection” of values like private lands and cabins, including improved data and interagency communication and efficient protection techniques. In short, the collection of viewpoints is very instructive about the “state of the art” of fire management as seen by the experts and executors of that art. A highlight of the paper is the Appendix, which includes 64 quotes from the interviews, allowing one to hear “from the horse’s mouth” about current priorities and challenges in Alaska fire management as well as potential future directions and requirements to meet new challenges.

Citation:  Rutherford, T. K., and C. A. Schultz. 2019. Adapting wildland fire governance to climate change in Alaska. Ecology and Society 24(1):27.

Download is Open Access at: https://www.ecologyandsociety.org/vol24/iss1/art27/

 

Building a Better Mousetrap to Estimate Dead Grass Fuel Moisture

Who says you can’t build a better mousetrap?  Local BLM Alaska Fire Service ecologist Eric Miller recently published a study using his extensive data on dead grass fuel moisture in Alaska to compare the performance of several models to predict moisture content from environmental variables.  Currently, Van Wagner’s (1969) model, with tweaks and add-on’s, is used in the Canadian Forest Fire Danger Rating System fire weather indices.  Eric notes that statistically Van Wagner’s model is overly complex, and at least 3 models used in industry or other processes that account for ambient temperature and relative humidity (or alternatively, dewpoint depression) can model standing dead grass fuel moisture in Alaska pretty well.  Fuel moisture content is integral to fire behavior prediction and fire danger ratings, and the reason standing dead grass moisture content can be predicted so reliably is that its thin structure and aeration keeps it very close to equilibrium with atmospheric moisture.  The drying lag time may be even less than 1 hour, although it is often referred to as a “1-hour lag time” fire fuel.  Eric’s working on some applications of his findings for the spring prescribed fire season in Alaska’s military land holdings.  Check out the paper:  Miller, Eric A. 2019. Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska. MDPI Fire 2(2):1-18. https://www.mdpi.com/2571-6255/2/1/2

Eric has several tools available for practitioners in Alaska on his website: http://www.taigafire.org/,  including simple rules-of-thumb and CFFDRS calculators for fuel moisture content.  These are partially described in a 2015 AFSC Research Brief What is the moisture content of standing dead grass?

 

Research Brief on What NASA is Contributing to Alaska Fire Science

Capture-thumbRB2018-4It’s hard to keep up with the myriad investigations NASA ABoVE campaign is working on in Alaska.  This short research brief is a round-up of recently published fire effects field studies and remote sensing products research and has some LINKS to show you where to access some intriguing new datasets and project results.  The “Big Data” coming from ABoVE is going to be a big boost to conducting regional or state-wide fire trends and assessments–you’ll want to know where that data lives. Access the Research Brief at:

https://www.frames.gov/catalog/56894