Dec 23 2020

Wildfire detection gets a boost from space

Historically, wildfire detection in Alaska relied on keeping an eye on neighborhoods and on lightning storms.  Much of the detection around the state was done by aerial patrols beginning in 1973.  In fact, in 1978 the BLM had 12 aircraft dedicated solely to fire detection and another six smokejumper aircraft which often did loaded patrols after widespread thunderstorm activity!

Alaska Department of Forestry T-28 Trojan on the ramp at Ft. Wainwright, 1988. (Photos by Linn Clawson).  Below, pilot and observer were required to wear parachutes on missions in the WW2 vintage fighter-turned-detection aircraft

Then, at the turn of this century, it became apparent that the weather satellites MODIS Terra and Aqua could detect heat signatures of fire—fortunate timing because the price of contracted aircraft had skyrocketed and those surplus WW2 airplanes were mostly out of service. 

Alaskan fire managers excitedly tested the use of spaceborne images to make wildfire detection faster and less expensive.  Although these satellites are now nearing the end of their useful life (after 20 years in orbit), the VIIRS instruments aboard two newer satellites are starting to provide data.

Alaskan fire managers again are eager to make use of the new capacity, and are receiving help from key partners, inside and outside of Alaska.  Several talks and posters at the 2020 American Geophysical Union (AGU) meeting held virtually in December highlighted important facets of ongoing efforts to harness the latest science and technology for use in Alaska.

The above figure (R. E. Wolfe, et al. FIRMS US/Canada – An Extension of NASA Near Real-Time FIRMS for the Forest Service and Inter-agency Wildfire Management Community—Fig. 1) diagrams the extensive network of agency and institution partnerships that have been established to gather and serve fire detection data to meet fire agencies needs around the country.  With respect to the polar-orbiting satellites carrying some of these sensors, including VIIRS, Alaska find itself advantaged by twice as many daily satellite passes.  Even more exciting, the Alaska Satellite Facility (ASF)—in the Geophysical Institute on the UAF campus– downloads the raw data directly, without waiting for processing and server functions in the lower 48.  In the last couple years, ASF and GINA (Geographic Information Network of Alaska) have teamed up to feed VIIRS satellite detection hotspots directly to a digital map layer that can be accessed by fire managers less than one hour after the satellite passes.  Compare the simplicity of the Alaska model with the above path for MODIS “rapid-response” data!

Figure 2 VIIRS Data processing, based on information from Dr. Peter Hickman, UAF-GINA presentation at AGU: VIIRS Direct Broadcast Advances for Improved Wildland Fire Monitoring in Alaska.

This kind of state-of-the-art service has taken a lot of logistical planning, hard work, and scrambles for funding both at the University of Alaska as well as the Alaska Interagency Coordination Center.  The hard works pays dividends by giving fire management in Alaska a much needed boost against the background of longer, hotter fire seasons with flat suppression funding. 

Not only has the satellite “hotspot” data proved useful for finding fires, it also is boosting our ability to monitor, model, and predict fire spread.  A talk by Dr. Chris Waigl outlined how maximizing the use of three key remotely-sensed data streams:  snow-off date to help start fire danger index models, improved wall-to-wall rasterized visuals of fuel drying by region, and improved regional algorithms to maximize the accuracy and sensitivity of satellite hotspot identification, were all used to good effect by Alaska fire management in 2020.  Having the remotely sensed data to help with prioritization and decision-making was especially strategic during the COVID pandemic when managers were striving to spare staffing and reduce travel to rural villages.

Figure 3 Example of VIIRS fire heat points use in fire spread monitoring, P. Hickman.

The potential utility of remotely-sensed data for wildfire management has been recognized by scientists and agencies for some time, but it’s not always easy to bring a product from the laboratory to the operations room.  There have been many discussions at various national levels and even a grassroots workshop in Fairbanks “Opportunities to Apply Remote Sensing in Boreal/Arctic Wildfire Management & Science”sponsored by NASA and facilitated by the Alaska Fire Science Consortium in 2017 addressing the potential application of remotely-sensed data.  And although the story seemed very successful, given the above, it’s not over!  Canada plans to launch the world’s first dedicated wildfire monitoring satellite constellation, WildFireSat, in 2025. And now we are thinking of ways to harness Artificial Intelligence for fire detection and spread monitoring!  Stay tuned.

List of the AGU talks/posters referenced with links:

Hickman, Pete; Jenkins, Jennifer; Schmunk, Gary; Delamere, Jennifer; Dierking, Carl; Cable, Jay; Wirth, Greg; Seaman, Curtis; York, Alison; Ziel, Robert. 2020, VIIRS Direct Broadcast Advances for Improved Wildland Fire Monitoring in Alaska. Talk, Presented at 2020 Fall Meeting, AGU, 15 Dec.

Waigl, Chris 2020, Science-to-Operations for Alaska Wildfire Management in Times of COVID-19: Usability Lessons from Rapid Data Tool Development. Talk, Presented at 2020 Fall Meeting, AGU, 15 Dec.

Wolfe, Edward; Quayle, Bard; Davies, Diana; Ederer Gergory; Olsina Otmar. 2020, FIRMS US/Canada – An Extension of NASA Near Real-Time FIRMS for the Forest Service and Inter-agency Wildfire Management Community. Talk, Presented at 2020 Fall Meeting, AGU, 15 Dec.

Ziel, Robert; Schmidt, Jennifer; Calef, Monika; Varvak, Anna. 2020, Detecting Temporal Changes in Land Cover Based on Disturbance in Alaska. Poster, presented at 2020 Fall Meeting, AGU, 15 Dec.

Nov 11 2020

EpsCor Fire and Ice Team 2020 Research Updates

At their “All Hands” meeting in November a diverse array of researchers presented quick overviews of their findings the University of Alaska National Science Foundation-sponsored research project called EpsCor Fire & Ice.  The scope of projects—many guided by the participation of fire managers and other stakeholder groups in Alaska—was remarkable.  Below are a few sample highlights that will convince you to check out their slide deck summary from the meeting [HERE].

  • Alaska’s first ever study of wildfire smoke-related health outcomes (respiratory and cardiovascular) by Micah Hahn at UAA.  She used a database on emergency room visits in Anchorage, Fairbanks, and Matsu (which collectively could account for 60% of Alaska’s population) during wildfire seasons 2015-2019.  The biggest correlative effect with smoke seemed to be asthma:  In Anchorage, for example, a 13% increase in ER visits was noted on days of elevated wildfire smoke (PM 2.5) exposure.  A paper with full results is expected to be out soon.
  • Remote sensing specialists in the Boreal Fires team (Smith, Bandola, Panda, Waigl) continue to make headway with using newly available multi-spetral remotely sensed imagery and high-tech computational processes to improve Alaska fire fuels maps (Figure, below).  Managers and fire modelers have repeatedly stressed that inaccurate mapping of fuels is one of the biggest limitations currently impeding better fire spread modeling.
  • Remote sensing products can also improve the quality of burn severity maps, even in WUI areas where suppression is still active (Schmidt).
  • Homeowner surveys in fire-impacted areas revealed how much risk homeowners thought they had prior to the fires and how they were directly impacted (Schmidt).
  • Why do some fire scars have great morel mushroom crops and others don’t?  That vexing question was tackled (Yamin-Pasternak) with input from lots of participating harvesters who also pronounced the fire season of 2020 as the longest ever! Hint: they also ranked recent fires around the state relative to their productivity—a result you’re going to want to examine.
  • Erik Schoen and Ben Meyer from the UAF Institute of Arctic Biology studied the effects of the 2019 Shovel Creek fire on juvenile salmon.  Although differences were found in water quality and food availability in burned vs. unburned reference areas, the growth rates of the fish were similar.
Comparing accuracy of LANDFIRE and AVIRIS fuels mapping (C. Smith, AHM Nov. 4, 2020).

Those highlights ought to convince you to spend a few minutes looking at the slide deck from the meeting, just to see more items from this amazing interdisciplinary collaboration of science and management!  Likely there’s a researcher looking for collaboration and input from you in your fire specialty, and Alaska Fire Science Consortium can help you make a connection.  Go to https://www.alaska.edu/epscor/publications-presentations-posters/  and look for the 2020 Alaska EPSCoR All Hands Meeting, Boreal Fires component.

Jul 29 2020

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).

Jul 13 2020

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.

Jun 3 2020

NASA ABoVE Science Comes Down to Earth

Now halfway through it’s 9-year funding life, the NASA Arctic Boreal Variability Experiment (ABoVE) is connecting some high-level science findings with practical applications for a variety of stakeholders. The 6th ABoVE Science Team meeting held virtually June 1-4 highlighted a number of these and happily, recorded them for you as well. The interactive posters are well done and a quick read–look for a subject of interest to select a group of interest (e.g. “Fire” returned 9 posters). Not surprisingly, much ABoVE research is focused on big-picture questions like– Can we see trends in vegetation composition, disturbance (like fire and insects), release of greenhouse gases into the atmosphere, and regional weather conditions across the North American boreal region?  Specifically, ABoVE is harnessing 30+ years of earth satellite observations, big data management, and brand new remote sensing platforms to answer these questions.

Notably, in this year’s meeting we are seeing the APPLICATION of the science directly with stakeholder groups in Alaska and Canada on very specific management questions. For example:

  • Helping Alaska wildlife biologists determine if snow conditions are suitable for conducting the winter moose counts without wasting lots of airplane hours and fuel (Boelman)
  • Assisting management of the Tanana Valley State Forest by providing detailed stocking and biomass information from remote sensing and modeling the effect of management (harvest) into the future for planning (Lutz)

Lutz slide-TVSF CAC

This project is using remotely sensed forest biomass and canopy height to help harvest plans for forest lands around Fairbanks is a great example of practical applications. (slide: David Lutz)

  • Correlating human health outcomes in Alaska with remotely-sensed smoke conditions (Loboda)
  • Mapping wetlands and determining waterfowl habitat suitability and future climate impacts with Ducks Unlimited (French).
  • Fire managers have long coveted a remote sensing method to track moisture content in deep organic soils to indicate drought level and potential for large wildfires and deep combustion and there has been terrific progress on this subject (Schaefer, Tabatabaeenejad).
  • Widlife managers have desired a way to map and inventory lichen cover on caribou ranges:  Matt Macander’s ABoVE team has come up lichen cover maps that validate very well with aerial surveys (Epstein–slide 1).

 

Connect with us for more information on these projects, or others YOU may want to be involved in or use information from, and browse the Agenda to learn more about what else ABoVE is up to!

Jun 1 2020

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.

Apr 28 2020

Arctic Urban Risks and Adaptations: a co-production framework for addressing multiple changing environmental hazards

This Fire Science Highlight is available as a standalone PDF

Arctic Urban Risks and Adaptations: a co-production framework for addressing multiple changing environmental hazards

Jen Schmidt – University of Alaska Anchorage

Arctic Urban Risks and Adaptations (AURA) is a new, four-year NSF project addressing changing environmental hazards around Anchorage, Fairbanks, and Whitehorse. Understanding how wildfire hazards are changing, and the associated risks and costs are a primary focus of the study. Overlapping risks associated with wildfire, including permafrost thaw and rain-in-winter, are being integrated as a unified approach to assessing environmental hazards. The co-production framework of the project strives to use land manager and community input to produce decadal wildfire hazard mapping and assessment tools by leveraging existing datasets other resources.

Wildfire management: statewide fuels treatment database

Currently fuels treatment datasets are spread across multiple agencies and are often lacking important information such as year of treatment. As part of a new four-year project, Jen Schmidt (PI) and the EPSCoR Boreal Fires Team hope to collaborate with others to provide comprehensive-online GIS layers that land managers and others could use to efficiently find where fuel treatments may exist. Having a centralized fuel treatment database with aerial imagery and management zones (Figure 1) would allow agencies to quickly check for prior fuel treatments, providing a valuable tool to aid in decision making and planning.

Aurafig1

Assessing wildfire hazards over time

As part of the hazard analysis, Jen is employing recently completed NASA ABoVE (Arctic – Boreal Vulnerability Experiment) 2014 vegetation maps. These maps categorize 1984-2014 LandSat imagery of arctic and boreal forests in 15 vegetation types. The dataset has great application for visualizing vegetation change over time. In this chord diagram (Figure 2), you can follow transitions of vegetation groups in the Fairbanks North Star Borough by looking at the lines in the center of the chart connecting the groups from 1984-2014. For example, note the many transformations of “woodland” (tan) to multiple vegetation types, including “evergreen” (green) and “sparse” (gray) forests over the 30-year time period. Fire is a key driver of these vegetation changes and being able to track the transition of vegetation types can improve understanding of how vegetation composition changes in response to fire, which can aid in modeling wildfire hazards.

 

Aurafig2

For more information, check out the Jen Schmidt’s Webinar from the AFSC 2020 Spring Fire Science Workshop

References

Wang, J.A., D. Sulla-Menashe, C.E. Woodcock, O. Sonnentag, R.F. Keeling, and M.A. Friedl. 2019. ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1691

Mar 25 2020

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
fsh highlights icon3

Feb 21 2020

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.”

ARF63_0-50m_2008

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

Feb 7 2020

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

video-only-site-roadsite-burnout-7

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!