Fuel Treatment Preferences of Alaskan Homeowners

A new study just published in Sustainability surveyed Fairbanks Northstar Borough and Kenai Peninsula Borough homeowners about their willingness to pay for types of fuelbreaks on their property, their neighbor’s property and how public land treatments nearby affected their choices. Molina et al. found that surveyed homeowners (n=358) had a greater willingness-to-pay for fire hazard reduction when a moderate number of neighbors (1-4 neighbors) engaged in property mitigation. They were less enthusiastic when nobody else was participating, or on the other hand–when they perceived too many neighbors were clearing fuels. Shaded fuel breaks–like thinning treatments–were preferred to clearcutting. Read the article (open access) here: https://www.mdpi.com/2071-1050/13/21/11754/htm

Fuelbreak around Tanacross, Alaska

Molina A, Little J, Drury S, Jandt R. Homeowner Preferences for Wildfire Risk Mitigation in the Alaskan Wildland Urban Interface. Sustainability. 2021; 13(21):11754. https://doi.org/10.3390/su132111754

Making Sense of Changing Forests in Alaska

By Randi Jandt

This research brief is available as a standalone PDF

Forestry research in Alaska indicates that coniferous-dominated interior boreal forests are being replaced by deciduous trees due to recent climate warming and changes in the wildfire regime. Mann et al. 20121 suggested a dramatic shift in forest species dominance was already happening, since the 1990’s. Interior Alaska forests have averaged about ⅔ coniferous to ⅓ deciduous over the last several hundred years. Using landscape disturbance models under various global climate scenarios, Mann’s team predicted the ratio would soon be 1:1 and even reverse by mid-century, with hardwood stands comprising ⅔ of the forests. Now, 30 years of satellite imagery and improved vegetation mapping methods using new technology offer a chance to test their predictions.

Post-fire succession: When Alaska spruce forests burn the trees are killed, but black spruce stands tend to “self-replace” about ⅔ of the time—by seeding into the remnant moss layers2. On the other hand, it has been well-documented that “severe” (deep-burning) forest fires favor regrowth of hardwoods (birch, aspen and poplar). These species can grow fast in deeper active layers that result after severe fires in which most of the moss duff is removed3. This can result in a “relay succession” recovery where hardwoods dominate the canopy for a time before slower growing spruce can regain dominance of the canopy. In either case, if the stand reburns within about 50 years, small black spruce in the understory are destroyed before developing a robust seed bank, and the stands tend to become increasingly hardwood dominated. Ongoing studies to refine these parameters in eastern interior Alaska reveal that pure black spruce stands transitioned to deciduous forest about half the time after a severe fire4. Coniferous forest with even a small percentage of deciduous stems (> 7%) succeeded into a mixed-wood composition (50:50) and stands which had >30% pre-fire deciduous stems became exclusively deciduous.

Why is a shift in forest species important? Spruce forests have vastly different properties than broadleaf forests. These differences include: higher flammability, lower water uptake, slower litter decomposition rates, different atmospheric heating properties (surface albedo), and carbon sequestration capacity. Of course, they also differ with respect to supplying wood, game, and berries to humans. Black spruce trees thrive in cold, moist soil conditions, and actually enhance these conditions by accumulating a deep mossy soil organic layer. The moss layer is often too moist–with snowmelt water, rainfall, and seasonally thawing ice below–to support combustion beyond a few centimeters deep. Most of the carbon (C) stored in these slow-growing forests is in the moss and soil organic layers. Warmer summers and extended burning seasons in late summer tend to allow the moss duff layers to dry faster and deeper. In western Canada, 90% of C combusted in extensive 2014 fires in black spruce forest came from the forest floor 5. Deciduous trees, on the other hand, thrive in nutrient rich, dry and (relatively) warm soils, and reinforce these conditions with high decomposition rates, so the soil organic layer tends to be shallow. Their faster growth rate (productivity 5-7 times greater than black spruce forest) means that they store most of their C aboveground in the trees themselves6. Deciduous trees also require more water to sustain that rapid growth—a lot more! It was recently discovered that Alaska broadleaf trees take up 25% of available spring snowmelt water (compared to about 1% for spruce)7. But mature spruce forests (aged 70-120 years) win out when it comes to long-term C storage — they had 4-10 times the C stored in their organic soils (2.0-5.7 kg C m-2) compared to 50-year-old stands8.

Was the forest shift prediction accurate? Back to the changing forest composition. What does the latest research tell us about the predictions of a decade ago? As part of a multi-year NASA study, Wang, et al. 20209 used an enormous data set of satellite-derived vegetation cover type estimates covering all Alaska and northwestern Canada from 1984 to 20149. They documented a net loss of 14.7% (+/- 3%) coniferous forest along with an equivalent net gain of deciduous forest. These results seem consistent with the predicted forest composition shift caused by a more active fire regime in boreal forests relative to the last several millennia. In an ongoing separate study, preliminary data showed that in burned areas of the boreal biome the ratio of deciduous to evergreen forest cover increased 14.4% (from 23.4% to 37.8%) from 2001-2016, which was 3.3 times higher than in unburned areas10. However, these same authors also documented that mixed and deciduous forest types may be burning more now compared to previous decades, perhaps even burning at a faster rate than they were being replaced during the study time window. In addition, by coupling two types of remotely sensed data for vegetation, the researchers could estimate forest biomass and tree-canopy structure. This analysis showed that recovering burned forest, whether it succeeded to deciduous forest or remained evergreen, still had overall lower biomass and canopy density compared to areas that did not burn. This finding has implications for management scenarios involving fire use to reduce future fuel loadings, and also for carbon sequestration studies. Separate investigations confirm the loss of forest biomass across eastern interior Alaska and predict this will become the modern trend as the climate warms4. Impacts on wildfire and permafrost dynamics will result in overall decreases in biomass (particularly for spruce within the interior Tanana Valley, despite increases in quaking aspen biomass) and result in a continued shift towards a higher deciduous fraction. To a lesser extent, increased biomass is seen at certain locations, such as cold or wet locations, and at high elevations, such as along the north slopes of the Alaska range11. In summary, recent studies seem to support the predictions made a decade ago1 with respect to spruce forest declining and broadleaf and mixed forest increasing across interior Alaska.

What about negative feedbacks to boreal burning? Deciduous forests are more resistant to fires than spruce forests—a guiding principle of much fire suppression and fuel treatments strategy12. Under average summer conditions, coniferous forest is more conducive to large fire spread and accounts for at least half of the average yearly burned extent. Since hardwood forests are less flammable, why doesn’t a negative feedback kick in to reduce flammability as deciduous forests become more common on the landscape? The short answer is that most mega-fires burn in unusually hot/dry summers, and once the fire weather gets extreme enough, deciduous forests and regenerating forests become equally receptive to burning13. The “unusual” heat is becoming more “usual” in recent decades, and future climate scenarios predict dramatic increases in high fire danger days. As for the future of forests in Alaska, new models at the stand level are being developed which include the latest understanding of forest environmental drivers and physiological constraints4,11. A research study funded by the Department of Defense in eastern interior will be sharing findings regarding fire-prone areas of Alaska from Fairbanks to the Canadian border, complete with web visualization tools for managers (beta version:

The takeaway: Alaska’s boreal forest is undergoing major changes driven by the domino effects of changing climate. Boreal forests are experiencing changes in the fire regime, which through interactions with the ecological and physiological attributes of trees are causing widespread shifts in forest composition. Those shifts in turn are causing a shift towards net C release to the atmosphere which could accelerate global warming. Overall, these shifts in forest species dominance are a fascinating example of how even a relatively simple biome like boreal forest can have complex responses to changes in climate.

Many thanks to Dr. Dan Mann and Dr. Eric Deutsch for helpful discussion and
interpretation and to Zav Grabinski for editing help.

1 Mann, D. H., T. S. Rupp, M. A. Olson, and P. A. Duffy. 2012. Is Alaska’s boreal
forest now crossing a major ecological threshold? Arctic Antarctic and Alpine
Research, v. 44, no. 3, p. 319-331.

2 Baltzer, Jennifer et al. 2019. Widespread ecological reorganization of boreal
forests following severe wildfires. AGU Poster B33B-07, San Francisco, CA Dec.
9-12, 2019.

3 Hollingsworth, T. N., J. F. Johnstone, E.L. Bernhardt, S. F. Chapin. 2013. Fire severity filters
regeneration traits to shape community assembly in Alaska’s boreal forest. Plos One 8(2):e56033.

4 Goetz, Scott, et al. (ongoing) SERDP Project RC18-C2-1183: Resiliency and Vulnerability of Boreal Forest Habitat across DoD Lands of Interior Alaska.

5 Walker, X. et al. 2018. Cross-scale controls on carbon emissions from boreal forest megafires. Global Change Biology 24 (9): 4251-4265.

6 Alexander, H. and M. Mack. 2016. A Canopy Shift in Interior Alaskan Boreal Forests: Consequences for Above and Belowground Carbon and Nitrogen Pools during Post-fire Succession. Ecosystems 19: 98-114.

7 Young-Robertson, J. M., W.R. Bolton, U.S. Bhatt, J. Cristóbal, R. Thoman. 2016. Deciduous trees are a large and overlooked sink for snowmelt water in the boreal forest. Scientific Reports 6:29504.

8 Hoy, E.E., M.R. Turetsky and E.S. Kasischke. 2016. More frequent burning increases vulnerability of Alaskan boreal black spruce forests. Environ. Res. Lett.11 095001

9 Wang, J. A., D. Sulla-Menashe, C.E. Woodcock, O. Sonnentag, R.F. Keeling and M.A. Friedl. 2020. Extensive Land Cover Change Across Arctic-Boreal Northwestern North America from Disturbance and Climate Forcing. Global Change Biology 26, 807–822.

10 Deutsch, E.J. and M.L. Chipman. 2020. Observations of Post-Wildfire Land Cover Trends in Boreal Alaska Using Geospatial Analyses. AGU Poster, Virtual, Dec. 1-17, 2020.

11 Foster, A.C. et al. 2019. Importance of tree- and species-level interactions with wildfire, climate, and soils in interior Alaska: Implications for forest change under a warming climate. Ecol. Modeling 409: 108765.

12 Beverly, J. L. 2017. Time since prior wildfire affects subsequent fire containment in black spruce. International Journal of Wildland Fire 26:919–929.

13 Barrett, K, T. Loboda, A.D. McGuire, H. Genet, E. Hoy, and E. Kasischke. 2016. Static and dynamic controls on fire activity at moderate spatial and temporal scales in the Alaskan boreal forest.
Ecosphere7(11): e01572. 10.1002/ecs2.1572.

14 Dash, C.B., J.M. Fraterrigo, and F.S. Hu. 2016. Land cover influences boreal forest fire responses to climate change: geospatial analysis of historical records from Alaska. Landscape Ecology 31:1781–1793.

Do Humans Contribute to Increased Fire in Southcentral Alaska?

A pair of new 2021 papers take different tracks to assess the impact of human activities and anthropogenic climate warming, on fire season in south central Alaska. We still remember how smoke choked the Kenai and Matsu boroughs in 2019, for most of June-August. A University of Alaska team tallies the impacts–in $$, losses, and human health, while also placing the season in a historical context to look for anthropogenic influence. They deemed human influences thus far were less important than weather, but would become more of a factor by mid-century. An important finding was that heating seemed to overpower increased precipitation (over longer timescales): “The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk.” Read the full paper HERE: Uma S. Bhatt, et al. 2021. Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019. Land. 2021; 10(1):82. https://doi.org/10.3390/land10010082

A second team, led by Princeton scientist Yan Yu, did a different type of analysis and tried to incorporate other factors such as anthropogenic ignitions, population density, and increased conifer biofuels. Although the increased fuels may be largely an assumption in their paper, at least for the Kenai region it is validated by Carson Baughmann’s (USGS) 2020 study: Four decades of land-cover change on the Kenai Peninsula, Alaska: detecting disturbance-influenced vegetation shifts using Landsat Legacy data. Yu’s team asserts there is evidence that part of the increase in fire risk is human-caused: “The . . . model indicates a threefold increased risk of Alaska’s [southcentral region] extreme fires during recent decades due to primarily anthropogenic ignition and secondarily climate-induced biofuel abundance.” Read their paper HERE: Yan Yu, et al. 2021. Increased Risk of the 2019 Alaskan July Fires due to Anthropogenic Activity, Bulletin of the American Meteorological Society 102(1): s1-s7. https://journals.ametsoc.org/view/journals/bams/102/1/BAMS-D-20-0154.1.xml

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.

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.

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.

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!

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


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

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.


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.



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


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