Monitoring change in Alaskan forests
Scientists are using aerial and ground survey methods to measure change in Alaska’s interior forests and its impact on ecosystem services.
The boreal forests of interior Alaska are changing rapidly as the climate warms. Wildfires are more frequent and more severe, and declines in growth of spruce trees may be driving a shift towards ecosystems normally found in warmer climates. These changes can have significant impacts on the quality of wildlife habitat and ecosystem services that support the subsistence economies of many native Alaskan communities. Despite their importance, the condition of these forests and the trends affecting them are still relatively poorly understood, due in part to the lack of a comprehensive inventory of this region. USGCRP observational efforts are investigating patterns and processes of change in Arctic and Alaskan forests, establishing a baseline for monitoring future change and understanding its broader impacts on regional ecosystem services and global climate change.
The USDA Forest Service’s Forest Inventory and Analysis Program, in conjunction with NASA’s Goddard Space Flight Center, carried out a pilot test of a new forest inventory and monitoring design for interior Alaska in 2014 [1]. Using field-based and airborne measurement techniques, researchers inventoried approximately 2.5 million acres (about 4000 square miles) of forestlands in the Tanana Valley State Forest and the Tetlin National Wildlife Refuge. NASA Goddard’s Light Detection and Ranging (LiDAR), Hyperspectral and Thermal (G-LiHT) airborne imaging system was used to map the forest ecosystem in detail, resulting in fine-scale (approximately one meter) data that allows assessment of forest health and the productivity of individual trees and groups of similar trees over time (see figure) [2]. Detailed terrain measurements acquired from G-LiHT can also be used to detect and characterize features on the land surface associated with permafrost distribution and change.
In addition, NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) has initiated research to understand drivers of wildland fires in North American boreal forests and the impacts of changes to the fire regime on ecosystem processes. ABoVE research has shown that recent increases in annual burned area is in part due to increases in the frequency of lightning strikes. [3] NASA-funded researchers also developed a way to determine a tree’s age using a LiDAR-based remote sensing mapper deployed as part of ABoVE. Scientists found that tree height, which can be mapped with LiDAR, correlates with tree age in northern treeline areas.
Results from these projects will help the Forest Service and others better understand the status and condition of Alaska’s interior forests and the impact of climate change on important ecosystem services. The inventory design developed and tested during this pilot study was refined and fully implemented in the FIA inventory of interior Alaska that began in 2016.
1 Pattison, Robert; Andersen, Hans-Erik; Gray, Andrew; Schulz, Bethany; Smith, Robert J.; Jovan, Sarah, tech. coords. 2018. Forests of the Tanana Valley State Forest and Tetlin National Wildlife Refuge, Alaska: results of the 2014 pilot inventory. Gen. Tech. Rep. PNW-GTR-967. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 80 p. https://www.fs.fed.us/pnw/pubs/pnw_gtr967.pdf
2Cook, B.D., L. Corp, R. Nelson, E. Middleton, D. Morton, J. McCorkel,… P. Montasano. 2013. NASA Goddard’s LidAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. Remote Sensing 2013 5 (8), 4045–4066. http://dx.doi.org/10.3390/rs5084045
3 Veraverbeke, S., B. M. Rogers, Goulden, M. L., Jandt, R. R., Miller, C. E., Wiggins, E. B., and Randerson, J. T. 2017. Lightning drives recent extreme fire years in the North American boreal forest, Nature Climate Change, 7, 529-534

G-LiHT measurements for an area within Bonanza Creek Experimental Forest near Fairbanks, Alaska that map the structure, function and composition of the forest ecosystem: a) terrain model, b) forest canopy height model, c) normal-color image derived from hyperspectral sensor data, and d) color-infrared image derived from hyperspectral sensor data. In c) and d), continuous spectral profiles for soil and forest features are shown in insets. Collectively, these images show detailed characteristics of the forest ecosystem that can be used to assess forest health and productivity and to predict ecosystem responses to change over time. Source: NASA.