Understanding change in forest carbon (C) is important for devising strategies to reduce emissions of greenhouse gases. National forest inventories (NFIs) are important to meet international accounting goals, but data are often incomplete going back in time, and the amount of time between remeasurements can make attribution of C flux to specific events difficult. The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. The study was conducted in the forests of southwest Oregon State, USA, a region of diverse forest types, disturbances, and landowner management objectives. Plot data consisted of 676 NFI plots with remeasured individual tree data over a mean interval (time 1 to time 2) of 10.0 years. We calculated change in live aboveground woody carbon (AWC), including separate components of growth, mortality, and harvest. We interpreted radiometrically corrected annual Landsat images with the TimeSync (TS) tool for a 90 m X 90 m area over each plot. Spectral time series were divided into segments of similar trajectories and classified as disturbance, recovery, or stability segments, with type of disturbance identified. We calculated a variety of values and segment changes from tasseled cap angle and distance (TCA and TCD) as potential predictor variables of C flux. Multiple linear regression was used to model AWC and net change in AWC from the TS change metrics. The TS attribution of disturbance matched the plot measurements 89% of the time regarding whether fire or harvest had occurred or not. The primary disagreement was due to plots that had been partially cut, mostly in vigorous stands where the net change in AWC over the measurement was positive in spite of cutting. The plot-measured AWC at time 2 was 86.0 ± 78.7 Mg C ha−1 (mean and standard deviation), and the change in AWC across all plots was 3.5 ± 33 Mg C ha−1 year−1. The best model for AWC based solely on TS and other mapped variables had an R2 = 0.52 (RMSE = 54.6 Mg C ha−1); applying this model at two time periods to estimate net change in AWC resulted in an R2 = 0.25 (RMSE = 28.3 Mg ha−1) and a mean error of −5.4 Mg ha−1. The best model for AWC at time 2 using plot measurements at time 1 and TS variables had an R2 = 0.95 (RSME = 17.0 Mg ha−1). The model for net change in AWC using the same data was identical except that, because the variable being estimated was smaller in magnitude, the R2 = 0.73. All models performed better at estimating net change in AWC on TS-disturbed plots than on TS-undisturbed plots. The TS discrimination of disturbance between fire and harvest was an important variable in the models because the magnitude of spectral change from fire was greater for a given change in AWC. Regional models without plot-level predictors produced erroneous predictions of net change in AWC for some of the forest types. Our study suggests that, in spite of the simplicity of applying a single carbon model to multiple image dates, the approach can produce inaccurate estimates of C flux. Although models built with plot-level predictors are necessarily constrained to making predictions at plot locations, they show promise for providing accurate updates or back-calculations of C flux assessments.
Wildland fire is an important disturbance agent in forests of the American Northwest. Historical fire suppression efforts have contributed to an accumulation of fuels in many Northwestern forests and may result in more frequent and/or more severe wildfire events. Here we investigate the extent to which atmospheric and climatic variability may contribute to variability in annual area burned on 20 National Forests in Washington, Oregon, and Idaho. Empirical orthogonal function (EOF) analysis was used to identify coherent patterns in area burned by wildfire in the Pacific Northwest. Anomaly fields of 500-hPa height were regressed onto the resulting principal-component time series to identify the patterns in atmospheric circulation that are associated with variability in area burned by wildfire. Additionally, cross-correlation functions were calculated for the Palmer drought severity index (PDSI) over the year preceding the wildfire season. Parallel analyses based on superposed epoch analysis focused only on the extreme fire years (both large and small) to discriminate the controls on extreme years from the linear responses identified in the regression analyses. Four distinct patterns in area burned were identified, each associated with distinct climatic processes. Extreme wildfire years are forced at least in part by antecedent drought and summertime blocking in the 500-hPa height field. However the response to these forcings is modulated by the ecology of the dominant forest. In more mesic forest types antecedent drought is a necessary precondition for forests to burn, but it is not a good predictor of area burned due to the rarity of subsequent ignition. At especially dry locations, summertime blocking events can lead to increases in area burned even in the absence of antecedent drought. At particularly xeric locations summertime cyclones can also lead to increased area burned, probably due to dry lightning storms that bring ignition and strong winds but little precipitation. These results suggest that fuels treatments alone may not be effective at reducing area burned under extreme climatic conditions and furthermore that anthropogenic climate change may have important implications for forest management.
The Pacific Northwest Research Station launched a Carbon Dynamics for Land and Watershed Stewardship research initiative in 2019. In this webinar, which took place January 21, 2020, working groups summarize their progress to date and talk about next steps and opportunities for additional scientists and stakeholders to contribute.
A comprehensive numerical modeling framework was developed to estimate the effects of collective global changes upon ozone pollution in the US in 2050. The framework consists of the global climate and chemistry models, PCM (Parallel Climate Model) and MOZART-2 (Model for Ozone and Related Chemical Tracers v.2), coupled with regional meteorology and chemistry models, MM5 (Mesoscale Meteorological model) and CMAQ (Community Multi-scale Air Quality model). The modeling system was applied for two 10-year simulations: 1990–1999 as a present-day base case and 2045–2054 as a future case. For the current decade, the daily maximum 8-h moving average (DM8H) ozone mixing ratio distributions for spring, summer and fall showed good agreement with observations. The future case simulation followed the Intergovernmental Panel on Climate Change (IPCC) A2 scenario together with business-as-usual US emission projections and projected alterations in land use, land cover (LULC) due to urban expansion and changes in vegetation. For these projections, US anthropogenic NOx (NO+NO2) and VOC (volatile organic carbon) emissions increased by approximately 6% and 50%, respectively, while biogenic VOC emissions decreased, in spite of warmer temperatures, due to decreases in forested lands and expansion of croplands, grasslands and urban areas. A stochastic model for wildfire emissions was applied that projected 25% higher VOC emissions in the future. For the global and US emission projection used here, regional ozone pollution becomes worse in the 2045–2054 period for all months. Annually, the mean DM8H ozone was projected to increase by 9.6 ppbv (22%). The changes were higher in the spring and winter (25%) and smaller in the summer (17%). The area affected by elevated ozone within the US continent was projected to increase; areas with levels exceeding the 75 ppbv ozone standard at least once a year increased by 38%. In addition, the length of the ozone season was projected to increase with more pollution episodes in the spring and fall. For selected urban areas, the system projected a higher number of pollution events per year and these events had more consecutive days when DM8H ozone exceed 75 ppbv.
Forests are considered a natural solution for mitigating climate change because they absorb and store atmospheric carbon. With Alaska boasting 129 million acres of forest, this state can play a crucial role as a carbon sink for the United States. Until recently, the volume of carbon stored in Alaska’s forests was unknown, as was their future carbon sequestration capacity.
In 2007, Congress passed the Energy Independence and Security Act that directed the Department of the Interior to assess the stock and flow of carbon in all the lands and waters of the United States. In 2012, a team composed of researchers with the U.S. Geological Survey, U.S. Forest Service, and the University of Alaska assessed how much carbon Alaska’s forests can sequester.
The researchers concluded that ecosystems of Alaska could be a substantial carbon sink. Carbon sequestration is estimated at 22.5 to 70.0 teragrams (Tg) of carbon per year over the remainder of this century. In particular, Alaska’s dense coastal temperate forests and soils are estimated to sequester 3.4 to 7.8 Tg of carbon per year. Forest management activities were found to have long-term effects on the maximum amount of carbon a site can sequester. These findings helped inform the carbon assessment sections of Chugach and Tongass National Forests’ land management plans.
A recent expansion in wood energy use at schools in Alaska has resulted in more than a dozen wood energy systems in operation. However, few have been evaluated for fuel efficiency and pollution impacts, both of which can be examined via combustion gas analysis. In this research, we monitored the wood energy system at a public school during winter heating conditions. Wood energy parameters were sampled on three occasions during early, mid, and late winter in northern Alaska. Combustion gas was sampled for a range of parameters that indicated boiler performance, including gas emmissions of oxygen (O2), carbon dioxide (CO2), carbon monoxide (CO), excess air, combustion efficiency, and stack temperature, which were monitored over 6 days. We observed differences in combustion gas composition between seasons as well as the response of combustion efficiency to gas concentrations. Combustion efficiency most strongly correlated with excess air (R2 = 0.693), but poorly correlated with stack temperature (R2 = 0.005). The primary combustion gases (O2, CO2, and CO) were moderately correlated with combustion efficiency (with R2 values of 0.40, 0.56, and 0.55, respectively). Seasonal differences were found between early, mid, and late winter, with generally less variation in combustion gas contents occurring during late winter. Mean combustion gas concentrations also varied with heating season. In all cases, mid-winter means were significantly different than early and late winter values. This research found that more efficient combustion of wood fuels should lead to cost savings, especially during early and late heating seasons. The findings should also be relevant to those of other wood-energy-using schools (in Alaska and elsewhere) that experience severe mid-winter conditions coupled with milder shoulder seasons.
From 2007 through 2017, Oregon implemented an incentive program for biomass collection and production. This research evaluates renewable biomass production and deliveries during a 3-year period (2012 to 2014) in which this tax credit was in place. We evaluated total delivered tons, average payments per load, delivered location, and average transportation distance of woody biomass. We found that total delivered tons of biomass decreased each year between 2012 and 2014, as did the number of users participating in the tax credit program. The average delivered tons, by participant, was more than double in 2014 its level in earlier years, suggesting that fewer, larger entities were participating. We also evaluated differences in biomass delivery, based on receipts, transportation distances, and tons delivered, for each land ownership class. There were statistically significant differences between private and public land ownership for 2012 and 2013 but not for 2014, which included fewer applicants. Our study showed that effective biomass utilization policies need to provide sufficient economic incentives to encourage adoption by both participants and biomass energy producers, and, to be effective, to consider the complete supply chain and type of energy produced. Future economic conditions in Oregon will most likely include rapid changes in renewable energy technologies and fluctuations in fossil fuel prices, and any truly effective renewable energy policies must be sufficiently nimble to account for these and other uncertainties.