Climate change mitigation potential of biochar from forestry residues under boreal condition.

Forest harvest residue is a low-competitive biomass feedstock that is usually left to decay on site after forestry operations. Its removal and pyrolytic conversion to biochar is seen as an opportunity to reduce terrestrial CO2 emissions and mitigate climate change. The mitigation effect of biochar is, however, ultimately dependent on the availability of the biomass feedstock, thus CO2 removal of biochar needs to be assessed in relation to the capacity to supply biochar systems with biomass feedstocks over prolonged time scales, relevant for climate mitigation. In the present study we used an assembly of empirical models to forecast the effects of harvest residue removal on soil C storage and the technical capacity of biochar to mitigate national-scale emissions over the century, using Norway as a case study for boreal conditions. We estimate the mitigation potential to vary between 0.41 and 0.78 Tg CO2 equivalents yr-1, of which 79% could be attributed to increased soil C stock, and 21% to the coproduction of bioenergy. These values correspond to 9-17% of the emissions of the Norwegian agricultural sector and to 0.8-1.5% of the total national emission. This illustrates that deployment of biochar from forest harvest residues in countries with a large forestry sector, relative to economy and population size, is likely to have a relatively small contribution to national emission reduction targets but may have a large effect on agricultural emission and commitments. Strategies for biochar deployment need to consider that biochar's mitigation effect is limited by the feedstock supply which needs to be critically assessed.

A century of National Forest Inventory in Norway - informing past, present, and future decisions.

PAST: In the early twentieth century, forestry was one of the most important sectors in Norway and an agitated discussion about the perceived decline of forest resources due to over-exploitation was ongoing. To base the discussion on facts, the young state of Norway established Landsskogtakseringen - the world's first National Forest Inventory (NFI). Field work started in 1919 and was carried out by county. Trees were recorded on 10 m wide strips with 1-5 km interspaces. Site quality and land cover categories were recorded along each strip. Results for the first county were published in 1920, and by 1930 most forests below the coniferous tree line were inventoried. The 2nd to 5th inventories followed in the years 1937-1986. As of 1954, temporary sample plot clusters on a 3 km × 3 km grid were used as sampling units. PRESENT: The current NFI grid was implemented in the 6th NFI from 1986 to 1993, when permanent plots on a 3 km × 3 km grid were established below the coniferous tree line. As of the 7th inventory in 1994, the NFI is continuous, and 1/5 of the plots are measured annually. All trees with a diameter ≥ 5 cm are recorded on circular, 250 m2 plots. The NFI grid was expanded in 2005 to cover alpine regions with 3 km × 9 km and 9 km × 9 km grids. In 2012, the NFI grid within forest reserves was doubled along the cardinal directions. Clustered temporary plots are used periodically to facilitate county-level estimates. As of today, more than 120 variables are recorded in the NFI including bilberry cover, drainage status, deadwood, and forest health. Land-use changes are monitored and trees outside forests are recorded. FUTURE: Considerable research efforts towards the integration of remote sensing technologies enable the publication of the Norwegian Forest Resource Map since 2015, which is also used for small area estimation at the municipality level. On the analysis side, capacity and software for long term growth and yield prognosis are being developed. Furthermore, we foresee the inclusion of further variables for monitoring ecosystem services, and an increasing demand for mapped information. The relatively simple NFI design has proven to be a robust choice for satisfying steadily increasing information needs and concurrently providing consistent time series.

Evaluating the terrestrial carbon dioxide removal potential of improved forest management and accelerated forest conversion in Norway.

As a carbon dioxide removal measure, the Norwegian government is currently considering a policy of large-scale planting of spruce (Picea abies (L) H. Karst) on lands in various states of natural transition to a forest dominated by deciduous broadleaved tree species. Given the aspiration to bring emissions on balance with removals in the latter half of the 21st century in effort to limit the global mean temperature rise to "well below" 2°C, the effectiveness of such a policy is unclear given relatively low spruce growth rates in the region. Further convoluting the picture is the magnitude and relevance of surface albedo changes linked to such projects, which typically counteract the benefits of an enhanced forest CO2 sink in high-latitude regions. Here, we carry out a rigorous empirically based assessment of the terrestrial carbon dioxide removal (tCDR) potential of large-scale spruce planting in Norway, taking into account transient developments in both terrestrial carbon sinks and surface albedo over the 21st century and beyond. We find that surface albedo changes would likely play a negligible role in counteracting tCDR, yet given low forest growth rates in the region, notable tCDR benefits from such projects would not be realized until the second half of the 21st century, with maximum benefits occurring even later around 2150. We estimate Norway's total accumulated tCDR potential at 2100 and 2150 (including surface albedo changes) to be 447 (±240) and 852 (±295) Mt CO2 -eq. at mean net present values of US$ 12 (±3) and US$ 13 (±2) per ton CDR, respectively. For perspective, the accumulated tCDR potential at 2100 represents around 8 years of Norway's total current annual production-based (i.e., territorial) CO2 -eq. emissions.

Detection and classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce Using RGB Images and Machine Learning.

Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution to addressing the problem without increasing workload complexity for the machine operator. In this study, we developed and evaluated an approach based on RGB images to automatically detect tree stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps into three classes of infestation; rot = 0%, 0% < rot < 50% and rot ≥ 50%. In this work we used deep-learning approaches and conventional machine-learning algorithms for detection and classification tasks. The results showed that tree stumps were detected with precision rate of 95% and recall of 80%. Using only the correct output (TP) of the stump detector, stumps without and with RBR were correctly classified with accuracy of 83.5% and 77.5%, respectively. Classifying rot into three classes resulted in 79.4%, 72.4%, and 74.1% accuracy for stumps with rot = 0%, 0% < rot < 50%, and rot ≥ 50%, respectively. With some modifications, the developed algorithm could be used either during the harvesting operation to detect RBR regions on the tree stumps or as an RBR detector for post-harvest assessment of tree stumps and logs.

Cooling aerosols and changes in albedo counteract warming from CO2 and black carbon from forest bioenergy in Norway.

Climate impacts of forest bioenergy result from a multitude of warming and cooling effects and vary by location and technology. While past bioenergy studies have analysed a limited number of climate-altering pollutants and activities, no studies have jointly addressed supply chain greenhouse gas emissions, biogenic CO2 fluxes, aerosols and albedo changes at high spatial and process detail. Here, we present a national-level climate impact analysis of stationary bioenergy systems in Norway based on wood-burning stoves and wood biomass-based district heating. We find that cooling aerosols and albedo offset 60-70% of total warming, leaving a net warming of 340 or 69 kg CO2e MWh-1 for stoves or district heating, respectively. Large variations are observed over locations for albedo, and over technology alternatives for aerosols. By demonstrating both notable magnitudes and complexities of different climate warming and cooling effects of forest bioenergy in Norway, our study emphasizes the need to consider multiple forcing agents in climate impact analysis of forest bioenergy.

Carbon-equivalent metrics for albedo changes in land management contexts: relevance of the time dimension.

Surface albedo is an important physical property by which the land surface regulates climate. A wide and growing body of literature suggests that failing to account for surface albedo can result in suboptimal or even counterproductive climate-motivated policies of the land-based sectors. As such, albedo changes are increasingly included in climate impact assessments of forestry and other land sector projects through conversion of radiative forcings into carbon or carbon dioxide equivalents. However, the prevailing methodology does not sufficiently accommodate dynamic albedo changes on land or CO2 in the atmosphere. We present two new metrics designed to address these deficiencies, referring to them as the time-dependent emissions equivalent and the time-independent emissions equivalent of albedo changes. We demonstrate their application in various land management contexts and discuss their merits and uncertainties.

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data.

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960-2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60-70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.

Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume.

Due to the availability of good and reasonably priced auxiliary data, the use of model-based regression-synthetic estimators for small area estimation is popular in operational settings. Examples are forest management inventories, where a linking model is used in combination with airborne laser scanning data to estimate stand-level forest parameters where no or too few observations are collected within the stand. This paper focuses on different approaches to estimating the variances of those estimates. We compared a variance estimator which is based on the estimation of superpopulation parameters with variance estimators which are based on predictions of finite population values. One of the latter variance estimators considered the spatial autocorrelation of the residuals whereas the other one did not. The estimators were applied using timber volume on stand level as the variable of interest and photogrammetric image matching data as auxiliary information. Norwegian National Forest Inventory (NFI) data were used for model calibration and independent data clustered within stands were used for validation. The empirical coverage proportion (ECP) of confidence intervals (CIs) of the variance estimators which are based on predictions of finite population values was considerably higher than the ECP of the CI of the variance estimator which is based on the estimation of superpopulation parameters. The ECP further increased when considering the spatial autocorrelation of the residuals. The study also explores the link between confidence intervals that are based on variance estimates as well as the well-known confidence and prediction intervals of regression models.

Sapling leaf trait responses to light, tree height and soil nutrients for three conifer species of contrasting shade tolerance.

We developed models to describe the responses of four commonly examined leaf traits (mass per area, weight, area and nitrogen (N) concentration) to gradients of light, soil nutrients and tree height in three conifer species of contrasting shade tolerance. Our observational dataset from the sub-boreal spruce forests of British Columbia included subalpine fir (Abies lasioscarpa [Hook.] Nutt; high shade tolerance), interior spruce (Picea glauca × Picea engelmannii [Moench] Voss; intermediate shade tolerance) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia; low shade tolerance) saplings from 0.18 to 4.87 m tall, in 8-98% of total incident light, from field sites with <17.6 kg ha(-1) to >46.8 kg ha(-1) total dissolved N. Leaf weights and areas showed strong positive responses to light and height, but little or no response to soil nutrients. Parameter estimates indicated that the shape of leaf weight and area responses to light corresponded with shade tolerance ranking for the three species; pine had the most linear response whereas spruce and fir had asymptotic responses. Leaf N concentration responded positively to soil nutrients, negatively to light and idiosyncratically to height. The negative effect of light was only apparent on sites of high soil nutrient availability, and parameter estimates for the shape of the negative response also corresponded to shade tolerance ranking (apine = -0.79, aspruce = -0.15, afir = -0.07). Of the traits we measured, leaf mass per area showed the least response to light, soil nutrient and height gradients. Although it is a common practice in comparisons across many species, characterizing these conifers by mean values of their leaf traits would miss important intraspecific variation across environmental and size gradients. In these forests, parameter estimates representing the intraspecific variability of leaf trait responses can be used to understand relative shade tolerances.

Climate change implications of shifting forest management strategy in a boreal forest ecosystem of Norway.

Empirical models alongside remotely sensed and station measured meteorological observations are employed to investigate both the local and global direct climate change impacts of alternative forest management strategies within a boreal ecosystem of eastern Norway. Stand-level analysis is firstly executed to attribute differences in daily, seasonal, and annual mean surface temperatures to differences in surface intrinsic biophysical properties across conifer, deciduous, and clear-cut sites. Relative to a conifer site, a slight local cooling of −0.13 °C at a deciduous site and −0.25 °C at a clear-cut site were observed over a 6-year period, which were mostly attributed to a higher albedo throughout the year. When monthly mean albedo trajectories over the entire managed forest landscape were taken into consideration, we found that strategies promoting natural regeneration of coniferous sites with native deciduous species led to substantial global direct climate cooling benefits relative to those maintaining current silviculture regimes ­ despite predicted long-term regional warming feedbacks and a reduced albedo in spring and autumn months. The magnitude and duration of the cooling benefit depended largely on whether management strategies jointly promoted an enhanced material supply over business-as-usual levels. Expressed in terms of an equivalent CO2 emission pulse at the start of the simulation, the net climate response at the end of the 21st century spanned −8 to −159 Tg-CO2-eq., depending on whether near-term harvest levels increased or followed current trends, respectively. This magnitude equates to approximately −20 to −300% of Norway's annual domestic (production) emission impact. Our analysis supports the assertion that a carbon-only focus in the design and implementation of forest management policy in boreal and other climatically similar regions can be counterproductive ­ and at best ­ suboptimal if boreal forests are to be used as a tool to mitigate global warming.