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Abstract

Forestry in coastal Norway has traditionally been a marginal activity with a low annual harvest rate. However, the region is now faced with large areas of spruce plantations that will reach harvest maturity within the next 25 years. Due to the poor infrastructure in the region, the current challenge is to harvest the maturing spruce plantations at an acceptable cost. Hence, there is considerable interest both from the forest sector and politicians to invest in infrastructure that can provide the basis for profitable forest sector development in coastal Norway. This paper presents a mathematical optimization model for timber transportation from stump to industry. The main decision variables are location of quays, upgrade of public road links, the length of new forest roads, and when the investments should happen. The main objective is to provide decision support for prioritization of infrastructure investments. The optimization model is combined with a dynamical forest resource model, providing details on available volumes and costs. A case study for coastal Norway is presented and solved to optimality. The instance includes 10 counties comprising more than 200 municipalities with forest resources, 53 possible new quays for timber export and 916 public road links that also can be upgraded. Compared with a no investment case, the optimal solution improved the objective by 23%. The study shows that consistent, informative and good analyses can be performed to evaluate trade-offs, prioritization, time and order of investment, and cost saving potentials of infrastructure investments in the forest industry. The solution seems reasonable based on present infrastructure and state of the forest.

Abstract

Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MSNFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

Abstract

The effectiveness of generating virtual transects on unmanned aerial vehicle-derived orthomosaics was evaluated in estimating the extent of soil disturbance by severity class. Combinations of 4 transect lengths (5–50 m) and five sampling intensities (1–20 transects per ha) were used in assessing traffic intensity and the severity of soil disturbance on six post-harvest, cut-to-length (CTL) clearfell sites. In total, 15% of the 33 ha studied showed some trace of vehicle traffic. Of this, 63% of was categorized as light (no visible surface disturbance). Traffic intensity varied from 787 to 1256 m ha−1, with a weighted mean of 956 m ha−1, approximately twice the geometrical minimum achievable with CTL technology under perfect conditions. An overall weighted mean of 4.7% of the total site area was compromised by severe rutting. A high sampling intensity, increasing with decreasing incidence of soil disturbance, is required if mean estimation error is to be kept below 20%. The paper presents a methodology that can be generally applied in forest management or in similar land-use evaluations.

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Abstract

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 climatealtering 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.

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Abstract

Predicting the surface albedo of a forest of a given species composition or plant functional type is complicated by the wide range of structural attributes it may display. Accurate characterizations of forest structure are therefore essential to reducing the uncertainty of albedo predictions in forests, particularly in the presence of snow. At present, forest albedo parameterizations remain a nonnegligible source of uncertainty in climate models, and the magnitude attributable to insufficient characterization of forest structure remains unclear. Here we employ a forest classification scheme based on the assimilation of Fennoscandic (i.e., Norway, Sweden, and Finland) national forest inventory data to quantify the magnitude of the albedo prediction error attributable to poor characterizations of forest structure. For a spatial domain spanning ~611,000 km2 of boreal forest, we find a mean absolute wintertime (December–March) albedo prediction error of 0.02, corresponding to a mean absolute radiative forcing ~0.4 W/m2. Further, we evaluate the implication of excluding albedo trajectories linked to structural transitions in forests during transient simulations of anthropogenic land use/land cover change. We find that, for an intensively managed forestry region in southeastern Norway, neglecting structural transitions over the next quarter century results in a foregone (undetected) radiatively equivalent impact of ~178 Mt‐CO2‐eq. year−1 on average during this period—a magnitude that is roughly comparable to the annual greenhouse gas emissions of a country such as The Netherlands. Our results affirm the importance of improving the characterization of forest structure when simulating surface albedo and associated climate effects.

Abstract

Background: Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and silver birch (Betula pendula Roth) in Norway are presented. The Norwegian National Forest Inventory (NFI) is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputation in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods: Generalized additive mixed models (gamm) are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand age, as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models (scam) were fit to incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results: Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. A two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatially correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions: In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scam may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.

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Abstract

Enabling automated 3D mapping in forests is an important component of the future development of forest technology, and has been garnering interest in the scientific community, as can be seen from the many recent publications. Accordingly, the authors of the present paper propose the use of a Simultaneous Localisation and Mapping algorithm, called graph-SLAM, to generate local maps of forests. In their study, the 3D data required for the mapping process were collected using a custom-made, mobile platform equipped with a number of sensors, including Velodyne VLP-16 LiDAR, a stereo camera, an IMU, and a GPS. The 3D map was generated solely from laser scans, first by relying on laser odometry and then by improving it with robust graph optimisation after loop closures, which is the core of the graph-SLAM algorithm. The resulting map, in the form of a 3D point cloud, was then evaluated in terms of its accuracy and precision. Specifically, the accuracy of the fitted diameter at breast height (DBH) and the relative distance between the trees were evaluated. The results show that the DBH estimates using the Pratt circle fit method could enable a mean estimation error of approximately 2 cm (7–12%) and an RMSE of 2.38 cm (9%), whereas for tree positioning accuracy, the mean error was 0.0476 m. The authors conclude that robust SLAM algorithms can support the development of forestry by providing cost-effective and acceptable quality methods for forest mapping. Moreover, such maps open up the possibility for precision localisation for forestry vehicles.

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Abstract

The forest understory is often associated with rapid rates of carbon and nutrient cycling, but cost-efficient quantification of its biomass remains challenging. We tested a new field technique for understory biomass assessment using an off-the-shelf handheld laser rangefinder. We conducted laser sampling in a pine forest with an understory dominated by invasive woody shrubs, especially Rhamnus frangula L. Laser sampling was conducted using a rangefinder, mounted on a monopod to provide a consistent reference height, and pointed vertically downward. Subsequently, the understory biomass was measured with destructive sampling. A series of metrics derived from the airborne LiDAR literature were evaluated alone and in combination for prediction of understory biomass using best-subsets regression. Resulting fits were good (r2 = 0.85 and 0.84 for the best single metric and best additive metric, respectively, and R2 = 0.93 for the best multivariate model). The results indicate that laser sampling could substantially reduce the need for costly destructive sampling within a double-sampling context.

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Abstract

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field.

Abstract

Unmanned aerial vehicles (UAVs) are increasingly used as tools to perform a detailed assessment of post-harvest sites. One of the potential use of UAV photogrammetric data is to obtain tree-stump information that can then be used to support more precise decisions. This study developed and tested a methodology to automatically detect, segment, classify, and measure tree-stumps. Among the potential applications for single stump data, this study assessed the possibility (1) to detect and map root- and butt-rot on the stumps using a machine learning approach, and (2) directly measure or model tree stump diameter from the UAV data. The results revealed that the tree-stumps were detected with an overall accuracy of 68–80%, and once the stump was detected, the presence of root- and butt-rot was detected with an accuracy of 82.1%. Furthermore, the root mean square error of the UAV-derived measurements or model predictions for the stump diameter was 7.5 cm and 6.4 cm, respectively, and with the former systematically under predicting the diameter by 3.3 cm. The results of this study are promising and can lead to the development of more cost-effective and comprehensive UAV post-harvest surveys.

Abstract

In many applications, estimates are required for small sub-populations with so few (or no) sample plots that direct estimators that do not utilize auxiliary variables (e.g. remotely sensed data) are not applicable or result in low precision. This problem is overcome in small area estimation (SAE) by linking the variable of interest to auxiliary variables using a model. Two types of models can be distinguished based on the scale on which they operate: i) Unit-level models are applied in the well-known area-based approach (ABA) and are commonly used in forest inventories supported by fine-resolution 3D remote sensing data such as airborne laser scanning (ALS) or digital aerial photogrammetry (AP); ii) Area-level models, where the response is a direct estimate based on a sample within the domain and the explanatory variables are aggregated auxiliary variables, are less frequently applied. Estimators associated with these two model types can make use of sample plots within domains if available and reduce to so-called synthetic estimators in domains where no sample plots are available. We used both model types and their associated model-based estimators in the same study area with AP data as auxiliary variables. Heteroscedasticity, i.e. for continuous dependent variables typically an increasing dispersion of re- siduals with increasing predictions, is often observed in models linking field- and remotely sensed data. This violates the model assumption that the distribution of the residual errors is constant. Complying with model assumptions is required for model-based methods to result in reliable estimates. Addressing heteroscedasticity in models had considerable impacts on standard errors. When complying with model assumptions, the precision of estimates based on unit-level models was, on average, considerably greater (29%–31% smaller standard errors) than those based on area-level models. Area-level models may nonetheless be attractive because they allow the use of sampling designs that do not easily link to remotely sensed data, such as variable radius plots.

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Abstract

This paper provides an overview of recent developments in remote and proximal sensing technologies and their basic applicability to various aspects of forest operations. It categorises these applications according to the technologies used and considers their deployment platform in terms of their being space-, airborne or terrestrial. For each combination of technology and application, a brief review of the state-of-the-art has been described from the literature, ranging from the measurement of forests and single trees, the derivation of landscape scale terrain models down to micro-topographic soil disturbance modelling, through infrastructure planning, construction and maintenance, to forest accessibility with ground and cable based harvesting systems. The review then goes on to discuss how these technologies and applications contribute to reducing impacts on forest soils, cultural heritage sites and other areas of special value or interest, after which sensors and methods necessary in autonomous navigation and the use of computer vision on forest machines are discussed. The review concludes that despite the many promising or demonstrated applications of remotely or proximately sensed data in forest operations, almost all are still experimental and have a range of issues that need to be addressed or improved upon before widespread operationalization can take place.

Abstract

The use of digital aerial photogrammetry (DAP) for forest inventory purposes has been widely studied and can produce comparable accuracy compared with airborne laser scanning (ALS) in small, homogeneous areas. However, the accuracy of DAP for large scale applications with heterogeneous terrain and forest vegetation has not yet been reported. In this study we examined the accuracy of timber volume, biomass and basal area prediction models based on DAP and national forest inventory (NFI) data on a large area in central Norway. Two separate point clouds were derived from aerial image acquisitions of 2010 and 2013. Vegetation heights were extracted by subtracting terrain elevation derived from ALS. A large number of NFI sample plots (483) measured between 2010 and 2014 were used as reference data to fit linear models for timber volume, biomass and basal area with height metrics derived from the DAP data as explanatory variables. Variables describing the heterogeneous environmental and image acquisition conditions were calculated and their influence on the model accuracy was tested. The results showed that forest parameter prediction using DAP works well when applied to a large area. The model fits of the timber volume, biomass and basal area models were good with R2 of 0.80, 0.81, 0.81 and RMSEs of 41.43 m3 ha−1 (55% of the mean observed value), 32.49 t ha−1 (47%), 5.19 m2 ha−1 (41%), respectively. Only a small proportion of the variation could be attributed to the heterogeneous conditions. The inclusion of the relative sun inclination led to an improvement of the model RMSEs by 2% of the mean observed values. The relatively low cost and stability across large areas make DAP an attractive source of auxiliary information for large scale forest inventories.

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Abstract

This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict stem density values for the centre point of all grid cells ina5m×5m lattice across the stand. Thirdly, due to nondetectability issues, a correction term was computed based on distance sampling theory. Lastly, the mean stem density at stand level was predicted as the mean of the point-level predictions multiplied with the correction factor, and the corresponding variance was estimated. Many factors contribute to the uncertainty of the stand-level prediction; in the variance estimator, we accounted for the uncertainties due to kriging prediction and due to estimating a detectability model from the laser scanning data. The results from our new approach were found to correspond fairly well to estimates obtained using field measurements from an independent set of 54 circular sample plots. The predicted number of stems in the stand based on the proposed methodology was 1366 with a 12.9% relative standard error. The corresponding estimate based on the field plots was 1677 with a 7.5% relative standard error.

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Abstract

Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY estimation substantially reduced the uncertainty in AGB change when compared with estimation using the field survey only. Relative efficiencies (relative variance) of 4.15 (MA) and 3.36 (HY) for overall estimates were found. The results suggest the MA estimator for single-time estimation and the HY as more appropriate for change estimation by cover class. With the HY estimator, a nested post-stratification scheme is demonstrated, combining cover classes with change classes, which enables detailed reporting for change according to cause within each cover class, and has the potential to improve the estimation precision. Finally, parametric bootstrapping is demonstrated as an empirical alternative to estimate the model-error component in the HY estimator. The model error estimated with parametric bootstrapping converged to the analytically determined value of the HY estimator within 1000 bootstrap samples.

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Abstract

REDD+, a climate change mitigation mechanism that values carbon in tropical forests, is expected to provide Africa with a range of environmental and socio-economic benefits. Drawing on a vast array of literature and personal experiences, this review analyzed particular features and challenges that REDD+ implementation has faced on the continent. The distinct contexts and major challenges regarding governance, finance and technical capacities are discussed, and mechanisms to fill these gaps are suggested. Radical land tenure reform and a perfect safeguard mechanism that transfers forest land and carbon to the communities are unlikely. REDD+ should rather look for systems that respect local institutional arrangements, and allow forest-based communities to participate in decision-making and benefit sharing, particularly benefits from emerging REDD+. Finances for REDD+ infrastructure and the results-based payment are in short supply. While negotiating for potential external sources in the short term, Africa should generate domestic financial resources and look for additional payments for ecosystem services. Africa should also negotiate for forest monitoring capacity building, while strengthening local community forest monitoring. This review contributes to an improved understanding of the contexts and challenges to consider in the capacity and policy development for REDD+ implementation.

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Abstract

Land surface albedo is a key parameter of the Earth’s climate system. It has high variability in space, time, and land cover and it is among the most important variables in climate models. Extensive large-scale estimates can help model calibration and improvement to reduce uncertainties in quantifying the influence of surface albedo changes on the planetary radiation balance. Here, we use satellite retrievals of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo (MCD43A3), high-resolution land-cover maps, and meteorological records to characterize climatological albedo variations in Norway across latitude, seasons, land-cover type (deciduous forests, coniferous forests, and cropland), and topography. We also investigate the net changes in surface albedo and surface air temperature through site pair analysis to mimic the effects of land-use transitions between forests and cropland and among different tree species. We find that surface albedo increases at increasing latitude in the snow season, and cropland and deciduous forests generally have higher albedo values than coniferous forests, but for few days in spring. Topography has a large influence on MODIS albedo retrievals, with values that can change up to 100% for the same land-cover class (e.g. spruce in winter) under varying slopes and aspect of the terrain. Cropland sites have surface air temperature higher than adjacent forested sites, and deciduous forests are slightly colder than adjacent coniferous forests. By integrating satellite measurements and high-resolution vegetation maps, our results provide a large semi-empirical basis that can assist future studies to better predict changes in a fundamental climate-regulating service such as surface albedo.

Abstract

The present study aims to develop biologically sound and parsimonious site index models for Norway to predict changes in site index (SI) under different climatic conditions. The models are constructed using data from the Norwegian National Forest Inventory and climate data from the Norwegian meteorological institute. Site index was modeled using the potential modifier functional form, with a potential component (POT) depending on site quality classes and two modifier components (MOD): temperature and moisture. Each of these modifiers was based on a portfolio of candidate variables. The best model for spruce-dominated stands included temperature as modifier (R2 = 0.56). In the case of pine- and deciduous-dominated stands, the best models included both modifiers (R2 = 0.40 and 0.54 for temperature and moisture, respectively). We illustrate the use of the models by analyzing the possible shift in SI for year 2100 under one (RCP4.5) of the benchmark scenarios adopted by the Intergovernmental Panel on Climate Change for its fifth assessment report. The models presented can be valuable for evaluating the effect of climate change scenarios in Norwegian forests.

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Abstract

National and international carbon reporting systems require information on carbon stocks of forests. For this purpose, terrestrial assessment systems such as forest inventory data in combination with carbon estimation methods are often used. In this study we analyze and compare terrestrial carbon estimation methods from 12 European countries. The country-specific methods are applied to five European tree species (Fagus sylvatica L., Quercus robur L., Betula pendula Roth, Picea abies (L.) Karst. and Pinus sylvestris L.), using a standardized theoretically-generated tree dataset. We avoid any bias due to data collection and/or sample design by using this approach. We are then able to demonstrate the conceptual differences in the resulting carbon estimates with regard to the applied country-specific method. In our study we analyze (i) allometric biomass functions, (ii) biomass expansion factors in combination with volume functions and (iii) a combination of both. The results of the analysis show discrepancies in the resulting estimates for total tree carbon and for single tree compartments across the countries analyzed of up to 140 t carbon/ha. After grouping the country-specific approaches by European Forest regions, the deviation within the results in each region is smaller but still remains. This indicates that part of the observed differences can be attributed to varying growing conditions and tree properties throughout Europe. However, the large remaining error is caused by differences in the conceptual approach, different tree allometry, the sample material used for developing the biomass estimation models and the definition of the tree compartments. These issues are currently not addressed and require consideration for reliable and consistent carbon estimates throughout Europe.

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Abstract

Individual tree mortality models based on logistic regression exist for different tree species and countries around the world. We examine two mortality models developed in Norway and two models from Austria for Norway spruce (Picea abies), Scots pine (Pinus sylvestris) and birch (Betula pubescens and Betula pendula) trees. We apply all models with their original coefficients on the Norwegian National Forest Inventory (NNFI) data. The dataset comprises 36,217 spruce, 17,483 pine and 24,418 birch trees. We show the differences in predictions that arise from newly paramete-rized predictor variables and the effect of the original calibration data from different geographic regions. Next we recalibrate the mortality functions with the NNFI data to show the improvements in the predictions and illustrate the impact of the different predictor variables. We apply statistical methods to assess which of the original and recalibrated models best mimic the observed mortality rates of the three species. Finally we provide the new coefficient set for the model functions for spruce, pine and birch in Norway.

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Abstract

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.

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Abstract

We demonstrate the efficacy of using close-range photogrammetry from a consumer grade camera as a tool in generating high-resolution, three-dimensional coloured point clouds for detailed analysis or monitoring of wheel ruts. Ground-based timber harvesting results in vehicle traffic on 12–70 per cent of the site, depending on the system used, with a variable probability of causing detrimental soil disturbance depending on climatic, hydrological and soil conditions at the time of harvest. Applying the technique described in this article can reduce the workload associated with the conventional manual measurement of wheel ruts, while providing a greatly enhanced source of information that can be used in analysing both physical and biological impact, or stored in a repository for later operation management or monitoring. Approaches for deriving and quantifying properties such as rut depths and soil displacement volumes are also presented. In evaluating the potential for widespread adoption of the method among forest or environmental managers, the study also presents the workflow and provides a comparison of the ease of use and quality of the results obtained from one commercial and two open source image processing software packages. Results from a case study showed no significant difference between packages on point cloud quality in terms of model distortion. Comparison of photogrammetric profiles against profiles measured manually resulted in root mean square errors of between 2.07 and 3.84 cm for five selected road profiles. Maximal wheel rut depth for three different models were 1.15, 0.99 and 1.01 m, and estimated rut volumes were 9.84, 9.10 and 9.09 m3, respectively, for 22.5 m long sections.

Abstract

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.

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Abstract

Sustainable forest management in an era of global changes has always been a central thematic area for the International Boreal Forest Research Association (IBFRA). At the 17th IBFRA conference held on 24–29 May 2015 in Rovaniemi, Finland, the theme of global change was accompanied by a new theme related to the use and value of big data in boreal forest management and research. Keynote presentations had a clear message that sustainably managed boreal forests and peatlands play a significant role in climate change mitigation. However, the choice of the most efficient mitigation options will vary with regional differences in ecology, institutional strength, and management intensity. In addition to changes in greenhouse gas fluxes linked to ecosystem dynamics, the design of climate change mitigation strategies should also account for the fate of harvested wood products and for the substitution of more energy-intensive materials such as concrete and steel. For climate change mitigation, it is therefore not only forest management that matters, but also ensuring the best possible end use for the produced biomass. Key note presentations on use and value of big data in the forest sector demonstrated the role of time series of remote sensing data in forest monitoring and research. In addition, new technologies and methods including terrestrial laser scanning are starting to provide detailed three-dimensional information from forest stands from which management tools and scientific understanding will be developed. Finally, citizen science was shown to offer a vast potential for the generation of forest-based data. Thus, new means are being developed by which forest scientists and managers will be able to obtain new, more frequent, and more detailed information on the forest. The ensuing development of knowledge will benefit the forest sector, create new opportunities for furthering boreal forest science, and finally benefit the society as a whole...

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Abstract

Soil organic carbon (C), accumulated over millennia, comprise more than half of the C stored in boreal and temperate forest landscapes. We used the Norwegian national forest inventory and soil survey network (n = 719, no deep organic soils) to explore the validity of a deterministic model representation of this pool (Yasso07). We statistically compared simulated and measured soil C stocks and related differences (measured – simulated) to site factors (drainage, topography, climate, vegetation, C-to-N ratio, and soil classification). Median C stocks were 5.0 kg C·m−2 (model) and 14.5 kg C·m−2 (measurements). Soil C differences related to site factors (r2 of 0.16 to 0.37). For Brunisols, Gleysols, and wet Organic soils, differences related primarily to topographic wetness. For Regosols, Podzols, and Dystric Eluviated Brunisols, they related to climate, profile depth, and, in some cases, drainage class and site index. We argue that soil moisture regimes in our study area overrule tree productivity effects in the determination of soil C stocks and present conditions for soil formation that the model cannot (and does not explicitly) account for. These are processes such as humification and podsolization that involve eluviation and illuviation of dissolved organic C (DOC) with sesquioxides to form spodic B horizons and carbon enrichment due to hampered decomposition in frequently anoxic conditions.

Abstract

This paper reviews emerging technology-based engineering solutions that may reduce the impact of forest operations on the environment while increasing the efficiency of operations resulting in an overall higher level of forest ecosystem service provision. Advances in forest machine control and automation systems, and the availability of remotely-sensed high resolution data now provide considerable potential to improve the management and precision of forest operations.

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Abstract

The accurate characterization of three-dimensional (3D) root architecture, volume, and biomass is important for a wide variety of applications in forest ecology and to better understand tree and soil stability. Technological advancements have led to increasingly more digitized and automated procedures, which have been used to more accurately and quickly describe the 3D structure of root systems. Terrestrial laser scanners (TLS) have successfully been used to describe aboveground structures of individual trees and stand structure, but have only recently been applied to the 3D characterization of whole root systems. In this study, 13 recently harvested Norway spruce root systems were mechanically pulled from the soil, cleaned, and their volumes were measured by displacement. The root systems were suspended, scanned with TLS from three different angles, and the root surfaces from the co-registered point clouds were modeled with the 3D Quantitative Structure Model to determine root architecture and volume. The modeling procedure facilitated the rapid derivation of root volume, diameters, break point diameters, linear root length, cumulative percentages, and root fraction counts. The modeled root systems underestimated root system volume by 4.4%. The modeling procedure is widely applicable and easily adapted to derive other important topological and volumetric root variables.

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Abstract

Nondetection of trees is a serious problem for the use of terrestrial laser scanning (TLS) in forest inventory applications. The use of multiple coregistered scans can reduce nondetection but may not eliminate it, and it carries substantial field and post-processing costs. We examined and extended previously developed theoretical approaches to modeling nondetection. The results suggested that tree size as well as multiple stand structural characteristics may be factors, but the theoretical models do not lend themselves to empirical estimation. We then used distance sampling techniques to identify detection probabilities and develop adjusted estimates for trees per hectare and basal area in nine forest stands in southern Norway. The results compared favorably with field estimates based on fixed-area plots. The estimated detection probabilities indicate that correction for nondetection is needed unless the search for trees is limited to very small distances from the scanner. Distance sampling appears promising when TLS is used in the context of temporary-plot forest inventories.

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Abstract

Sallow (Salix caprea L.) and rowan (Sorbus aucuparia L.) constitute small proportions of the deciduous tree volume in Scandinavia, but are highly preferred winter forage for moose and red deer, which occur at historically high densities. Thus, a possible decline of these tree species has been indicated. Against this background, we have reviewed the life histories of relevance for browsing, as well as the basic biology and genetics of sallow and rowan. The species show similarities with respect to short lifespan, small size and sympodial growth pattern, which are risk factors in a browsing context. They also have high juvenile growth rate, important for growing quickly out of reach of browsers. Sallow depends strongly on disturbance for establishment and is more demanding with respect to soil and light conditions than rowan, possibly important for the substantially lower abundance of sallow on the Norwegian Forest Inventory plots. Similarly, the relative recruitment of small size classes of sallow is less than for rowan. Although recruitment is reported to be hampered in wintering areas with high moose or red deer densities, the inventory data, however, dating only back to 1994, do not suggest a general decrease in any of the species. Sallow and rowan saplings show low mortality in moose and deer dominated areas and the species can be characterised as rather resilient to browsing. Of more concern is that browsing can constrain the development of mature rowan and sallow trees locally, with possible consequences for associated epiphytic biodiversity.

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Abstract

Terrestrial lidar (TLS) is an emerging technology for deriving forest attributes, including conventional inventory and canopy characterizations. However, little is known about the influence of scanner specifications on derived forest parameters. We compared two TLS systems at two sites in British Columbia. Common scanning benchmarks and identical algorithms were used to obtain estimates of tree diameter, position, and canopy characteristics. Visualization of range images and point clouds showed clear differences, even though both scanners were relatively high-resolution instruments. These translated into quantifiable differences in impulse penetration, characterization of stems and crowns far from the scan location, and gap fraction. Differences between scanners in estimates of effective plant area index were greater than differences between sites. Both scanners provided a detailed digital model of forest structure, and gross structural characterizations (including crown dimensions and position) were relatively robust; but comparison of canopy density metrics may require consideration of scanner attributes.

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Abstract

1. Whether plant competition grows stronger or weaker across a soil fertility gradient is an area of great debate in plant ecology. We examined the effects of competition and soil fertility and their interaction on growth rates of the four dominant tree species in the sub-boreal spruce forest of British Columbia. 2. We tested separate soil nutrient and moisture indices and found much stronger support for models that included the nutrient index as a measure of soil fertility. 3. Competition, soil fertility and their interaction affected radial growth rates for all species. 4. Each species supported a different alternate hypothesis for how competitive interactions changed with soil fertility and whether competition intensity was stronger or weaker overall as soil fertility increased depended on the context, specifically, species, neighbourhood composition and type of competition (shading vs. crowding). 5. The four species varied slightly in their growth response to soil fertility. 6. Individual species had some large variations in the shapes of their negative relationships between shading, crowding and tree growth, with one species experiencing no net negative effects of crowding at low soil fertility. 7. Goodness-of-fit was not substantially increased by models including competition–soil fertility interactions for any species. Tree size, soil fertility, shading and crowding predicted most of the variation in tree growth rates in the sub-boreal spruce forest. 8. Synthesis. The intensity of competition among trees across a fertility gradient was species- and context-specific and more complicated than that predicted by any one of the dominant existing theories in plant ecology.

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Abstract

The aim of this study was to determine whether forest clear-cuts during 2000–2011 could be detected as a decrease in surface height by combining Digital Surface Models (DSMs) from the Shuttle Radar Topography Mission (SRTM) and Tandem-X, and to evaluate the performance of this method using SRTM X- and C-band data as references representing the heights before logging. The study area was located in a Norway spruce-dominated forest estate in southeastern Norway. We interpolated 11-year DSM changes into a 10 m × 10 m raster, and averaged these changes per forest stand. Based on threshold values for DSM decreases we classified the pixels and stands into the categories “clear-cut” and “not clear-cut”, and compared this to a complete record of logged stands during 2000–2011. The classification accuracy was moderate or fairly good. A correct detection was achieved for 59%–67% of the clear-cut stands. Omission errors were most common, occurring in 33%–42% of the stands. Commission errors were found in 13%–21% of the clear-cut stands. The results obtained for X-band SRTM were only marginally better than for C-band. In conclusion, the combination of SRTM and Tandem-X has the potential of providing near global data sets for the recent 12 years’ logging, which should be particularly valuable for deforestation mapping.

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Abstract

An 11-year remotely sensed surface albedo dataset coupled with historical meteorological and stand-level forest management data for a variety of stands in Norway’s most productive logging region is used to develop regression models describing temporal changes in forest albedo following clear-cut harvest disturbance events. Datasets are grouped by dominant tree species, and two alternate multiple regression models are developed and tested following a potential-modifier approach. This result in models with statistically significant parameters (p < 0.05) that explain a large proportion of the observed variation, requiring a single canopy modifier predictor coupled with either monthly or annual mean air temperature as a predictor of a stand’s potential albedo. Models based on annual mean temperature predict annual albedo with errors (RMSE) in the range of 0.025–0.027, while models based on monthly mean temperature predict monthly albedo with errors ranging between of 0.057–0.065 depending on the dominant tree species. While both models have the potential to be transferable to other boreal regions with similar forest management regimes, further validation efforts are required. As active management of boreal forests is increasingly seen as a means to mitigate climate change, the presented models can be used with routine forest inventory and meteorological data to predict albedo evolution in managed forests throughout the region, which, together with carbon cycle modeling, can lead to more holistic climate impact assessments of alternative forest harvest scenarios and forest product systems.

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Abstract

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.

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Abstract

Use of harvest residues for bioenergy is minimal in Norway, and the proposed increase of 14 TWh in annual bioenergy use by year 2020 may thus to a large part be based on residues from conventional timber harvesting. To judge the potential of harvest residues for bioenergy both in the short and long run, we present cost-supply curves for residue harvesting at national and regional levels. We produce different harvesting scenarios using the detailed forest model Gaya/J and a representative description of the Norwegian forest area from Norwegian national forest inventory (NFI) sample plots including environmental restrictions. Forest information is sufficiently detailed to estimate necessary biomass fractions and calculate costs of harvest residue extraction at plot level. We estimate a maximum annual energy production of 5.3 TWh from harvest residues with the present harvest level, which is far from the official target. In principle, there are two solutions for achieving this target; increase harvests and thus the corresponding residue supply, or increase the use of roundwood for energy purposes on the expense of pulpwood. Scenarios with long-run increase in timber production shows an annual energy potential from harvest residues in the range 6–9 TWh. Thus, to reach the political target roundwood must be used for energy production.

Abstract

There is a need for monitoring methods for forest volume, biomass and carbon based on satellite remote sensing. In the present study we tested interferometric X-band SAR (InSAR) from the Tandem-X mission. The aim of the study was to describe how accurate volume and biomass could be estimated from InSAR height and test whether the relationships were curvilinear or not. The study area was a spruce dominated forest in southeast Norway. We selected 28 stands in which we established 192 circular sample plots of 250 m2, accurately positioned by a Differential Global Positioning System (dGPS). Plot level data on stem volume and aboveground biomass were derived from field inventory. Stem volume ranged fromzero to 596 m3/ha, and aboveground biomass up to 338 t/ha.We generated 2 Digital Surface Models (DSMs) fromInSAR processing of two co-registered, HH-polarized TanDEM-X image pairs – one ascending and one descending pair.We used a Digital TerrainModel (DTM) from airborne laser scanning (ALS) as a reference and derived a 10 m × 10 m Canopy Height Model (CHM), or InSAR height model. We assigned each plot to the nearest 10 m × 10 m InSAR height pixel. We applied a nonlinear, mixed model for the volume and biomass modeling, and from a full model we removed effects with a backward stepwise approach. InSAR heightwas proportional to volume and aboveground biomass, where a 1 m increase in InSAR height corresponded to a volume increase of 23 m3/ha and a biomass increase of 14 t/ha. Root Mean Square Error (RMSE) values were 43–44% at the plot level and 19–20% at the stand level.

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Abstract

National Forest Inventories (NFIs) provide estimates of forest parameters for national and regional scales. Many key variables of interest, such as biomass and timber volume, cannot be measured directly in the field. Instead, models are used to predict those variables from measurements of other field variables. Therefore, the uncertainty or variability of NFI estimates results not only from selecting a sample of the population but also from uncertainties in the models used to predict the variables of interest. The aim of this study was to quantify the model-related variability of Norway spruce (Picea abies [L.] Karst) biomass stock and change estimates for the Norwegian NFI. The model-related variability of the estimates stems from uncertainty in parameter estimates of biomass models as well as residual variability and was quantified using a Monte Carlo simulation technique. Uncertainties in model parameter estimates, which are often not available for published biomass models, had considerable influence on the model-related variability of biomass stock and change estimates. The assumption that the residual variability is larger than documented for the models and the correlation of within-plot model residuals influenced the model-related variability of biomass stock change estimates much more than estimates of the biomass stock. The larger influence on the stock change resulted from the large influence of harvests on the stock change, although harvests were observed rarely on the NFI sample plots in the 5-year period that was considered. In addition, the temporal correlation between model residuals due to changes in the allometry had considerable influence on the model-related variability of the biomass stock change estimate. The allometry may, however, be assumed to be rather stable over a 5-year period. Because the effects of model-related variability of the biomass stock and change estimates were much smaller than those of the sampling-related variability, efforts to increase the precision of estimates should focus on reducing the sampling variability. If the model-related variability is to be decreased, the focus should be on the tree fractions of living branches as well as stump and roots.

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Abstract

A mountain pine beetle (MPB) epidemic is currently ravaging large areas of interior British Columbia (BC) with significant implications for ecosystem services including future timber supply and community economic stability. Information is needed on future stand dynamics in areas of impacted forests that are unlikely to be salvaged logged. Of greatest concern are stands dominated by lodgepole pine (>50% timber volume). Predicting how surviving trees in these areas respond and grow and the timing and species composition of natural regeneration ingress is of critical importance for multiple forest values. We undertook a retrospective study in the Flathead Valley of southeastern British Columbia where an intense MPB epidemic peaked in 1979–1980. Our objective was to gain insight into stand recovery and stand self-organization as influenced by species-specific growth responses of different sized secondary structure trees (individual seedling, sapling, sub-canopy and canopy trees surviving the epidemic) and post-beetle regeneration dynamics. MPB mortality rates, the percent of basal area killed by beetles, varied from 42% to 100% with most stands between 60% and 80%. In general, all surviving secondary structure released but the extent of growth release exhibited species variability. Release of surviving canopy lodgepole pine trees was often dramatic and greatest in stands with high total stand MPB mortality rates. Ingress of natural regeneration was slow in the first few years after MPB attack but there was a strong pulse of recruitment 10–20 years post disturbance which then slowed considerably. Nearly 30 years after the MPB attack, the stocking and composition of the understories have changed dramatically. Overall, the occurrence of the MPB epidemic resulted in more structurally and compositionally diverse stands leading to multiple successional pathways different from those of even-age pine dominated stands. The recovery and self-organization of unsalvaged natural stands in the Flathead Valley was a complicated process. It has provided insights for future forest management in areas impacted by the current massive MPB epidemic ongoing for the past decade in western North America.

Abstract

Harvest activity directly impacts timber supply, forest conditions, and carbon stock. Forecasts of the harvest activity have traditionally relied on the assumption that harvest is carried out according to forest management guidelines or to maximize forest value. However, these rules are, in practice, seldom applied systematically, which may result in large discrepancies between predicted and actual harvest in short-term forecasts. We present empirical harvest models that predict final felling and thinning based on forest attributes such as site index, stand age, volume, slope, and distance to road. The logistic regression models were developed and fit to Norwegian national forest inventory data and predict harvest with high discriminating power. The models were consistent with expected landowners behavior, that is, areas with high timber value and low harvest cost were more likely to be harvested. We illustrate how the harvest models can be used, in combination with a growth model, to develop a national business-as-usual scenario for forest carbon. The business-as-usual scenario shows a slight increase in national harvest levels and a decrease in carbon sequestration in living trees over the next decade.

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Abstract

In this paper two sampling and estimation strategies for regional forestinventory were investigated in detail and results were presented for various geographical scales. Airbornelaser scanner (ALS) data were acquired to augment data from a systematic sample of NationalForestInventory (NFI) ground plots in HedmarkCounty, Norway (27,390 km2). Approximately 50% of the NFI fieldplots were covered by the systematic ALS sample of 53 parallel flight lines spaced 6 km apart. The area was stratified into eight cover classes and independent log-transformed regression models were developed for each class to predict total above-ground dry biomass (AGB). The two laser-ground estimation strategies tested were a model-dependent (MD), two-phase approach that rests on the assumption that the predictive models are correctly specified, and a model-assisted (MA) approach with a two-stage probability sampling design which utilizes design-unbiased estimators. ALS AGB estimates were reported by land cover class and compared to the NFI ground estimates. The ALS-based MA and MD mean estimates differed from the NFI AGB estimates by about 2% and 8%, respectively, for the entire County. At the county level the smallest estimated standard error (SE) for the estimates was obtained using the field data alone. However, the SEs calculated from field and ALS data were based on unequal numbers of ground plots. When considering only the NFI plots in the ALS strips, the smallest SEs were obtained using the MD framework. However, we also illustrated the sensitivity of the estimates of applying different plausible models. All the applied estimators assumed simple random sampling while the selection of flight lines as well as ground plots followed a systematic design. Thus, the estimates of SE were most likely conservative. Simulated sampling undertaken in a parallel research effort suggests that the overestimation of the SEs was probably much larger for the ALS-based estimates compared to the NFI estimates. ALS-based estimates were also derived for sub-county political units and thereby demonstrated how limited sample sizes affect the standard error of the biomass estimates.

Abstract

The combined effects of light, soil fertility, and ontogenetic changes on plant growth rates are poorly understood, yet these three factors play fundamental roles in structuring plant communities. We sought to determine how lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia), interior spruce (Picea glauca engelmanii (Moench) Voss), and subalpine fir (Abies lasiocarpa (Hook.) Nutt.) sapling growth responds to the combination of light, soil fertility, and ontogeny and how these three dominant conifer species in sub-boreal forests of British Columbia differ in their responses.Using maximum likelihood methods, we found that 0.204 m tall sapling growth rates changed during ontogeny and were limited by both light and soil resources. The strongest differences among species growth rates were due to tree size, with smaller differences due to soil fertility, and there were no differences among species in the shape of their growth responses to light. Rank order in growth rates for small saplings (pine spruce fir) inversely corresponded to classic shade-tolerance ratings, thus supporting the carbon balance theory. Interior spruce height growth rates increased relative to lodgepole pine with increasing soil fertility, clearly matching the landscape-scale increase in canopy dominance of interior spruce over lodgepole pine with increasing soil fertility.

Abstract

The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small sub-populations such as municipalities or protected areas. Frequently, too few sampled observations are available for such small areas to allow estimates with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates. The study aimed at estimating the mean above-ground forest biomass for small areas with high precision and accuracy, using SAE techniques. For this purpose, the simple random sampling (SRS) estimator, the generalized regression (GREG) estimator, and the unit-level empirical best linear unbiased prediction (EBLUP) estimator were compared. Mean canopy height obtained from a photogrammetric canopy height model (CHM) was the auxiliary variable available for every population element. The small areas were 14 municipalities within a 2,184 km2 study area for which an estimate of the mean forest biomass was sought. The municipalities were between 31 and 527 km2 and contained 1–35 NNFI sample plots located within forest. The mean canopy height obtained from the CHM was found to have a strong linear correlation with forest biomass. Both the SRS estimator and the GREG estimator result in unstable estimates if they are based on too few observations. Although this is not the case for the EBLUP estimator, the estimators were only compared for municipalities with more than five sample plots. The SRS resulted in the highest standard errors in all municipalities. Whereas the GREG and EBLUP standard errors were similar for small areas with many sample plots, the EBLUP standard error was usually smaller than the GREG standard error. The difference between the EBLUP and GREG standard error increased with a decreasing number of sample plots within the small area. The EBLUP estimates of mean forest biomass within the municipalities ranged between 95.01 and 153.76 Mg ha−1, with standard errors between 8.20 and 12.84 Mg ha−1.

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Abstract

Stand and disturbance dynamics are key processes that need to be assessed along with climate-species interactions if we are to better understand the impacts of climate change on species. In this study we investigated the biotic interactions (competition) between species, the influence of disturbance type, and changes in resource availability (moisture and light) on the response of six tree species to climate change in the northwest region of central British Columbia, Canada. Two ecological models were parameterized, linked together and coupled to climate change scenarios to explore the interactions between: (1) the response of species in the regeneration phase and (2) the role of disturbance, resource availability and competition on determining stand composition and productivity. Climate change was found to reduce soil moisture availability which resulted in a decline in regeneration potential for all species on dry sites and negative to neutral responses on sites with higher water availability. Following fire, stand dynamics and composition were modeled to undergo significant changes under the 2080s climate compared to current climate conditions on dry and mesic sites. Changes in stand dynamics under climate change were marginal following bark beetle disturbances. While significant changes to stand dynamics were found on dry sites, the presented results suggest that the sites with the highest moisture availability maintain the same general stand dynamics and composition following disturbances under climate change. This study highlights the need to consider species response to climate change in interaction with existing stand conditions, disturbance type, competition, resource availability, not just temperature and precipitation.

Abstract

Vegetation height information is one of the most important variables for predicting forest attributes such as timber volume and biomass. Although airborne laser scanning (ALS) data are operationally used in forest planning inventories in Norway, a regularly repeated acquisition of ALS data for large regions has yet to be realized. Therefore, several research groups analyze the use of other data sources to retrieve vegetation height information. One very promising approach is the photogrammetric derivation of vegetation heights from overlapping digital aerial images. Aerial images are acquired over almost all European countries on a regular basis making image data readily available. The Norwegian Forest and Landscape Institute (Skog og Landskap) invited researchers and practitioners that produce and utilize photogrammetric data to share their experiences. More than 30 participants followed the invitation and contributed to a successful event with interesting presentations and discussions. We wish to thank the speakers for their contributions and hope that all participants found the seminar useful. These short proceedings of the seminar include summaries of the talks. The presentations, which provide more information, can be found at the end of this document.

Abstract

The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small subpopulations such as municipalities or protected areas. Frequently, too few sampled observations are available for those small areas to allow an estimate with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates.

Abstract

Competition for canopy space is a fundamental structuring feature of forest ecosystems and remains an enduring focus of research attention. We used a spatial neighborhood approach to quantify the influence of local competition on the size of individual tree crowns in north-central British Columbia, where forests are dominated by subalpine fir (Abies lasiocarpa), lodgepole pine (Pinus contorta) and interior spruce (Picea glaucax engelmanii).Using maximum likelihood methods, we quantified crown radius and length as functions of tree size and competition, estimated by the species identity and spatial arrangement of neighboring trees.Tree crown size depended on tree bole size in all species. Given low levels of competition, pine displayed the widest, shortest tree crowns compared to the relatively long and narrow crowns found in spruce and fir. Sensitivity to crowding by neighbors declined with increasing tree height in all but the pine crown radius model. Five of the six selected best models included separate competition coefficients for each neighboring tree species, evidence that species generally differ in their competitive effects on neighboring tree crowns.The selected crown radius model for lodgepole pine, a shade-intolerant species, treated all neighbors as equivalent competitors. In all species, competition from neighbors exerted an important influence on crown size. Per-capita effects of competition across different sizes and species of neighbors and target trees varied, but subalpine fir generally displayed the strongest competitive effects on neighbors.Results from this study provide evidence that species differ both in their response to competition and in their competitive influence on neighbors, factors that may contribute to maintaining coexistence.

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Abstract

The suitability of interferometric X-band radar for forest monitoring was investigated. Working in a spruce-dominated forest in southeast Norway, top height, mean height, stand density, stem volume, and biomass were related to space shuttle interferometric height above ground. A ground truth dataset was produced for each radar data pixel in the study area by combining a field inventory and automatic tree detection with airborne laser scanning data. Pixels were aggregated to forest stands. Interferometric height was strongly related to all of the five forest variables, and most strongly to top height with R-2 = 0.71 and RMSE = 13% at the pixel level and R-2 = 0.82 and RMSE = 5.6% at the stand level. Interferometric height was linearly related to stem volume and biomass up to 400 m(3)/ha and 200 t/ha, respectively, and RMSE was approximately 19% for both variables. These errors contain error components caused by the 3.5-year time lag between the radar acquisition and the laser scanning. It is concluded that interferometric X-band radar has potential for use in forest monitoring.

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Abstract

The primary aim of this study was to investigate the suitability of interferometric X-band SAR (InSAR) for inventory of boreal forest biomass. We investigated the relationship between SRTM X-band InSAR height and above-ground biomass in a study area in southern Norway. We generated biomass reference data for each SRTM pixel from a field inventory in combination with airborne laser scanning (ALS). One set of forest inventory plots served for calibrating ALS based biomass models, and another set of field plots was used to validate these models. The biomass values obtained in this way ranged up to 250 t/ha at the stand level. The relationship between biomass and InSAR height was linear, no apparent saturation effect was present, and the accuracy was high (RMSE = 19%). The relationship differed between Norway spruce and Scots pine, where an increase in InSAR height of 1 m corresponded to an increase in biomass of 9.9 and 7.0 t/ha. respectively. Using a high-quality terrain model from ALS enabled biomass to be estimated with a higher accuracy as compared to using a terrain model from topographic maps. Interferometric X-band SAR appears to be a promising method for forest biomass monitoring. (C) 2010 Elsevier Inc. All rights reserved.

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Abstract

Ambitious targets for renewable energy production in Norway draw attention to biomass potent-ials. The objective of this report is to review the state of the art regarding research on estimation methods, the availability and production of tree biomass resources for energy purposes in Norway in order to indentify knowledge gaps and thus facilitate appropriate focus, development and priorities regarding research for the coming years. The review focuses on biomass from pri-mary forest production with emphasis on Norwegian conditions, but also considers international research, especially from the other Nordic countries. Three main subject areas are considered: - biomass estimation - biomass resources and availability - biomass production. The first part of this report comprises an overview of existing biomass equations and associated inventory methods applied for estimating biomass in Norway. The overview includes a description of the Norwegian National Forest Inventory data as a basis for large-scale biomass assessments. The second part of the report comprises an overview of previous Norwegian assessments of biomass as an energy supplier as well as suggestions for improvements in such assessments. Improvement possibilities regarding the impacts of environmentally oriented restrictions, appropriate models for productivity and cost calculations regarding biomass harvesting systems, and implementation of biomass-related features in existing decisions support systems to facilitate analyses, where timber production and biomass production for energy purposes are equally important, are identified. The final part of the review focuses on silvi-cultural options aiming at optimizing the value of total biomass instead of the conventional approach to silviculture where the main focus is timber values.

Abstract

There is an increasing need for forest resource monitoring methods, as more attention is paid to deforestation, bio-energy and forests as habitats. Most national forest inventories are based on networks of field inventory plots, sometimes together with satellite data, and airborne laser scanning (ALS) is increasingly used for local forest mapping. These methods are expensive to establish or carry out, and many countries, including some severely affected by deforestation, do not apply such methods.Satellite based remote sensing methods in use today are hampered by problems caused by clouds and saturation at moderate biomass levels. Satellite SAR is not hampered by cloud problems, and monitoring of canopy surface elevation, which is correlated to key forest resource variables, might be a future method in forest monitoring.We here present the main findings of three studies (Solberg et al. 2010, a, b, c) investigating the potential of interferometric SAR (InSAR) for forest monitoring, by describing the relationship between InSAR height above ground and key forest variables. We based this study on InSAR data from the Shuttle Radar Topographic Mission (SRTM) with its acquisition in February 2000. We obtained SRTM InSAR DEM data from DLR for two forest areas in Norway, and built a ground-truth from the combination of field inventory and ALS.The forest areas were dominated by Norway spruce and Scots pine. In each forest area we laid out a number of field inventory plots, where we recorded standard forest variables such as Dbh and tree height, and from this derived plot aggregated variables of top height, mean height, stand density (mean tree height divided by the mean tree spacing), volume and biomass. We used this to calibrate and validate ALS based models, from which we derived estimates of the same variables for each SRTM pixel. This served as reference data for the SRTM data.From the X-band SRTM digital surface model (DSM) image we subtracted a high quality digital terrain model (DTM) derived from the ALS data. This was based on an extraction of ground echoes from the data provider, and the elevations of these echoes were interpolated into a grid fitting the SRTM grid.This produced data on the RADAR echo height above ground (InSAR height), which we related to the forest variables. With digital stand maps we aggregated the variables to the stand level. The X-band microwaves penetrate a little into the canopy, and the InSAR height was on average about 1.2 m below the mean tree height. InSAR height was strongly related to all forest variables, most strongly to top height.Particularly valuable was that stem volume and biomass, ranging up to 400 m3/ha and 200 t/ha, respectively, were linearly related to InSAR height with an accuracy, RMSE, of 19% at the stand level. However, these relationships had an intercept, which represents the microwave penetration into the vegetation, and due to this the relationships were non-linear for forest stands having heights and biomass values close to zero.With a lower quality DTM derived from topographic maps, the relationships were weaker. However, as long as a forest variable is within the ranges of the linear relationship, any change in InSAR elevation would be proportional to a change in forest height, volume or biomass. And, any logging should be detectable as a sudden decrease in InSAR elevation.Hence, a forest monitoring based on X-band InSAR might be suitable even without a DTM. An application of space borne InSAR for forest monitoring would be feasible for large areas at low cost, whereas an ALS acquisition for a part of the area would serve as reference data for calibration.

Abstract

Evaluation of climate change consequences and national carbon reporting such as under the Kyoto protocol require long-term monitoring of carbon fluxes. We report on an ongoing project aimed at a national-level assessment of the terrestrial carbon sequestration potential under present conditions and under various climate and land use change scenarios, in particular in terms of their temperature effect. We develop empirical models for national soil carbon stock assessment and evaluate process-based soil carbon models for prediction of future carbon dynamics.....