Rasmus Astrup
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Clemens Blattert Mikko Mönkkönen Daniel Burgas Fulvio Di Fulvio Astor Toraño Caicoya Marta Vergarechea Julian Klein Markus Hartikainen Clara Antón Fernández Rasmus Astrup Michael Emmerich Nicklas Forsell Jani Lukkarinen Johanna Lundström Samuli Pitzén Werner Poschenrieder Eeva Primmer Tord Snäll Kyle EyvindsonSammendrag
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Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.
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Ove Wolfgang Siri Gulaker Mathisen Dilip Khatiwada Pavinee Nojpanya Kristoffer Andersen Øyvind Skreiberg Ignacio Sevillano Elisa Magnanelli Elvira Molin Sarah Schmidt Peter Hagström Signe Kynding Borgen Sofia Poulikidou Akram Sandvall Kenneth Karlsson Fumi Maeda Harahap Catarina Almeida Abhijith Kapothanillath Rasmus AstrupSammendrag
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Stephan Hoffmann Marian Schönauer Joachim Heppelmann Antti Asikainen Emmanuel Cacot Benno Eberhard Hubert Hasenauer Janis Ivanovs Dirk Jaeger Andis Lazdins Sima Mohtashami Tadeusz Moskalik Tomas Nordfjell Krzysztof Stereńczak Bruce Talbot Jori Uusitalo Morgan Vuillermoz Rasmus AstrupSammendrag
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The preservation of the functionality of forest soil is a key aspect in planning mechanized harvesting operations. Therefore, knowledge and information about stand and soil characteristics are vital to the planning process. In this respect, depth-to-water (DTW) maps were reviewed with regard to their potential use as a prediction tool for wheel ruts. To test the applicability of open source DTW maps for prediction of rutting, the ground surface conditions of 20 clear-cut sites were recorded post harvesting, using an unmanned aerial vehicle (UAV). In total, 80 km of machine tracks were categorized by the severity of occurring rut-formations to investigate whether: i) operators intuitively avoid areas with low DTW values, ii) a correlation exists between decreasing DTW values and increasing rut severity, and iii) DTW maps can serve as reliable decision-making tool in minimizing the environmental effects of big machinery deployment. While the machine operators did not have access to these predictions (DTW maps) during the operations, there was no visual evidence that driving through these areas was actively avoided, resulting in a higher density of severe rutting within areas with DTW values <1 m. A logistic regression analysis confirmed that the probability of severe rutting rapidly increases with decreasing DTW values. However, significant differences between sites exist which might be attributed to a series of other factors such as soil type, weather conditions, number of passes and load capacity. Monitoring these factors is hence highly recommended in any further follow-up studies on soil trafficability.
Forfattere
Marta Vergarechea Rasmus Astrup Carolin Fischer Knut Øistad Clemens Blattert Markus Hartikainen Kyle Eyvindson Fulvio Di Fulvio Nicklas Forsell Daniel Burgas Astor Toraño-Caicoya Mikko Mönkkönen Clara Antón FernándezSammendrag
To mitigate climate change, several European countries have launched policies to promote the development of a renewable resource-based bioeconomy. These bioeconomy strategies plan to use renewable biological resources, which will increase timber and biomass demands and will potentially conflict with multiple other ecosystem services provided by forests. In addition, these forest ecosystem services (FES) are also influenced by other, different, policy strategies, causing a potential mismatch in proposed management solutions for achieving the different policy goals. We evaluated how Norwegian forests can meet the projected wood and biomass demands from the international market for achieving mitigation targets and at the same time meet nationally determined targets for other FES. Using data from the Norwegian national forest inventory (NFI) we simulated the development of Norwegian forests under different management regimes and defined different forest policy scenarios, according to the most relevant forest policies in Norway: national forest policy (NFS), biodiversity policy (BIOS), and bioeconomy policy (BIES). Finally, through multi-objective optimization, we identified the combination of management regimes matching best with each policy scenario. The results for all scenarios indicated that Norway will be able to satisfy wood demands of up to 17 million m3 in 2093. However, the policy objectives for FES under each scenario caused substantial differences in terms of the management regimes selected. We observed that BIES and NFS resulted in very similar forest management programs in Norway, with a dominance of extensive management regimes. In BIOS there was an increase of set aside areas and continuous cover forestry, which made it more compatible with biodiversity indicators. We also found multiple synergies and trade-offs between the FES, likely influenced by the definition of the policy targets at the national scale.
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Johannes Breidenbach David Ellison Hans Petersson Kari T. Korhonen Helena M. Henttonen Jörgen Wallerman Jonas Fridman Terje Gobakken Rasmus Astrup Erik NæssetSammendrag
Key message: Using satellite-based maps, Ceccherini et al. (Nature 583:72-77, 2020) report abruptly increasing harvested area estimates in several EU countries beginning in 2015. Using more than 120,000 National Forest Inventory observations to analyze the satellite-based map, we show that it is not harvested area but the map’s ability to detect harvested areas that abruptly increases after 2015 in Finland and Sweden. Keywords: Global Forest Watch, Landsat, Remote sensing, National Forest Inventory, Greenhouse Gas Inventory
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SiTree is a flexible, cross-platform, open-source framework for individual-tree simulators intended to facilitate accurate and flexible analyses of forest growth and yield, or more generally forest dynamics simulations. SiTree provides generic functionality to build customized individual-tree simulators using additional user-written code. In the forestry literature there are a wide variety of individual models that describe the different parts of forest growth and dynamics and new models are continuously developed and published. The aim of SiTree is to provide a broad community of R-users within forestry with an easily adaptable individual-tree simulator framework and an easily accessible tool for testing and combining new and existing models describing parts of forest growth dynamics.
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Marian Schönauer Robert Prinz Kari Väätäinen Rasmus Astrup Dariusz Pszenny Harri Lindeman Dirk JaegerSammendrag
Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations. The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset. Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45). We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management.
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Norway’s most common tree species, Picea abies (L.) Karst. (Norway spruce), is often infected with Heterobasidion parviporum Niemelä & Korhonen and Heterobasidion annosum (Fr.) Bref.. Because Pinus sylvestris L. (Scots pine) is less susceptible to rot, it is worth considering if converting rot-infested spruce stands to pine improves economic performance. We examined the economically optimal choice between planting Norway spruce and Scots pine for previously spruce-dominated clear-cut sites of different site indexes with initial rot levels varying from 0% to 100% of stumps on the site. While it is optimal to continue to plant Norway spruce in regions with low rot levels, shifting to Scots pine pays off when rot levels get higher. The threshold rot level for changing from Norway spruce to Scots pine increases with the site index. We present a case study demonstrating a practical method (“Precision forestry”) for determining the tree species in a stand at the pixel level when the stand is heterogeneous both in site indexes and rot levels. This method is consistent with the concept of Precision forestry, which aims to plan and execute site-specific forest management activities to improve the quality of wood products while minimising waste, increasing profits, and maintaining environmental quality. The material for the study includes data on rot levels and site indexes in 71 clear-cut stands. Compared to planting the entire stand with a single species, pixel-level optimised species selection increases the net present value in almost every stand, with average increase of approximately 6%.
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Stand-level growth and yield models are important tools that support forest managers and policymakers. We used recent data from the Norwegian National Forest Inventory to develop stand-level models, with components for dominant height, survival (number of survived trees), ingrowth (number of recruited trees), basal area, and total volume, that can predict long-term stand dynamics (i.e. 150 years) for the main species in Norway, namely Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and birch (Betula pubescens Ehrh. and Betula pendula Roth). The data used represent the structurally heterogeneous forests found throughout Norway with a wide range of ages, tree size mixtures, and management intensities. This represents an important alternative to the use of dedicated and closely monitored long-term experiments established in single species even-aged forests for the purpose of building these stand-level models. Model examination by means of various fit statistics indicated that the models were unbiased, performed well within the data range and extrapolated to biologically plausible patterns. The proposed models have great potential to form the foundation for more sophisticated models, in which the influence of other factors such as natural disturbances, stand structure including species mixtures, and management practices can be included.
Forfattere
Johannes Schumacher Håvard Toft Larsen Paul McLean Marius Hauglin Rasmus Astrup Johannes BreidenbachSammendrag
The number of people affected by snow avalanches during recreational activities has increased over the recent years. An instrument to reduce these numbers are improved terrain classification systems. One such system is the Avalanche Terrain Exposure Scale (ATES). Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to (i) map forest stem density and canopy-cover based on National Forest Inventory and remote sensing data and, (ii) use these forest attributes as input to the AutoATES model. We predicted stem density and directly calculated canopy-cover in a 20 Mha study area in Norway. The forest attributes were mapped for 16 m × 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy-cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski-touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%.
Forfattere
Stephan Hoffmann Marian Schönauer Joachim Heppelmann Antti Asikainen Emmanuel Cacot Benno Eberhard Hubert Hasenauer Janis Ivanovs Dirk Jaeger Andis Lazdins Sima Mohtashami Tadeusz Moskalik Tomas Nordfjell Krzysztof Stereńczak Bruce Talbot Jori Uusitalo Morgan Vuillermoz Rasmus AstrupSammendrag
Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.
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Benjamin Allen Michele Dalponte Hans Ole Ørka Erik Næsset Stefano Puliti Rasmus Astrup Terje GobakkenSammendrag
Numerous species of pathogenic wood decay fungi, including members of the genera Heterobasidion and Armillaria, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (Picea abies L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13).
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Stefano Puliti Grant D. Pearse Michael S. Watt Edward Mitchard Ian McNicol Magnus Bremer Martin Rutzinger Peter Surovy Luke Wallace Markus Hollaus Rasmus AstrupSammendrag
Survey-grade laser scanners suitable for drones (UAV-LS) allow the efficient collection of finely detailed three-dimensional (3D) information on tree structures allowing to resolve the complexity of the forest into discrete individual trees and species as well as into different component of the tree. Current developments are hindered by the limited availability of survey-grade UAV-LS data and by the lack of a publicly available benchmark dataset for developing and validating methods. We present a new benchmarking dataset composed of manually labelled UAV-LS data covering forests in different continents and eco-regions. Such data consists in single-tree point clouds, with each point classified as either stem, branches, and leaves. This benchmark dataset offers new possibilities to develop single-tree segmentation algorithms and validate existing ones.
Forfattere
Stefano Puliti Grant Pearse Michael Watt Edward Mitchard Ian McNicol Magnus Bremer Martin Rutzinger Peter Surovy Luke Wallace Markus Hollaus Rasmus AstrupSammendrag
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Proximal sensing technologies are becoming widely used across a range of applications in environmental sciences. One of these applications is in the measurement of the ground surface in describing soil displacement impacts from wheeled and tracked machinery in the forest. Within a period of 2–3 years, the use photogrammetry, LiDAR, ultrasound and time-of-flight imaging based methods have been demonstrated in both experimental and operational settings. This review provides insight into the aims, sampling design, data capture and processing, and outcomes of papers dealing specifically with proximal sensing of soil displacement resulting from timber harvesting. The work reviewed includes examples of sensors mounted on tripods and rigs, on personal platforms including handheld and backpack mounted, on mobile platforms constituted by forwarders and skidders, as well as on unmanned aerial vehicles (UAVs). The review further highlights and discusses the benefits, challenges, and some of the shortcomings of the various technologies and their application as interpreted by the authors. The majority of the work reviewed reflects pioneering approaches and innovative applications of the technologies. The studies have been carried out almost simultaneously, building on little or no common experience, and the evolution of standardized methods is not yet fully apparent. Some of the issues that will likely need to be addressed in developing this field are (i) the tendency toward generating apparently excessively high resolution micro-topography models without demonstrating the need for or contribution of such resolutions on accuracy, (ii) the inadequacy of conventional manual measurements in verifying the accuracy of these methods at such high resolutions, and (iii) the lack of a common protocol for planning, carrying out, and reporting this type of study.
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Stefano Puliti Johannes Breidenbach Johannes Schumacher Marius Hauglin Torgeir Ferdinand Klingenberg Rasmus AstrupSammendrag
This study aimed at estimating total forest above-ground net change (ΔAGB; Gg) over five years (2014–2019) based on model-assisted estimation utilizing freely available satellite imagery. The study was conducted for a boreal forest area (approx. 1.4 Mha) in Norway where bi-temporal national forest inventory (NFI), Sentinel-2, and Landsat data were available. Biomass change was modelled based on a direct approach. The precision of estimates using only the NFI data in a basic expansion estimator was compared to four different alternative model-assisted estimates using 1) Sentinel-2 or Landsat data, and 2) using bi- or uni-temporal remotely sensed data. We found that spaceborne optical data improved the precision of the purely field-based estimates by a factor of up to three. The most precise estimates were found for the model-assisted estimation using bi-temporal Sentinel-2 (standard error; SE = 1.7 Gg). However, the decrease in precision when using Landsat data was small (SE = 1.92 Gg). We also found that ΔAGB could be precisely estimated when remotely sensed data were available only at the end of the monitoring period. We conclude that satellite optical data can considerably improve ΔAGB estimates, when repeated and coincident field data are available. The free availability, global coverage, frequent update, and long-term time horizon make data from programs such as Sentinel-2 and Landsat a valuable data source for consistent and durable monitoring of forest carbon dynamics.
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Niels Strange Peter Bogetoft Giovanna Ottaviani Aalmo Bruce Talbot Anders Holm Holt Rasmus AstrupSammendrag
The forestry sector is constantly looking for ways for making data-driven decisions and improving efficiency. The application of Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) allow the users to go beyond looking at simple key performance indicators. Benchmarking is one of the most common tools in business for improving efficiency and competitiveness. This study searched for benchmarking studies in Web of Science until December 2020. It reviewed 56 benchmarking studies in forestry and discusses the potential advantages of using benchmarking in forestry. More than 80% of the studies apply DEA. This review found that almost half of the benchmarking studies in forestry have attempted to estimate the efficiency of forest management organizations at regional scale, mostly being public or state-owned forest districts. A bit more than one-third of the studies have focused on benchmarking forest industries and one-fifth, benchmarking of forest operations. Forest management organizations mainly applied benchmarking for internal comparison and forest industries entirely focused on competitive benchmarking. Surprisingly, in most cases the studies do not necessarily overlap geographically with forest rich countries (e.g., Russian Federation or Brazil). A number of studies address multiple criteria. The future potential for applying automatic data transfer from harvest machines to interactive benchmarking systems are discussed. Finally, the paper discusses the advantages and weaknesses of benchmarking and future research on improving usefulness and usability of benchmarking in forest businesses.
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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.
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Johannes Breidenbach Janis Ivanovs Annika Kangas Thomas Nord-Larsen Mats Nilsson Rasmus AstrupSammendrag
Policy measures and management decisions aimed at enhancing the role of forests in mitigating climate change require reliable estimates of carbon (C)-stock dynamics in greenhouse gas inventories (GHGIs). The aim of this study was to assemble design-based estimators to provide estimates relevant for GHGIs using National Forest Inventory (NFI) data. We improve basic expansion (BE) estimators of living-biomass C-stock loss using only field data, by leveraging with remote sensing auxiliary data in model-assisted (MA) estimators. Our case studies from Norway, Sweden, Denmark, and Latvia covered an area of >70 Mha. Landsat-based forest cover loss (FCL) and one-time wall-to-wall airborne laser scanning (ALS) served as auxiliary data. ALS provided information on the C stock before a potential disturbance indicated by FCL. The use of FCL in MA estimators resulted in considerable efficiency gains, which in most cases were further increased by adding ALS. A doubling of efficiency was possible for national estimates and even larger efficiencies were observed at the subnational level. Average annual estimates were considerably more precise than pooled estimates of the NFI data from all years at once. The combination of remotely sensed and NFI field data yields reliable estimators, which is not necessarily the case when using remotely sensed data without reference observations.
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Statskog eier om lag 8% av Norges produktive skogareal. Skogen er dominert av en stor andel hogstmoden skog og en overvekt av skog på lavere boniteter. Tilveksten i skogen er svakt avtagende noe som sannsynligvis skyldes skjev aldersklasse fordeling med mye eldre skog. Hogsten i skogen er kun om lag en tredjedel av tilveksten og fører til en sterk oppbygging av det stående volum på Statskog sine eiendommer. Når tilveksten er høyere enn avvirking vil man vanligvis forvente et opptak av karbon i skogen. Dette er også tilfellet for Statskog hvor det er estimert et karbonopptak på om lag 1,5 mill. ton CO2 per år. Karbon opptaket er litt mindre nå enn det var tidligere ettersom tilveksten er fallende og hogsten har vært svakt økende. Når man driver hogst er det fossile utslipp knyttet til hogst, terrengtransport, og tømmerbil transport. Mellom 2010 og 2019 har utslippene fra hogst og transport variert mellom 1 600 tonn CO2 og 4 900 tonn CO2 avhengig av hogstkvantum. Det er viktig å fremheve at utslippene fra transport og hogst er minimale sammenlignet med opptaket av CO2 i skogen til Statskog. Når man avvirker skog produseres det materialer som kan erstatte fossil intensive materialer til andre sektorer slik som bygg og energi. Det er vanskelig å direkte kvantifisere substitusjonen av fossil intensive materialer da effekten er avhengig av de spesifikke materialene som erstattes og effektiviteten i hele verdikjeden. På den andre siden er substitusjon en viktig del av klimaeffekten ved hogst og bør inkluderes når man vurderer klimaeffekter av skogsdrift. Hvis vi antar at skurlast produsert fra avvirkningen til Statskog benyttes til å erstatte stål er det estimert at substitusjonen mellom 2010 og 2019 har variert mellom 32 000 og 99 000 tonn CO2 per år. Substitusjonseffekten er dermed mye høyere enn utslippene fra hogst og transport, men likevel små i forhold til opptaket av karbon i skogen til Statskog. Gjennom skogbehandlingen kan man kraftig påvirke opptaket av karbon i skogen. På lang sikt, er det muligheter for å øke opptaket av karbon gjennom økt plantetetthet og økt bruk av foredlet plantemateriale. Ved å gjødsle skogen kan man oppnå raskt økende opptak av karbon, men den samlede effekten er ikke nødvendigvis så stor da det er begrenset med arealer som er egnet til økt gjødslingsintensitet. Andre tiltak slik som forlenget omløpstid kan også vurderes, men må ses i sammenheng med skogens helsetilstand og effekter på det tilgjengelige hogstkvantum.
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Rasmus AstrupSammendrag
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Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.
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Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM), and k nearest-neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.
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Butt rot (BR) damage of a tree results from a decay caused by a pathogenic fungus. BR damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, maps of BR damages are typically lacking in forest information systems. Timber volume damaged by BR was predicted at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). This study utilized Random Forests models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). Our findings showed that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation resulted in an RMSE of 11.4 m3 · ha−1 (pseudo-R2: 0.66) whereas the mapping case resulted in a pseudo-R2 of 0.60. When spatially distinct clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo-R2 associated with the mapping case were 15.6 m3 · ha−1 and 0.37, respectively. The findings associated with the different cross-validation schemes indicated that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages.
Forfattere
Marian Schönauer Kari Väätäinen Robert Prinz Harri Lindeman Dariusz Pszenny Martin Jansen Joachim Maack Bruce Talbot Rasmus Astrup Dirk JaegerSammendrag
The utilization of detailed digital terrain models entails an enhanced basis for supporting sustainable forest management, including the reduction of soil impacts through predictions of site trafficability during mechanized harvesting operations. Since wet soils are prone to traffic-induced damages, soil moisture is incorporated into several systems for spatial predictions of trafficability. Yet, only few systems consider temporal dynamics of soil moisture, impeding the accuracy and practical value of predictions. The depth-to-water (DTW) algorithm calculates a cartographic index which indicates wet areas. Temporal dynamics of soil moisture are simulated by different DTW map-scenarios derived from set flow initiation areas (FIA). However, the concept of simulating seasonal moisture conditions by DTW map-scenarios was not analyzed so far. Therefore, we conducted field campaigns at six study sites across Europe, capturing time-series of soil moisture and soil strength along several transects which crossed predicted wet areas. Assuming overall dry conditions (FIA = 4.00 ha), DTW predicted 20% of measuring points to be wet. When a FIA of 1.00 ha (moist conditions) or 0.25 ha (wet conditions) were applied, DTW predicted 29% or 58% of points to be wet, respectively. De facto, 82% of moisture measurements were predicted correctly by the map-scenario for overall dry conditions – with 44% of wet measurements deviating from predictions made. The prediction of soil strength was less successful, with 66% of low values occurring on areas where DTW indicated dryer soils and subsequently a sufficient trafficability. The condition-specific usage of different map-scenarios did not improve the accuracy of predictions, as compared to static map-scenarios, chosen for each site. We assume that site-specific and non-linear hydrological processes compromise the generalized assumptions of simulating overall moisture conditions by different FIA.
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Long-term machine-derived data sets comprising 140,000 trees were collected from four harvesters of equal age and similar working conditions, into two machine size classes, viz. two Ponsse Bears and two smaller Ponsse Beavers. Productivity functions for each size class were modelled using a nonlinear mixed effects approach. Based on these functions, unit costs and their sensitivity to utilization rates and cost of capital were assessed. Results showed that despite considerably higher capital costs (32%) on the Bear, a 50% higher mean productivity resulted in a unit cost only 17% higher than the Beaver in a disadvantageous scenario (high interest rates and low utilisation), and a 6% lower unit cost than the Beaver in an advantageous scenario (low interest and high utilisation), within the range of tree sizes observed. Between these extremes, only marginal differences in unit costs were observed. This demonstrates that the difference in ownership and operating costs between larger and smaller harvesters is largely negated by the difference in productivity rates. These results can provide useful insight into timber harvester investment decisions. Harvesters from two adjacent size classes can be used interchangeably at the same unit cost within a wide range of tree sizes despite productivity differences. It should be noted that increased repair costs and an eventual reduction in expected economic lifetime on a smaller harvester, or the negative effects of using a larger harvester in smaller trees, e.g. thinning operations, were not taken into account in this work.
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Key message Large-scale forest resource maps based on national forest inventory (NFI) data and airborne laser scanning may facilitate synergies between NFIs and forest management inventories (FMIs). A comparison of models used in such a NFI-based map and a FMI indicate that NFI-based maps can directly be used in FMIs to estimate timber volume of mature spruce forests. Context Traditionally, FMIs and NFIs have been separate activities. The increasing availability of detailed NFI-based forest resource maps provides the possibility to eliminate or reduce the need of field sample plot measurements in FMIs if their accuracy is similar. Aims We aim to (1) compare a timber volume model used in a NFI-based map and models used in a FMI, and (2) evaluate utilizing additional local sample plots in the model of the NFI-based map. Methods Accuracies of timber volume estimates using models from an existing NFI-based map and a FMI were compared at plot and stand level. Results Estimates from the NFI-based map were similar to or more accurate than the FMI. The addition of local plots to the modeling data did not clearly improve the model of the NFI-based map. Conclusion The comparison indicates that NFI-based maps can directly be used in FMIs for timber volume estimation in mature spruce stands, leading to potentially large cost savings.
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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. Landuse 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.
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Forest structural properties largely govern surface fluxes of moisture, energy, and momentum that strongly affect regional climate and hydrology. Forest structural properties are greatly shaped by forest management activities, especially in the Fennoscandia (Norway, Sweden, and Finland). Insight into transient developments in forest structure in response to management intervention is therefore essential to understanding the role of forest management in mitigating regional climate change. The aim of this study is to present a simple grid-based framework – the Fennoscandic Forest State Simulator (F2S2) -- for predicting time-dependent forest structural trajectories in a manner compatible with land models employed in offline or asynchronously coupled climate and hydrological research. F2S2 enables the prescription of future regional forest structure as a function of: i) exogenously defined scenarios of forest harvest intensity; ii) forest management intensity; iii) climate forcing. We demonstrate its application when applied as a stand-alone tool for forecasting three alternative future forest states in Norway that differ with respect to background climate forcing, forest harvest intensity (linked to two Shared Socio-economic Pathways (SSPs)), and forest management intensity. F2S2 captures impacts of climate forcing and forest management on general trends in forest structural development over time, and while climate is the main driver of longer-term forest structural dynamics, the role of harvests and other management-driven effects cannot be overlooked. To our knowledge this is the first paper presenting a method to map forest structure in space and time in a way that is compatible with land surface or hydrological models employing sub-grid tiling.
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A new stand-level growth and yield model, consisting of component equations for stand volume, basal area, survival, and dominant stand height, was developed from a dataset of long-term trials for managed thinned and unthinned even-aged Norway spruce (Picea abies (L.) Karst.) forests in Norway. The developed models predict considerably faster growth rates than the existing Norwegian models. Further, it was found that the existing Norwegian stand-level models do not match the data from the thinning trails. The significance of thinning response functions indicated that thinning increases basal area growth while reducing competition related mortality. No significant effects of thinning were found in the dominant stand height growth. Model examination by means of cross-validation indicated that the models were unbiased and performed well within the data range. An application of the developed stand-level model highlights the potential use for these models in comparing different management scenarios.
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Laser scanning data from unmanned aerial vehicles (UAV-LS) offer new opportunities to estimate forest growing stock volume ( V ) exclusively based on the UAV-LS data. We propose a method to measure tree attributes and using these measurements to estimate V without the use of field data for calibration. The method consists of five steps: i) Using UAV-LS data, tree crowns are automatically identified and segmented wall-to-wall. ii) From all detected tree crowns, a sample is taken where diameter at breast height (DBH) can be recorded reliably as determined by visual assessment in the UAV-LS data. iii) Another sample of crowns is taken where tree species were identifiable from UAV image data. iv) DBH and tree species models are fit using the samples and applied to all detected tree crowns. v) Single tree volumes are predicted with existing allometric models using predicted species and DBH, and height directly obtained from UAV-LS. The method was applied to a Riegl-VUX data set with an average density of 1130 points m−2 and 3 cm orthomosaic acquired over an 8.8 ha managed boreal forest. The volumes of the identified trees were aggregated to estimate plot-, stand-, and forest-level volumes which were validated using 58 independently measured field plots. The root-mean-square deviance ( RMSD% ) decreased when increasing the spatial scale from the plot (32.2%) to stand (27.1%) and forest level (3.5%). The accuracy of the UAV-LS estimates varied given forest structure and was highest in open pine stands and lowest in dense birch or spruce stands. On the forest level, the estimates based on UAV-LS data were well within the 95% confidence interval of the intense field survey estimate, and both estimates had a similar precision. While the results are encouraging for further use of UAV-LS in the context of fully airborne forest inventories, future studies should confirm our findings in a variety of forest types and conditions.
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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.
Forfattere
Johannes Breidenbach Janis Ivanovs Annika Kangas Mats Nilsson Thomas Nord-Larsen Rasmus AstrupSammendrag
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Skogen i Norge har et årlig netto opptak i underkant av 30 mill. tonn CO2. Størrelsen på opptaket påvirkes av forvaltningen av skogarealene, både gjennom endringer i totalarealet (avskoging og påskoging), og forvaltningen av de eksisterende skogarealene. I denne rapporten presenteres en første vurdering av syv klimatiltak som ikke tidligere er utredet, en kunnskapsoppdatering av noen tidligere utredede klimatiltak, og en framskrivning av mulige effekter på netto CO2-opptak av ulike nivå på implementerte tiltak. Rapporten er skrevet på bestilling fra Landbruksdirektoratet og Miljødirektoratet, og det er direktoratene som har gjort utvalget av tiltak....
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Det årlige netto opptaket i skogen i Norge økte frem til 2009 (over 35 mill. tonn), og har etter det vist en avtakende trend. I 2018 var det et netto opptak på i underkant av 28 millioner tonn CO2- ekvivalenter. Størrelsen på opptaket påvirkes av forvaltningen av skogarealene, både gjennom endringer i totalarealet (avskoging og påskoging), og forvaltningen av de eksisterende skogarealene. I en første rapport til Klimakur 2030 – skrevet på oppdrag fra Miljødirektorat og Landbruksdirektoratet - ble det presentert en første vurdering av syv klimatiltak som ikke tidligere var utredet, samt en kunnskapsoppdatering for noen tidligere utredede klimatiltak. I denne rapporten presenteres ytterligere vurderinger av fire av disse tiltakene; ungskogpleie, grøfterensk, stubbebehandling mot råte og gjødsling med treaske. Rapporten er skrevet på bestilling fra Landbruks- og matdepartementet (LMD) og Klima- og miljødepartementet (KLD), og det er departementene som har gjort utvalget av tiltak som skulle vurderes videre...
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Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.
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Stefano Puliti Marius Hauglin Johannes Breidenbach P. Montesano C.S.R. Neigh Johannes Rahlf Svein Solberg T. F. Klingenberg Rasmus AstrupSammendrag
Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to-wall throughout the boreal domain. ArcticDEM is a collection of high-resolution (2 m) space-borne stereogrammetric digital surface models (DSM) covering the entire land area north of 60° of latitude. The free-availability of ArcticDEM data offers new possibilities for aboveground biomass mapping (AGB) across boreal forests, and thus it is necessary to evaluate the potential for these data to map AGB over alternative open-data sources (i.e., Sentinel-2). This study was performed over the entire land area of Norway north of 60° of latitude, and the Norwegian national forest inventory (NFI) was used as a source of field data composed of accurately geolocated field plots (n=7710) systematically distributed across the study area. Separate random forest models were fitted using NFI data, and corresponding remotely sensed data consisting of either: i) a canopy height model (ArcticCHM) obtained by subtracting a high-quality digital terrain model (DTM) from the ArcticDEM DSM height values, ii) Sentinel-2 (S2), or iii) a combination of the two (ArcticCHM+S2). Furthermore, we assessed the effect of the forest- and terrain-specific factors on the models’ predictive accuracy. The best model (,i.e., ArcticCHM+S2) explained nearly 60% of the variance of the training set, which translated in the largest accuracy in terms of root mean square error (RMSE=41.4 t ha−1 ). This result highlights the synergy between 3D and multispectral data in AGB modelling. Furthermore, this study showed that despite the importance of ArcticCHM variables, the S2 model performed slightly better than ArcticCHM model. This finding highlights some of the limitations of ArcticDEM, which, despite the unprecedented spatial resolution, is highly heterogeneous due to the blending of multiple acquisitions across different years and seasons. We found that both forest- and terrain-specific characteristics affected the uncertainty of the ArcticCHM+S2 model and concluded that the combined use of ArcticCHM and Sentinel-2 represents a viable solution for AGB mapping across boreal forests. The synergy between the two data sources allowed for a reduction of the saturation effects typical of multispectral data while ensuring the spatial consistency in the output predictions due to the removal of artifacts and data voids present in ArcticCHM data. While the main contribution of this study is to provide the first evidence of the best-case-scenario (i.e., availability of accurate terrain models) that ArcticDEM data can provide for large-scale AGB modelling, it remains critically important for other studies to investigate how ArcticDEM may be used in areas where no DTMs are available as is the case for large portions of the boreal zone.
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Stefano Puliti Marius Hauglin Johannes Breidenbach Paul M. Montesano C.S.R. Neigh Johannes Rahlf Svein Solberg Torgeir Ferdinand Klingenberg Rasmus AstrupSammendrag
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Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupSammendrag
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Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupSammendrag
Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine-, and deciduous-dominated forest in Norway at a 16 m × 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42 000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by ∼10%. The produced map was subsequently used to generate model-assisted (MA) and poststratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.
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Foredrag – Presis stedfesting av trær med hogstmaskin
Erik Sørngård, Rasmus Astrup, Erik Næsset, ...
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In a climate model, surface energy and water fluxes of the vegetated ecosystem largely depend on important structural attributes like leaf area index and canopy height. For forests, management can greatly alter these attributes with resulting consequences for the surface albedo, surface roughness, and evapotranspiration. The sensitivity of surface energy and water budgets to alterations in forest structure is relatively unknown in boreal regions, particularly in Nordic Fennoscandia (Norway, Sweden, and Finland), where the forest management footprint is large. Here we perform offline simulations to quantify the sensitivity of surface heat and moisture fluxes to changes in forest composition and structure across daily, seasonal, and annual time scales. For the region on average, it is found that broadleaved deciduous forests cool the surface by 0.16 K annually and 0.3 K in the growing season owed to higher year‐round albedo and lower Bowen ratio, yet in some locations the local cooling can be as much as 2.4 K and 3.0 K, respectively. Moreover, fully developed forests cool the surface by 0.04 K annually in our domain owed to higher evapotranspiration, reaching up to 0.4 K locally in some locations, whereas undeveloped forests warm annually by 0.14 K owed to much lower evapotranspiration reaching up to 0.8 K for some locations. If regional forests are ever to be managed for the local climate regulation services that they provide, our results are an important first step illuminating the potential adverse impacts or benefits across space and time.
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An understanding of the relationship between volume increment and stand density (basal area, stand density index, etc.) is of utmost importance for properly managing stand density to achieve specific management objectives. There are two main approaches to analyse growth–density relationships. The first relates volume increment to stand density through a basic relationship, which can vary with site productivity, age, and potentially incorporates treatment effects. The second is to relate the volume increment and density of thinned experimental plots relative to that of an unthinned experimental plot on the same site. Using a dataset of 229 thinned and unthinned experimental plots of Norway spruce, a growth model is developed describing the relationship between gross or net volume increment and basal area. The models indicate that gross volume increases with increasing basal area up to 50 m2 and thereafter becomes constant out to the maximum basal area. Alternatively, net volume increment was maximized at a basal area of 43 m2 and decreased with further increases in basal area. However, the models indicated a wide range where net volume increment was essentially constant, varying by less than 1 m3 ha−1 year−1. An analysis of different thinning scenarios indicated that the relative relationship between volume increment and stand density was dynamic and changed over the course of a rotation.
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Global Forest Watch (GFW) provides a global map of annual forest cover loss (FCL) produced from Landsat imagery, offering a potentially powerful tool for monitoring changes in forest cover. In managed forests, FCL primarily provides information on commercial harvesting. A semi-autonomous method for providing data on the location and attributes of harvested sites at a landscape level was developed which could significantly improve the basis for catchment management, including risk mitigation. FCL in combination with aerial images was used for detecting and characterising harvested sites in a 1607 km2 mountainous boreal forest catchment in south-central Norway. Firstly, the forest cover loss map was enhanced (FCLE) by removing small isolated forest cover loss patches that had a high probability of representing commission errors. The FCLE map was then used to locate and assess sites representing annual harvesting activity over a 17-year period. Despite an overall accuracy of >98%, a kappa of 0.66 suggested only a moderate quality for detecting harvested sites. While errors of commission were negligible, errors of omission were more considerable and at least partially attributed to the presence of residual seed trees on the site after harvesting. The systematic analysis of harvested sites against aerial images showed a detection rate of 94%, but the area of the individual harvested site was underestimated by 29% on average. None of the site attributes tested, including slope, area, altitude, or site shape index, had any effect on the accuracy of the area estimate. The annual harvest estimate was 0.6% (standard error 12%) of the productive forest area. On average, 96% of the harvest was carried out on flat to moderately steep terrain (<40% slope), 3% on steep terrain (40% to 60% slope), and 1% on very steep terrain (>60% slope). The mean area of FCLE within each slope category was 1.7 ha, 0.9 ha, and 0.5 ha, respectively. The mean FCLE area increased from 1.0 ha to 3.2 ha on flat to moderate terrain over the studied period, while the frequency of harvesting increased from 249 to 495 sites per year. On the steep terrain, 35% of the harvesting was done with cable yarding, and 62% with harvester-forwarder systems. On the very steep terrain (>60% slope), 88% of the area was harvested using cable yarding technology while harvesters and forwarders were used on 12% of the area. Overall, FCL proved to be a useful dataset for the purpose of assessing harvesting activity under the given conditions.
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Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the effective spatial resolution of the satellite-based retrieval. This is particularly true for MODIS products and for topographically complex regions, such as Norway, which makes it difficult to separate the environmental drivers (e.g., temperature and snow) from those related to land cover and vegetation structure. In the present study, we employ high resolution datasets of Norwegian land cover and structure to spectrally unmix MODIS surface albedo retrievals (MCD43A3 v6) to study how surface albedo varies with land cover and structure. Such insights are useful for constraining land cover-dependent albedo parameterizations in models employed for regional climate or hydrological research and for developing new empirical models. At the scale of individual land cover types, we found that the monthly surface albedo can be predicted at a high accuracy when given additional information about forest structure, snow cover, and near surface air temperature. Such predictions can provide useful empirical benchmarks for climate model predictions made at the land cover level, which is critical for instilling greater confidence in the albedo-related climate impacts of anthropogenic land use/land cover change (LULCC).
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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 in 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 to 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 root and butt-rot were correctly classified with accuracy of 83.5% and 77.5%. Classifying rot to 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 algorithm developed could be used either during the harvesting operation to detect RBR regions on the tree-stumps or as a RBR detector for post-harvest assessment of tree-stumps and logs.
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Forfattere
Rasmus Astrup Johannes Rahlf Knut Bjørkelo Misganu Debella-Gilo Arnt Kristian Gjertsen Johannes BreidenbachSammendrag
This paper describes the development and utility of the Norwegian forest resources map (SR16). SR16 is developed using photogrammetric point cloud data with ground plots from the Norwegian National Forest Inventory (NFI). First, an existing forest mask was updated with object-based image analysis methods. Evaluation against the NFI forest definitions showed Cohen's kappa of 0.80 and accuracy of 0.91 in the lowlands and a kappa of 0.73 and an accuracy of 0.96 in the mountains. Within the updated forest mask, a 16×16 m raster map was developed with Lorey's height, volume, biomass, and tree species as attributes (SR16-raster). All attributes were predicted with generalized linear models that explained about 70% of the observed variation and had relative RMSEs of about 50%. SR16-raster was segmented into stand-like polygons that are relatively homogenous in respect to tree species, volume, site index, and Lorey's height (SR16-vector). When SR16 was utilized in a combination with the NFI plots and a model-assisted estimator, the precision was on average 2–3 times higher than estimates based on field data only. In conclusion, SR16 is useful for improved estimates from the Norwegian NFI at various scales. The mapped products may be useful as additional information in Forest Management Inventories.
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I denne rapporten presenteres framskrivninger for opptak og utslipp fra arealbrukssektoren (eng. Land Use, Land-Use Change and Forestry; LULUCF) frem til 2100. Framskrivninger av opptak og utslipp av CO2 og andre klimagasser fra arealbrukssektoren er utført i tråd med metodikken brukt i klimagassregnskapet for Norge i 2019 (Miljødirektoratet mfl. 2019), og basert på data rapportert for 2010 – 2017 som referanseperiode. Framskrivningen for opptak og utslipp i skog er basert på tilsvarende metodikk som i referansebanen for forvaltede skogarealer (eng. Forest Reference Level, FRL), som publisert i National Forest Accounting Plan (Klima- og miljødepartementet 2019), men basert på nyeste tilgjengelige data og med implementert politikk. Framskrivningene er utført basert på rapporteringen under FNs klimakonvensjon og Kyotoprotokollen, samt EUs LULUCF-forordning.
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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.
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Climate change could increase fire risk across most of the managed boreal forest. Decreasing this risk by increasing the proportion of broad-leaved tree species is an overlooked mitigation–adaption strategy with multiple benefits.
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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.
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This paper presents an optimization model designed to find productivity functions for timber forwarding. Timber forwarding or skidding has for some 25 years been calculated using shortest path formulations on grid networks. Unfortunately, few productivity studies relate to such grids. Here, an inverse shortest path problem is presented, basically panning out costs on the grid based on point cost estimates. The formulation is tested using point cost estimates from the national forest inventories of Norway, together with a terrain model and other public spatial data (e.g. roads, water). The problem is optimized using the metaheuristic variable neighborhood search. The results of the test cases were achieved in reasonable time, and indicate that part of the solution space might be convex. The productivity function found for one of the test cases was used to create a variable forwarding cost map of the case area.
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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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Med ny teknologi kan skader forårsaket av rotråte reduseres, og dermed gi et mer lønnsomt og bærekraftig skogbruk.
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Anders Arvesen Francesco Cherubini Gonzalo del Alamo Serrano Rasmus Astrup Michael Becidan Helmer Belbo Franziska Goile Tuva Grytli Geoffrey Guest Carine Lausselet Per Kr. Rørstad Line Rydså Morten Seljeskog Øyvind Skreiberg Veena Sajith Vezhapparambu Anders Hammer StrømmanSammendrag
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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Ryan Bright Stephanie Eisner Marianne Tronstad Lund Titta Majasalmi Gunnar Myhre Rasmus AstrupSammendrag
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.
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Jonathan Rizzi Clara Antón Fernández Paulo Jorge de Almeida Borges Ryan Bright Rasmus AstrupSammendrag
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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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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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Ryan Bright Stephanie Eisner Titta Majasalmi Marianne Tronstad Lund Gunnar Myhre Rasmus AstrupSammendrag
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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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Annika Kangas Rasmus Astrup Johannes Breidenbach Jonas Fridman Terje Gobakken Kari T. Korhonen Matti Maltamo Mats Nilsson Thomas Nord-Larsen Erik Næsset Håkan OlssonSammendrag
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.
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Rasmus Astrup Niels Strange Pieter Kerstens Peter Bogetoft Uli Dietz Giovanna Ottaviani Aalmo Bruce TalbotSammendrag
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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.
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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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Johannes Breidenbach Sebastian Eiter Rune Eriksen Knut Bjørkelo Gregory Taff Gunnhild Søgaard Stein Michael Tomter Lise Dalsgaard Aksel Granhus Rasmus AstrupSammendrag
I henhold til det Norske klimagassregnskap leder avskoging til en betydelig del av de nasjonale klimagassutslipp. Målet med denne rapporten er en kartlegging av størrelse og årsaker til avskoging som kan forbedre forståelsen av avskogingsprosesser, og på sikt kan være et første steg for å redusere utslippene fra avskogingen. I Kyotoprotokollen er avskoging betegnet som menneskeskapte endringer fra skog til en annen arealkategori siden 1990. I Norge har avskoging siden 1990 vært på om lag 58 km2 per år. På grunn av påskoging (på aktivt forvaltede arealer) og skogutvidelse (naturlig etablering på ikke forvaltede arealer) har skogarealet ikke forandret seg nevneverdig. Men den teoretiske produksjonsevnen, altså skogens evne til å produsere biomasse og dermed også til å ta opp karbon fra atmosfæren i et gitt tidsrom, av det samlede arealet av påskoging og skogutvidelse er mindre enn produksjonsevnen av avskogingsarealet. Hovedgrunnen til avskoging var utbygging (68 % av avskogingsarealet), men også omlegging til beite (18 %) eller nydyrking (13 %) bidro. I denne rapporten er alle areal og utslippsestimater basert på Landsskogtakseringen som er en landsdekkende utvalgsundersøkelse. Grunnet det lille totale areal av avskoging i Norge er arealestimatene assosiert med relativ stor usikkerhet relatert til antall prøvefelter i utvalgskartleggingen. Blant utbyggingskategoriene var vei og bebyggelse de viktigste grunnene til avskoging. Traktor- og skogsbilveier var de største enkeltkategoriene blant veikategoriene og til sammen står de for om lag 13 % av avskogingsarealet. Bolig og fritidsbolig var de største enkeltkategoriene blant bebyggelseskategoriene og til sammen står de for om lag 13 % av avskogingsarealet...
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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.
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Forskere skal teste de nye skogsmaskinene, som forhåpentligvis gjør mindre skade i skogen etter hogst.
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Finske Ponsse skal i samarbeid med italienske Prinoth utvikle en beltegående lassbærer.
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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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Annika Kangas Rasmus Astrup Johannes Breidenbach Jonas Fridman Terje Gobakken Kari T. Korhonen Matti Maltamo Mats Nilsson Thomas Nord-Larsen Erik Næsset Håkan OlssonSammendrag
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Svetlana Saarela Johannes Breidenbach Pasi Raumonen Anton Grafström Göran Ståhl Mark J. Ducey Rasmus AstrupSammendrag
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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Forskere skal teste de nye skogsmaskinene, som forhåpentligvis gjør mindre skade i skogen.
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Forfattere
Victor Strimbu Liviu Theodor Ene Terje Gobakken Timothy G. Gregoire Rasmus Astrup Erik NæssetSammendrag
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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Belachew Gizachew Zeleke Rasmus Astrup Pål Olav Vedeld Eliakimu M. Zahabu Lalisa A. DugumaSammendrag
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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Gunnhild Søgaard Rasmus Astrup Micky Allen Kjell Andreassen Even Bergseng Inger Sundheim Fløistad Aksel Granhus Kjersti Holt Hanssen Ari Hietala Harald Kvaalen Svein Solberg Halvor Solheim Arne Steffenrem Jogeir N. Stokland Bjørn ØklandSammendrag
Fokus i denne rapporten er på skogbehandlingen, og på maksimering av verdiproduksjon. En forutsetning for dette er høy sagtømmerproduksjon, samtidig som det er viktig å vurdere arealets totale tømmerverdi. Følgende forutsetninger ligger til grunn for rapporten: Bevaring av biologisk mangfold og ivaretakelse av andre flerbrukshensyn Det legges til grunn at all skogbehandling utføres i henhold til dagens lovverk og frivillige sertifiseringsordninger, og at dette ivaretar hensyn til biologisk mangfold og andre flerbrukshensyn. Vi går derfor ikke inn på betydningen ulik skogbehandling vil ha for biologisk mangfold eller andre flerbrukshensyn, eller tilpasninger av skogbehandlingen for dette. Bærekraftig skogbruk i klimasammenheng Miljødirektoratet mfl. (2016) drøfter vern eller bruk av skog som klimatiltak. I rapporten konkluderes det med at det ikke er grunnlag for å vektlegge vern av norsk skog som klimatiltak. En forutsetning er at det drives et bærekraftig skogbruk i klimasammenheng. Dette ble definert som følger: «Bærekraftig skogbruk i klimasammenheng innebærer at skogens produktivitet og evne til å lagre karbon ikke forringes, og at karbonbeholdninger ikke reduseres permanent.» (Miljødirektoratet mfl. 2016). Vi forutsetter gjennom rapporten at skogbehandling drives bærekraftig i tråd med denne definisjonen, uten at vi går nærmere inn på betydningen og eventuelle nødvendige tilpasninger. Skogbehandling for å motvirke klimaendringer Skogbehandling som motvirker klimaendringer, for eksempel ved å øke karbonopptaket, vil i mange tilfeller være i samsvar med skogbehandling for maksimal verdiproduksjon, men ikke alltid. Vi har i denne rapporten kun fokusert på verdiproduksjon, og betydning av skogbehandling på ulike karbonbeholdninger er ikke vurdert. Driftstekniske forhold («hvordan ta ut tømmeret») Driftskostnader vil være av stor betydning for skogeiers økonomiske resultat, og både de endringer vi allerede ser og forventede klimaendringer er forventet å gi større driftstekniske utfordringer. I denne rapporten ser vi imidlertid utelukkende på den betydning skogbehandlingen vil ha for antatt verdiproduksjon.
Forfattere
Francesco Cherubini Sajith Vezhapparambu Wiley Steven Bogren Rasmus Astrup Anders Hammer StrømmanSammendrag
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.
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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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Mathias Neumann Adam Moreno Volker Mues Sanna Härkönen Matteo Mura Olivier Bouriaud Mait Lang Wouter M.J. Achten Alain Thivolle-Cazat Karol Bronisz Jan Merganič Mathieu Decuyper Iciar Alberdi Rasmus Astrup Frits Mohren Hubert HasenauerSammendrag
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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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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Mathias Neumann Adam Moreno Christopher Thurnher Volker Mues Sanna Härkönen Matteo Mura Olivier Bouriaud Mait Lang Giuseppe Cardellini Alain Thivolle-Cazat Karol Bronisz Jan Merganic Iciar Alberdi Rasmus Astrup Frits Mohren Maosheng Zhao Hubert HasenauerSammendrag
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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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.
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Lise Dalsgaard Rasmus Astrup Clara Antón Fernández Signe Kynding Borgen Johannes Breidenbach Holger Lange Aleksi Lehtonen Jari LiskiSammendrag
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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Tuva Grytli Geoffrey Guest Carine Lausselet Francesco Cherubini Per Kr. Rørstad Helmer Belbo Rasmus Astrup Øyvind Skreiberg Morten Seljeskog Franziska Goile Anders Hammer StrømmanSammendrag
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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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Lise Dalsgaard Holger Lange Line Tau Strand Ingeborg Callesen Signe Kynding Borgen Jari Liski Rasmus AstrupSammendrag
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.
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Ketil Flugsrud Rasmus Astrup Ellen Bruzelius Backer Linn Viken Bøe Aksel Granhus Geir Grønningsæter Ole-Kristian Kvissel Audun Rosland Gunnhild Søgaard Torleif Terum Elin ØkstadSammendrag
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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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Gunnhild Søgaard Rasmus Astrup Clara Antón Fernández Lise Dalsgaard Signe Kynding Borgen Nikolas Von LüpkeSammendrag
Framskrivninger av opptak og utslipp av CO2 og andre klimagasser fra skog og andre landarealer (LULUCF-sektoren) fram til 2120, utført i tråd med metodikken brukt i klimagassregnskapet for Norge i 2014 (Miljødirektoratet mfl. 2014), presenteres i denne rapporten. Framskrivingene er basert på tre ulike scenarier for fremtidig klima: • Dagens klima. • 2-gradersmålet, det vil si en global temperaturøkning som flater ut på to grader i 2100 (”oppfyller” 2-gradersmålet). Her er RCP 2.6 lagt til grunn. • Business-as-usual, det vil si forventet klimaendring dersom nye tiltak ikke gjennomføres. Her er RCP 8.5 lagt til grunn. iii En videreføring av dagens politikk og virkemiddelbruk er lagt til grunn for framskrivningene. Videre legges til grunn en sannsynlig utvikling for avvirkning ut fra hogstmodenhet og tilgjengelighet av tømmer i norske skoger (Antón Fernández og Astrup 2012). Skog er den viktigste arealkategorien for opptak av klimagasser, med et netto opptak på 30 741 Gg CO2-ekvivalenter i 2012 (Miljødirektoratet mfl. 2014). Framskrivningene viser at skog også i fremtiden vil være den viktigste arealkategorien for opptak av klimagasser, men opptaket vil reduseres betydelig over de nærmeste 100 år. Dette vil skje uavhengig av klimascenario, og skyldes en kombinasjon av økende avvirkning og endring av skogens alderssammensetning. Den skogen som ble plantet i tiårene etter 2. verdenskrig begynner nå å bli hogstmoden, og det vil være et betydelig volum i skog som vil bli hogstmoden de kommende 30 år (Granhus mfl. 2014). Det gir større arealer med hogstmoden skog nær vei, og denne skogen har også større volum per arealenhet enn eldre hogstmoden skog. Dette vil gi økt avvirkning, og dermed høyere utslipp (ettersom all avvirkning regnes som utslipp ved avvirkningstidspunktet i klimagassregnskapet). Skogen i Norge har i dag en ujevn alderssammensetning, med en stor andel av arealet i de mest produktive faser (høy tilvekst, som gir høyt opptak). Med fortsatt forvaltning som i dag vil alderssammensetningen langsomt bli mer jevn, og vi vil få en større andel av gammel skog. Dette resulterer i redusert tilvekst. Lageret av karbon i levende biomasse i skog har økt gjennom hele rapporteringsperioden (1990 – 2012), og fortsetter å øke i framskrivningene. Økningen i rapporteringsperioden skyldes blant annet en aktiv skogforvaltning de siste 60 – 70 årene. Den fremtidige økningen forutsetter at skogen som avvirkes re-etableres med samme treslag og produktivitet som skogen har i dag. Utslippsendringer for de øvrige arealkategoriene vil i større grad være betinget av arealendringer, hvor den totale størrelsen på opptak og utslipp påvirkes av størrelsen på arealet. Gitt en fortsettelse av trenden for perioden 2006 – 2010, vil den største endringen være i arealkategorien bebyggelse, som øker mest både i areal og prosent. Arealet med vann og myr og annen utmark vil være noenlunde stabilt, arealet skog og dyrket mark reduseres, mens arealet beite øker noe. Arealkategorien vann og myr bidrar til netto opptak. Dette skyldes karbonopptak i trær på tresatt myr (myrarealer med trær, men som ikke når skogdefinisjonen). Arealkategoriene dyrket mark, beite, bebyggelse og annen utmark har alle netto utslipp. Dyrket mark vil ha en reduksjon i netto utslipp gjennom de nærmeste 100 år, grunnet reduksjon i areal, mens utslippene fra beite kun vil ha små endringer.
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Tuva Grytli Geoffrey Guest Carine Lausselet Francesco Cherubini Ryan Bright Per Kr. Rørstad Helmer Belbo Rasmus Astrup Øyvind Skreiberg Morten Seljeskog Franziska Goile Anders Hammer StrømmanSammendrag
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Tuva Grytli Geoffrey Guest Carine Lausselet Francesco Cherubini Ryan Bright Per Kr. Rørstad Helmer Belbo Rasmus Astrup Øyvind Skreiberg Morten Seljeskog Franziska Goile Anders Hammer StrømmanSammendrag
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Ryan Bright Clara Antón Fernández Rasmus Astrup Francesco Cherubini Maria Malene Kvalevåg Anders Hammer StrømmanSammendrag
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Lise Dalsgaard Clara Antón Fernández Rasmus Astrup Signe Kynding Borgen Johannes Breidenbach Holger Lange Jogeir N. Stokland Gunnhild SøgaardSammendrag
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Ryan Bright Francesco Cherubini Rasmus Astrup Clara Antón Fernández Anders Hammer Strømman Maria Malene KvalevågSammendrag
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Johannes Breidenbach Juha Heikkinen Göran Ståhl Hans Petersson Anna Ringvall Rasmus AstrupSammendrag
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Aksel Granhus Nikolas Von Lüpke Rune Eriksen Gunnhild Søgaard Stein Michael Tomter Clara Antón Fernández Rasmus AstrupSammendrag
Formålet med denne rapporten er å gi en oversikt over skogressursene som grunnlag for vurdering av avvirkningsmulighetene, med hensyntaken til miljø og driftskostnader, i de neste 30 år. I rapporten gis en oversikt over dagens stående volum i hogstmoden skog (hogstklasse V), samt volum i skog som vil bli hogstmoden de kommende 30 år. Resultatene vises i form av tabeller og figurer der det er gjort ulike fratrekk for å ta høyde for reduksjoner grunnet miljøhensyn, driftskostnader og svinn. Brutto volum i dagens hogstklasse V utgjør vel 400 millioner kubikkmeter med bark. Vi har med bakgrunn i tilgjengelige data og et sett med forutsetninger estimert at miljøhensyn inkludert vern utgjør en reduksjon av tilgjengelig volum på 14 prosent. Kvantumet som vokser inn i hogstklasse V vil øke gjennom hele 30-årsperioden 2014-2043, fra litt under ni millioner kubikkmeter per år i første tiårsperiode til 13,7 millioner kubikkmeter per år i den tredje perioden (etter fradrag for miljøhensyn). Det er her tatt utgangspunkt i en framskriving av volumet til hogstmodenhetsalder. Dette volumet inkluderer imidlertid topp, bult, småtrær som ikke er nyttbare, og trær som ikke holder tømmerkvalitet (ofte kalt topp og avfall). Vi har med bakgrunn i data fra de permanente flatene i Landsskogtakseringen estimert reduksjonen ved omregning fra stående volum («skogskubikk») til volum som kan omsettes («tømmerkubikk») til 15 prosent. En vesentlig del av skogressursene er lokalisert i områder langt fra vei og/eller i bratt terreng, og gir ikke grunnlag for lønnsom skogsdrift gitt dagens driftskostnader, virkepriser og infrastruktur (skogsveier). Dette gjelder i størst grad den skogen som allerede er hogstmoden, mens den arealmessige fordelingen i forhold til driftsveilengde og terrengbratthet er gunstigere for skog som vokser inn i hogstklasse V de kommende tiår. Gitt at det relative forholdet mellom driftskostnader og virkepriser ikke endres vesentlig, vil vi få en en økt andel areal med positiv driftsnetto, noe som sannsynliggjør økt virketilgang i årene framover. Det er betydelige strukturelle forskjeller mellom den eksisterende hogstmodne skogen og skogen som blir hogstmoden de neste 30 år. En vesentlig forskjell er treslagsfordelingen, der gran utgjør 56 prosent av tilgangen av ny hogstmoden skog de neste 30 år, mens granandelen er kun 41 prosent i dagens hogstmodne skog. En økning av granandelen kan forventes i alle landets regioner og er en logisk følge av de historiske endringer i skogskjøtsel med økt planting av gran. En annen viktig observasjon er at over halvparten av tilgangen av hogstmoden skog de neste 30 år vil komme på det sentrale Østlandet, noe som vil styrke denne regionens rolle som det viktigste området for skogproduksjon. Etter fradrag for miljøhensyn og svinn, og ved kun å inkludere arealer med en estimert driftskostnad på maksimalt 250 kroner per kubikkmeter, synes det fullt forsvarlig ut fra ressursgrunnlaget å øke årlig hogstkvantum til om lag 15 millioner kubikkmeter («tømmerkubikk»). Dette under forutsetning av at tynningsuttaket framover holder seg minst på samme nivå som i dag. For å komme fram til et anslag på hva som er tilgjengelig for industriell bruk må det gjøres et ytterligere fradrag for virke som ikke kommer for salg (hjemmeforbruk, ved).
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Aaron Smith Rasmus Astrup Pasi Raumonen Jari Liski Anssi Krooks Sanna Kaasalainen Markku Åkerblom Mikko KaasalainenSammendrag
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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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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Tor Myking Erling Johan Solberg Gunnar Austrheim James David Mervyn Speed Fredrik Bøhler Rasmus Astrup Rune EriksenSammendrag
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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Ryan Bright Rasmus Astrup Anders Hammer Strømman Clara Antón Fernández Maria Malene Kvalevåg Francesco CherubiniSammendrag
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Mark J. Ducey Rasmus Astrup Stefan Seifert Hans C. Pretzsch Bruce C. Larson K. David CoatesSammendrag
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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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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Hanne Kathrine Sjølie Antón-Fernández Clara Gregory S. Latta Rasmus Astrup Birger SolbergSammendrag
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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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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.
Sammendrag
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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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.
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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.
Forfattere
Johannes Breidenbach Clara Antón Fernández Hans Petersson Ronald E. McRoberts Rasmus AstrupSammendrag
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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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.
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Forfattere
Ryan Bright Francesco Cherubini Rasmus Astrup Neil Bird Annette L. Cowie Mark J. Ducey Gregg Marland Kim Pingoud Ilkka Savolainen Anders Hammer StrømmanSammendrag
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Forfattere
Erling Johan Solberg Christer Moe Rolandsen Rasmus Astrup Rune Eriksen Tor Myking Gunnar AustrheimSammendrag
Antallet hjortevilt i Norge når stadig nye høyder, og stadig flere spør seg om konsekvensene for andre arter i skogøkosystemet. Blant de mer synlige effektene er det høye beitetrykket på rogn, osp og selje, eller hva vi kaller ROS-artene. Disse forekommer ved relativt lav tetthet i alle skogområder, men har en uforholdsmessig stor betydning som mat og leveområde for andre organismer. Dersom de skulle synke i frekvens og utbredelse vil det derfor ha økologiske konsekvenser som går langt utover deres verdi som beiteplanter for elg og hjort.
Sammendrag
Bruken av bioenergi vil øke betraktelig i årene som kommer. Politisk ligger det målsettinger om en dobling av årlig bruk fra 14 TWh i 2008 til 28 TWh i 2020. For å nå målet om en økning med 14 TWh innen 2020 vil skogen være en helt avgjørende ressurs. Det er derfor et stort behov for å få frem tall for hvilke ressurser som finnes i norske skoger delvis for å kunne vurdere om myndighetenes mål er realistiske, men også for å kunne planlegge nye bioenergianlegg og ny bruk av bioenergi ut fra hvor mye og hvor ressursene finnes. Formålet med den foreliggende rapporten har vært å produsere en ”tabellsamling” som kan brukes direkte, eller som et utgangspunkt, i utredninger, vurderinger og analyser knyttet til spørsmål omkring bruk av biomasse fra skog til energiformål. Rapporten viser tall for biomasseressurser både på lands- og regionnivå, den viser tall for hvor ressursene finnes basert på driftsveglengder og terrengklasser, samt størrelsen på typiske energisortimenter som GROT (greiner og topper) og stubber og røtter. Det er også regnet noe på det energipotensialet som ligger i biomassetallene. Disse beregningene viser at ressursene i utgangspunktet er mer enn store nok til å nå de politiske målsettingene. Mulighetene for å nå disse målene er imidlertid avhengig av kostnadene ved å ta ut biomassen, konkurransedyktige priser for biomassen,samt myndighetenes rammevilkår. Dette er ikke diskutert i rapporten.
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I en bærekraftig skogforvaltning er det mange hensyn som må balanseres. Mange av disse hensynene er formelt ivaretatt i lovverk og forskrifter og gjennom miljøsertifisering. Skogbruksloven med tilhørende forskrifter er styrende for mye av skogbehandlingen, men også lover som naturmangfoldloven, vannressursloven og friluftsloven gir føringer for skogbehandling og forvaltning. De fleste skogeiendommer er miljøsertifisert gjennom Skogbrukets miljøstandard, basert på Levende Skog standard av 2006 (med presiseringer gjeldende f.o.m. juli 2009 og juli 2010). I tillegg til formelle krav som påvirker skogforvaltningen direkte er det flere andre miljø- og samfunnsmessige hensyn som berører skogforvaltningen. Vi har sett på hvordan ulike hensyn, hver for seg og samlet, påvirker tilgjengelighet av produktivt skogareal. Analysen er i hovedsak basert på data fra Landsskogtakseringen og supplert med andre kildedata (MiS-databasen, litteraturstudie, spørreundersøkelse) ....
Sammendrag
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.
Forfattere
Terje Gobakken Erik Næsset Ross F. Nelson Ole Martin Bollandsås Timothy G. Gregoire Göran Ståhl Sören Holm Hans Ole Ørka Rasmus AstrupSammendrag
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.
Sammendrag
Mulighetene for å kartlegge skogressurser med satellittbaserte RADAR-instrumenter er undersøkt. Det er bildedannende RADAR som er brukt, såkalt syntetisk aperture RADAR (SAR). Vi har testet ut C-bånd polarimetri (PolSAR) fra Radarsat-2 og X-bånd interferometri (InSAR) fra Tandem-X. PolSAR som metode bygger på at ulike typer arealklasser gir ulike typer ekko, mens InSAR bygger på at RADAR-ekkoets høyde over bakken er korrelert med volum og biomasse. Resultatene viser at polarimetrisk C-bånd SAR alene ser ut til å være verdiløst for skogkartlegging. Årsaken er at de korte C-bånd bølgelengdene ikke penetrerer noe særlig ned i vegetasjonen, og store trær vi derfor gi tilnærmet samme ekko som bunnvegetasjon. For InSAR, derimot, har vi oppnådd gode resultater, og dette er i tråd med tidligere utprøvinger basert på liknende data fra romferge-misjonen SRTM i år 2000. InSAR er en lovende metode for kartlegging av volum og biomasse i skog, med en nøyaktighet på bestandnivå på 23%.
Sammendrag
I en 5-årsperiode har Landsskogtakseringen registrert beitetrykk og beitetilbud på 11 000 prøveflater innenfor store deler av elgens utbredelsesområde i Norge. Resultatene viser at elgen i nord kan velge fra øverste hylle, både sommer og vinter, mens elgen i sør må dele på godene. I Troms er det fire ganger flere beitetrær tilgjengelig pr elg enn hva som er tilfelle i Agder, og en langt større andel består av de mest selekterte artene. I tillegg er arealet av attraktive feltsjiktplanter 50 ganger større pr elg i nord enn i sør.