Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further

A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway

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.publishedVersio

Stand-level growth models for long-term projections of the main species groups in Norway

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.publishedVersio

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

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

Radiative forcing bias of simulated surface albedo modifications linked to forest cover changes at northern latitudes

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Skogtilstanden i verneområder og vurderinger av mulighetene for intensivert overvåking gjennom Landsskogtakseringen

Med grunnlag i Landsskogtakseringens prøveflater beskriver denne rapport skogtilstanden på vernet areal samt vurder mulighetene for økt overvåking av vernet areal gjennom Landsskogtakseringen. I henhold til Landsskogtakseringens utvalgskartlegging er 2,3% av den produktive skogen vernet, mens andelen er 5,5% for den uproduktive skogen. Dette betyr at 3,1% (343 000 ha) av det totale skogarealet er vernet. Størsteparten av den vernete produktive skogen er i naturreservater (134 000 ha), mens nasjonalparkene utgjør en relativt liten del av det vernete produktive skogarealet (55 000 ha). Den mest vanlige skogtypen i vernområdene er bjørkedominert skog, det vil si arealer hvor over 70% av det stående volum er bjørk (120 000 ha). Et nesten like stort areal er furudominert (106 000 ha), mens grandominert skog utgjør et noe mindre areal (60 000 ha). Fordelingen av den vernete skogen på produktivitetsklasser viser at høyproduktiv og middels produktiv skog er underrepresentert, mens lavproduktiv og uproduktiv skog er overrepresentert. […]publishedVersio

Effect of Volcano-Polluted Seawater on the Corrosion Behaviour of Different Alloys

During a subsea volcano eruption, gases and thermal water emissions are released. This might change the behaviour of the materials that are in contact with the seawater caused by the decrease of the pH value. For this reason, the materials for marine applications are selected to maintain the integrity of the structure and to be corrosion resistant. In spite of this, corrosion can cause great damage to marine steel infrastructures such as bridges, wharfs, platforms and pipeline systems. These corrosion problems could be aggravated if the medium is altered, due to volcano emissions, since the resistance of the surface film is influenced by the environmental conditions

Stand-level mortality models for Nordic boreal forests

New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age

Framskrivninger for arealbrukssektoren (LULUCF) under FNs klimakonvensjon og EUs klimarammeverk

Arealbrukssektoren (engelsk: Land Use, Land-Use Change and Forestry, LULUCF) omfatter arealbruk og arealbruksendringer, med tilhørende utslipp og opptak av CO2, CH4 og N2O, og er en del av det nasjonale klimagassregnskapet under FNs klimakonvensjon. Framskrivningene presentert her er basert på data og metodikk fra Norges siste rapportering til FNs klimakonvensjon (IPCC), Norges National Inventory Report (NIR), innsendt 8. april 2022 (Miljødirektoratet mfl. 2022). Perioden 2006 – 2020 har vært lagt til grunn som referanseperiode, og framskrivning av arealutvikling og utslipp er i all hovedsak basert på rapporterte data for denne tidsperioden. Utviklingen i gjenværende skog er framskrevet ved hjelp av simuleringsverktøyet SiTree og Yasso07. Klimaendringer under klimascenariet i RCP 4.5 er lagt til grunn. Framskrivingen er framstilt på to ulike formater: Både i henhold til FNs klimakonvensjon sitt regelverk (alle arealbrukskategorier og kilder) og basert på EUs regelverk under LULUCF-forordningen (2018/841) (European Union 2018).publishedVersio

Framskrivninger for arealbrukssektoren – under FNs klimakonvensjon, Kyotoprotokollen og EUs rammeverk

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.publishedVersio