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Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand.

The nature areas surrounding the capital of Norway (Oslomarka), comprising 1 700 km of forest land, are the recreational home turf for a population of 1.2 mill. people. These areas are highly valuable, not only for recreational purposes and biodiversity, but also for commercial activities. To assess the impacts of the challenges that Oslo municipality forest face in their management, we developed four optimization problems with different levels of management constraints. The constraints consider control of harvest level, guarantee of minimum old-growth forest area and maximum open area after final harvest. For the latter, to date, no appropriate analyses quantifying the impact of such a constraint on economy and biomass production have been carried out in Norway. The problem solved is large due to both the number of stands and number of treatment schedules. However, the model applied demonstrated its relevance for solving large problems involving maximum opening areas. The inclusion of maximum open area constraints caused 7.0% loss in NPV compared to the business as usual case with controlled harvest volume and minimum old-growth area. The estimated supply of 20-30 GWh annual energy from harvest residues could provide a small, but stable supply of energy to the municipality.2

Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.

In this paper, we analyse how optimal forest management of even aged Norway spruce changes when economic values are placed on carbon fixation, release, and saved greenhouse gas emissions from using wood instead of more energy intensive materials or fossil fuels. The analyses are done for three different site qualities in Norway, assuming present climate and with a range of CO prices and real rates of return. Compared to current recommended management, the optimal number of plants per ha and harvest age are considerably higher when carbon benefits are included, and increase with increasing price on CO. Furthermore, planting becomes more favourable compared to natural regeneration. At the medium site quality, assuming 2% p.a. real rate of return and 20 euros per ton CO, optimal planting density increases from 1500 per ha to 3000 per ha. Optimal harvest age increases from 90 to 140 years. Including saved greenhouse gas emissions when wood is used instead of more energy intensive materials or fossil fuels, i.e. substitution effects, does not affect optimal planting density much, but implies harvesting up to 20 years earlier. The value of the forest area increases with increasing price on CO, and most of the income is from carbon. By using the current recommended management in calculations of carbon benefit, our results indicate that the forestâs potential to provide this environmental good is underestimated. The study includes many uncertain factors. Highest uncertainty is related to the accuracy of the forest growth and mortality functions at high stand ages and densities, and that albedo effects and future climate changes are not considered. As such, the results should be viewed as exploratory and not normative.2222

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

The albedo of boreal forests depends on the properties of the forest and is a key parameter for understanding the climate impact of forest management practices at high northern latitudes. While high-resolution albedo retrievals from satellites remain challenging, unmanned aerial vehicles (UAVs) offer the ability to obtain albedo corresponding to the typical size of forest stands or even smaller areas, such as forest plots. Plots and pixels of sizes in the typical range of 200–400 m2 are used as the basic units in forest management in the Nordic countries. In this study, the aim was to evaluate the effect of the differences in the footprint size of the measured albedo and fixed-area forest plots on the relationship between albedo and forest attributes. This was performed by examining the correlation between albedo and field-measured forest attributes and metrics derived from airborne laser scanner data using linear regression models. The albedo was measured by a UAV above 400 m2, circular forest plots (n = 128) for seven different flight heights above the top of the canopy. The flight heights were chosen so the plots were always smaller than the footprint of the measured albedo, and the area of a forest plot constituted 30–90% of the measured albedo. The applied pyranometer aboard the UAV measured the albedo according to a cosine response across the footprint. We found the strongest correlation when there was the greatest correspondence between the spatial size of the albedo footprint and the size of the forest plots, i.e., when the target area constituted 80–90% of the measured albedo. The measured albedo of the plots in both regeneration forests and mature forests were highly sensitive (p-values ≤ 0.001) to the footprint size, with a mean albedo difference of 11% between the smallest and largest footprints. The mean albedo of regeneration forests was 33% larger than that of mature forests for footprint sizes corresponding to 90%. The study demonstrates the importance of corresponding spatial sizes of albedo measurements and the target areas subject to measurements. [ABSTRACT FROM AUTHOR]

The transition zone between the upper forest boundary and the treeless alpine vegetation (i.e., the treeline ecotone) is expected to shift upward in elevation with global warming. Multiple factors in addition to climate are influencing tree establishment, but it is uncertain to what degree. In this study, we took advantage of a unique dataset with field measurements of mountain birch trees acquired from 32 boreal–alpine treeline ecotone sites situated along a 1100‐km latitudinal gradient in Norway to investigate the role of climate, herbivory, topography, and vegetation on the establishment success of trees. To represent tree establishment, we recorded occurrence, survival, and height growth of short juvenile birch trees (stem height ≤1 m) as response variables at each site in 2008, 2012, and 2018. We used already available climate and herbivory data, and airborne laser scanning (ALS) and unmanned aerial vehicle (UAV) surveyed imagery together with field measurements to compute topography and vegetation explanatory variables per site on microsite scale (approx. 1 m2). We observed a clear decrease in short juvenile birch tree establishment due to livestock herbivory, evidenced by lower occurrence, reduced survival rate, and diminished height growth in treeline ecotones with higher livestock densities. Winter precipitation and the two land cover classes, tree‐covered area and lee side vegetation, were also important for tree establishment. However, the direction and magnitude of the relationships depended on the stage of establishment. While winter precipitation can lead to higher tree occurrence, long‐term survival is not necessarily ensured, reflecting diverse requirements for optimal conditions of tree establishment. Thus, our results show that occurrence, survival, and height growth of short juvenile birch trees differ in their requirements to climate, topography, and vegetation, but tree establishment is consistently low with livestock herbivory. Overall, our results suggest that treeline ecotones will shift toward higher elevations in areas with little or no livestock herbivory and close to already established trees and in lee sides. Regarding management and conservation issues, our results suggest that free‐roaming livestock can reduce tree establishment and hinder treeline ecotone shifts into threatened alpine ecosystems, at least for birch‐dominated treelines. [ABSTRACT FROM AUTHOR]

In many countries, airborne laser scanning (ALS) inventories are implemented to produce predictions for stand-level forest attributes. Nevertheless, mature stands are usually field-visited prior to clear-cutting, so some measurements can be conducted on these stands to calibrate the ALS-based predictions. In this paper, we developed a seemingly unrelated multivariate mixed-effects model system that includes component models for basal area, merchantable volume, and sawlog volume for 225 [m.sup.2] cells. We used ALS data and accurately positioned cut-to-length harvester observations from clear-cut stands dominated by Norway spruce (Picea abies (L.) Karst.). Our aim was to study the effect of 1-10 local angle-gauge basal area measurements on the accuracy of predicted merchantable and sawlog volumes. A seemingly unrelated mixed-effect model system was fitted to estimate cross-model correlations in residuals and random effects, which were then utilized to predict all the random effects of the system for calibrated stand-level predictions. The 10 angle-gauge plots decreased the relative root mean square error (RMSE%) of the basal area and merchantable volume predictions from 16.8% to 10.5% and from 15.8% to 11.9%, respectively. Cross-model correlations of the stand effects of sawlog volume with the other responses were low; therefore, the initial RMSE% of ~22% was decreased only marginally by the calibration. Key words: LiDAR, area-based approach, quality, estimated best linear unbiased predictor, harvester data. Dans de nombreux pays, des inventaires par balayage laser aeroporte (BLA) sont mis en reuvre afin de produire des previsions pour les attributs forestiers a l'echelle des peuplements. Neanmoins, les peuplements matures sont generalement visites sur le terrain avant d'etre coupes a blanc, ainsi certaines mesures peuvent etre effectuees sur ces peuplements pour calibrer les previsions basees sur le BLA. Dans cet article, nous avons developpe un systeme de modeles a effets mixtes multivaries sans correlation apparente qui inclut des composantes pour la surface terriere, le volume marchand et le volume en bois de sciage, pour des cellules de 225 [m.sup.2]. Nous avons utilise les donnees de BLA et positionne avec precision les observations des abatteuses-tronconneuses dans des peuplements coupes a blanc domines par l'epicea commun (Picea abies (L.) Karst.). Notre objectif consistait a etudier l'effet de 1 a 10 mesures locales de la surface terriere avec une jauge d'angle sur la precision des volumes marchand et de bois d'reuvre prevus. Un systeme de modeles a effets mixtes sans correlation apparente a ete ajuste pour estimer les correlations croisees des modeles dans les residus et les effets aleatoires, qui ont ensuite ete utilises pour predire tous les effets aleatoires du systeme pour les previsions calibrees a l'echelle des peuplements. L'utilisation des donnees des 10 placettes a la jauge d'angle ont diminue l'erreur quadratique moyenne relative (EQM %) des previsions de la surface terriere et du volume marchand de respectivement 16,8 a 10,5% et de 15,8 a 11,9%. Les correlations croisees entre les modeles d'effets du peuplement pour le volume de bois de sciage et pour les autres reponses etaient faibles : l'EQM % initial d'environ 22% n'a par consequent ete diminue que marginalement par l'etalonnage. [Traduit par la Redaction] Mots-cles : balayage laser aeroporte, approche par surface, qualite, meilleur predicteur lineaire sans biais estime, donnees de l'abatteuse-tronconneuse., Introduction Many remote sensing technologies have proven to be useful in forest inventories around the world. In Norway, for example, almost all forest management inventories are based on airborne laser [...]

We propose design-based inference with finite mixture models (FMM) in settings where heterogeneity cannot be addressed by more conventional modelling. In FMM, a model is estimated for each of K latent model subgroups in a population under study. We evaluated the FMM approach with a difference estimator with K = 2 in 600 replications of simulated equal probability sampling from 12 artificial populations. An example with a forest population in southern Norway demonstrated a practical implementation. The artificial populations were composed of one, two, three, or four actual model subgroups generated from models that were either of the same form as the estimation model or different. We compare bias and variance in estimates of a population mean with standard results for K =1. All estimates with K =2 were nearly unbiased. Bias was largest when actual subgroups were clustered on y. Variances in sample means with K =1 were 60% larger than with K =2. An important reduction in variance with K =2 was confirmed in the case study. A reliable estimate of variance requires a medium to large sample size. Key words: bias, simulated sampling, subgroup models, expectation-maximization algorithm, model calibration. Nous proposons une inference basee sur le plan d'experience avec des modeles de melanges finis (MMF) pour des contextes oU l'heterogeneite ne peut etre traitee par une modelisation plus conventionnelle. Avec les MMF, un modele est estime pour chacun des K sous-groupes de modeles latents dans une population a l'etude. Nous avons evalue l'approche MMF avec un estimateur de difference et avec K =2 sur 600 repetitions d'echantillonnage simule a probabilite egale provenant de 12 populations artificielles. Un exemple avec une population forestiere du sud de la Norvege demontre la mise en oeuvre pratique. Les populations artificielles etaient composees d'un, deux, trois ou quatre sous-groupes de modeles reels generes a partir de modeles de formes similaires ou differentes a celle du modele d'estimation. Nous comparons le biais et la variance dans les estimations de la moyenne d'une de population aux resultats standards pour K = 1. Avec K = 2, toutes les estimations etaient presque sans biais. Le biais etait plus important lorsque les sous-groupes reels etaient groupes selon y. Les variances dans les moyennes d'echantillon avec K =1 etaient 60 % plus grandes qu'avec K =2. Une reduction importante de la variance avec K = 2 a ete confirmee dans l'etude de cas. Une estimation fiable de la variance necessite une taille d'echantillon moyenne a grande. [Traduit par la Redaction] Mots-cles : biais, echantillonnage simule, sous-groupes de modeles, algorithme esperance-maximisation, etalonnage de modeles., Introduction Forest inventories at all levels (enterprise, regional, and national) are increasingly supported by remotely sensed auxiliary variables (x) obtained from satellite or airborne sensors (Kangas et al. 2018; McRoberts [...]

Two approaches to greenhouse gas (GHG) inventories are common, namely the stock-change approach and the gain-loss approach. With the stock-change approach, mean annual emissions are estimated as the ratio of the difference in stock estimates at two points in time and the number of intervening years. The stock-change approach is fairly easy to implement for countries with well-established forest sampling programs. However, countries without established forest sampling programs more commonly use the gain-loss approach. With this approach, emissions are estimated as the product of the areas of classes of land use change, characterized as activity data, and the responses of carbon stocks for those classes, characterized as emission factors. Regardless of the approach, the Intergovernmental Panel on Climate Change (IPCC) good practice guidelines specify that GHG inventories produce neither over- nor under-estimates and reduce uncertainties to the degree possible. For a study area in southeastern Norway, the objectives of the study were to compare the stock-change and gain-loss approaches with respect to estimates of carbon emissions for the aboveground biomass pool and to illustrate statistically rigorous methods for complying with the two IPCC good practice guidelines for both approaches. The primary conclusions were that the two approaches produced comparable estimates of mean annual emissions, but that the stock-change approach produced considerably smaller estimates of uncertainty.Key words: statistical estimators, remote sensing, uncertainty.Deux methodes d'inventaire des gaz a effet de serre (GES) sont couramment utilisees : la methode de difference des stocks et la methode gains-pertes. Dans le cas de la methode de difference des stocks, les emissions annuelles moyennes sont estimees par le rapport entre la difference des estimations des stocks a deux moments et le nombre le nombre d'annees ecoulees entre les deux. Cette methode est relativement facile a implanter dans les pays ou des programmes d'inventaire forestier sont bien etablis. Cependant, les pays ou ce n'est pas le cas utilisent generalement la methode gains-pertes. Avec cette methode, les emissions sont estimees par le produit de la variation dans la superficie des classes d'utilisation des terres, caracterisees comme donnees d'activites, et de la reaction des stocks de carbone correspondant a ces classes, caracterisee comme facteur d'emission. Peu importe l'approche, le guide des bonnes pratiques du Groupe d'experts intergouvernemental sur revolution du climat (GIEC) precise que l'inventaire des GES ne devrait ni surestimer, ni sous-estimer tout en cherchant a reduire dans la mesure du possible les incertitudes. A partir d'une zone d'etude situee dans le sud de la Norvege, cette etude avait pour but de comparer les methodes de difference des stocks et de gains-pertes en ce qui concerne les estimations des emissions de carbone pour le reservoir de biomasse aerienne et de presenter des methodes statistiquement rigoureuses pour respecter les directives du guide des bonnes pratiques du GIEC pour les deux methodes. Les principales conclusions sont que les deux methodes produisent des estimations comparables des emissions annuelles moyennes mais que l'incertitude estimee par la methode de difference des stocks est considerablement plus faible. [Traduit par la Redaction]Mots-cles: estimateurs statistiques, teledetection, incertitude., 1. IntroductionGreenhouse gas (GHG) inventories focus on carbon accounting and assess the scale of carbon emissions for multiple land uses including the Agriculture, Forestry, and Other Land Use sector (Intergovernmental [...]