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Forest management inventories assisted by airborne laser scanning (ALS) can be used to predict different forest attributes. These predictions are utilized in practical forestry, but in the case of timber assortment-specific volumes, the ALS-based predictions can be inaccurate. This causes uncertainty in harvest planning. However, ALS-based predictions can be calibrated to achieve greater accuracy with local measurements. In this study, we used ALS data and accurately positioned cut-to-length harvester measurements from Norway spruce (Picea abies (L.) Karst.) dominated clear-cuts. We fitted linear mixed-effects (LME) models with exponential correlation structure for merchantable volume and sawlog volume for 225 m2 cells. Our aim was to study the effect of local harvester measurements on the accuracy of stand level merchantable and sawlog volumes. LME-based predictions were calibrated repeatedly up to 40 times as the cutting progressed. ALS data and harvester measurements were used to predict both the random effects and residual errors for each validation unit. At best, relative root mean square error (RMSE%) of initial predictions of 15.4 per cent for merchantable volume and 22.1 per cent for sawlog volume were reduced to 4.1 and 5.3 per cent, respectively, when measurements from 40 harvested cells of size 225 m2 were used. These results suggest that spatially accurate harvester data could be utilized during harvesting to increase the accuracy of volume and timber assortment predictions.

Bush encroachment affects ~45 million ha of Namibia and, without appropriate restoration measures, it negatively affects rangeland productivity and biodiversity. Thinning is a common method to counteract bush encroachment. The thinning strategy applied in north-central Namibia was assessed to examine how effective it has been in reducing bush encroachment. Trees/shrubs were selectively thinned manually, targeting all height classes, except individuals with stem diameters ≥18 cm. We investigated the effects on the vegetation and soil properties using surveys on three freehold farms (in 2016 and 2017) in bush-encroached and previously thinned habitats. Our results revealed significant differences in the mean total nitrogen (TN) content between the treatments; thinned areas had higher TN content which would be beneficial for fast-growing grasses. In the thinned plots, the occurrence probability of red umbrella thorn (Vachellia reficiens Warwa) was significantly reduced, indicating that it was the most harvested species; and umbrella thorn (Vachellia tortilis (Burch.) Brenan spp. heteracantha) was increased, indicating that it favoured reduced densities of dominant species. Natural regeneration was rapid; the tree/shrub abundance in the 0–1-m height class in the thinned area surpassed those in the non-thinned by 34 per cent, ~7.2 years since thinning. Thinning significantly reduced tree/shrub abundances of the 1–3- and >3-m height classes, which was still evident 7.2 years since thinning. Based upon the generalized linear mixed-effects model, tree/shrub counts between treatments may equalize in ~14 and 15 years for the 1–3- and >3-m height classes, respectively. Thinning was effective in reducing tree/shrub abundances and can be used to restore wildlife habitat on the Namibian farmland: however, post-thinning management is required to maintain an open savannah vegetation structure as the 0–1-m height class cohort will eventually grow into mature trees/shrubs.

Tree growth is expected to be responsive to climatic drivers across a spectrum of temporal scales, ranging from yearly growth to daily water use and photosynthesis. Automatic dendrometers offer the potential to provide continuous high-resolution measurements of tree radius changes. The signal recorded contains three components: (1) a long-term seasonal growth component, (2) a mid-term component representing swelling after rainfall and subsequent drying and (3) daily cycles of water-uptake related to tree transpiration. For 91 trees at 4 sites (Picea abies: 58, Pinus cembra: 17, Fagus sylvatica: 14, Pinus sylvestris: 2) monitored in Austria between 2012 and 2015, we simultaneously modelled these three processes using a hierarchical nonlinear mixed-effects model represented by two logistic growth curves. The focus was on the mid-term and daily component, and therefore long-term growth that is typically modelled by including tree size, competition or site variables was represented by random effects only. Both mid-term and short-term components were species-specific. In general, P. cembra and F. sylvatica were less sensitive to climate variables than P. abies. For all species, the mid-term component was best represented using a 14-day moving average difference between rainfall and potential evapotranspiration, a 24-h moving average of precipitation and its 1–3 days lags, a 24-h moving average temperature and its 1–3 days lags. The daily cycles of water uptake were best related to hourly humidity and its 3-h lag, and interactions with the 14-day moving average difference between rainfall and potential evapotranspiration accounted for attenuating cycles after rainy events and increasing cycles in dry periods.

We quantified the role of spatially varying vegetation composition in seasonal and interannual changes in a boreal bog’s CO2 uptake. We divided the spatially heterogeneous site into six microform classes based on plant species composition and measured their net ecosystem exchange (NEE) using chamber method over the growing seasons in 2012–2014. A nonlinear mixed-effects model was applied to assess how the contributions of microforms with different vegetation change temporally, and to upscale NEE to the ecosystem level to be compared with eddy covariance (EC) measurements. Both ecosystem respiration (R) and gross photosynthesis (PG) were the largest in high hummocks, 894–964 (R) and 969–1132 (PG) g CO2 m−2 growing season−1, and decreased toward the wetter microforms. NEE had a different spatial pattern than R and PG; the highest cumulative seasonal CO2 sink was found in lawns in all years (165–353 g CO2 m−2). Microforms with similar wetness but distinct vegetation had different NEE, highlighting the importance of vegetation composition in regulating CO2 sink. Chamber-based ecosystem-level NEE was smaller and varied less interannually than the EC-derived estimate, indicating a need for further research on the error sources of both methods. Lawns contributed more to ecosystem-level NEE (55–78%) than their areal cover within the site (21.5%). In spring and autumn, lawns had the highest NEE, whereas in midsummer differences among microforms were small. The contributions of all microforms to the ecosystem-level NEE varied seasonally and interannually, suggesting that spatially heterogeneous vegetation composition could make bog CO2 uptake temporally more stable.

Errors in individual tree detection and delineation affect diameter distribution predictions based on crown attributes extracted from the detected trees. We develop a methodology for circumventing these problems. The method is based on matching cumulative distribution functions of field measured tree diameter distributions and crown radii distributions extracted from airborne laser scanning data through individual tree detection presented by Vauhkonen and Mehtatalo (2015). In this study, empirical distribution functions and a monotonic, nonlinear model curve are introduced. Tree crown radius distribution produced by individual tree detection is corrected by a method taking into account that all trees cannot be detected. The evaluation is based on the ability of the developed model sequence to predict quadratic mean diameter and total basal area. The studied data consists of 36 field plots in a typical boreal managed forest area in eastern Finland. The suggested enhancements to the model sequence produce improved results in most of the test cases. Most notably, in leaveone-out cross-validation experiments the modified models improve RMSE of basal area 13% in the full data and RMSE of quadratic mean diameter and basal area 69% and 11%, respectively, in pure pine plots. Better modeling of the crown radius distribution and improved matching between crown radii and stem diameters add the operational premises of the full distribution matching.

Northern peatlands form a major soil carbon (C) stock. With climate change, peatland C mineralization is expected to increase, which in turn would accelerate climate change. A particularity of peatlands is the importance of soil aeration, which regulates peatland functioning and likely modulates the responses to warming climate. Our aim is to assess the impacts of warming on a southern boreal and a sub-arctic sedge fen carbon dioxide (CO2) exchange under two plausible water table regimes: wet and moderately dry. We focused this study on minerotrophic treeless sedge fens, as they are common peatland types at boreal and (sub)arctic areas, which are expected to face the highest rates of climate warming. In addition, fens are expected to respond to environmental changes faster than the nutrient poor bogs. Our study confirmed that CO2 exchange is more strongly affected by drying than warming. Experimental water level draw-down (WLD) significantly increased gross photosynthesis and ecosystem respiration. Warming alone had insignificant impacts on the CO2 exchange components, but when combined with WLD it further increased ecosystem respiration. In the southern fen, CO2 uptake decreased due to WLD, which was amplified by warming, while at northern fen it remained stable. As a conclusion, our results suggest that a very small difference in the WLD may be decisive, whether the C sink of a fen decreases, or whether the system is able to adapt within its regime and maintain its functions. Moreover, the water table has a role in determining how much the increased temperature impacts the CO2 exchange. This article is protected by copyright. All rights reserved.

In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters.

The increasing availability of wall-to-wall remote sensing datasets in combination with accurate field data enables the mapping of different ecosystem services more accurately and over larger areas than before. The provision of wild berries is an essential ecosystem service, and berries are the most used non-wood forest products in Nordic countries. The aim of the study was to 1) develop general prediction models for bilberry and cowberry yield based on metrics derived from airborne laser scanning (ALS) data and other existing wall-to-wall data and 2) to identify laser-based structural features of forests that can be linked to locations of the highest berry yields. We used the indirect approach where the correlation between forest structure described by the ALS data and the berry yields are utilized. Berry data collected in the Swedish National Forest Inventory (NFI) 2007–2016 were used for training the models and ALS data from 2009 to 2014 from the national ALS campaign of Sweden. Berry yields were modelled using generalised linear mixed models (GLMMs), and forest structural differences were demonstrated in histograms of presence/absence data. The ALS-based canopy cover was an important variable both in bilberry and cowberry models. Other significant variables were ALS-based height variance, shrub cover, height above sea level, slope, soil wetness and terrain ruggedness, satellite-based species-specific volume and percentage, seasonality of temperature and precipitation and annual precipitation, inventory year, soil type and land use class. In addition, the time difference between the inventory day and the Julian day when berries were expected to be ripe showed a 1.5% decrease for bilberry and a 1.1% decrease for cowberry yield per day during the season. The highest bilberry yield was identified in forests with a canopy cover of 50% and the highest cowberry yield in forests with a canopy cover close to zero. The canopy height of 15 m reflected the highest bilberry yield, whereas a canopy height close to 0 m resulted in the highest cowberry yield. The shrub cover was close to zero both with highest bilberry and cowberry yields. This is the first study combining ALS metrics with other wall-to-wall variables and NFI field data to model bilberry and cowberry yields. Prediction models can be used to produce maps showing the most potential locations for berry picking. Further, the models may, in the future, be imported into forest planning systems to obtain stand-level prognoses of berry yield development under different forest management strategies.

Northern wetlands with organic soil i.e., mires are significant carbon storages. This key ecosystem service may be threatened by anthropogenic activities and climate change, yet we still lack a consensus on how these major changes affects their carbon sink capacities. We studied how forestry drainage and restoration combined with experimental warming, impacts greenhouse gas fluxes of wetlands with peat. We measured CO2 and CH4 during two and N2O fluxes during one growing season using the chamber method. Gas fluxes were primarily controlled by water table, leaf area and temperature. Land use had a clear impact of on CO2 exchange. Forestry drainage increased respiration rates and decreased field layer net ecosystem CO2 uptake (NEE) and leaf area index (LAI), while at restoration sites the flux rates and LAI had recovered to the level of undrained sites. CH4 emissions were exceptionally low at all sites during our study years due to natural drought, but still somewhat lower at drained compared to undrained sites. Moderate warming triggered an increase in LAI across all land use types. This was accompanied by an increase in cumulative seasonal NEE. Restoration appeared to be an effective tool to return the ecosystem functions of these wetlands as we found no differences in LAI or any gas flux components (PMAX, Reco, NEE, CH4 or N2O) between restored and undrained sites. We did not find any signs that moderate warming would compromise the return of the ecosystem functions related to C sequestration. (C) 2018 Elsevier B.V. All rights reserved.

Secondary succession that occurs on abandoned farmlands is an important source of biomass carbon stocks. Both direct and indirect tree biomass estimation methods are applied on forest lands. Using empirical data from 148 uprooted trees, we developed a seemingly unrelated mixed-effects models system for the young silver birch that grows on post agricultural lands in central Poland. Tree height, biomass of stem, branches, foliage, and roots are used as dependent variables, the diameter at breast height is used as the independent variable. During model elaboration we used restricted cubic spline: 5 knots at the quantiles (0.05, 0.275, 0.5, 0.725, and 0.95) of diameter at breast height provided sufficiently flexible curves for all biomass components. In this study, we demonstrate the use of the model system through cross-model calibration of the biomass component model using tree height measured from 0, 2, 3, and 4 available extreme trees feature in the plot in question. A different number of extreme trees were measured for final model system and our results indicated that for all analyzed components, random-effect predictions are characterized by higher accuracy than fixed-effects predictions.

Climate change affects peatlands directly through increased air temperatures and indirectly through changes in water-table level (WL). The interactions of these two still remain poorly known. We determined experimentally the separate and interactive effects of temperature and WL regime on factors of relevance for the inputs to the carbon cycle: plant community composition, phenology, biomass production and shoot:root allocation in two wet boreal sedge-dominated fens, ‘southern’ at 62°Ν and ‘northern’ at 68°Ν. Warming (1.5 °C higher average daily air temperature) was induced with open-top chambers and WL drawdown (WLD; 3-7 cm on average) by shallow ditches. Total biomass production varied from 250 to 520 g m−2, with belowground production comprising 25–63%. Warming was associated with minor effects on phenology and negligible effects on community composition, biomass production and allocation. WLD clearly affected the contribution of different plant functional types (PFTs) in the community and the biomass they produced: shrubs benefited while forbs and mosses suffered. These responses did not depend on the warming treatment. Following WLD, aboveground biomass production decreased mainly due to reduced growth of mosses in the southern fen. Aboveground vascular plant biomass production remained unchanged but the contribution of different PFTs changed. The observed changes were also reflected in plant phenology, with different PFTs showing different responses. Belowground production increased following WLD in the northern fen only, but an increase in the contributions of shrubs and forbs was observed in both sites, while sedge contribution decreased. Moderate warming alone seems not able to drive significant changes in plant productivity or community composition in these wet ecosystems. However, if warming is accompanied by even modest WL drawdown, changes should be expected in the relative contribution of PFTs, which could lead to profound changes in the function of fens. Consequently, hydrological scenarios are of utmost importance when estimating their future function. This article is protected by copyright. All rights reserved.

This study examines the potential of airborne laser scanning (ALS) to predict diameter distributions in an even-aged plantation of Eucalyptus urograndis in Brazil. The single-species plantation conditions allow different modelling alternatives to be compared without the presence of minor tree species or an understory layer affecting the results. Three modelling alternatives based on the two-parametric Weibull function form; parameter prediction, parameter recovery and distribution matching were tested with a k-nearest neighbour prediction (k-nn) method. We also evaluated a parameter prediction alternative, in which the Weibull distribution was predicted using field attributes. The results showed that ALS information can predict diameter distributions with an error margin of slightly more than 10 per cent of the RMSE of the mean of the third power of diameter, and with error index values between 50 and 60. The degree of accuracy was only slightly improved when the Weibull distribution was predicted using field attributes. According to the accuracy metrics, the differences between modelling alternatives were minor but parameter recovery and k-nn seem to be the most favourable ALS-based prediction methods. To conclude, the results showed a strong relationship between ALS information and diameter distributions in a tropical single-species plantation and we discuss how these results could be applied in other types of forests.

We used national scenario analyses to examine the effects of harvesting intensity on the development of forest resources, timber supply, carbon balance, and biodiversity indicators of Finnish forestry in nine 10-year simulation periods (90-year simulation period) under the current climate. Data from the 11th National Forest Inventory of Finland were used to develop five even-flow harvesting scenarios for non-protected forests with the annual harvest ranging from 40 to 100 million m3. The results show that the highest annual even-flow harvest level, which did not decrease the growing stock volume over the 90-year simulation period, was 73 million m3. The total 90-year timber production, consisting of harvested volume and change in growing stock volume, was maximized when the annual harvest was 60 million m3. Volume increment increased for several decades when harvested volume was less than the current volume increment. The total carbon balance of forestry was the highest with low volume of harvested wood. Low harvested volume increased the values of biodiversity indicators, namely volume of deciduous trees, amount of deadwood and area of old forest., final draft, peerReviewed

Climate change has influenced a range of species across the globe. Yet, to state a noted decline in the abundance of a given species as a consequence of a specific environmental change, for instance, spatially explicit long-term data are a prerequisite. This study assessed the extent to which prolonged snow-free periods in autumn and spring have contributed to the decline of the willow grouse, the only forest grouse changing into a white winter plumage. Time-series data of willow grouse numbers from summer surveys across the study area were integrated with local data on weather (snow cover), mammalian predator abundance and hunting intensity. Modelling was conducted with a hierarchical Bayesian Poisson model, acknowledging year-, area- and location-specific variability. The results show that while willow grouse numbers had decreased continuously across the study landscapes, the decrease was accelerated at the sites where, and during the years when the preceding April was the most snow-free. This indicates a mismatch between the change into a white winter plumage and the presence of snow, turning the bird into an ill-camouflaged prey. The results thus also confirm past hypotheses where local declines of the species have been attributed to prolonged snow-free periods. Across our study area, autumns and springs have become more snow-free, and the trend has been predicted to continue. Thus, in addition to conservation actions, the future of a species such as the willow grouse is also dependent on its ability to adapt to the changed environmental conditions.

McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural to describe a continuous variable (X) such as height. In this contribution we aim to provide a mathematical framework for determining maximum entropy in 3D remote sensing datasets based on the Gini Coefficient of theoretical continuous distributions, intended to replace FHD as entropy measure in vertical profiles of LiDAR heights (1D, X), with extensions to variables expressing dimensions of higher order (2D or 3D, Z ∝ X2 or X3). Then we apply this framework to Boreal forests in Finland to describe landscape heterogeneity with the intention to improve the modelling of forest aboveground biomass (AGB), hypothesizing that LiDAR models of AGB should essentially be different in areas of differing structural characteristics. We carried out a pre-stratification of LiDAR data collected in 2012 using simple rules applied to the L-skewness (Lskew) and L-coefficient of variation of LiDAR echo heights (Lcv; equivalent to the Gini coefficient, GCH), determining a new threshold at GCH = 0.33 as a consequence of the newly developed mathematical proofs. We observed only moderate improvements in terms of model accuracies: RMSDs reduced from 41.7% to 38.9 or 37.0%. More remarkably, we identified critical differences in the metrics selected at each stratum, which is useful to understand what predictor variables are more important for estimating AGB at each area of a forest. We observed that higher LiDAR height percentiles are more relevant at open canopies and heterogeneous forests, whereas closed canopies in homogeneous forests obtain most accurate predictions from a combination of cover metrics and percentiles around the median. Without stratification, the overall model would neglect explained variability in the structural types of lower occurrence, and predictions from a model influenced by structural types of higher occurrence would be biased at those areas. These results are thus useful in terms of improving our understanding on the relationships underlying LiDAR-AGB models.

Individual tree detection methods leave smaller trees hiding below larger trees undetected. This is a problem for remote sensing forest inventories, leading, for example, to severe underestimation ...

Peatlands are one of the major sources of the powerful greenhouse gas methane (CH4). Our aim was to detect responses of methanogenic archaeal and methane-oxidizing bacterial (MOB) communities that control the methane (CH4) cycle to climatic warming. This study took place in two boreal fens three years after experimental warming in un-manipulated wet and drier regimes, thus simulating future climate scenarios. We determined active methanogen and MOB communities as transcripts of mcrA and pmoA genes, along with the abundance of these genes, CH4 production and oxidation potentials, and in situ CH4 fluxes. Methanogenic community remained similar, although methanogen abundance decreased after warming. In the wet regime, this decrease resulted in a small but significant reduction on the potential CH4 production in such peat layers where the average production potential was high. Drying alone, however, reduced the potential CH4 production more than warming, and this impact was strong enough to mask the small warming impact in the drier regime. Warming did not affect the MOB community or the potential CH4 oxidation in the wet regime; however, type Ib MOB abundance decreased and MOB related to genus Methylocapsa became typical after warming in the drier regime of the southern fen. The in situ measured CH4 fluxes indicated similar patterns as potential measurements; both warming and drying reduced methane emissions, drying more than warming. These results indicate that methanogens and MOB may have different controlling patterns on CH4 fluxes when facing global warming. These patterns may further differ not only between moisture regimes, but inside the same habitat type, here boreal fen. Irrespective of this variation, the in situ CH4 fluxes still seem to respond similarly across sites.

During the last decade, Airborne Laser Scanning (ALS) data has been used increasingly in numerous studies to assess e.g. bird habitats and biodiversity, but less often in studies focusing on ground dwelling mammals. Here, we utilized ALS data to study the role of forest structure in moose habitat use. The data consisted of 18 GPS-collared moose in western Finland. ALS data was extracted around the moose locations. The aim was to examine the habitat selection patterns of moose within a year. Special attention was given: 1) to winter to detect when moose-related forest damages occur (when moose favor young forests) and 2) to the calving period to examine the possible role of forest structure in determining where females give birth and where they move with the newborn calves. During calving, females were occupying forests with minimal amounts of vegetation below the height of five meters. Shortly after calving, the females and their calves moved to forests with dense vegetation under the height of five meters, which is explained by the increasing demand of food both for the growing calf and the lactating mother. In summer and autumn, all moose were found more often in mature forests with higher and denser canopies, which is explained by the fact that their most preferred food during autumn (e.g. berries and twigs) grows in mature forests. This trend diminished as autumn turned to winter and moose started to favor vegetation below five meters. In general, we were able to see clear patterns and differences in habitat use between sexes, as well as with calves, and we gained new and more accurate information about the role of the forest structure to calving females. The results show that ALS data alone can yield valuable additional information about wildlife ecology.

Drone applications are becoming increasingly common in the arena of forest management and forest inventories. In particular, the use of photogrammetrically derived drone-based image point clouds (DIPC) in individual tree detection has become popular. Use of an area-based approach (ABA) in small areas has also been considered. However, in-situ field measurements of sample plots substantially increase the cost of small area forest inventories. Therefore, we examined whether small-scale forest management inventories could be carried out without local field measurements. We used nationwide and regional ABA models for stem volumes fitted with airborne laser scanning (ALS) data to predict stem volumes using corresponding metrics calculated from DIPC data. The stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m). Height metrics for the dominant tree layer from the DIPC data showed strong correlations with similar metrics computed from the ALS data. The ALS-based models applied with DIPC metrics performed well, especially if the ABA model was fitted in the same geographical area (regional model) and the inventory units were disaggregated to coniferous and deciduous dominated stands using auxiliary information from Multi-source National Forest Inventory data (root mean square error at 30 × 30 m level was 13.1%). The corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%).

The responses in growth and defense after tissue damage are highly variable in plants depending on species, damaged-tissue type and the intensity of damage. The prevailing abiotic conditions can also influence these responses. In this study, our aim was to examine how the removal of lateral vegetative buds affects the growth and accumulation of phenolics in saplings of the dioecious Populus tremula grown under simulated climate change. For three growing seasons, the saplings were grown under ambient conditions (control), elevated temperature (+2°C) and elevated UV radiation (30%) (UVB and UVA as its control), or a combination of these. In the fourth growing season, all saplings were grown under ambient conditions. The bud removal was performed twice - in summer and autumn - in the third year. The responses of growth and the accumulation of phenolics to the bud removal were measured at the end of the fourth growing season. Removal of 5% of the lateral buds resulted in higher leaf, stem and total plant biomass in both sexes of P. tremula saplings, compared to intact plants. The effects were greater in the temperature-treated plants, especially in the temperature-treated females. The concentrations of flavonoids and condensed tannins were higher in the bud-removed individuals. The concentration of condensed tannins was also higher in the males than in the females, opposite to the concentration of phenolic acids. There was no significant interaction between bud removal and UVB treatment on either growth or phenolics. Our results suggest that plants can allocate resources to both growth and defense simultaneously in response to tissue loss, and that global warming can modify the responses to some extent.

This work presents applications of the linear mixed-effects model calibration to predict individual tree volumes of Eucalyptus grandis W. Hill ex Maiden plantations on first and second rotations located in different farms of the same region in São Paulo, Brazil. We started with the Schumacher and Hall equation in its linearized form to develop our mixed-effects model. Some parameters were considered as random among the different farms, and the calibration was made at the farm level using a small number of sample trees. The approach was developed for univariate models of the first rotation, which were calibrated with first- and second-rotation trees, and for bivariate models of the two rotations, which were calibrated with first-rotation trees. The results showed that the calibrated mixed model provides more reliable predictions than the fixed part of the model alone; however, the benefit is only moderate due to the rather small variation of the stem form between farms and rotations. The results indicate that the approach can reduce the measurement requirements on second-rotation crops.

Large herbivores can have large impacts on their habitats through extensive browsing. Similarly, human actions can have large impacts both on habitats and on the animals utilizing the habitats. In Finland, the increase in clear-cut areas has been highly positive for moose in particular, because these areas provide an easy and abundant source of winter food. For the forest owners, moose browsing causes growth and quality losses or even the destruction of whole stand. We aimed to identify moose browsing damage from airborne laser scanning (ALS) data and to predict damaged areas. The data was used to detect the difference in forest structure caused by moose browsing (lost branches and twigs) in relation to reference areas without moose browsing. The damaged areas were located, measured, and confirmed by forestry professionals, and ALS data was collected after the damage. In the end, the structural differences that browsing caused proved to be clear enough to be detected with metrics calculated from ALS data. Many variables were significantly different between the damage and no-damage areas. With logistic regression, we were able to differentiate the areas with significant, large-scale damage from no-damage areas with a 76% accuracy. However, the model was too keen to predict false-positive cases (classifying no-damage areas as damaged). It was shown that ALS data can be used in detecting moose browsing damage in a case where the damage is extremely severe (like in here). Yet, to make the results more accurate, better field data about the damaged areas would be needed.

Summary Peatland rewetting aims to restore biomass accumulation from peat-forming plants for climate change mitigation, biotope conservation and water purification purposes. Boreal spruce swamp forests in Europe have suffered heavily from drainage for forestry and are now a focus of restoration efforts. We measured Sphagnum height and biomass increment by allowing Sphagnum to grow through mesh nets located in nine undrained, nine drained and 18 rewetted boreal spruce swamp forests. At the moss patch level, rewetting led to a recovery of Sphagnum growth: height increment and biomass increment at the rewetted sites (5·9 ± 0·7 cm year−1 and 147 ± 15 g m−2 year−1, mean ± SE) were similar to increment at the undrained sites (4·9 ± 0·5 cm year−1 and 128 ± 12 g m−2 year−1), while remnant patches of Sphagnum at the drained sites showed smaller increment (2·8 ± 0·8 cm year−1 and 76 ± 19 g m−2 year−1). Sphagnum in the ditches at the drained sites showed similar increment as the moss at the undrained and rewetted sites, while ditches at the rewetted sites had the greatest increment (8·7 ± 0·7 cm year−1 and 183 ± 16 g m−2 year−1). A higher water-table increased Sphagnum growth, and Sphagnum riparium grew more rapidly than the other species. At the ecosystem level, where information on moss cover was utilized, rewetting had increased Sphagnum production to values close to the undrained sites. At the drained sites, biomass production was 8 ± 17 g (mean ± SE) m−2 year−1, at the rewetted sites 42 ± 15 g m−2 year−1 and at the undrained sites 66 ± 12 g m−2 year−1. Sphagnum cover was the most important variable that determined Sphagnum production in the ecosystem. Synthesis and applications. Drained spruce swamp forests appear to be a suitable target for restoration as they respond readily to rather inexpensive rewetting. The result points out that even in boreal conditions, restoration results can be achieved in a time-scale of years rather than decades. At the ecosystem level, rewetting had resulted in a biomass gain of 34 g m−2 year−1 when compared to drained sites.

The mean temperature during the potential growing season (AprilâSeptember) may increase by 1 °C by 2030, and by 4 °C, or even more, by 2100, accompanied by an increase in atmospheric CO concentrations of 536â807 ppm, compared to the current climate of 1981â2010, in which atmospheric CO is at about 350 ppm. This may affect both the growth and frost hardiness of boreal trees. In this work, we studied the responses of height and autumn frost hardiness development in 22 half-sib genotypes of one-year-old Norway spruce ( (L.) Karst.) seedlings to elevated temperatures and atmospheric CO concentration under greenhouse conditions. The three climate treatments used were: T+1 °C above ambient and ambient CO; T+4 °C above ambient and ambient CO; and T+4 °C above ambient and elevated CO (700 ppm). The height growth rate and final height were both higher under T+4 °C compared to T+1 °C. Temperature increase also delayed the onset, and shortened the duration, of autumn frost hardiness development. Elevated CO did not affect the development of height or frost hardiness, when compared to the results without CO elevation under the same temperature treatment. Higher temperatures resulted in greater variation in height and frost hardiness development among genotypes. Three genotypes with different genetic backgrounds showed superior height growth, regardless of climate treatment; however, none showed a superior development of autumn frost hardiness. In future studies, clonal or full-sib genetic material should be used to study the details of autumn frost hardiness development among different genotypes.22Picea abies222222

Data assimilation (DA) has a broad category of mathematical procedures for updating and calibrating existing predictions or parameter estimates using new observations. Typical DA procedures such as the Kalman filter require that the observations of a variable of interest used in a forecast of this variable to be collected just prior to the updating procedure and that they continue to be collected over time, a requirement that is not always practical for forest inventory problems. Distinct from the Kalman filter, the best linear unbiased predictor (BLUP) circumvents this time constraint, because it can calibrate the predicted values of a variable of interest using observations from other variables correlated with this variable of interest. Remote sensing played a major role in this study by providing the latter observations. The objective of this study was threefold: (1) to propose and illustrate a DA calibration procedure that incorporates simultaneous predictions and BLUP to improve prediction accuracy; (2) to expand this procedure in the framework of model-based inference; and (3) to compare the inferential properties before and after DA calibration for multiple scenarios. Four major conclusions are relevant. First, the prediction accuracies for all variables of interest were consistently increased by 10%–74% after implementing the DA procedure. Second, the DA procedure supported simultaneous inference, and the precision of the simultaneously estimated population means for the variables of interest were increased by 55%–76% after implementing this procedure. Third, the parametric bootstrap procedures used to estimate the inferential precisions for this complex estimation problem were effective and converged rapidly. In addition, this DA calibration procedure is universally applicable for DA calibrations using other data sources such as airborne or terrestrial laser scanning and spectral data, given that cross-model correlations exist. Overall, the proposed procedure demonstrated considerable potential for better serving forest inventory with respect to both simultaneous inference and inferential precision.

Forest ecological restoration by burning is widely applied to promote natural, early-successional sites and increase landscape biodiversity. Burning is also used as a forest management practice to facilitate forest regeneration after clearcutting. Besides the desired goals, restoration burnings also affect soil biogeochemistry, particularly soil organic matter (SOM) and related soil carbon stocks but the long-term effects are poorly understood. However, in order to study these effects, a reliable estimate of spatial variability is first needed for effective sampling. Here we investigate spatial variability of SOM and vegetation features 13 years after burnings and in combination with variable harvest levels. We sampled four experimental sites representing distinct management and restoration treatments with an undisturbed control. While variability of vegetation cover and biomass was generally higher in disturbed sites, soil parameter variability was not different between the four sites. The joint ecological patterns of soil and vegetation parameters across the whole sample continuum support well the prior assumptions on the characteristic disturbance conditions within each of the study sites. We designed and employed statistical simulations as a means to plan prospective sampling. Sampling six forest sites for each treatment type with 30 independent soil cores per site would provide enough statistical power to adequately capture the impacts of burning on SOM based on the data we obtained here and statistical simulations. In conclusion, we argue that an informed design-based approach to documenting the ecosystem effects of forest burnings is worth applying both through obtaining new data and meta-analysing the existing., published version, peerReviewed

This article introduces a novel methodology for automated classification of forest areas from airborne laser scanning (ALS) datasets based on two direct and simple rules: L-coefficient of variation Lcv = 0.5 and L-skewness Lskew = 0, thresholds based on descriptors of the mathematical properties of ALS height distributions. We observed that, while Lcv > 0.5 may represent forests with large tree size inequality, Lskew > 0 can be an indicator for areas lacking a closed dominant canopy. Lcv = 0.5 discriminated forests with trees of approximately equal sizes (even tree size classes) from those with large tree size inequality (uneven tree size classes) with kappa κ = 0.48 and overall accuracy OA = 92.4%, while Lskew = 0 segregated oligophotic and euphotic zones with κ = 0.56 and OA = 84.6%. We showed that a supervised classification could only marginally improve some of these accuracy results. The rule-based approach presents a simple method for detecting structural properties key to tree competition and potential for natural regeneration. The study was carried out with low-density datasets from the national program on ALS surveying of Finland, which shows potential for replication with the ALS datasets typically acquired at nation-wide scales. Since the presented method was based on deductive mathematical rules for describing distributions, it stands out from inductive supervised and unsupervised classification methods which are more commonly used in remote sensing. Therefore, it presents an opportunity for deducing physical relations which could partly eliminate the need for supporting ALS applications with field plot data for training and modelling, at least in Boreal forest ecosystems.

In boreal bogs plant species are low in number, but they differ greatly in their growth forms and photosynthetic properties. We assessed how ecosystem carbon (C) sink dynamics were affected by seasonal variations in the photosynthetic rate and leaf area of different species. Photosynthetic properties (light response parameters), leaf area development and areal cover (abundance) of the species were used to quantify species-specific net and gross photosynthesis rates (PN and PG, respectively), which were summed to express ecosystem-level PN and PG. The ecosystem-level PG was compared with a gross primary production (GPP) estimate derived from eddy covariance (EC) measurements.Species areal cover, rather than differences in photosynthetic properties, determined the species with the highest PG of both vascular plants and Sphagna. Species-specific contributions to the ecosystem PG varied over the growing season, which, in turn, determined the seasonal variation in ecosystem PG. The upscaled growing season PG estimate, 230 g C m−2, agreed well with the GPP estimated by the EC (243 g C m−2).Sphagna were superior to vascular plants in ecosystem-level PG throughout the growing season but had a lower PN. PN results indicated that areal cover of the species, together with their differences in photosynthetic parameters, shape the ecosystem-level C balance. Species with low areal cover but high photosynthetic efficiency appear to be potentially important for the ecosystem C sink. Results imply that functional diversity, i.e., the presence of plant groups with different seasonal timing and efficiency of photosynthesis, may increase the stability of C sinks of boreal bogs.

Errors in forest planning data are known to have various undesired effects, which have been examined previously by simulating their impact on forest planning systems. In most cases, the simulation of forest inventory errors has been simplified by assuming the error distribution to be Gaussian, possibly with a constant bias, and neglecting possible correlations between the errors in various attributes. The first aim here was to examine the distributions, correlations, and trends in errors when using alternative forest inventory methods, and the second was to analyse how different error simulation methods affect the estimated economic losses caused by suboptimal harvest timing on account of errors. We found that the errors were not normally distributed, had notable trends, and showed significant correlations between the errors for the various attributes. The most important factor affecting the inoptimality losses was the powerful tendency to underestimate the growing stock properties of mature stands. The error simulation method clearly makes a difference when analysing the effects of errors, and it is therefore important to use a simulation method that generates realistic errors.

Forest development can be predicted by the use of forest simulators based on various statistical models describing the forest and its dynamics. One potential approach to study the reliability of the simulators is to utilise Monte Carlo simulation techniques to generate a predictive distribution of a forest characteristic. One problem in examining the effect of model uncertainty in forestry decision making, however, is correlation between the models. If this is not taken into account, predictions of the model systems may become biased, and the effect of errors on decision making may be underestimated. In reality, the models often are interdependent, but the correlations usually are not known because the models have been estimated in separate studies. The aim of this paper is to study the impacts of between-model dependencies on the predictive distribution of forest characteristics by Monte Carlo simulation techniques. We utilise a case of predicting seedling establishment of planted Norway spruce (Picea abies (L.) Karst.) stands as an example with multivariate multilevel model structures. Regardless of low cross-correlations between the models, ignoring them led to significant underestimation of the amount of competing broadleaves to be removed in pre-commercial thinning. Therefore, we recommend that between-model dependencies are clarified and considered in stochastic simulations. In our case, between-model interdependencies can be reliably estimated with a limited dataset. In addition, estimating the models separately and using the model residuals to estimate interdependencies between models were also sufficient to take the between-model dependencies into account when producing stochastic predictions for silvicultural decision making.

Most airborne laser scanning (ALS) studies have been carried out in semi-natural forests, but some research has also been carried out in plantations. Results indicate that methods similar to those which are used in semi-natural forest are also usable in plantation forestry. The study was conducted in a pulpwood plantation growing Eucalyptus urograndis in Bahia State, Brazil. The aims of this study are to investigate (1) how accurately the plot volume may be estimated by ALS data in eucalyptus plantations and (2) how to estimate the site index directly by combining ALS data and stand age. The plot volume and site index were estimated by means of nonlinear mixed-effect modeling in order to take into account the stand-within-clone hierarchy of the data. The obtained accuracies are quite good if compared to those obtained in semi-natural forests. The root-mean-square error was 8.2% for plot volume and 2.7% for site index when the clone effect was used in prediction. Precision forestry applied in plantations differs in many ways from the forestry practiced in a semi-natural environment. ALS-based forest inventory methods have a great deal of potential in pulpwood plantations when the unique features of plantation forestry are taken into account.