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In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0â20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

The tree stem volume models of Norway spruce, Scots pine and silver and downy birch currently used in Finland are based on data collected during 1968â1972. These models include four different formulations of a volume model, with three different combinations of independent variables: 1) diameter at height of 1.3 m above ground (), 2) and tree height () and 3) , and upper diameter at height of 6 m (). In recent National Forest Inventories of Finland, a difference in the mean volume prediction between the models with and without the upper diameter as predictor has been observed. To analyze the causes of this difference, terrestrial laser scanning (TLS) was used to acquire a large dataset in Finland during 2017â2018. Field-measured predictors and volumes predicted using spline functions fitted to the TLS data were used to re-calibrate the current volume models. The trunk form is different in these two datasets. The form height is larger in the new data for all diameter classes, which indicates that the tree trunks are more slender than they used to be. One probable reason for this change is the increase in stand densities, which is at least partly due to changed forest management. In models with both and as predictors, the volume is smaller a given class in the data new data than in the old data, and vice versa for the diameter classes. The differences between the old and new models were largest with pine and smallest with birch.dbhdbhhdbhhd6dbhhh

Since the 1990âs, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric -nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 Ã 16 m pixel size and units of 240 Ã 240 m, 1200 Ã 1200 m and 12â000 Ã 12â000 m aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12â000 Ã 12â000 m. The multiple testing corrections decreased the amount of significant -values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.k22222p

There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30â076 images (1972â2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 ±30 days,

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.

Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km. Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.2

This paper is based on a session âHow to make forest science available for all? Publishersâ, editorsâ, and authorsâ challengesâ at the IUFRO XXIV world conference, organized by Pekka Nygren and Eeva Korpilahti from the Finnish Society of Forest Science. The presenters dealt with the topical problems of publishing scientific knowledge from different perspectives. The talks covered the development of journals, publications and submissions, benefits and drawbacks of open access publishing as well as electronic and traditional publishing, and possibilities to promote interesting papers either from the journalâs or from the authorâs perspective, and the problems of disseminating the scientific results to the end users. In this paper, a few prevalent viewpoints, inspired by the session, are raised and discussed with some suggestions included.

Tree species identification using optical remote sensing is challenging. Modern digital photogrammetric cameras enable radiometrically quantitative remote sensing and the estimation of reflectance images, in which the observations depend largely on the reflectance properties of targets. Previous research has shown that there are species-specific differences in how the brightness observed changes when the viewing direction in an aerial image is altered. We investigated if accounting for such directional signatures enhances species classification, using atmospherically corrected, real and simulated multispectral Leica ADS40 line-camera data. Canopy in direct and diffuse illumination were differentiated and species-specific variance-covariance structures were analyzed in real reflectance data, using mixed-effects modeling. Species classification simulations aimed at elucidating the level of accuracy that can be achieved by using images of different quality, number and view-illumination geometry. In real data, a substantial variance component was explained by tree effect, which demonstrates that observations from a tree correlate between observation geometries as well as spectrally. Near-infrared band showed the strongest tree effect, while the directionality was weak in that band. The gain from directional signatures was insignificant in real data, while simulations showed a potential gain of 1â3 percentage points in species classification accuracy. The quality of reflectance calibration was found to be important as well as the image acquisition geometry. We conclude that increasing the number of image observations cancels out random observation noise and reflectance calibration errors, but fails to eliminate the tree effect and systematic calibration inaccuracy. Directional reflectance constitutes a marginal improvement in tree species classification.

Airborne laser scanning based forest inventories have recently shown to produce accurate results. However, the accuracy varies according to the test area and used methodology and therefore, an unambiguous and practical quality assessment will be needed as a part of each inventory project. In this study, the accuracy of an ALS inventory was evaluated with a field sampling based control inventory. The agreement between the ALS inventory and the control inventory was analysed with four methods: 1) root mean square error (RMSE) and bias, 2) scatter plots with 95% confidence intervals, 3) Bland-Altman plots and 4) tolerance limits within Bland-Altman plots. Each method has its own special features which have to be taken into account when the agreement is analysed. The pre-defined requirements of the ALS inventory were achieved. A simplified control inventory approach with a slightly narrower focus is proposed to be used in the future. The Bland-Altman plots with the tolerance limits are proposed to be used in quality assessments of operational ALS inventories. Further studies to improve the efficiency of quality assessment are needed.

In recent years, optimal inventory accuracy has been analyzed with a cost-plus-loss methodology, where the total costs of inventory include both the measurement costs and the losses from the decisions based on the collected information. Losses occur, when the inaccuracies in the data lead to sub-optimal decisions. In almost all cases, it has been assumed that the accuracy of the once collected data remains the same throughout the planning period, and the period has been from 10 up to 100 years. In reality, the quality of the data deteriorates in time, due to errors in the predicted growth. In this study, we carried out a cost-plus-loss analysis accounting for the errors in (stand-level) growth predictions of basal area and dominant height. In addition, we included the inventory errors of these two variables with several different levels of accuracy, and costs of inventory with several different assumptions of cost structure. Using the methodology presented in this study, we could calculate the optimal inventory interval (life-span of data) minimizing the total costs of inventory and losses through the 30-year planning period. When the inventory costs only to a small extent depended on the accuracy, the optimal inventory period was 5 years and optimal accuracy RMSE 0%. When the costs more and more heavily depended on the accuracy, the optimal interval turned out to be either 10 or 15 years, and the optimal accuracy reduced from RMSE 0% to RMSE 20%. By increasing the accuracy of the growth models, it was possible to reduce the inventory accuracy or lengthen the interval, i.e. obtain clear savings in inventory costs.

Metsähallitus in Finland is a state enterprise that manages about 3.5 million hectares of productive commercial state-owned forest land. Metsähallitus has a forest management planning system which uses information stored in a GIS-based forest resource information system. The information on forest resources is currently collected using a standwise inventory system with ocular estimation of stand characteristics. New promising inventory methods based on laser scanning have been introduced. Before taking a new system into use, the information needs of Metsähallitus must be analysed. In this study, information needs in operational harvest planning have been analysed with a qualitative approach. A total of eight team leaders in the forestry business unit were interviewed, six of them representing the process responsible for the operational harvest planning and two representing the process responsible for the harvest and deliveries. Based on the study, two main decision making points with different information needs were confirmed. The first decision making point is related to finding the areas potential for immediate or near future harvesting. Here, geographical information on the need for the treatment as well as rough information on the harvestable volume is needed. In the second decision making point, a final decision of sites to be harvested is made with rather intensive field work. Precise delineations of the treatment are needed as well as good estimates of volumes of different timber assortments. When considering a new inventory system it is justified to consider how much of the information needs in these decision making points can be covered. Two different approaches are proposed for further analysis. The interviews revealed a need for a more structured tactical planning system. Some of the findings of this study â especially the decision making points and information needs in them â may be transferable to other large-scale forest owners.

In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.

Models for large areas (global models) are often biased in smaller sub-areas, even when the model is unbiased for the whole area. Localization of the global model removes the local bias, but the problem is to find homogenous sub-areas in which to localize the function. In this study, we used the eCognition Professional 4.0 (later versions called Definies Pro) segmentation process to segment the study area into homogeneous sub-areas with respect to residuals of the global model of the form height and/or local Getis statistics calculated for the residuals, i.e., G-indices. The segmentation resulted in four different rasters: 1) residuals of the global model, 2) the local G-index, and 3) residuals and the local G-index weighted by the inverse of the variance, and 4) without weighting. The global model was then localized (re-fitted) for these sub-areas. The number of resulting sub-areas varied from 4 to 366. On average, the root mean squared errors (RMSEs) were 3.6% lower after localization than the global model RMSEs in sub-areas before localization. However, the localization actually increased the RMSE in some sub-areas, indicating the sub-area were not appropriate for local fitting. For 56% of the sub-areas, coordinates and distance from coastline were not statistically significant variables, in other words these areas were spatially homogenous. To compare the segmentations, we calculated an aggregate standard error of the RMSEs of the single sub-areas in the segmentation. The segmentations in which the local index was present had slightly lower standard errors than segmentations based on residuals.i*i*i*

In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha when the inventory interval was 5 years and 860 euro ha when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.â1â1

In participatory forest management planning, the perceived values of local inhabitants concerning the area under planning are collected. The results may, however, depend on the methods used. In this study, values of residents of Hyrynsalmi municipality concerning the nearby forests owned by UPM-Kymmene Ltd. were evaluated with a questionnaire consisting of general value questions and mapping of social values of forests. The data was collected from a public meeting and from a mailed survey from randomly sampled people and from members of municipal council. The aims of the study were to 1) test the social value mapping method in commercial forests in a rural-urban interface as well as to examine the benefits and drawbacks 2) in place-specific and non-specific data collection, and 3) in different data collection methods, from the viewpoint of forest management planning. We noted that while all respondents can claim to represent local values, different data collection methods produced statistically significantly different local values. This needs to be accounted for when planning a participatory process. In operational forest planning, place-specific information is more useful than questions concerning the general values, while the latter may help in defining forest policy goals. The social values mapping method is also relatively easy for the participants. However, in the studied case about one fifth of the area was delineated by the participants per each positive value. The answers were quite scattered, suggesting that most of the area had some social values for local people. This indicates that utilising a social values mapping method in planning needs further development in rural areas, where distinctive patches can not be easily detected.

In percentile method, percentiles of the diameter distribution are predicted with a system of models. The continuous empirical diameter distribution function is then obtained by interpolating between the predicted values of percentiles. In Finland, the distribution is typically modelled as a basal-area weighted distribution, which is transformed to a traditional density function for applications. In earlier studies it has been noted that when calculated from the basal-area weighted diameter distribution, the density function is decreasing in most stands, especially for Norway spruce. This behaviour is not supported by the data. In this paper, we investigate the reasons for the unsatisfactory performance and present possible solutions for the problem. Besides the predicted percentiles, the problems are due to implicit assumptions of diameter distribution in the system. The effect of these assumptions can be somewhat lessened with simple ad-hoc methods, like increasing new percentiles to the system. This approach does not, however, utilize all the available information in the estimation, namely the analytical relationships between basal area, stem number and diameter. Accounting for these, gives further possibilities for improving the results. The results show, however, that in order to achieve further improvements, it would be recommendable to make the implicit assumptions more realistic. Furthermore, height variation within stands seems to have an important contribution to the uncertainty of some forest characteristics, especially in the case of sawnwood volume.

This study deals with the prediction of the basal area diameter distribution of a stand without using a complete sample of diameters from the target stand. Traditionally, this problem has been solved by either the parameter recovery method or the parameter prediction method. This study uses the parameter prediction method and the percentile based diameter distribution with a recent development that makes it possible to improve these predictions by using sample order statistics. A sample order statistic is a tree whose diameter and rank at the plot are known, and is referred to in this paper as a quantile tree. This study tested 13 different strategies for selection of the quantile trees from among the trees of horizontal point sample plots, and compared them with respect to RMSE and the bias of four criterion variables in a dataset of 512 stands. The sample minimum was found to be the most promising alternative with respect to RMSE, even though it introduced a rather large amount of bias in the criterion variables. Other good and less biased alternatives are the second and third smallest trees and the tree closest to the plot centre. The use of minimum is recommended for practical inventories because its rank is probably easiest to determine correctly in the field.

The decision support methods most often used in timber-harvesting planning are based on a single criterion. In this study, a voting-theory-based method called multicriteria approval (MA) is introduced to the group decision support of timber-harvesting. The use of voting methods alleviates the problems caused by the multitude of decision objectives involved in forestry decision-making and by the poor quality of information concerning both the preferences of decision-makers and the evaluation of decision alternatives with respect to the objectives often faced in practical timber-harvesting planning. In the case study, the tactical forest management plan of a forest holding jointly owned by three people was specified at the operative timber-harvesting level. The task was to find the best actual operative alternatives for the harvesting that had been proposed in the tactical plan. These timber-harvesting alternatives were combinations of treatment, timber-harvesting system and the timing of logging. Forest owners established multiple criteria under which the alternatives were evaluated. Two versions of MA were tested, one of them based on individual decision analyses and other one based on a composite analysis. The first was markedly modified from the original MA, combining properties of MA and Borda count voting. The other was an original MA with the order of importance for criteria estimated either using Borda count or cumulative voting. The results of the tested MA versions produced were very similar to each other. MA was found to be a useful tool for the group decision support of timber-harvesting.

Several models and/or several variable combinations could be used to predict the diameter distribution of a stand. Typically, a fixed model and a fixed variable combination is used in all conditions. The calibration procedure, however, makes it possible to choose the measurement combination from among many possibilities, although the model used is fixed. In this study, the usefulness of utilizing additional stand characteristics for calibrating the predicted diameter distribution is examined. Nine measurement strategies were tested in predicting the total stand volume, sawlog volume and pulpwood volume. The observed errors of these variables under each strategy were modeled as a function of basal area, basal area median diameter and number of stems. The models were estimated in three steps. First, an Ordinary Least Squares (OLS) model was fitted to the observed errors. Then, a variance function was estimated using the OLS residuals. Finally, a weighted Seemingly Unrelated Regression (SUR) analysis was used to model the observed errors, using the estimated variance functions as weights. The estimated models can be used to anticipate the precision and accuracy of predicted volume characteristics for each stand with different variable combinations and, consequently, to choose the best measurement combination in different stands.

Two outranking methods, ELECTRE III and PROMETHEE II, commonly used as decision-aid in various environmental problems, and their applications to decision support for natural resources management are presented. These methods represent âthe European schoolâ of multi-criteria decision making (MCDM), as opposed to âthe American schoolâ, represented by, for instance, the AHP method. On the basis of a case study, outranking methods are compared to so far more usually applied techniques based on the ideas of multi attribute utility theory (MAUT). The outranking methods have been recommended for situations where there is a finite number of discrete alternatives to be chosen among. The number of decision criteria and decision makers may be large. An important advantage of outranking methods, when compared to decision support techniques most often applied in todayâs natural resources management, is the ability to deal with ordinal and more or less descriptive information on the alternative plans to be evaluated. Furthermore, the uncertainty concerning the values of the criterion variables can be taken into account using fuzzy relations, determined by indifference and preference thresholds. The difficult interpretation of the results, on the other hand, is the main drawback of the outranking methods.

The purpose of this study was to examine the use of non-parametric methods in estimating tree level growth models. In non-parametric methods the growth of a tree is predicted as a weighted average of the values of neighbouring observations. The selection of the nearest neighbours is based on the differences between tree and stand level characteristics of the target tree and the neighbours. The data for the models were collected from the areas owned by Kuusamo Common Forest in Northeast Finland. The whole data consisted of 4051 tally trees and 1308 Scots pines (Pinus sylvestris L.) and 367 Norway spruces (Picea abies Karst.). Models for 5-year diameter growth and bark thickness at the end of the growing period were constructed with two different non-parametric methods: the k-nearest neighbour regression and k-Most Similar Neighbour method. Diameter at breast height, tree height, mean age of the stand and basal area of the trees larger than the subject tree were found to predict the diameter growth most accurately. The non-parametric methods were compared to traditional regression growth models and were found to be quite competitive and reliable growth estimators.

Diameter distribution is used in most forest management planning packages for predicting stand volume, timber volume and stand growth. The prediction of diameter distribution can be based on parametric distribution functions, distribution-free parametric prediction methods or purely non-parametric methods. In the first case, the distribution is obtained by predicting the parameters of some probability density function. In a distribution-free percentile method, the diameters at certain percentiles of the distribution are predicted with models. In non-parametric methods, the predicted distribution is a linear combination of similar measured stands. In this study, the percentile based diameter distribution is compared to the results obtained with the Weibull method in four independent data sets. In the case of Scots pine, the other methods are also compared to k-nearest neighbour method. The comparison was made with respect to the accuracy of predicted stand volume, saw timber volume and number of stems. The predicted percentile and Weibull distributions were calibrated using number of stems measured from the stand. The information of minimum and maximum diameters were also used, for re-scaling the percentile based distribution or for parameter recovery of Weibull parameters. The accuracy of the predicted stand characteristics were also compared for calibrated distributions. The most reliable results were obtained using the percentile method with the model set including number of stems as a predictor. Calibration improved the results in most cases. However, using the minimum and maximum diameters for parameter recovery proved to be inefficient.

Information about diameter distribution is used for predicting stand total volume, timber volume and stand growth for forest management planning. Often, the diameter distribution is obtained by predicting the parameters of some probability density function, using means and sums of tree characters as predictors. However, the results have not always been satisfactory: the predicted distributions practically always have a similar shape. Also, multimodal distributions cannot be obtained. However, diameter distribution can also be predicted using distribution-free methods. In the percentile method, the diameters at certain percentiles of the distribution are predicted with models. The empirical diameter distribution function is then obtained by interpolating between the predicted diameters. In this paper, models for diameters at 12 percentiles of stand basal area are presented for Scots pine, Norway spruce and birch species. Two sets of models are estimated: a set with and one without number of stems as a predictor. Including the number of stems as a predictor improved the volume and saw timber volume estimates for all species, but the improvements were especially high for number of stems estimates obtained from the predicted distribution. The use of number of stems as predictor in models is based on the possibility of including this characteristic to measured stand variables.

The yield of various forest variables is predicted by means of a simulation system to provide information for forest management planning. These predictions contain many kinds of uncertainty, for example, prediction and measurement errors. Inevitably, this has an effect on forest management planning. It is well known that uncertainty in the forest yields causes optimistic bias in the observed values of the objective function. This bias increases with the error variances. The amount of bias, however, also depends on the error structure and the relations between the objective variables. In this paper, the effect of uncertainty in forest yields on optimization is studied by simulation. The effect of two different sources of error, the correlation structure of these errors and relations among the objective variables are considered, as well as the effect of two different optimization approaches. The relations between the objective variables and the error structure had a notable effect on the optimization results.

Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.

In this study, model-based and design-based inference methods are used for estimating mean volume and its standard error for systematic cluster sampling. Results obtained with models are compared to results obtained with classical methods. The data are from the Finnish National Forest Inventory. The variation of volume in ten forestry board districts in Southern Finland is studied. The variation is divided into two components: trend and correlated random errors. The effect of the trend and the covariance structure on the obtained mean volume and standard error estimates is discussed. The larger the coefficient of determination of the trend model, the smaller the model-based estimates of standard error, when compared to classical estimates. On the other hand, the wider the range and level of autocorrelation between the sample plots, the larger the model-based estimates of standard error.

Old inventory data has widely been used as prior information in forest inventory using the method of sampling with partial replacement (SPR). In this method knowledge about forest growth has not been utilized. However, the accuracy of the inventory results can be improved if this knowledge is utilized. The usability of the inventory results can be improved if the prior information is updated by treewise growth models. In this paper a statistical basis is presented for a method in which such information can be used. The applicability of the method is also discussed. An example is given to demonstrate the method. The PDF includes an abstract in Finnish.

A common task in forestry is to determine the value of a forest property, and timber is the most valuable component of that property. Remotely sensed data collected by an unoccupied aerial vehicle (UAV) are suited for this purpose as most forest properties are of a size that permits the efficient collection of UAV data. These UAV data, when linked to a probability sample of field plots, enable the model-assisted (MA) estimation of the timber value and its associated uncertainty. Our objective was to estimate the value of timber (€/ha) in a 40-ha forest property in Finland. We used a systematic sample of field plots (n  = 160) and 3D image point cloud data collected by an UAV. First, we studied the effects of spatial autocorrelation on the variance estimates associated with the timber value estimates produced using a field data-based simple expansion (EXP) estimator. The variance estimators compared were simple random sampling, Matérn, and a variant of the Grafström–Schelin estimator. Second, we compared the efficiencies of the EXP and MA estimators under different sampling intensities. The sampling intensity was varied by subsampling the systematic sample of 160 field plots. In the case of the EXP estimator, the simple random sampling variance estimator produced the largest variance estimates, whereas the Matérn estimator produced smaller variance estimates than the Grafström–Schelin estimator. The MA estimator was more efficient than the EXP estimator, which suggested that the reduction of sampling intensity from 160 to 60 plots is possible without deterioration in precision. The results suggest that the use of UAV data improves the precision of timber value estimates compared to the use of field data only. In practice, the proposed application improves the cost-efficiency of the design-based appraisal of a forest property because expensive field workload can be reduced by means of UAV data. [ABSTRACT FROM AUTHOR]

Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry. Key words: forest inventory, hierarchical Bayes model, Kalman filter, Markov chain Monte Carlo (MCMC), credible interval. L'evaluation des ressources forestieres fondee sur des donnees de nombreuses sources et multitemporelles est devenue plus frequente. Par consequent, l'amelioration des capacites de previsions de la dynamique forestiere au moyen du regroupement et de l'analyse efficaces des donnees sequentielles spatiales et de series chronologiques est maintenant plus cruciale. Le filtrage et le nivellement bayesiens fournissent un formalisme bien defini pour la fusion ou l'assimilation des diverses donnees. Nous avons determine la frequence a laquelle le cadre bayesien generique et normalise est utilise dans la litterature scientifique et nous avons evalue si une telle approche est avantageuse pour les applications forestieres. Une revue de la litterature a montre que l'utilisation des methodes bayesiennes semble etre moins frequente en foresterie que dans d'autres disciplines, particulierement la teledetection. On a constate plus particulierement que les analyses des series chronologiques favorisaient les methodes ad hoc. Notre revue n'a pas revele de preuves numeriques importantes pour une meilleure performance par diverses approches bayesiennes, mais ce resultat peut etre du en partie a la difficulte de comparer un eventail de methodes pour differentes taches de previsions. Nous avons identifie des defis methodologiques relies a l'assimilation des previsions du developpement forestier; plus particulierement, en combinant la croissance modelisee aux perturbations dues aux operations forestieres et aux phenomenes naturels. Neanmoins, les cadres bayesiens offrent des possibilites de combiner efficacement et de faire une mise a jour avant et apres les distributions predictives et de calculer les mesures connexes de l'incertitude qui semblent sous-utilisees en foresterie. [Traduit par la Redaction] Mots-cles : inventaire forestier, modele hierarchique bayesien, filtre de Kalman, Monte Carlo par chaine de Markov (MCCM), intervalle de credibilite., 1. Introduction 1.1. Motivation and objectives Systematic forest inventories have been carried out for over a century (Kangas et al. 2018a) and have utilized remote sensing and other digital map [...]

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. Key words: greenhouse gas inventory, national forest inventory, model-assisted estimator, climate convention, Global Forest Watch. Les mesures politiques et les decisions de gestion visant a mettre en valeur le role des forets dans l'attenuation des changements climatiques necessitent des estimations fiables de la dynamique des stocks de carbone (C) dans les inventaires de gaz a effet de serre (IGES). Le but de cette etude etait de reunir des estimateurs bases sur un plan d'experience afin de fournir des estimations appropriees pour les IGES a l'aide des donnees de l'inventaire forestier national (IFN). Nous ameliorons les estimateurs de base avec facteur d'extension de la perte de stock de C de la biomasse vivante en utilisant uniquement des donnees de terrain et en les appuyant par des donnees auxiliaires de teledetection au moyen d'estimateurs assistes par un modele (AM). Nos etudes de cas couvraient une superficie de plus de 70 Mha en Norvege, en Suede, au Danemark et en Lettonie. Nous avons utilise la perte de couvert forestier (PCF) basee sur des donnees Landsat et une seule couverture complete par balayage laser aeroporte (BLA) comme donnees auxiliaires. Le BLA a fourni des informations sur le stock de C avant une perturbation potentielle indiquee par la PCF. L'utilisation de la PCF dans les estimateurs AM a permis des gains d'efficacite considerables qui, dans la plupart des cas, ont ete encore augmentes en ajoutant le BLA. Un doublement de l'efficacite etait possible pour les estimations nationales et des gains d'efficacite encore plus importants ont ete observes a l'echelle sous-nationale. Les estimations annuelles moyennes etaient considerablement plus precises que les estimations regroupees des donnees de l'IFN de toutes les annees a la fois. La combinaison de donnees de teledetection et de donnees terrain de l'IFN produit des estimateurs fiables, ce qui n'est pas necessairement le cas lors de l'utilisation de donnees de teledetection sans observations de reference. [Traduit par la Redaction] Mots-cles: inventaire des gaz a effet de serre, inventaire forestier national, estimateur assiste par un modele, convention pour le climat, surveillance forestiere avec l'application Global Forest Watch., 1. Introduction The struggle to achieve a 'well-below' 2 [degrees]C mean temperature increase by 2100 as concerted in the Paris agreement (United Nations 2015) requires policy measures to fulfill emission [...]

Forestry can help to mitigate climate change by storing carbon in trees, forest soils, and wood products. Forest owners can be subsidized if forestry removes carbon from the atmosphere and taxed if forestry produces emissions. Errors in forest inventory data can lead to losses in net present value (NPV) if management prescriptions are selected based on erroneous data. This study assesses the effect of inventory errors on economic losses in forest management when the objective is to maximize the total NPV of timber production and carbon payments. Errors similar to those in airborne laser scanning based forest inventory were simulated in stand attributes with a vine copula approach and nearest neighbor method. Carbon payments were based on the total carbon balance of forestry (including trees, soil, and wood-based products) and calculations were carried out for 30 years using carbon prices of 0, 50, 75, 100, 125, and 150 [euro] * [t.sup.-1]. The results revealed that increasing the carbon price and decreasing the level of errors led to decreased losses in NPV. The inclusion of carbon payments for the maximization of the NPV decreased the effect of errors on the losses, which suggests that the value of collecting more accurate forest inventory data may decrease when the carbon price increases. Key words: carbon credit, carbon sequestration, net present value, random error, value of information. La foresterie peut aider a attenuer le changement climatique en stockant du carbone dans les arbres, les sols forestiers et les produits du bois. Les proprietaires prives peuvent recevoir des subventions si leurs pratiques permettent de reduire les emissions de carbone dans l'atmosphere ou, au contraire, etre imposes si leurs pratiques sont une source de carbone atmospherique. Les erreurs dans les donnees d'inventaire forestier peuvent mener a des pertes de valeur actuelle nette (VAN) si les prescriptions d'amenagement sont selectionnees sur la base de donnees erronees. Cette etude evalue les effets des erreurs d'inventaire sur les pertes economiques en amenagement forestier lorsque l'objectif vise a maximiser la VAN totale de la production de bois et des paiements de carbone. Des erreurs similaires a celles obtenues lors de l'inventaire forestier fait par balayage laser aeroporte ont ete simulees dans les attributs de peuplement en utilisant une approche de copules en vignes et la methode du plus proche voisin. Les paiements de carbone etaient bases sur le bilan de carbone total de la foresterie, incluant les arbres, le sol et les produits du bois. Les calculs ont ete effectues sur une periode de 30 ans en utilisant des valeurs de 0, 50, 75,100,125 et 150 euros par tonne de carbone. Les resultats revelent que l'augmentation du prix du carbone et la diminution du niveau d'erreur entrainent une diminution des pertes de la VAN. La prise en compte des paiements de carbone pour la maximisation de la VAN diminue l'effet des erreurs sur les pertes economiques, ce qui laisse entendre que la valeur de donnees d'inventaire forestier plus precises peut diminuer lorsque le prix du carbone augmente. [Traduit par la Redaction] Mots-cles: credit carbone, sequestration de carbone, valeur actuelle nette, erreur aleatoire, valeur de l'information., Introduction Decisions are commonly made under uncertainty, but the decision-maker can reduce the uncertainty by collecting more relevant and accurate information. Value of information (VOI) in decision-making can be defined [...]

There is considerable interest in identifying and locating natural forests as accurately as possible, because they are deemed essential in preventing biodiversity loss. In the boreal region, natural forests contain a substantial amount of dead wood and exhibit considerable variation in tree age, size, and species composition. However, it is difficult to define natural forests in a quantitative manner. This is an issue, for example, in the Finnish national forest inventory. If naturalness could be related to the metrics derived from tree measurements, it would be easier to locate natural forests based on the inventory data. In this study, we investigated the value of metrics computed from tree locations and tree sizes for the characterization of a key aspect of naturalness, namely, structural naturalness as defined in the Finnish national forest inventory. We used L-moments, Gini coefficient, Lorenz asymmetry, and interquartile range to quantify the variations in tree size at the plot level. We summarized the spatial pattern of trees with a spatial aggregation index. We compared the structural metrics, species proportions, and stand age using the classes of structural naturalness described in the Finnish national forest inventory, which have been determined in the field without strict numerical rules. These categories are 'natural', 'near-natural', and 'non-natural'. We found that the forests evaluated as structurally natural had larger variations in tree size and species composition and showed a more clustered spatial pattern of trees on average, although the variation in the structural metrics was considerable in all three classes. In addition, we used the structural metrics to predict naturalness by employing a random forest algorithm. Based on the structural metrics, it was possible to obtain high precision in the classification only if we simultaneously accepted low recall, and vice versa; the link between the inspected metrics and naturalness evaluated in the field was weak. The stand age separated the three classes more clearly and it also improved the classification. [ABSTRACT FROM AUTHOR]

In forest management planning, errors in predicted stand attributes might lead to suboptimal decisions that result in decreased net present value (NPV). Forest inventory data will have higher value if the amount of suboptimal decisions can be decreased. Therefore, the value of information can be measured through the decrease in inoptimality losses, which are the NPV differences between the optimal and suboptimal decisions. In this study, four alternative sample plot selection strategies with different numbers of sample plots were compared in terms of expected mean inoptimality losses. Stand-level mean inoptimality losses varied between 41.1 x [ha.sup.-1] [euro] and 80.7 x [ha.sup.-1] [euro], depending on the sample plot selection strategy and the number of sample plots used as training data in the k-nearest neighbors imputation method. Mean inoptimality losses decreased substantially when the number of sample plots increased from 25 to 100, and the decreasing trend continued until 500 sample plots. Total inoptimality losses can decrease by approximately 1 million [euro] in an inventory area of 100 000 ha when the number of sample plots is increased from 100 to 500. The measurement of more sample plots can be justified as long as the field measurement costs do not exceed the decrease in inoptimality losses. Key words: forest inventory, forest management planning, inoptimality loss, value of information, cost-plus-loss analysis. Lors de la planification de l'amenagement forestier, des erreurs dans les predictions des attributs de peuplement pourraient conduire a des decisions sous-optimales, entrainant une diminution de la valeur actuelle nette (VAN). Les donnees d'inventaire forestier auront une plus grande valeur si elles servent a diminuer le nombre de decisions sous-optimales. Par consequent, la valeur de l'information peut etre mesuree par la diminution des pertes dues a la sous-optimalite, qui correspondent a la difference de VAN entre les decisions optimales et sous-optimales. Dans cette etude, quatre strategies differentes de selection des placettes d'echantillonnage, avec differents nombres de placettes d'echantillonnage, ont ete comparees en termes de moyennes attendues des pertes dues a la sous-optimalite. Les pertes moyennes dues a la sous-optimalite a l'echelle du peuplement ont varie entre 41,1 [euro] et 80,7 [euro] par hectare, en fonction de la strategie de selection des placettes d'echantillonnage et du nombre de celles-ci utilisees comme donnees d'apprentissage dans la methode d'imputation des k-plus proches voisins. Les pertes moyennes dues a la sous-optimalite ont sensiblement diminue lorsque le nombre de placettes d'echantillonnage est passe de 25 a 100, et la tendance a la baisse s'est poursuivie jusqu'a 500 placettes d'echantillonnage. Les pertes totales dues a la sous-optimalite peuvent diminuer d'environ 1 million [euro] dans une zone d'inventaire de 100 000 ha lorsque le nombre de placettes d'echantillonnage augmente de 100 a 500. La mesure d'un plus grand nombre de placettes d'echantillonnage peut etre justifiee a condition que les couts de mesure sur le terrain ne depassent pas la diminution des pertes par sous-optimalite. [Traduit par la Redaction] Mots-cles: inventaire forestier, planification de l'amenagement forestier, perte due a la sous-optimalite, valeur de l'information, analyse combinee de cout plus perte., 1. Introduction Forest inventories based on airborne laser scanning (ALS) usually consist of data acquisition, model-building, and accuracy assessment with training data, the prediction of stand attributes in a wall-to-wall [...]