Search

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m à 30 m) were 9.0%, and 33.4â62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] â13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD â2.0% to 2.8%, with the exception of the Tornio region â RMSE 11.4%, MD â9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration.

Very high point density laser scanning data from unmanned aerial systems (UAS) can be used to segment the trunks of individual trees. Such segmentation (e.g., individual trunk segmentation (ITS)) is useful, for example, in the estimation of diameter at breast height (DBH), which in turn is needed for the estimation of other tree and stand attributes, such as stem volume and diameter of the basal area median tree (DGM). In this paper, we assess the estimation of DBH directly from UAS LiDAR data in open and closed canopy conditions that represent the range of operational conditions encountered in boreal forests in Finland. We also compare trunk-based DBH estimates to corresponding estimates from individual tree crown segmentation (ITC) and fuse the results from the trunk- and crown-based estimates. The results showed that trunk segmentation performed slightly better than ITC in open canopy areas, whereas ITC performed better in closed canopy areas. The DBH prediction error was smaller for ITS (3.0 cm) than ITC (3.9 cm) when considering the trees that were recognized by both methods. We also conclude that a hybrid method, where both segmented tree trunks and tree crowns are fused, considerably increases the number of correctly segmented trees but does not decrease the prediction error associated with DGM compared to using either ITC or ITS individually. Key words: forest inventory, laser scanning, drone, segmentation. Les donnees obtenues par balayage par faisceau laser avec densite de points tres elevee par les systemes d'aeronefs sans pilote (UAS) peuvent etre utilisees pour segmenter les troncs des arbres individuels. Une telle segmentation (p. ex., segmentation d'un tronc individuel (STI)) est utile, par exemple, dans l'estimation du diametre a hauteur d'homme (DHH), laquelle est par ailleurs requise pour l'estimation des attributs des autres arbres et du peuplement forestier, par exemple le volume du tronc et la surface terriere moyenne des arbres (DGM). Dans la presente communication, nous evaluons l'estimation du DHH a partir directement des donnees de l'UAS LiDAR dans des conditions de couvert ouvert et ferme qui representent l'eventail des conditions operationnelles rencontrees dans les forets boreales en Finlande. Nous comparons egalement les estimes du DHH fonde sur les troncs aux estimes correspondants de la segmentation de la couronne des arbres individuels (CAI) et nous fusionnons les resultats des estimes fondes sur les troncs et les couronnes. Les resultats ont indique que la segmentation des troncs fonctionnait un peu mieux que la CAI dans les secteurs de couverts ouverts alors que la CAI fonctionnait mieux dans des secteurs de couverts fermes. L'erreur de prevision du DHH etait plus petite pour la STI (3,0 cm) que la CAI (3,9 cm) lorsqu'on tenait compte des arbres qui etaient reconnus par les deux methodes. Nous concluons egalement qu'une methode hybride oU les troncs et les couronnes des arbres segmentes sont fusionnes accroit considerablement le nombre d'arbres correctement segmentes, mais ne diminue par l'erreur de prevision associee a la DGM comparativement a l'utilisation de la CAI ou de la STI individuellement. [Traduit par la Redaction] Mots-cles : inventaire forestier, balayage par faisceau laser, drone, segmentation., 1. Introduction The rapid development of unmanned aerial systems (UAS) and the miniaturization of optical sensors and light detection and ranging (LiDAR) instruments has been followed by an extensive research [...]

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]

Most forest growth studies using airborne laser scanning (ALS) consider how the changes in forest attributes are observed in repeated ALS data acquisitions, but the prediction of future forest growth from ALS data is still a rarely discussed topic. This study examined the prediction of the periodic annual increment (PAI) of the width of tree rings over a period of 10 years. The requirement for this approach is that ALS data are acquired at the beginning of the growth period. This is followed by field measurements of growth by drilling after a given growth period. The PAI was modelled in terms of ALS metrics by using the principle of the area-based approach. The metrics related to intensity were particularly significant as predictors, whereas the effective leaf area index was not. The root-mean-square error (RMSE) of the predictions was slightly over 21%. Additional field information (soil type, management operations) improved the RMSE by 2.7 percentage units. Key words: area-based approach, diameter increment, forest growth, leaf area index, LiDAR. La plupart des etudes sur l'accroissement forestier utilisant le balayage par laser aeroporte (BLA) tiennent compte de la facon dont les changements des attributs forestiers sont observes dans les acquisitions de donnees repetees du BLA, mais la prevision d'un accroissement forestier futur a partir des donnees du BLA est un theme qui fait encore rarement l'objet de discussions. La presente etude a examine la prevision de l'augmentation annuelle periodique (AAP) de la largeur des anneaux de croissance des arbres sur une periode de 10 ans. La necessite d'adopter cette approche est que les donnees du BLA sont acquises au debut de la periode de croissance. Ceci est suivi de mesures de la croissance sur le terrain en faisant un forage apres une periode donnee de croissance. L'AAP a ete modelee en fonction des mesures du BLA en utilisant le principe de l'approche locale. Les mesures reliees a l'intensite etaient particulierement importantes comme variable explicative alors que l'indice foliaire reel ne l'etait pas. L'EMQ des previsions etait legerement au-dessus de 21%. Des renseignements additionnels sur le terrain (type de sol, operations de gestion) ont ameliore l'EMQ de 2,7 unites de pourcentage. [Traduit par la Redaction] Mots-cles : approche locale, augmentation du diametre, accroissement forestier, indice foliaire, LiDAR., Introduction Data acquired through airborne laser scanning (ALS) are widely used in forestry, with the forest inventories being one of the main applications of this ALS data. The main interest [...]

Horvitz-Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than those of previous papers. We also present and discuss the methods for estimating the tuning parameter of the estimator using a functional k-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a 13% lower RMSE (root-mean-square error) than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data on the results. Key words: segmentation, stochastic geometry, detectability, nearest neighbor, forest inventory. L'estimation de type Horvitz-Thompson de la densite de peuplement est une methode d'estimation de la densite de peuplement a partir d'objets qui sont des couronnes d'arbres obtenues par detection d'arbres individuels a partir de donnees de balayage laser aeroporte. L'estimateur est base sur la geometrie stochastique et la morphologie mathematique de l'ensemble (planaire) forme par les couronnes d'arbres detectees. Cet ensemble est utilise pour approximer les probabilites de detection des arbres. Ces probabilites sont ensuite utilisees pour calculer l'estimation. La methode comprend un parametre d'ajustement qui doit etre connu pour pouvoir l'appliquer. Nous presentons un raffinement de la methode afin de permettre des conditions plus generales de detection que celles des articles precedents. Nous presentons et discutons des methodes d'estimation du parametre d'ajustement de l'estimateur en utilisant la methode fonctionnelle des k plus proches voisins. Nous testons l'ajustement et la prediction du modele pour deux ensembles de donnees spatialement distincts et nous examinons la precision de l'estimation a l'echelle de la parcelle. L'estimateur a produit une REQM (racine de l'erreur quadratique moyenne) 13% inferieure a celle de la methode de reference pour un ensemble de donnees de validation externe. Nous analysons egalement les effets de la similitude et de la dissemblance des donnees d'entrainement et de validation sur les resultats. [Traduit par la Redaction] Mots-cles : segmentation, geometrie stochastique, detectabilite, plus proche voisin, inventaire forestier., 1. Introduction Volume and biomass related attributes are usually of primary interest in remote-sensing-based forest inventories. If these quantities are estimated at tree level, scaling them to area level requires [...]

Semi- and nonparametric models are popular in the area-based approach (ABA) using airborne laser scanning. It is unclear, however, how many predictors and training plots are needed to provide accurate predictions without overfitting. This work aims to explore these limits for various approaches: ordinary least squares regression (OLS), generalized additive models (GAM), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and Gaussian process regression (GPR). We modeled timber volume ([m.sup.3]*[ha.sup.-1]) for four boreal sites using ABA with 2-39 predictors and 20-500 training plots. OLS, GAM, LASSO, and SVM overfitted as the number of predictors approached the number of training plots. They required [greater than or equal to] 15 plots per predictor to provide accurate predictions (RMSE [less than or equal to] 30%). GAM required [greater than or equal to] 250 plots regardless of the number of predictors. The number of predictors only mildly affected RF and GPR, but they required [greater than or equal to] 200 and [greater than or equal to] 250 training plots, respectively. RF did not overfit in any circumstances, whereas GPR overfit even with 500 training plots. Overall, using up to 39 predictors did not generally result in overfit, and for most model types, it resulted in better accuracy for sufficiently large datasets ([greater than or equal to] 250 plots). Key words: LiDAR, machine learning, remote sensing, area-based approach, sampling size. Les modeles semi et non parametriques sont populaires avec l'approche territoriale (AT) qui utilise le balayage laser aeroporte. On ignore cependant combien de predicteurs et de parcelles d'entrainement sont necessaires pour generer des predictions justes sans surapprentissage. Cet article vise a explorer ces limites pour differentes approches : regression des moindres carres ordinaire (MCO), modeles additifs generaux (MAG), operateur de selection et reduction par moindres valeurs absolues (LASSO); foret aleatoire (FA), machine a vecteurs de support (MVS), et regression de processus gaussien (RPG). Nous avonsmodelise le volume de bois ([m.sup.3]*[ha.sup.-1]) dans quatre stations boreales a l'aide de l'AT avec 2 a 39 predicteurs et 20 a 500 parcelles d'entrainement. Les approches MCO, MAG, LASSO et MVS surajustaient lorsque le nombre de predicteurs approchait le nombre de parcelles d'entrainement. Elles exigeaient au moins 15 parcelles par predicteur pour generer des predictions justes (EMQ [less than or equal to] 30%). L'approche MAG necessitait au moins 250 parcelles peu importe le nombre de predicteurs. Le nombre de predicteurs influencait seulement legerement les approches FA et RPG mais elles exigeaient respectivement au moins 200 et 250 parcelles. L'approche FA ne surajustait pas quelles que soient les circonstances, tandis que l'approche RPG surajustait meme avec 500 parcelles d'entrainement. Dans l'ensemble, l'utilisation de 39 predicteurs n'engendrait generalement pas de surapprentissage et, avec la plupart des types de modeles, cela se traduisait par une precision accrue pour des jeux de donnees suffisamment importants (au moins 250 parcelles). [Traduit par la Redaction] Mots-cles : LiDAR, apprentissage machine, teledetection, approche territoriale, taille de l'echantillon., 1. Introduction The development of prediction models is an essential step when performing forest assessments through airborne laser scanning (ALS). In the area-based approach (ABA) to forest inventory with ALS [...]

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 [...]

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

An area-based approach (ABA) is the most common method used to predict forest attributes with airborne laser scanning (ALS) data. Individual-tree detection (ITD) offers an alternative to ABA; however, few studies have examined the selection of these two alternatives for the prediction of diameter distributions. We predicted diameter distributions by applying ABA and ITD in coniferous-dominated boreal forests using ALS data and examined their predictive performance based on the shapes of the diameter distributions (Gaussian, bimodal, and reverse-J). We proposed an ABA-ITD fusion for diameter distribution prediction. Firstly, the fusion was optimized and its potential was evaluated using an error index. Secondly, we offer two alternatives to incorporate the fusion into ALS-based forest inventories. Our results indicate that ITD is more prone to errors than ABA and that the predictive performance of ITD is more sensitive than ABA to the shape of the diameter distribution. The results show that ITD outperforms ABA with Gaussian diameter distributions. In contrast, ABA was seen as preferable to ITD with bimodal- or reverse-J-shaped diameter distributions. The findings indicate that ABA-ITD fusion has potential for predicting diameter distributions, although the predictive capability of ITD is limited compared with that of ABA. Key words: area-based approach, individual-tree detection, multispectral ALS, nearest neighbor imputation, tree size distribution. L'approche par zones (APZ) est la methode la plus courante pour predire les attributs de la foret a l'aide de donnees de balayage laser aeroporte (BLA). La detection individuelle d'arbre (DIA) offre une alternative a l'APZ. Cependant, peu d'etudes ont compare ces deux approches pour la prediction de la distribution des diametres. Nous avons predit la distribution des diametres en appliquant les techniques d'APZ et de DIA dans des forets boreales dominees par les coniferes a l'aide de donnees de BLA, et avons examine leur performance predictive en fonction de la forme des distributions de diametre (gaussienne, bimodale et en J inverse). Nous avons propose une fusion des techniques d'APZ et de DIA pour la prediction de la distribution des diametres. Premierement, la fusion a ete optimisee et son potentiel a ete evalue a l'aide d'un indice d'erreur. Deuxiemement, nous proposons deux methodes pour incorporer la fusion dans les inventaires forestiers bases sur le BLA. Nos resultats indiquent que la DIA est plus sujette aux erreurs que l'APZ et que ses performances predictives sont plus sensibles que celles de l'APZ a la forme de la distribution de diametre. Les resultats montrent que la DIA surpasse l'APZ avec les distributions de diametre gaussiennes. En revanche, l'APZ a ete consideree comme preferable a la DIA avec des distributions de diametre de forme bimodale ou en J inverse. Les resultats indiquent que, bien que la capacite predictive de la DIA soit limitee par rapport a celle de l'APZ, la fusion APZ-DIA a un potentiel de prediction des distributions de diametre. [Traduit par la Redaction] Mots-cles : approche par zones, detection individuelle d'arbre, balayage laser aeroporte multispectral, imputation par la methode du plus proche voisin, distribution de diametre., 1. Introduction Forest inventory practices that rely on airborne laser scanning (ALS) data are divided into two main categories: area-based approaches (ABA; Noesset 1997) and individual-tree detection (ITD; Hyyppa and [...]

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 [...]

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh [greater than or equal to] 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree- level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar. Key words: airborne laser scanning, individual-tree detection, k-nearest neighbor, k-NN, transferability. Dans cet article, nous examinons la transferabilite des modeles fondes sur le balayage laser aeroporte (BLA) pour la detection d'arbres individuels (DAI) d'une zone inventoriee par BLA (Z1) a deux autres zones (Z2 et Z3). Toutes les zones etaient situees dans l'est de la Finlande, a moins de 100 km les unes des autres, et ont ete numerisees a l'aide de differents dispositifs et parametres de BLA. Les attributs pertinents des arbres etaient le diametre a hauteur de poitrine (DHP), la hauteur (H), la hauteur de la base de la cime (HBC), le volume de la tige (V) et le volume theorique des billes de sciage (VSci) dans le tronc de pins sylvestres (Pinus sylvestris L.) avec un DHP [greater than or equal to] 16 cm. Tous les arbres ont d'abord ete delimites a partir des modeles de hauteur du couvert forestier et diverses metriques du BLA ont ete derivees pour chaque cime identifiee. Puis, seules les cimes correctement detectees comme des pins ont ete choisies pour la suite des evaluations. Les attributs de l'arbre ont ete predits a l'aide de l'imputation des k-plus proches voisins (k-PPV). Les resultats ont montre que les erreurs quadratiques moyennes augmentaient pour chaque attribut apres les transferts avec respectivement pour Z1, Z2 et Z3 : DHP, 13,5 /14,8 /18,1 %; H, 3,2 / 5,9 / 6,2 %; HBC, 13,3 /15,3 / 18,3 %; V, 29,3 / 35,4 / 39,1 %; et VSci, 38,2 / 54,4 / 51,8 %. Les valeurs observees indiquent qu'il est possible d'utiliser des modeles k-PPV, fondes sur un BLA a l'echelle de l'arbre, sur differentes zones d'inventaire sans reduction excessive de la precision, a condition que l'espece d'arbre soit reconnue comme similaire. [Traduit par la Redaction] Mots-cles : balayage laser aeroporte, detection d'arbres individuels, k-plus proches voisins, k-PPV, transferabilite., Introduction Airborne laser scanning (ALS) has proven to be very useful in forest inventories (e.g., Vauhkonen et al. 2014a). The two main approaches for deriving forest information from ALS data [...]