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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

This study examines the alternatives to include crown base height (CBH) predictions in operational forest inventories based on airborne laser scanning (ALS) data. We studied 265 field sample plots in a strongly pine-dominated area in northeastern Finland. The CBH prediction alternatives used area-based metrics of sparse ALS data to produce this attribute by means of: 1) Tree-level imputation based on the -nearest neighbor (-nn) method and full field-measured tree lists including CBH observations as reference data; 2) Tree-level mixed-effects model (LME) prediction based on tree diameter (DBH) and height and ALS metrics as predictors of the models; 3) Plot-level prediction based on analyzing the computational geometry and topology of the ALS point clouds; and 4) Plot-level regression analysis using average CBH observations of the plots for model fitting. The results showed that all of the methods predicted CBH with an accuracy of 1â1.5 m. The plot-level regression model was the most accurate alternative, although alternatives producing tree-level information may be more interesting for inventories aiming at forest management planning. For this purpose, -nn approach is promising and it only requires that field measurements of CBH is added to the tree lists used as reference data. Alternatively, the LME-approach produced good results especially in the case of dominant trees.kkk

This study examined the suitability of airborne laser scanner (ALS) data collected under leaf-off conditions in a forest inventory, in which deciduous and coniferous trees need to be separated. All analyses were carried out with leaf-on and leaf-off ALS data collected from the same study area. Additionally, aerial photographs were utilized in the Nearest Neighbor (NN) imputations. An area-based approach was used in this study. Regression estimates of plot volume were more accurate in the case of leaf-off than leaf-on data. In addition, regression models were more accurate in coniferous plots than in deciduous plots. The results of applying leaf-on models with leaf-off data, and vice versa, indicate that leaf-on and leaf-off data should not be combined since this causes serious bias. The total volume and volume by coniferous and deciduous trees was estimated by the NN imputation. In terms of total volume, leaf-off data provided more accurate estimates than leaf-on data. In addition, leaf-off data discriminated between coniferous and deciduous trees, even without the use of aerial photographs. Accurate results were also obtained when leaf-off ALS data were used to classify sample plots into deciduous and coniferous dominated plots. The results indicate that the area-based method and ALS data collected under leaf-off conditions are suitable for forest inventory in which deciduous and coniferous trees need to be distinguished.

Tree species identification constitutes a bottleneck in remote sensing-based forest inventory. In passive images the differentiating features overlap and bidirectional reflectance hampers analysis. Airborne LiDAR provides radiometric and geometric information. We examined the single-trees-level response of two LiDAR sensors in over 13 000 forest trees in southern Finland. We focused on the commercially important species. Our aims were to 1) explore the relevant LiDAR features and study their dependencies on stand and tree variables, 2) examine two sensors and their fusion, 3) quantify the gain from intensity normalizations, 4) examine the importance of the size of the training set, and 5) determine the effects of stand age and site fertility. A set of 570 semiurban broad-leaved trees and exotic conifers was analyzed to 6) examine the LiDAR signal in the economically less important species. An accuracy of 88 90% was achieved in the classification of Scots pine, Norway spruce, and birch, using intensity variables. Spruce and birch showed the highest levels of confusion. Downsizing the training set from 30% to 2.5% of all trees had only a marginal effect on the performance of classifiers. The intensity features were dependent on the absolute and relative sizes of trees, especially for birch. The results suggest that leaf size, orientation, and foliage density affect the intensity, which is thus not affected by reflectance only. Some of the ecologically important species in Finland may be separable, since they gave rise to high intensity values. Comparison of the sensors implies that performance of the intensity data for species classification varies between sensors for reasons that remained uncertain. Both range and gain receiver normalization improved species classification. Weighting of the intensity values improved the fusion of two LiDAR datasets.

Airborne laser scanning (ALS) data have become the most accurate remote sensing technology for forest inventories. When planning new inventories the costs of fieldwork could be reduced if datasets of old inventory areas are effectively reused in the new area. The aim of this study was to apply mixed estimation using a combination of existing and new field datasets in area-based approach. Additionally, combining datasets with mixed estimation was compared with constructing new local models with smaller datasets. The two forest study areas were in Juuka and Matalansalo, which are located about 120 km apart in eastern Finland. ALS-based regression models were constructed using datasets of Matalansalo (472 reference plots) and Juuka (10â212 reference plots). Models were developed for the basal area median tree diameter and height, mean tree height, stem number, basal area and volume. The work was based on a simulation approach which involved five methods for approximating the regression coefficients. The first method merged the datasets using ordinary least squares (OLS) regression models, whereas the second and third methods combined datasets using mixed estimation on different weighting principles, and the final two estimated local models with predetermined and new independent variables. The results indicate that mixed estimation can improve the accuracy of derived stand variables compared with basic OLS models. Additionally, a sample of 40â50 plots was enough to build local models for basal area and volume and produce at least the equal accuracy of results than any other methods in this study.

The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree and area-based plot level approaches. The main goal of such applications has usually been to estimate accurate information on timber quantities. Prediction of timber quality has not been focused to the same extent. Thus, in this study we consider here the prediction of both basic tree attributes (tree diameter, height and volume) and characteristics describing tree quality more closely (crown height, height of the lowest dead branch and sawlog proportion of tree volume) by means of high resolution ALS data. The tree species considered is Scots pine (Pinus sylvestris), and the field data originate from 14 sample plots located in the Koli National Park in North Karelia, eastern Finland. The material comprises 133 trees, and size and quality variables of these trees were modeled using a large number of potential independent variables calculated from the ALS data. These variables included both individual tree recognition and area-based characteristics. Models for the dependent tree characteristics to be considered were then constructed using either the non-parametric k-MSN method or a parametric set of models constructed simultaneously by the Seemingly Unrelated Regression (SUR) approach. The results indicate that the k-MSN method can provide more accurate tree-level estimates than SUR models. The k-MSN estimates were in fact highly accurate in general, the RMSE being less than 10% except in the case of tree volume and height of the lowest dead branch.

The low-density airborne laser scanning (ALS) data based estimation methods have been shown to produce accurate estimates of mean forest characteristics and diameter distributions, according to several studies. The used estimation methods have been based on the laser canopy height distribution approach, where various laser pulse height distribution -derived predictors are related to the stand characteristics of interest. This approach requires very delicate selection methods for selecting the suitable predictor variables. In this study, we introduce a new nearest neighbor search method that requires no complicated selection algorithm for choosing the predictor variables and can be utilized in multipurpose situations. The proposed search method is based on Minkowski distances between the distributions extracted from low density ALS data and aerial photographs. Apart from the introduction of a new search method, the aims of this study were: 1) to produce accurate species-specific diameter distributions and 2) to estimate factual saw log recovery, using the estimated height-diameter distributions and a stem data bank. The results indicate that the proposed method is suitable for producing species-specific diameter distributions and volumes at the stand level. However, it is proposed, that the utilization of more extensive and locally emphasized reference data and auxiliary variables could yield more accurate saw log recoveries.

The harvesting of energy wood from young stands is increasing as the demand for renewable wood fuel is growing. Energy wood consists of stems, tree tops, branches and needles, depending on the size of the trees and the logging method used. The current forest inventory and planning systems used in private forests in Finland do not produce estimates of energy wood components. In stands typical for energy wood harvesting, a large share of energy wood consists of trees smaller than the minimum size for pulpwood. In this study, energy wood was included into the calculation system of compartment inventory, and a procedure for simulating the thinning treatments in young stands was developed. The results for six inventory alternatives and prediction of energy wood were compared with the use of inventory material from 37 young stands that have plenty of energy wood. The measurement of additional stand characteristics and the use of a calibration estimation method was tested, as well as the use of plot-level inventory data instead of stand level data. The results showed that the measurement of the number of trees per hectare, in addition to stand basal area and mean diameter, improved the energy wood estimates. The additional minimum and maximum diameters improved the precision of the estimates, but did not affect bias. The removal estimates were more precise when plot-level data was used, rather than stand-level data. The removal estimates were higher with plot-level data. The results suggest that, in heterogeneous young stands, plot by plot prediction would give more accurate removal estimates than the calculation of a corresponding prediction at the stand-level.

Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.

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.

Abundance and species number of the tree and shrub vegetation in different canopy layers were analysed according to site quality class and drainage succession phase on permanent sample plots on spruce mires (n = 268) and pine mires (n = 628) in the Finnish National Forest Inventory in 1995. The abundances based on the crown coverage were compared with the abundances based on the parallel basal area of the tree stand. The canopy coverages and species number for peatland forests were also compared with those for mineral soil forests on the permanent sample plots (n = 1725) in 1995. In general, effective temperature sum correlated positively, although not very strongly, with the coverages and species number in most of the canopy layers, as well as with the mean range of the diameter distribution. The effects of both site quality class and drainage phase were stronger on pine mires than on spruce mires, most probably due to the longer fertility gradient and large potential free growing space in the former group. On pine mires, drainage increased the abundances and species number in the different canopy layers, as well as the structural inequality of the tree stands. On spruce mires, the increase was principally allocated to the abundances of the dominant and intermediate tree layers. The correlations between the total crown coverage of the tree layers and stand basal area were r = 0.45 for spruce mires and r = 0.70 for pine mires. Compared to mineral soil forests, in addition to having a higher abundance of Betula pubescens, the dominant layer was not as pronounced in peatland forests. On spruce mires, the coverage of the shrub layer on mesotrophic and meso-oligotrophic sites was higher than that in mineral soil forests. The average species number in different canopy layers did not differ significantly between spruce mires and mineral soil forests in corresponding site quality classes. On pine mires, the species number was generally lower (except for the mesotrophic sites) than that in corresponding mineral soil forests.

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.

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.

The aim of the study was to develop a yield prediction model using the non-parametric Most Similar Neighbour (MSN) reference method. The model is constructed on stand level but it contains information also on tree level. A 10-year projection period was used for the analysis of stand growth. First, the canonical correlation matrix was calculated for the whole study material using stand volumes at the beginning and at the end of the growth period as independent variables and stand characteristics as dependent variable. Secondly, similar neighbour estimates were searched from the data categories reclassified according to thinnings. Due to this, it was possible to search for growth and yield series which is as accurate as possible both at the beginning and at the end of the growth period. The reliability of the MSN volume predictions was compared to the volumes predicted with the simultaneous yield model. The MSN approach was observed to be more reliable volume predictor than the traditional stand level yield prediction model both in thinned and unthinned stands.

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.

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.

The purpose of this study was to compare the Weibull distributions estimated for the entire growing stock of a stand and separately for Scots pine ( L.) and Norway spruce ( (L.) H. Karst.) in describing the basal area diameter distributions in mixed stands. The material for this study was obtained by measuring 553 stands located in eastern Finland. The parameters of the Weibull distribution were estimated using the method of maximum likelihood. The models for these parameters were derived using regression analysis. Also, some parameter models from previous studies were compared with the measured distribution. The obtained distributions were compared using the diameter sums of the entire growing stock, diameter sums by tree species and of the sawtimber part of the growing stock. The results showed that far more accurate results were obtained when the distributions were formed using parameter models separately for the different tree species than when using parameter models for the entire growing stock. This was already true when considering the entire growing stock of the stand and especially when the results were examined by tree species. When the models for the entire growing stock were applied by tree species in relation to basal areas, the results obtained were overestimates for Norway spruce and underestimates for Scots pine. The models from earlier studies, where parameter models were estimated separately for tree species from the National Forest Inventory data, showed good fits also in regard to the data of this study.Pinus sylvestrisPicea abies

Comparisons were made between artificially and naturally regenerated stands in the south-eastern part of North Karelia, Finland, and naturally regenerated stands in the western parts of the Republic of Karelia, Russian Federation. The effect of soil fertility and silvicultural operations on the stand structure was also investigated. The results of the study show clearly that when forests are artificially regenerated the stand structure includes less variation when compared with the stands naturally regenerated. Differences between the regeneration methods are clearer the more fertile the forest site is. Within the regeneration method there is also a clear trend in stand structure, with the variation decreasing the poorer the site. The effect of silvicultural operations, i.e. the cleaning of the sapling stand, has disappeared by the time of first thinning, although it appears to have a permanent effect on the dynamics of the tree species within a stand. The variation of the stand structure can be regarded as an essential factor for the potential biodiversity of the stand also at its young vegetation succession stage. This capacity for maintaining the forest biodiversity, developed at the young vegetation succession stage, becomes increasingly important in subsequent vegetation succession stages. Natural regeneration provides improved possibilities for the operations preserving forest biodiversity, as it generates more dense stands with a wider variation in stand structure, compared to artificial regeneration.

Correlation functions of the mean volume, land use class and soil class were estimated using the data of the Finnish National Forest Inventory. Estimated functions were used for approximating the standard error of e.g. the mean volume of a cluster of plots. Standard error estimates can be used for comparing different inventory designs. The PDF includes an abstract in Finnish.

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 [...]

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 [...]

European aspen is a keystone species in boreal forests, which support numerous ecologically important and endangered species. As detection of those species by remote sensing is impossible, we instead investigated the detection of large aspen trees using airborne laser scanning and aerial image data. However, this is a challenge due to their low quantity and scattered occurrence. The performance was assessed with representative and unrepresentative (where aspens were over-represented) samples of the population. First, we detected individual trees and then the random forest (RF) classifier was used to identify large aspens. The RF classification was implemented with and without synthetic minority oversampling technique (SMOTE) to balance the training data due to the rarity of large aspens. At the tree-level, the best F1-score (0.44) was obtained when the unrepresentative plot data were used with SMOTE. However, the F1-score decreased to 0.21 when the representative data were used. The best plot-level (plots with at least one aspen tree) F1-score with the representative plot data was 0.41. We conclude that although data augmentation may improve the result, it is difficult to detect large aspen trees in genuine populations. [ABSTRACT FROM AUTHOR]

Trestima® is a computer vision-based smartphone application that utilises relascope theory to obtain estimates of forest attributes from smartphone photographs. The aim of this study was to investigate the accuracy of Trestima es - timation and evaluate whether it is sufficiently accurate for operational use in forestry. Our data consisted of 37 forest stands, encompassing 73.5 ha in southeastern Finland, where Trestima estimates were obtained by forestry professionals during their work. The results were compared with harvester data obtained from clear-cut stands. The number of photographs taken per stand ranged between 1-29 (average: 7.3; standard deviation: 5.0). The total amount of industrial roundwood harvested from the stands was 21,531 m3 and the average harvest removal per hectare was 282 m3 . The accuracy of Trestima estimation was relatively good when ≥ 10 photographs per stand were taken. In this case, the root mean square error percent (RMSE%) value associated with roundwood volume was 17.7%. When the number of photographs per stand was < 10, the accuracy of Trestima was much weaker (RMSE% 22.7- 55.3%). On average, Trestima underestimated harvested volumes in Scots pine (Pinus sylvestris L.) stands (Bias% 11.4-89.2), although the bias was smaller (Bias% –12.7-12.4) with Norway spruce (Picea abies [L.] Karst.) stands. The Trestima smartphone application is a possible option for traditional field measurements in operational forestry, provided that its usage instructions are strictly followed, which is not always the case in practice. [ABSTRACT FROM AUTHOR]

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

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 [...]

The paper continues an earlier study by Kilkki and Päivinen concerning the use of the Weibull function in modelling the diameter distribution. The data consists of spruces ( (L.) H. Karst.) measured on angle count sample points of the National Forest Inventory of Finland. First, maximum likelihood estimation method was used to derive the Weibull parameters. Then, regression models to predict the values of these parameters with stand characteristics were calculated. Several methods to describe the Weibull function by a tree sample were tested. It is more efficient to sample the trees at equal frequency intervals than at equal diameter intervals. It also pays to take separate samples for pulpwood and saw timber.Picea abies

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 [...]