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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

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.

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

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.

Estimation of forest canopy cover has recently been included in many forest inventory programmes. In this study, after discussing how canopy cover is defined, different ground-based canopy cover estimation techniques are compared to determine which would be the most feasible for a large scale forest inventory. Canopy cover was estimated in 19 Scots pine or Norway spruce dominated plots using the Cajanus tube, line intersect sampling, modified spherical densiometer, digital photographs, and ocular estimation. The comparisons were based on the differences in values acquired with selected techniques and control values acquired with the Cajanus tube. The statistical significance of the differences between the techniques was tested with the nonparametric Kruskall-Wallis analysis of variance and multiple comparisons. The results indicate that different techniques yield considerably different canopy cover estimates. In general, labour intensive techniques (the Cajanus tube, line intersect sampling) provide unbiased and more precise estimates, whereas the estimates provided by fast techniques (digital photographs, ocular estimation) have larger variances and may also be seriously biased.

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 [...]

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 [...]

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 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 [...]

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The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method's applicability in diverse forest structures and biomes is warranted. [ABSTRACT FROM AUTHOR]

Large-diameter trees are important for both ecological and economic reasons, but they have become increasingly rare. Thus, there is an interest in easily locating such trees, and for this purpose, the use of airborne laser scanning (ALS) seems suitable. Our objective was to assess the accuracy of area-based ALS estimation in predicting the number of large- diameter Scots pines (Pinus sylvestris L.). A sample of 856 trees with a diameter > 35 cm were measured from 1109 sample plots located in eastern Norway. We fitted negative binomial and zero-inflated negative binomial models for predicting large-diameter tree counts. ALS-derived and external variables were used as predictors when fitting the models. The accuracy was assessed based on the weighted kappa coefficient and cross validation. Our best model was based on three ALS height distribution variables, one horizontal ALS variable, and plot elevation. Its overall accuracy was 65.8% and the weighted kappa was 0.55. Although there was a clear relationship between the response and the proposed predictor variables, fairly large errors in the predicted large-diameter tree counts were common. Key words: large trees, LiDAR, area-based method, negative binomial regression, old growth. Les arbres de grand diametre sont importants, a la fois pour des raisons ecologiques et economiques, mais ils sont devenus de plus en plus rares. Il y a donc un interet a localiser facilement ces arbres et l'utilisation du balayage laser aeroporte (BLA) semble appropriee pour ce faire. Notre objectif etait d'evaluer la precision d'une estimation basee sur la surface obtenue par BLA pour predire le nombre de pins sylvestres (Pinus sylvestris L.) de grand diametre. Un echantillon de 856 arbres, d'un diametre > 35 cm, a ete mesure a partir de 1109 placettes echantillons situees dans l'est de la Norvege. Nous avons ajuste des modeles binomiaux negatifs et des modeles binomiaux negatifs a exces de zeros pour predire le nombre d'arbres de grand diametre. Des variables obtenues par BLA et des variables externes ont ete utilisees comme predicteurs lors de l'ajustement des modeles. La precision a ete evaluee sur la base du coefficient kappa pondere et par validation croisee. Notre meilleur modele etait base sur trois variables de distribution de hauteur obtenues par BLA, une variable horizontale obtenue par BLA et l'altitude de la placette. Sa precision globale etait de 65,8 % et le coefficient kappa pondere etait de 0,55. Bien qu'il y ait une relation claire entre la variable de reponse et les variables predictives proposees, d'assez grandes erreurs dans la prediction du nombre d'arbres de grand diametre etaient courantes. [Traduit par la Redaction] Mots-cles: grands arbres, LiDAR, methode d'estimation basee sur la surface, regression binomiale negative, foret ancienne., 1. Introduction Large trees are rare in many forests and difficult to find. They are important for both economic and ecological reasons. Large trees dominate the structure, dynamics, and function [...]

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001–2017) of eddy‐covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter‐annual variability (IAV). The forest was a mean annual carbon sink (252 [±42] gC m‐2a‐1), and NEP increased at rate +6.4–7.0 gC m‐2a‐1 (or ca. +2.5% a‐1) during the period. This was attributed to the increasing gross‐primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a‐1, and increase in GPP and NEP outside the main growing season contributed ca. one‐third and one‐fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long‐term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi‐layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30–40% of the observed ecosystem GPP increase could be attributed to CO2. The increasing trend in leaf‐area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid‐rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone. [ABSTRACT FROM AUTHOR]

Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%, root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias -10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias -1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR [(R.sup.2] = 0.47 and 0.34, respectively), and lowest with PBM_FIELD [(R.sup.2] = 0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions. Resume: L'utilisation croissante de la detection et de la telemetrie au moyen du laser aeroporte (LiDAR) dans les inventaires forestiers offre de nouvelles possibilites pour appliquer des modeles forestiers a base ecophysiologique (PBM) de facon operationnelle. Nous presentons une nouvelle approche dans laquelle un PBM simplifie est applique en utilisant des intrants derives des donnees LiDAR. Le PBM a ete elabore en combinant plusieurs modeles existants et il a ete teste avec 52 placettes echantillons dominees par le pin sylvestre (Pinus sylvestris L.) en Finlande, en utilisant des intrants provenant du LiDAR (PBM_LIDAR) et du terrain PBM_FIELD). Les resultats ont ete compares a des predictions empiriques de croissance (EM_FIELD) et a un taux de croissance de reference sur le terrain. En moyenne, les variables specifiques au peuplement derivees du LiDAR (hauteur moyenne des arbres et de la base de la cime, indice de surface foliaire) correspondaient bien aux mesures prises sur le terrain. La croissance en surface terriere etait legerement sous-estimee par le PBM_LIDAR (biais de 4,1 %; erreur-type de prediction de 26,7 %) et legerement surestimee par le PBM_FIELD (biais de -10,2 %; erreur-type de prediction de 33,3 %) alors que les EM_FIELD etaient les moins biaisees (biais de -1,9 %; erreur-type de prediction de 24,6 %). Le biais variait selon l'age du peuplement et la fertilite de la station. La dependance au taux de croissance de reference sur le terrain etait la plus forte pour les EM_FIELD et le PBM_LIDAR [(R.sup.2] = respectivement 0,47 et 0,34) et la plus faible pour le PBM_FIELD [(R.sup.2] = 0,18). Bien qu'elle necessite plusieurs ameliorations, cette approche est prometteuse pour incorporer facilement des donnees sur le couvert forestier et des donnees meteorologiques dans les predictions de la croissance forestiere. [Traduit par la Redaction], Introduction Using airborne light detection and ranging (LiDAR) in forest inventories has increased extensively in recent years, and it has become a realistic alternative for operational use. For example, LiDAR [...]

Angle count or relascope sampling has traditionally been used in forestry to estimate stand basal area. In this paper, we present an extension to the basal area relascope, the crown relascope, which differs from the normal relascope in that the relascope's slot is very high and wide. We describe the theoretical basis of the instrument and present results from a field test in which a crown relascope with a basal area factor of 250 [m.sup.2]/ha (0.025) was used to estimate canopy cover of 73 sample plots in northern Finland. The crown relascope estimates had a root mean square difference of 9.3% and an average difference of -3.1% when compared with estimates obtained with the control method, line intersect sampling using the Cajanus tube. The results indicated that the crown relascope is a quick and fairly reliable instrument for canopy cover estimation, especially in relatively sparse forests where crown overlap is insignificant and visibility does not limit an efficient use of the instrument. L'echantillonnage par balayage sous angle constant ou l'echantillonnage a l'aide du relascope a traditionnellement ete utilise en foresterie pour estimer la surface terriere du peuplement. Dans cet article, nous presentons une nouvelle utilisation du relascope de la surface terriere, le relascope de la cime, pour estimer le couvert forestier. Le relascope de la cime differe du relascope normal de la surface terriere par le fait que sa fente est tres haute et tres large. Nous decrivons la base theorique de l'instrument et presentons les resultats d'un essai sur le terrain. Dans cet essai, le relascope de la cime avec un facteur de surface terriere de 250 [m.sup.2]/ha (0,025) a ete utilise pour estimer le couvert forestier de 73 placettes d'echantillonnage dans le nord de la Finlande. Les estimations avec le relascope de la cime ont une erreur relative moyenne de 9,3 % et une difference moyenne de -3,1 % par rapport aux estimations obtenues avec la methode de controle, l'echantillonnage par transects d'intersection lineaire a l'aide du tube de Cajanus. Les resultats indiquent que le relascope de la cime est un instrument rapide et relativement fiable pour estimer le couvert forestier, particulierement dans les forets relativement clairsemees ou le chevauchement des cimes est peu important et ou la visibilite est suffisante pour une utilisation efficace de l'instrument. [Traduit par la Redaction], Introduction Forest canopy cover is a key variable in international forestry statistics. For example, FAO defines forest as 'a land area with a potential canopy cover exceeding 10% and potential [...]

Helicopter based simultaneous measurements of broadband surface albedo and the effective leaf area index (LAIeff) were carried out in subarctic area of Finnish Lapland in spring 2008, 2009, and 2010 under varying illumination and snow cover conditions. Vertical profile measurements show that the found relationship between albedo and LAIeff seems to be rather independent of the flight altitude and therefore the footprint scale. Actually, flights above 500 m in altitude revealed low variations of the surface albedo approaching an aerial average at 1 km, meaning that a footprint of 20 km is representative of the landscape. The albedo of the area was beta distributed, and without LAIeff values below 0.25, the average albedo value of the area would decrease from 0.49 to 0.44 showing the albedo sensitivity to sparse vegetation. The results agreed with the photon recollision probability based model PARAS and the MODIS satellite albedo product MCD43A3. However, differences between satellite based and airborne albedo values were noticed, which could be explained by a difference in footprint size and/or the strong local heterogeneity as certain flights were operated on specific targets. Plain Language Summary: Helicopter based measurements were used to assess how much a forest stand laying over a snow slab reduces the surface albedo at high latitudes where the sun zenith angle is large and shadow cast is always important. The effect is amplified in the case of sparse vegetation as there is less mutual shadowing. Model results and satellite observations are found in good agreement with the airborne data sets. Key Points: Surface albedo and effective leaf area index (LAI) can be measured at fine resolution and landscape scale simultaneously using helicopterSurface albedo and effective LAI are coherently retrieved based on a photon recollision probability based modelAirborne and satellite‐based surface albedo show a good agreement [ABSTRACT FROM AUTHOR]

In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error (⁠|$\overline{\mathrm{RMSE}}$|⁠) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation (⁠|$\overline{\mathrm{RMSE}}$|⁠ : 17.6 percent, 17.4 percent), followed by ABA (⁠|$\overline{\mathrm{RMSE}}$|⁠ : 19.3 percent, 18.2 percent) and ITD (⁠|$\overline{\mathrm{RMSE}}$|⁠ : 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level (⁠|$\overline{\mathrm{RMSE}}$| 15.5 and 15.3 percent), with poorer ITD performance (⁠|$\overline{\mathrm{RMSE}}$| 19.4 percent). [ABSTRACT FROM AUTHOR]

In this study, for five sites around the world, we look at the effects of different model types and variable selection approaches on forest yield modelling performances in an area-based approach (ABA). We compared ordinary least squares regression (OLS), k-nearest neighbours (kNN) and random forest (RF). Our objective was to test if there are systematic differences in accuracy between OLS, kNN and RF in ABA predictions of growing stock volume. The analyses are based on a 5-fold cross-validation at five study sites: an eucalyptus plantation, a temperate forest and three different boreal forests. Two completely independent validation datasets were also available for two of the boreal sites. For the kNN, we evaluated multiple measures of distance including Euclidean, Mahalanobis, most similar neighbour (MSN) and an RF-based distance metric. The variable selection approaches we examined included a heuristic approach (for OLS, kNN and RF), exhaustive search among all combinations (OLS only) and all variables together (RF only). Performances varied by model type and variable selection approaches among sites. OLS and RF had similar accuracies and were more efficient than any of the kNN variants. Variable selection did not affect RF performance. Heuristic and exhaustive variable selection performed similarly for OLS. kNN fared the poorest amongst model types, and kNN with RF distance was prone to overfitting when compared with a validation dataset. Additional caution is therefore required when building kNN models for volume prediction though ABA, being preferable instead to opt for models based on OLS with some variable selection, or RF with all variables together. [ABSTRACT FROM AUTHOR]

Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under- and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing. [ABSTRACT FROM AUTHOR]

Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of normalized difference vegetation index (NDVI) of forest understory with multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 meter daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were hence tested across diverse forest conditions and phenological phases during the growing season, to examine its applicability on ecosystems other than boreal forests. Here we report the method can deliver good retrievals especially over different forest types with open canopies (low foliage cover). The performance of the method was found limited over forests with closed canopies (high foliage cover), where the signal from understory gets much attenuated. The spatial heterogeneity of individual field sites as well as the limitations and documented quality of the MODIS BRDF product are shown to be important for correct assessment and validation of the retrievals obtained with remote sensing. [ABSTRACT FROM AUTHOR]