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A study on forest ownership groups in central Finland showed that there are substantial differences in forest stand structure. This research revealed that private-owned forests have more tree storeys and occupy a greater diameter range in moist heaths as compared to forests owned by the state or industrial companies while dryish or dry heaths induce similar variances in tree species. Through this study, the state of forests has been measured which may lead to improved forest management and better regional forest management planning and ecological planning.

Highlights • Dependence between diameter and crown was modeled by locally-estimated copulas. • The copula method decreased diameter RMSE down to 2.1 cm. • Variance in diameter RMSE was sixteen times lower in the copula method. • Less vulnerable to outliers, the copula method was more robust than the LOESS. • The copula method predicted median and other quantiles in addition to the mean. Abstract Biomass inventories that employ airborne laser scanning (ALS) require models that can predict tree diameter at breast height (DBH) from ALS-derived tree dimensions, as ALS can usually not directly measure DBH due to scanning angle, inadequate point density and canopy obstruction. Although some work has been done in using correlation as a measure of dependence to describe the linear relationship between variable means, none has investigated the copula-based measure of dependence for the prediction of DBH from ALS-derived height and crown diameter. Following the application of a locally-estimated copula method to 79 sample plots in eastern Finland, we compared the performance of the copula method with a baseline local regression (LOESS) model and an ordinary least squares (OLS) model. We found that the copula method outperformed the OLS model by decreasing 30% of the root-mean-squared error (RMSE). The copula method performed slightly better than the LOESS model for the original sample, but the results of the bootstrap samples showed that the variance in RMSE was sixteen times lower in the copula method than the LOESS model, suggesting that the copula had a more consistent and robust model performance across the 10,000 bootstrap samples. Moreover, while the LOESS model only predicts the conditional mean of the response variable, the copula method can also predict median and other quantiles. [ABSTRACT FROM AUTHOR]

Highlights • A simple two-tier approach to classify forest structural types (FSTs). • Higher tier classifies single storey/multi-layered/reversed J. • A lower tier classifies young/mature and dense/sparse subtypes. • Airborne laser scanning was employed for a multisite FST classification. • This approach paves the way toward transnational assessments of FSTs. Abstract Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (Q M D) , Gini coefficient (G C) , basal area larger than mean (B A L M) and density of stems (N) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS – maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions. [ABSTRACT FROM AUTHOR]

In-situ field measurements of sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories. Field measurements on new inventory areas can be reduced by utilizing existing stand attribute models from former inventory areas. We constructed a nationwide model for stem volume, and examined seven different calibration scenarios using 22 inventory areas distributed evenly throughout Finland. These scenarios can be divided into three main categories: 1) calibration with additional predictor variables, 2) calibration with 200 geographically nearest sample plots, and 3) calibration with MS-NFI (Multi-source National Forest Inventory of Finland) volume at the target inventory area. Calibration with degree days, precipitation, and proportion of birch resulted in the greatest decrease in error rate of stem volume predictions. The mean of the root mean square errors (RMSE) among the 22 inventory areas decreased from 28.6% to 25.9%, and the standard deviation of RMSEs from 4.3% to 3.9% using three additional predictor variables. Correspondingly, the mean and standard deviation of absolute values of mean differences (|MD|) decreased from 8.3% to 5.6% and from 5.6% to 4.4%, respectively. All calibration scenarios decreased the error rate, especially the high |MDs| observed in the northern part of Finland. Calibration with sample plots from geographically nearest inventory areas was useful when there were sample plots that offered a good representation of the target area. MS-NFI based calibration was also a reasonable option if loggings and other inconsistencies between datasets could be detected and accounted for. [ABSTRACT FROM AUTHOR]

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

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland. [ABSTRACT FROM AUTHOR]

Abstract: This article performs an in-depth examination on whether indices of diversity and equitability among tree size classes are adequate for studying the structural complexity of forests. Diversity profiles and the intrinsic diversity ordering of several field plots were compared. Results demonstrated that even-sized stands are intrinsically non-comparable to uneven-sized stands with regard to their diversity of size classes. Indices describing the diversity of size classes are consequently inadequate, as they order forest structural types (FSTs) inconsistently. The concept of equitability, obtained when removing the richness component from entropy, seemed more adequate for this purpose. Indices of equitability among size classes provided more consistent measures, since the field plots had comparable intrinsic equitability ordering. Furthermore, ranking individual trees by their size is a better approach than ranking size classes, and therefore it is more correct to measure the inequality of tree sizes rather than equitability among size classes. A particular interpretation of Lorenz curves applies when they are used for the study of forest structures, as they should also be compared to a theoretical uniform distribution, and not just the diagonal line-of-absolute-equality. Advised indices are Gini coefficient (GC), De Camino homogeneity (CH), and structure index based on variance (STVI), as they all are consistent with the Lorenz ordering. [Copyright &y& Elsevier]

Abstract: Boreal forest stands with high herbaceous plant species diversity have been found to be one of the main habitats for many endangered species, but the locations and sizes of these herb-rich forest stands are not well known in many areas. Better identification of the stands could improve both their conservation and management. A new approach is proposed here for locating the mature herb-rich forest stands using airborne laser scanner (ALS) data and logistic regression, or the k-NN classifier. We show that ALS technology is capable of distinguishing the ecologically important herb-rich forests from those growing on less fertile site types, mainly on the basis of unique but quantifiable crown structure and vertical profile that characterise forests on high fertility sites. The study site, Koli National Park, is located on the border of the southern and middle boreal vegetation zones in Finland, and includes 63 herb-rich forest stands of varying sizes. The model and test data comprised 274 forest stands belonging to five forest site types varying from very fertile to poor. The best overall classification accuracy achieved with the k-NN method was 88.9%, the herb-rich forests being classified correctly in 65.0% of cases and the other forest site types in 95.7%. The best overall classification accuracy achieved with logistic regression was 85.6%, being 55.0% for the herb-rich forests and 94.3% for the other forest site types. Both methods demonstrated promising potential for separating herb-rich forests from other forest site types, although slightly better results were obtained with the non-parametric k-NN method, which was capable of utilising a higher number of explanatory variables. It is concluded that ALS-based data analysis techniques are applicable to the detection of mature boreal herb-rich forests in large-scale forest inventories. [Copyright &y& Elsevier]

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

Various studies have been presented within the last 10 years on the possibilities for predicting forest variables such as stand volume and mean height by means of airborne laser scanning (ALS) data. These have usually considered tree stock as a whole, even though it is tree species-specific forest information that is of primary interest in Finland, for example. We will therefore concentrate here on prediction of the species-specific forest variables volume, stem number, basal area, basal area median diameter and tree height, applying the non-parametric k-MSN method to a combination of ALS data and aerial photographs in order to predict these stand attributes simultaneously for Scots pine, Norway spruce and deciduous trees as well as total characteristics as sums of the species-specific estimates. The predictor variables derived from the ALS data were based on the height distribution of vegetation hits, whereas spectral values and texture features were employed in the case of the aerial photographs. The data covered 463 sample plots in 67 stands in eastern Finland, and the results showed that this approach can be used to predict species-specific forest variables at least as accurately as from the current stand-level field inventory for Finland. The characteristics of Scots pine and Norway spruce were predicted more accurately than those of deciduous trees. [Copyright &y& Elsevier]

The aim of the study was to model the development of the basal area diameter distribution of Pinus kesiya (Royle ex Gordon) stands in Zambian and Zimbabwean forest plantations. The development of stand mean characteristics and basal area diameter distributions are predicted using a combination of a simultaneous yield model (SYM) for stand characteristics and models for the basal area diameter distribution. The stand basal area diameter distribution was calculated from prediction and projection models for diameters at selected percentile points of the basal area, i.e. the percentile based distribution (PBD) model was applied. The models for the percentiles formed a system of equations. Earlier models were used to construct a simulator, which was used in validity tests. According to the reliability tests of total stand volume predictions, the accuracy of the PBD model is comparable to the SYM model and better than the Weibull model, which was used as a reference method for the PBD model. However, the PBD model provides a more detailed description of the stand structure and its development than the SYM model. The models of this study may be flexibly used in yield predictions of P. kesiya plantations in Zambia and Zimbabwe. [Copyright &y& Elsevier]

Compares percentile-based prediction methods and the Weibull distribution in describing the diameter distribution of heterogenous Scots pine stands. Differences in empirical diameter distributions and mean stand characteristics; Absolute biases; Absolute root mean square error of basal area estimates.

This study investigated if primary and managed forests of Southeast Asian tropical forests can be distinguished by quantitative forest structure characteristics, which are easy to measure in the field. Such a method could help select analytically and objectively small fragments of primary forests for conservation purposes within planning areas. The test area was located close to Tanah Grogot in the southern part of East Kalimantan, Indonesia. The field inventory of the test area was made with systematic cluster sampling. Altogether, the data included 304 clusters with 6475 sample plots, which included 47 386 measured trees. Differences in the structure between primary and managed forests were analyzed with quantitative characteristics: number of stems per hectare; total volume per hectare; Shannon's index of tree species richness; Hill's index of tree species richness and evenness of the abundances; Q-statistics of the cumulative tree species abundance curves; average shape of diameter distributions; and peaks of smoothed diameter distributions. Characteristics of the forest structure were investigated in the whole data, by tree species classes, diameter classes, and forest management status classes. The results of the study show that it is possible to find a set of easily measurable characteristics that reveal the changes caused by the management in tropical forests. However, because management affects mostly the economically and also ecologically valuable large dipterocarps, it is reasonable to examine the conducted changes separately by diameter classes and preferably by different tree species classes rather than in the tree data as a whole. [ABSTRACT FROM AUTHOR]

The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests. The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation. The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported. • ICESat-2 data was used to estimate biomass of a boreal forest with good results. • The ICESat-2 model was validated using data from a neighboring area and year. • Variance of the estimated biomass was quantified using a novel statistical approach. [ABSTRACT FROM AUTHOR]

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

Examines the possibilities of using computerized digital aerial photograph interpretation in determining the spatial distribution of trees in Finland. Structure of forest stand; Recognition of the pattern of tree crowns with sub-pixel accuracy; Image coverage pattern indices between the various spatial distribution categories.

Airborne Light Detection and Ranging (LiDAR) information alone is insufficient for species-specific prediction of forest stand attributes, and therefore auxiliary optical image features (OIF) are commonly used to decrease the prediction errors associated with species-specific tree attributes. However, this requires collection and merging of two data sources, LiDAR and OIF, which increases the costs of the inventory. The recently introduced multispectral LiDAR (M-ALS) provides a potential single-sensor solution for obtaining species-specific information, as its multispectral intensity values resemble optical image data. Image point clouds (IPC) derived from aerial stereo images are another single-sensor option that provides both geometric and optical information. We compared two single-sensor options, M-ALS and IPC, with two LiDAR data sets (leaf-on and leaf-off) with auxiliary OIF, for the prediction of boreal tree species' volumes. In terms of root-mean-square error (RMSE) in the validation data, the LiDAR+OIF combination performed best (leaf-on RMSE: 33.3%; leaf-off RMSE 34.3%), followed by M-ALS + OIF (RMSE = 35.2%) and IPC + OIF (RMSE = 42.4%). The mean RMSE value associated with M-ALS increased to the same level (44.7%) as the IPC + OIF combination when optical image features were not included. Both IPC and M-ALS are potential single sensor solutions for forest inventories, but the use of both LiDAR and OIF provides the most accurate results. • Species-specific volumes predicted with multiple sensor configurations. • LiDAR with optical image features performed better than multispectral LiDAR alone. • IPC data performed markedly worse than LiDAR data in predicting total volume. • Single wavelength leaf-on LiDAR with image features performed the best. [ABSTRACT FROM AUTHOR]

Abstract: Forest inventories based on single-tree interpretation of airborne laser scanning (ALS) data often rely on an allometric estimation chain in which inaccuracies in the estimates of the diameter at breast height (DBH) propagate to other characteristics of interest such as the stem volume. Our purpose was to test nearest neighbor imputation by the k-Most Similar Neighbor (k-MSN) and the Random Forest (RF) methods for the simultaneous estimation of species, DBH, height and stem volume using ALS data. The predictors included computational alpha shape metrics and variables based on the height and intensity distributions in the ALS data. Separate data sets covering 1898 and 1249 dominant to intermediate trees in a typical Scandinavian stand structure were used for training and validation, respectively. RF proved to be a flexible method with an ability to handle 1846 predictors with no need for their reduction. Classification of Scots pine, Norway spruce and deciduous trees showed an accuracy of 78%, and the estimates of DBH, height and volume had root mean square errors of 13%, 3%, and 31%, respectively, when evaluated against the validation data. The two selection strategies implemented here reduced the number of candidate variables effectively without any substantial effect on the accuracy relative to the use of all predictors. Differences in k-MSN and RF imputations were marginal when the reduced sets of variables were used. Estimation accuracies could be maintained practically unchanged with only 12.5% of the initial reference data (237 trees), provided the distribution of the observations was similar in the reference and target data. Since we used information collected in the field for extracting the ALS point clouds for individual trees, our results represent an optimal case and should nevertheless be validated against automated tree delineation. [Copyright &y& Elsevier]

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

In this study, two forest sites located in Finland were compared by means of predictions of Gini coefficient ( GC ) obtained from airborne laser scanning (ALS). We discuss the potential of the proposed method for identifying differences in structural complexity in relation with the management history of forests. The first study site (2200 ha), the Koli National Park (NP), includes areas where human intervention was restricted after 1907, in addition to forests which were protected only after the 1990s. The second study site in the municipality of Kiihtelysvaara (800 ha) has been under intensive management. These are commercial forests which include areas with different types of ownership: a large estate owned by an industrial company together with smaller private properties. We observed that GC predictions may be used to evaluate the effects of management practice on forest structure. Conservation and commercial forests showed significant differences, with the old-protected area of Koli having the highest, and the most intensively managed area in Kiihtelysvaara the lowest GC values. The effect of management history was revealed, as the 1990s’ extensions of Koli NP were more similar to unprotected areas than to forests contained within the original borders of the 1907s’ state property. Yet, their conservation status for almost two decades has been sufficient for developing significant differences against the outside of the NP. In Kiihtelysvaara, we found significant differences in GC according to the type of ownership. Moreover, the ALS predictions of GC also detected differences near lakeshores, which are driven by limitations on logging governed by Finnish law. Estimating this indicator with ALS remote sensing allowed to observe its spatial distribution and to detect peculiarities which would otherwise be unavailable from field plot sampling. Consequently, the method presented appears to be well suited for monitoring the effects of management practice, as well as verifying its compliance with legal restrictions. [ABSTRACT FROM AUTHOR]

• BALM is an important indicator that could be used to enhance cohort balance in forests. • BALM estimation at smaller plot sizes and sample sizes is unstable. • Stabilization of BALM starts at the plot size 6 m radius and sample size 6 trees. • The optimal plot size and sample size are 380–615 m2 and 50–80 trees, respectively. • ALS point density of at least 5 points m−2 is required for reliable BALM estimation. Airborne laser scanning (ALS) assisted basal area larger than mean (BALM) estimation measures the cohort balance in forests and provides adequate opportunities to describe forest structure. However, a problem still exists that how the plot size, sample size (number of trees), and ALS point density affect the BALM estimation. We tackled this question by using both field and ALS data from a typical managed boreal forest area in Finland. Various concentric circular plots (1–15 m radii) were simulated within the actual field plots (squared) and the optimal plot size and sample size were selected by observing changes in the absolute correlation between BALM estimates and various ALS metrics. Instability in the correlation was found at the smaller concentric circular plots (1–5 m radii) and sample sizes (less than 6 trees) but as the plot size and sample size increased, the correlation followed a convex curve. The maximum correlation was found between a concentric circular plot size 11–14 m radii (380–615 m2 area) and sample size 50–80 trees which could be the optimal plot size and sample size for a reliable BALM estimation. With regards to the ALS point density, no major effects were observed on the relationship between BALM estimates and various ALS metrics unless the point density is less than at least 5 points m−2. The point density of the current nationwide ALS survey is matching the minimum point density requirement obtained in this study and thus it is suitable for a reliable forest structural assessment. [ABSTRACT FROM AUTHOR]

Abstract: Remote sensing of forest canopy cover has been widely studied recently, but little attention has been paid to the quality of field validation data. Ecological literature has two different coverage metrics. Vertical canopy cover (VCC) is the vertical projection of tree crowns ignoring within-crown gaps. Angular canopy closure (ACC) is the proportion of covered sky at some angular range around the zenith, and can be measured with a field-of-view instrument, such as a camera. We compared field-measured VCC and ACC at 15° and 75° from the zenith to different LiDAR (Light Detection and Ranging) metrics, using several LiDAR data sets and comprehensive field data. The VCC was estimated to a high precision using a simple proportion of canopy points in first-return data. Confining to a maximum 15° scan zenith angle, the absolute root mean squared error (RMSE) was 3.7–7.0%, with an overestimation of 3.1–4.6%. We showed that grid-based methods are capable of reducing the inherent overestimation of VCC. The low scan angles and low power settings that are typically applied in topographic LiDARs are not suitable for ACC estimation as they measure in wrong geometry and cannot easily detect small within-crown gaps. However, ACC at 0–15° zenith angles could be estimated from LiDAR data with sufficient precision, using also the last returns (RMSE 8.1–11.3%, bias –6.1–+4.6%). The dependency of LiDAR metrics and ACC at 0–75° zenith angles was nonlinear and was modeled from laser pulse proportions with nonlinear regression with a best-case standard error of 4.1%. We also estimated leaf area index from the LiDAR metrics with linear regression with a standard error of 0.38. The results show that correlations between airborne laser metrics and different canopy field characteristics are very high if the field measurements are done with equivalent accuracy. [Copyright &y& Elsevier]

This paper demonstrates the applicability of small footprint, high sampling density airborne laser scanners for boreal forest change detection, i.e. the estimation of forest growth and monitoring of harvested trees. Two laser acquisitions were carried out on a test site using a Toposys-1 laser scanner. Three-dimensional canopy height models were calculated for both data sets using raster-based algorithms. Object-oriented algorithms were developed for detecting harvested and fallen trees, and for measuring forest growth at plot and stand levels. Out of 83 field-checked harvested trees, 61 could be automatically and correctly detected. All mature harvested trees were detected; it was mainly the smaller trees that were not. Forest growth was demonstrated at plot and stand levels using an object-oriented tree-to-tree matching algorithm and statistical analysis. The precision of the estimated growth, based on field checking or statistical analysis, was about 5 cm at stand level and about 10–15 cm at plot level. The authors expect that the methods may be feasible in large area forest inventories that make use of permanent sample plots. Together with methods for detecting individual sample trees, the methods described may be used to replace a large number of permanent plots with laser scanning techniques. [Copyright &y& Elsevier]

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