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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

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

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

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]

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Multispectral light detection and ranging (LiDAR) instruments, such as Optech Titan, record intensities at multiple wavelengths and these intensities can be used for tree species prediction in the same way as multispectral image data. In this paper, our main objective was to compare the accuracy of tree species prediction in a boreal forest area using multispectral LiDAR, the 1064-nm wavelength channel (“unispectral LiDAR”), and unispectral LiDAR with auxiliary aerial image data. We also evaluated the effect of the widely used intensity range correction method. We classified the main tree species of field plots using linear discriminant analysis (LDA) and predicted the species-specific volume proportions (%) for Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and broadleaved trees using the $k$ -nearest neighbor imputation. The effect of intensity correction on prediction errors for the dominant tree species was evaluated using optimal parameters derived from: 1) minimal intensity difference between flight lines; 2) parameters suggested by theory; and 3) uncorrected data. Although the range correction increased the classification accuracy slightly, it was observed to be ambiguous, and not consistent with theory for canopy echoes. Regardless, the intensity values were useful for the prediction of dominant tree species and species’ volume proportions. The results for the dominant tree species classification using multispectral LiDAR [overall accuracy (OA) 88.2%, kappa 0.79] were comparable to the use of unispectral LiDAR and aerial images (OA 89.1%, kappa 0.81). We conclude that the multispectral LiDAR may become a useful tool in operational species-specific forest inventories. [ABSTRACT FROM AUTHOR]

Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas. [ABSTRACT FROM AUTHOR]