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Airborne scanning LiDAR is a promising technique for efficient and accurate biomass mapping due to its capacity for direct measurement of the three-dimensional structure of vegetation. A combination of individual tree detection (ITD) and an area-based approach (ABA) introduced in Vastaranta et al.[1] to map forest aboveground biomass (AGB) and stem volume (VOL) was investigated. The main objective of this study was to test the usability and accuracy of LiDAR in biomass mapping. The nearest neighbour method was used in the ABA imputations and the accuracy of the biomass estimation was evaluated in the Finland, where single tree-level biomass models are available. The relative root-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9% OPEN ACCESSRemote Sens. 2013, 5 2258 and 26.4% when field measurements were used in training the ABA. When ITD measurements were used in training, the respective accuracies ranged between 28.5%-34.9% and 29.2%-34.0%. Overall, the results show that accurate plot-level AGB estimates can be achieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL was encouraging when visually corrected ITD (ITD visual ) was used in training. We conclude that it is not feasible to use ITD visual in wall-to-wall forest biomass inventory, but it could provide a cost-efficient application for acquiring training data for ABA in forest biomass mapping.

Digital twin is a virtual entity that is linked to a real-world entity. Both the link and the virtual representation can be realized in several different ways. However, the ambiguous meanings associated with the term digital twin are causing unnecessary miscommunications as people have different interpretations of what can be accomplished with it. To provide clarity around the concept, we introduce a general approach to analyze and construct digital twins in various applications. We identify the common features of digital twins from earlier literature and propose an analysis method that compares digital twin instances based on these features. The method is used to verify the existence of the features and can be further enhanced. We formulate the observations to a feature-based digital twin framework (FDTF) to universally define and structure digital twins. The framework consists of three main principles: i) the idea that all digital twins consist of a definite set of features, ii) the features can be used to compare digital twin instances to each other, and iii) the features can be combined via a data link feature to construct future digital twins more efficiently. As key contributions, we found that the features can be identified in existing digital twin implementations and the feature combinations of the implementations are diverse. We suggest that the features should be leveraged to provide clarity and efficiency in digital twin discussion and implementation. We further propose a general procedure for building digital twins.INDEX TERMS Digital twin, enterprise systems, Industrial Internet of Things, cyber-physical systems.

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Pölönen I., Sarkeala J., Viitala R. (2015). Unmanned aerial system imagery and photogrammetric canopy height data in area-based estimation of forest variables. Silva Fennica vol. 49 no. 5 article id 1348. 19 p. Highlights• Orthoimage mosaic and 3D canopy height model were derived from UAV-borne colourinfrared digital camera imagery and ALS-based terrain model. • Features extracted from orthomosaic and canopy height data were used for estimating forest variables.• The accuracy of forest estimates was similar to that of the combination of ALS and digital aerial imagery. AbstractIn this paper we examine the feasibility of data from unmanned aerial vehicle (UAV)-borne aerial imagery in stand-level forest inventory. As airborne sensor platforms, UAVs offer advantages cost and flexibility over traditional manned aircraft in forest remote sensing applications in small areas, but they lack range and endurance in larger areas. On the other hand, advances in the processing of digital stereo photography make it possible to produce three-dimensional (3D) forest canopy data on the basis of images acquired using simple lightweight digital camera sensors. In this study, an aerial image orthomosaic and 3D photogrammetric canopy height data were derived from the images acquired by a UAV-borne camera sensor. Laser-based digital terrain model was applied for estimating ground elevation. Features extracted from orthoimages and 3D canopy height data were used to estimate forest variables of sample plots. K-nearest neighbor method was used in the estimation, and a genetic algorithm was applied for selecting an appropriate set of features for the estimation task. Among the selected features, 3D canopy features were given the greatest weight in the estimation supplemented by textural image features. Spectral aerial photograph features were given very low weight in the selected feature set. The accuracy of the forest estimates based on a combination of photogrammetric 3D data and orthoimagery from UAV-borne aerial imaging was at a similar level to those based on airborne laser scanning data and aerial imagery acquired using purpose-built aerial camera from the same study area.

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Abstract. The objective of this study was to make preliminary investigations between accurately measured field biomasses and terrestrial laser scanning (TLS) measurements including tree crown and stem diameters. Stem and crown biomass were determined based on detailed field measurements of the individual tree stem, bark, branch and needles. At the tree level, field measurements were intensive and thus material consisted of only 20 trees located at 11 stands. Stem and crown diameters were extracted manually from TLS point clouds and used as predictors for total biomass. Correlations from 0.96 to 0.99 between predicted and field measured biomass estimates were obtained. Examination of stem form predictions showed that various diameters measured by TLS could enhance the tree level stem curve predictions. Results are rather promising, but more field data is needed for developing practical modelling means. Our further studies will concentrate on automation of TLS data processing and use the of TLS features in the biomass estimation.