Over the last decades, the natural disturbance is increasingly putting pressure on European forests. Shifts in disturbance regimes may compromise forest functioning and the continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central to many European policies, we lack the long‐term empirical data needed for thoroughly understanding disturbance dynamics, modeling them, and developing adaptive management strategies. Here, we present a unique database of >170,000 records of ground‐based natural disturbance observations in European forests from 1950 to 2019. Reported data confirm a significant increase in forest disturbance in 34 European countries, causing on an average of 43.8 million m³ of disturbed timber volume per year over the 70‐year study period. This value is likely a conservative estimate due to under‐reporting, especially of small‐scale disturbances. We used machine learning techniques for assessing the magnitude of unreported disturbances, which are estimated to be between 8.6 and 18.3 million m³/year. In the last 20 years, disturbances on average accounted for 16% of the mean annual harvest in Europe. Wind was the most important disturbance agent over the study period (46% of total damage), followed by fire (24%) and bark beetles (17%). Bark beetle disturbance doubled its share of the total damage in the last 20 years. Forest disturbances can profoundly impact ecosystem services (e.g., climate change mitigation), affect regional forest resource provisioning and consequently disrupt long‐term management planning objectives and timber markets. We conclude that adaptation to changing disturbance regimes must be placed at the core of the European forest management and policy debate. Furthermore, a coherent and homogeneous monitoring system of natural disturbances is urgently needed in Europe, to better observe and respond to the ongoing changes in forest disturbance regimes.

Purpose of Review: The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure.Recent Findings: Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories— – approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies.Summary: To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.

The effects of topographic features and the soil parent rock on the site index of Norway spruce [Picea abies (L.) Karst.] were examined. Field data were collected on 347 plots located in the Polish West Carpathian. On these plots, the diameter at breast height and the height of trees were measured and the age of trees was determined. On 64 plots one dominant or codominant tree was felled for stem analysis. In addition, topographic features of each site were measured, including elevation above sea level, aspect and slope. Stem analysis data were used in the development of a local site index system, which was used for calculating the site index for particular plots. In order to interpret site index as functions of elevation, aspect, slope, size of a mountain massif and soil parent rock, the multiple regression model was used, which explained 79.0% of the variation in Norway spruce site index. Elevation above sea level was the major factor responsible for variation in site index, and was negatively correlated (R ² _adj=0.610) with site index. Site index also depended on the soil parent rock. The stands growing on the tertiary Magura sandstones had considerably higher site indices than those located on the geological substratum composed of Istebna and Godula sandstones formed during the Cretaceous period. The site index of Norway spruce is also determined by the aspect, slope and height of mountain massif.