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Recent observations of tree regeneration failures following large and severe disturbances, particularly under warm and dry conditions, have raised concerns about the resilience of forest ecosystems and their recovery dynamics in the face of climate change. We investigated the recovery of temperate forests in Europe after large and severe disturbance events (i.e., resulting in more than 70% canopy loss in patches larger than 1 ha), with a range of one to five decades since the disturbance occurred. The study included 143 sites of different forest types and management practices that had experienced 28 disturbance events, including windthrow (132 sites), fire (six sites), and bark beetle outbreaks (five sites). We focused on assessing post‐disturbance tree density, structure, and composition as key indicators of forest resilience. We compared post‐disturbance height‐weighted densities with site‐specific pre‐disturbance densities to qualitatively assess the potential for structural and compositional recovery, overall and for dominant tree species, respectively. Additionally, we analyzed the ecological drivers of post‐windthrow tree density, such as forest management, topography, and post‐disturbance aridity, using a series of generalized additive models. The descriptive results show that European temperate forests have been resilient to past large and severe disturbances and concurrent climate conditions, albeit with lower resilience to high‐severity fire compared with other disturbance agents. Across sites and disturbance agents, the potential for structural recovery was greater than that of compositional recovery, with a large proportion of plots becoming dominated by early‐successional species after disturbance. The models showed that increasing elevation and salvage logging negatively affect post‐windthrow regeneration, particularly for late‐successional species, while pioneer species are negatively affected by increasing summer aridity. These findings provide a key baseline for assessing future recovery and resilience following the recent occurrence of widespread disturbance in the region and in anticipation of future conditions characterized by increasing heat and drought stress. [ABSTRACT FROM AUTHOR]

This study aimed to examine the dynamics of soil functional indicators under the influence of deadwood in old mixed beech forests in northern Iran. In January and February 2006, several strong wind events occurred in the forest of Khanikan, which led to severe windthrow. All the fallen trees remained in the protected parcel and were not removed. Immediately after tree felling in the studied areas, five common tree species [Fagus orientalis (Fagus), Acer velutinum (Acer), Carpinus betulus (Carpinus), Alnus subcordata (Alnus), and Tilia begonifolia (Tilia)] with the highest abundance of deadwood (with 14 individual trees) were marked as permanent plots for monitoring their effect on soil functions. Wood samples were collected from the trunks and soil samples (0–10 cm depth) from the lower part of deadwood in August 2011, 2016, and 2021. In addition, soil samples were taken from areas that did not have deadwood as control areas. For the study of soil biological parameters, soil samples were collected in August and November (2011, 2016, and 2021). However, for monitoring soil N transformation, soil samples were taken in August, September, November, and December (2011, 2016, and 2021). We found that Alnus and Carpinus deadwood had higher N, K, P, and Ca contents than deadwood of other species. Higher values of C and Mg were observed in Fagus deadwood in the final stages of sampling (2021). Anlus deadwood enhanced the stability of soil aggregates, soil nutrient contents, as well as soil enzyme and microbial activities, and soil fauna and microflora populations during the decay process. Based on our findings, the intensity of soil functions increased in the order control < Fagus < Tilia < Acer < Carpinus < Alnus, which corresponds to the increasing quality of the deadwood of the tree species. Our data emphasize that retaining deadwood is highly effective in boosting carbon and nutrient cycling in forest ecosystems, which may have implications for forest management strategies to improve soil ecological functions and forest sustainability. Although our observations were obtained in an old mixed beech forest, the importance of deadwood on soil functional characteristics can also be assumed in a broader range of forest environments, but this requires further investigation to determine the mechanisms that explain these phenomena. [ABSTRACT FROM AUTHOR]

Studying how deadwood mitigates the rockfall hazard in mountain forests is key to understanding the influence of climate-induced disturbances on the protective capacity of mountain forests. Both experimental quantification and numerical process modeling are needed to address this question. Modeling provides detailed insights into the rock–deadwood interaction and can therefore be used to develop effective forest management strategies. Here, we introduce an automatic deadwood generator (ADG) for assessing the impact of fresh woody storm debris on the protective capacity of a forest stand against rockfall. The creation of various deadwood scenarios allows us to directly quantify the mitigation potential of deadwood. To demonstrate the functionality of the proposed ADG method, we compare deadwood log patterns, deadwood effective height, and mesoscale surface ruggedness observed in field surveys in a natural windthrow area with their simulated counterparts. Specifically, we consider two sites near Lake Klöntal, Switzerland, where a major windthrow event occurred in 2019. We perform rockfall simulations for the time (a) before, (b) directly after, and (c) 10 years after the windthrow event. We further compare the results with (d) a simulation with complete clearing of the thrown wood: in other words, a scenario with no standing forest remaining. We showcase an integration of deadwood into rockfall simulations with realistic deadwood configurations alongside a diameter at breast height (DBH)- and rot-fungi-dependent maximum deadwood breaking energy. Our results confirm the mitigation effect of deadwood, which significantly reduces the jump heights and velocities of 400 kg rocks. Our modeling results suggest that, even a decade after the windthrow event, deadwood has a stronger protective effect against rockfall than that provided by standing trees. We conclude that an ADG can contribute to the decision-making involved in forest and deadwood management after disturbances. [ABSTRACT FROM AUTHOR]

Simple Summary: The most important disturbances in European forests are windstorms and fire. In 2004, 12,000 ha of High Tatra Mts. (Slovakia) forests were seriously damaged by strong winds. Furthermore, in 2005 the area of 250 ha of not cleared forest from fallen wood biomass after windstorms was burned down. Our study brings an overview on the influences of irregular wind and fire disturbances and subsequently human activities upon the structure of thrips communities there, where eight study plots with different after-calamity management were chosen. There were two study plots that were affected by wind and subsequent management with the extraction of fallen wood biomass and influenced by fire that were characterized by low species richness. In comparison, not cleared habitats from fallen wood biomass with significantly higher species richness of thrips and higher values of diversity were characterized mainly by the presence of grass-living species Chirothrips manicatus and Limothrips denticornis. The meadow communities with longer development and higher values of diversity were inhabited by the species-rich community mainly with Thrips fuscipennis, T. brevicornis, T. flavus, and T. tabaci. Gradually, in the process of secondary succession these thrips assemblages will be probably replaced by the thrips of the shaded and long-term forests that are mainly composed of Oxythrips bicolor, O. ajugae, and Thrips pini. Strong winds, fire, and subsequent forest management impact arthropod communities. We monitored the diversity and changes in the community structure of forest thrips assemblages in the context of secondary succession and anthropogenic impact. There were eight study plots that were affected to varying degrees by the mentioned disturbances that were selected in the Central European spruce (Picea abies (L.) Karst.) forests in Slovakia. The soil photoeclectors were used to obtain thrips in the study plots during two vegetation seasons. The thrips assemblages and their attributes were analyzed by non-metric multidimensional scaling (NMDS). The significant changes in community structure, composition, stratification, species richness, and diversity of thrips assemblages that were caused by natural- (wind) and human-induced disturbance (forestry and fire) were observed in our research. Our analyses revealed a clear relationship between different thrips assemblages and impacted environment. Moreover, our results indicate that silvicolous thrips species may be useful for indicating changes and disturbances in forest ecological systems. [ABSTRACT FROM AUTHOR]

Disturbances in forest ecosystems are expected to increase by the end of the twenty-first century. An understanding of these disturbed areas is critical to defining management measures to improve forest resilience. While some studies emphasize the importance of quick salvage logging, others emphasize the importance of the deadwood for biodiversity. Unmanned aerial vehicle (UAV) remote sensing is playing an important role to acquire information in these areas through the structure-from-motion (SfM) photogrammetry process. However, the technique faces challenges due to the fundamental principle of SfM photogrammetry as a passive optical method. In this study, we investigated a UAV video-based technology called full motion video (FMV) to identify fallen and snapped trees in a windthrow area. We compared the performance of FMV and an orthomosaic, created by the SfM photogrammetry process, to manually identify fallen and snapped trees, using a ground survey as a reference. The results showed that FMV was able to identify both types of damaged trees due to the ability of video to deliver better context awareness compared to the orthomosaic, although providing lower position accuracy. In addition to its processing being simpler, FMV technology showed great potential to support the interpretation of conventional UAV remote sensing analysis and ground surveys, providing forest managers with fast and reliable information about damaged trees in windthrow areas. [ABSTRACT FROM AUTHOR]

Rising temperatures could increase forest ecosystem disturbance frequency and severity, transferring large amounts of live biomass to coarse woody debris (CWD) pools that emit carbon (C). The type of disturbance-created CWD influences the rate, amount, and duration of this C emission. We studied the effect of a large-scale disturbance on CWD dynamics in spruce-dominated forests of the Kenai Peninsula, Alaska, by determining CWD decomposition rate constants (k) using the chronosequence and decomposition-vectors methods and by modeling the hypothetical CWD dynamics occurring after a bark beetle outbreak versus a windthrow. Chronosequence-based k's for mass ranged between 0.020 and 0.022 year−1 for logs and between 0.000 and 0.003 year−1 for snags. Decomposition-vectors-based k's for log mass ranged between 0.022 and 0.045 year−1 among three decomposition phases and between 0.014 and 0.048 year−1 among five decay classes. Our analysis showed that snag-generating disturbances delayed C flux from CWD to the atmosphere, produced a smaller magnitude of C flux, and had the potential to store 10% to 66% more C in the system over time than disturbances generating logs. Thus, landscapes affected by disturbances creating snags (versus logs) may revert faster to C neutrality, suggesting forest management practices should reflect these differences. [ABSTRACT FROM AUTHOR]

On the basis of long‐term surveys of permanent plots and traps, we examined the communities of saproxylic beetles, fungi, herbs, and trees on an untreated 22 ha large beech forest windthrow and asked whether the results lend support to the intermediate disturbance hypothesis (IDH). We studied species richness and the similarity of community composition. Additionally, we grouped species by their frequency trend over time to successional model types to examine whether, corresponding to the IDH, the diversity of these groups explained peak richness at intermediate intervals after the disturbance. In line with the IDH, species richness showed a hump‐backed temporal course for alpha and gamma diversity. We found evidence for a linear succession directly after the disturbance. This, however, did not continue, and in all species groups, a partial recovery of the initial community was observed. In the case of fungi, herbs, and trees, but not for saproxylic beetles, alpha diversity was driven by the diversity of the successional model types. Our results underline that the mechanisms driving species richness after disturbances are more complex than the IDH suggests and that these mechanisms vary with species group. We assumed that, besides competition, legacy effects, facilitation, habitat heterogeneity, and random saturation of the species pool are important. In case of trees and herbs, we found indications for strong legacy and competition effects. For fungi and beetles, substrate heterogeneity and microclimate were assumed to be important. We concluded that disturbances contribute to increasing species richness not only by reducing the effectiveness of competitors but also by increasing the amount and diversity of resources, as well as their rate of change over time. [ABSTRACT FROM AUTHOR]

• Wind damage can heavily impact montane forests. • Models can be used to produce wind vulnerability maps for montane forests. • New parametrisations can be validated and added to existing models. • Mapping vulnerability to wind damage can help forest managers and practitioners. The risk of wind damage to European forests is expected to increase due to the changed climate. Research efforts in forestry have been focussing on the development of analytical and modelling tools to aid with the evaluation and the prediction of forests' vulnerability to wind damage, and ultimately to inform forest management decisions aimed at promoting wind-resistance in forest stands. Recent catastrophic wind damage to European forests has shown that wind damage risk applies also to montane forests. These are of particular importance for the various ecosystem services they provide, including protection from gravitational hazards and defence against soil erosion. At present, the available forest wind risk models have been tested and used mainly on production or planted forests in different countries, but never in the complexity of mountainous terrains. The aim of this study is to introduce a methodology for the validation of a new parametrisation of the ForestGALES wind risk model, specifically designed for the alpine scenario. To do so, we used parameters from pulling tests in the field, and laboratory mechanical tests. We also developed a workflow that, starting from data obtained by LiDAR, aims at producing wind damage vulnerability maps. After validating this new parameterisation, we investigated the variations in wind vulnerability for a case study in the Italian Alps by introducing the Difference of Vulnerability (DoV) raster. Among our results, the DoV proves to be an innovative tool whose future use can increase understanding of the factors predisposing a forest stand to wind instability. [ABSTRACT FROM AUTHOR]

Highlights • Machine learning techniques were accurate in predicting wind damage to trees. • Random forests proved the most accurate and discriminating methodology. • Models were sensitive to removal of site and stand but not tree characteristics. • All models were able to accurately replicate a mechanistic wind risk model. • Machine learning techniques could help the management of wind damage to forests. Abstract This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory plots damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle-Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage. [ABSTRACT FROM AUTHOR]

Windstorms are among the main factors causing damages to forest ecosystems. These meteorological phenomena cannot be predicted, prevented or controlled. They are occurring rapidly and take place in a meteorological context characterized by high velocities of the air currents. This paper analyses the characteristics of the severe meteorological events on 20 July 2011 which have led to windthrows on extended areas within the Apuseni Mountains in the Romanian Carpathians. The study highlights the evolution of the synoptic processes both at regional scale and at mesoscale. The meteorological analysis is carried out based on synoptic maps and by means of the data coming from the doppler WSR-98D radar in Bobohalma. The main results indicate the occurrence of the severe meteorological phenomena against the background of alternating atmospheric circulation types. The main cause is the transition of the zonal circulation into maritime tropical circulation. This contributed to the destabilizing of the warm and moist air masses that have generated strong storms at national level. [ABSTRACT FROM AUTHOR]

We employed simulations by forest ecosystem (SIMA) and mechanistic wind damage (HWIND) models in upland boreal forests throughout Finland to study regional risks of wind damage under changing management preferences and climates (current and RCP4.5 and RCP8.5 scenarios) over 2010-2099. We used a critical wind speed for the uprooting of trees as a measure of vulnerability, which together with the probability of such wind speed defined a level of risk. Based on that, we also predicted the stem volume of growing stock at risk and the amount of damage. In this work, medium fertility sites were planted to one of Scots pine ( Pinus sylvestris L.), Norway spruce ( Picea abies (L.) Karst.), or silver birch ( Betula pendula Roth) or to the tree species that was dominant before the final clear-felling. The vulnerability to wind damage, the volume of growing stock at risk, and the amount of damage all increased, increasing the most in the south when the proportion of Norway spruce (with shallow rooting) of the growing stock increased. Under a severe climate warming, the proportion of Norway spruce decreased the most in the south, opposite to that of birch. This decreased the risk of damage in autumn (when birch is leafless), unlike in summer. The low risk of damage in the north was due to the large proportion of Scots pine. [ABSTRACT FROM AUTHOR]

Actual status of mountain forests especialy, of Norway spruce (Picea abies Karst. (L)) stands is a result of combined action between forest management measures and biotic and abiotic risk factors. In this context, tree growth dynamics is a result of multifactor interaction (competition, disturbance, management practice, climate etc.). Wind damage affect the stand structure and normal ecosystem functions including the relationship between individual tree growth and competition processes. Though, it is recorded a diminishing of productive capacity for affected stands as well as a significant diameter increment for remaining standing trees. The correlation between tree growth (cumulate basal area increment in the last 10 years) and competition (Hegyi and Schutz competition indices) was analysed in stands with different windfall intensity. No relationship between tree growth and competition was observed in highly affected stands. Schutz competition index explains better individual tree growth comparing with Hegyi index in low or no-affected stand by windthrow. The variability of the basal area increment (In△g10), explained by competition indices, is rather low (<2%) in the stand that had been highly affected by windfall, regardless of the selected competition index. Thus, significant reduction of the stand density related to number of trees (low density), as a consequence of high intensity windfalls, crown competition has a very small influence on the basal area increment comparing with situations met in low (high density) and moderate (medium density) affected stands. [ABSTRACT FROM AUTHOR]

On Nov. 30 to Dec. 1, 2011, an extreme wind event affected the central Sierra Nevada mountain region of California, causing extensive windthrow of trees. The wind event was caused by on extreme pressure gradient from north to south over Nevada and the Sierra that was unusual for the region in its duration, atypical wind direction, and high-intensity wind. Within Devils Postpile National Monument, there were approximately 118.5 windthrown trees km-1. The average diameter at 1.37 m of windthrown trees was 55.36 cm, 2.2 times greater than that for pre-windstorm standing trees. Trees differed in damage type; 86% of trees were uprooted, whereas 14% were snapped, and dead trees were more likely to snap than uproot relative to live trees. Tree species was not a factor in likelihood of windthrow because species composition and relative abundance of windthrown trees were representative of the preexisting forest composition. This wind event is the most extensive on record for California's Sierra Nevada range and may have long-lasting effects on forest composition and function. [ABSTRACT FROM AUTHOR]

Abstract: This study contributes to the design of public policies for the forestry sector in Madre de Dios, Peru. We developed a timber rent model that estimates optimal stumpage fees and compared three scenarios of harvestable areas access versus two harvest methods to calculate potential revenues to the State. We found that current stumpage fees undervalue timber resources and thus provide windfall profits to loggers. Annual forest revenues to the State could be increased from US$1 million to a maximum annual average of US$23.4±1.4 million over a 20-year period if the fee structure suggested from our estimations were adopted. Similarly, we show that the spatial distribution of current fees encourages timber harvesting outside of timber concessions, in particular from Brazil-nut concessions, which compete with timber concessions to supply timber to markets. Our results suggest that timber harvesting should be limited to a maximum volume of 5m3/ha inside Brazil-nut concessions and that timber harvesting in all Madre de Dios could be increased by up to ~200% over the next 20years without threatening conservation areas. This would in turn provide additional revenues to the State that could be applied to better monitoring and forest management. [Copyright &y& Elsevier]

This study deals with the problem of including the risk of wind damage in long-term forestry management. A model based on Graph-Based Markov Decision Processes (GMDP) is suggested for development of silvicultural management policies. The model can both take stochastic wind events into account and be applied to forest estates containing a large number of stands. The model is demonstrated for a forest estate in southern Sweden. Treatment of the stands according to the management policy specified by the GMDP model increased the expected net present value (NPV) of the whole forest only slightly, less than 2%, under different wind-risk assumptions. Most of the stands were managed in the same manner as when the risk of wind damage was not considered. For the stands that were treated differently, however, the expected NPV increased by 3% to 8%. [ABSTRACT FROM AUTHOR]

This study demonstrated how cellular automata, using irregular grids, can be used to minimize the risk of wind damage in forest management planning. The development of a forest in central Finland was simulated for a 30-year period with three subplanning periods. A forest growth and yield model in association with a mechanistic wind damage model was applied to simulate forest growth and to calculate the length of stand edges at risk. Irregular cellular automata were utilized to optimize the harvest schedules for reducing the risk and maintaining a sustainable harvest level. The cellular automata produced rational results, i.e., new clearcuts were often placed next to open gaps, thereby, reducing the amount of vulnerable stand edges. The algorithms of the cellular automata rapidly converged and optimized the harvest schedules in an efficient way. especially when risk minimization was the only objective. In a planning problem that included even-flow timber harvesting objectives (harvest level equal to the total timber growth), the targets were almost achieved. Although the cellular automaton had slightly larger deviations of harvesting from the targets compared with other tested heuristic approaches (simulated annealing, tabu search, and genetic algorithms), it had the best performance when minimizing the expected wind damage. [ABSTRACT FROM AUTHOR]

We (1) estimated how the possible changes in wind climate due to climatic change may affect the probability of exceeding critical wind speeds (CWS) expected to cause significant wind damage within a forest management unit located in southern Sweden, (2) analysed how the probability of exceeding an approximate CWS as observed in the management unit would change in different regions in Sweden if expecting a similar kind of forested area to occur in different geographical locations. The topography of the management unit was relatively gentle and the forests were dominated by Norway spruce (Picea abies (L.) Karst.). Seven regions across Sweden were selected for comparison of possible future probability of damaging wind speed. The model-system WINDA was modified and used for calculations of the probability of wind damage together with regionally downscaled climate change scenario (CCS) data. In total, two climate scenarios downscaled using the RCAO model for the control period 1961-1990 and four for the period 2071-2100 were used. The CCSs represent fairly central projections on a 100-year time scale in terms of global mean warming. Although there is ambiguity between different CCSs, the results indicated that the present pattern of more windy conditions in southern than in northern Sweden will remain. For most sites the probability of exceeding the CWS from westerly to south-westerly directions was indicated to remain comparatively high and the probability of damaging wind from south-westerly to south-easterly directions was indicated to increase in many places. For southernmost Sweden increasing probability of exceeding the CWS from the north-westerly to south-easterly wind directions were indicated for all but one CCS. The results were discussed with respect to spatial planning in forestry under a changing wind climate. [ABSTRACT FROM AUTHOR]

Wind throw is an important risk factor in forest management in North-western Europe. In recent years, mechanistic models have been developed to estimate critical wind speeds needed to break or uproot the average tree of a forest stand. Based on these models, we developed a wind damage module for the individual tree model ForGEM (Forest Genetics, Ecology and Management). For a given wind speed this module assesses the forces on each individual tree, based on the tree dimensions, and support and sheltering provided by other trees. Due to this individual approach, irregular stands can also be assessed. The module is demonstrated on Douglas fir stands (Pseudotsuga menziesii (Mirb.) Franco) of different densities in the Netherlands. Patterns of damage are explained, both in freshly exposed stands as well as in sheltered stands. Wind speeds needed to cause damage approximated those of known wind throw events. The wind damage module proved to be very sensitive to simulated tree heights and diameters. Furthermore, the newly introduced support mechanism played an important role in the stability of trees and stands. Lower individual tree stability in dense stands was clearly compensated for by the support of other trees. [Copyright &y& Elsevier]

We used drift fences with pitfall traps to compare soricid abundance, richness, and demographic parameters among intact multiple-tree windthrow gaps, salvaged gaps, and mature forest in a xeric southern Appalachian forest type during 1997-1999. We also tested whether capture rates were correlated with rainfall, and whether similar-sized species did not co-occur as predicted by multi-species assemblage rules. We captured six species: Blarina brevicauda (Say) (northern short-tailed shrew). Cryptotis parva (Say) (least shrew), Sorex cinereus Kerr (masked shrew), S. fumeus G.M. Miller (smoky shrew), S. hoyi Baird (pygmy shrew), and S. longirostris Bachman (southeastern shrew). Tree basal area, forest structural features, and arthropod abundance differed among treatments, but species richness and abundance of most shrews did not. Captures during June-October were higher in 1998 than in 1997, although 1998 summer rainfall was low. Rainfall was correlated with shrew activity, but explained little of the variability in capture rates (r² = 0.05). The sex ratio within each species was similar among gap treatments and controls, but differed from 1:1 for S. hoyi. S. longirostris, and S. fumeus. The soricid assemblage did not conform to multi-species assemblage rules: three small-bodied species co-occurred in similar numbers at all study sites. Our results suggest that forest management that mimics conditions created by multiple windthrows in xeric forest of the southern Appalachians is unlikely to affect shrew communities adversely, at least in the short-term. [ABSTRACT FROM AUTHOR]

Wind disturbances are significant phenomena in forest spatial structure and succession dynamics. They cause changes in biodiversity, impact on forest ecosystems at different spatial scales, and have a strong influence on economics and human beings. The reliable recognition and mapping of windthrow areas are of high importance from the perspective of forest management and nature conservation. Recent research in artificial intelligence and computer vision has demonstrated the incredible potential of neural networks in addressing image classification problems. The most efficient algorithms are based on artificial neural networks of nested and complex architecture (e.g., convolutional neural networks (CNNs)), which are usually referred to by a common term—deep learning. Deep learning provides powerful algorithms for the precise segmentation of remote sensing data. We developed an algorithm based on a U-Net-like CNN, which was trained to recognize windthrow areas in Kunashir Island, Russia. We used satellite imagery of very-high spatial resolution (0.5 m/pixel) as source data. We performed a grid search among 216 parameter combinations defining different U-Net-like architectures. The best parameter combination allowed us to achieve an overall accuracy for recognition of windthrow sites of up to 94% for forested landscapes by coniferous and mixed coniferous forests. We found that the false-positive decisions of our algorithm correspond to either seashore logs, which may look similar to fallen tree trunks, or leafless forest stands. While the former can be rectified by applying a forest mask, the latter requires the usage of additional information, which is not always provided by satellite imagery. [ABSTRACT FROM AUTHOR]

Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer's accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user's accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user's needs. [ABSTRACT FROM AUTHOR]

The uncertainty about future changes in forest disturbance regimes has highlighted the need to consider disturbances in forest management. In order to effectively reduce the vulnerability of forests to disturbances, detailed information about the risks is needed to support management decisions.Here, our aim is to provide such information by creating high-resolution maps of forest wind and snow damage probability for Finland. First, damage probability models separately for wind and snow were developed based on damage observations in the Finnish national forest inventory (NFI). Three different statistical and machine learning methods were used and compared for the damage probability models – generalized linear models (GLM), generalized additive models (GAM) and boosted regression trees (BRT). Then, the damage probability maps were created in 16 m x 16 m grid resolution by combining the damage models with national coverage spatial data about the model predictors (forest properties, management history, soil properties and climatic conditions) compiled from different sources. Finally, we test the predictive performance of the maps with a large test data from new NFI damage observations not included in model fitting, demonstrating the ability of the maps to identify forests vulnerable to wind and snow disturbances.The maps provide a tool for identifying the vulnerable forest areas and communicating disturbance risks to forest owners and managers. Therefore, they have potential to steer forest management practices to a more disturbance-aware direction by providing easily available high-resolution information about damage probabilities to support management decisions. [ABSTRACT FROM AUTHOR]