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The frequency and intensity of insect outbreaks have increased in boreal forests, along with associated impacts on the growth and economic losses of host trees. In Finland, the common pine sawfly (Diprion pini L.) is a serious pest, causing declines in health and growth responses of Scots pine (Pinus sylvestris L.). We focused on investigating the species' defoliating impact on tree radial and volume growth and estimated the economic value of the declined growth. Managed P. sylvestris forests in our study area in eastern Finland have suffered from extended defoliation by D. pini for 15 years since 1999 at varying intensity levels. We classified 184 trees into four defoliation classes and compared annual growth, expressed as growth indices between the classes. We modelled tree volume, estimated economic loss, and compared those to a reference period preceding the initial outbreak. We found significant differences in growth indices between the defoliation classes. Growth losses of 4.2%, 20.8%, and 40.4% were obtained for the mild, moderate, and high defoliation classes, with related economic impacts of 51 €, 272 €, and 734 € per ha for 11 years, respectively. Growth was slightly enhanced in the lowest defoliation class. We suggest that growth-related economic loss caused by D. pini may be significant and depend on defoliation intensity and outbreak duration. [ABSTRACT FROM AUTHOR]

New measures for effective monitoring and controlling of bark beetle infestations are needed as a response to intensified outbreaks caused by the climate change. Various environmental factors affect tree health and susceptibility, as well as stand predisposition to bark beetles. European spruce bark beetle Ips typographus L. abundance and outbreak frequency in Finland has significantly increased during the last decade. The ability to identify sites under a high risk of infestation would facilitate adaptation to this new situation and help target limited forest health management resources. Accordingly, our goal was to investigate the importance of various stand, soil and topographic characteristics in the assessing predisposition of Norway spruce dominated urban forest in southern Finland to I. typographus infestations. Information on the environmental factors was assessed in the field in 2014 and derived from a digital elevation model. Ips typographus infestation intensity was classified into three infestation index classes based on tree-wise symptoms of resin flow, discoloration and defoliation. Cumulative logit link models were utilized for investigating stand-level infestation probability. The best explanatory factors were aspect, slope, site type and soil texture. Models with the highest cumulative probabilities for severe infestation were linked with eastern aspect, moderate steep slope and rich site type fertility (0.72) and eastern aspect, shallow soil and rich site type fertility (0.71). Higher soil C/N ratios with east aspect and rich site type fertility was associated with an increased risk of severe infestation in a third model. The lowest risk was associated with southern and southwestern aspects, fine soil texture, moderate site fertility and gentle slopes. [ABSTRACT FROM AUTHOR]

Forest disturbances caused by pest insects are threatening ecosystem stability, sustainable forest management and economic return in boreal forests. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, particularly at higher latitudes. Due to rapid responses to elevating temperatures, forest insect pests can flexibly change their survival, dispersal and geographic distributions. The outbreak pattern of forest pests in Finland has evidently changed during the last decade. Projection of shifts in distributions of insect-caused forest damages has become a critical issue in the field of forest research. The Common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini has resulted in severe growth loss and mortality of Scots pine (Pinus sylvestris L.) (Pinaceae) in eastern Finland. In this study, tree-wise defoliation was estimated for five different needle loss category classification schemes and for 10 different simulated airborne laser scanning (ALS) pulse densities. The nearest neighbor (NN) approach, a nonparametric estimation method, was used for estimating needle loss of 701 Scots pines, using the means of individual tree features derived from ALS data. The Random Forest (RF) method was applied in NN-search. For the full dense data (+20 pulses/m2), the overall estimation accuracies for tree-wise defoliation level varied between 71.0% and 86.5% (kappa-values of 0.56 and 0.57, respectively), depending on the classification scheme. The overall classification accuracies for two class estimation with different ALS pulse densities varied between 82.8% and 83.7% (kappa-values of 0.62 and 0.67, respectively). We conclude that ALS-based estimation of needle losses may be of acceptable accuracy for individual trees. Our method did not appear sensitive to the applied pulse densities. [ABSTRACT FROM AUTHOR]

The mapping of changes in the distribution of insect-caused forest damage remains an important forest monitoring application and challenge. Efficient and accurate methods are required for mapping and monitoring changes in insect defoliation to inform forest management and reporting activities. In this research, we develop and evaluate a LiDAR-driven (Light Detection And Ranging) approach for mapping defoliation caused by the Common pine sawfly (Diprion pini L.). Our method requires plot-level training data and airborne scanning LiDAR data. The approach is predicated on a forest canopy mask created by detecting forest canopy cover using LiDAR. The LiDAR returns that are reflected from the canopy (that is, returns > half of maximum plot tree height) are used in the prediction of the defoliation. Predictions of defoliation are made at plot-level, which enables a direct integration of the method to operational forest management planning while also providing additional value-added from inventory-focused LiDAR datasets. In addition to the method development, we evaluated the prediction accuracy and investigated the required pulse density for operational LiDAR-based mapping of defoliation. Our method proved to be suitable for the mapping of defoliated stands, resulting in an overall mapping accuracy of 84.3% and a Cohen's kappa coefficient of 0.68. [ABSTRACT FROM AUTHOR]