Abstract
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.
Highlights
Boreal forest ecosystems normally are highly dynamic and resilient to a variety of changes which promotes stable development of forest stands across broad temporal and spatial scales
(2–20 pulses/m2), we found that Random Forest (RF) classification was not overly sensitive to varying pulse density and that the overall classification accuracies did not vary considerably between different pulse densities
Results of this study suggest that it may be possible to detect trees with differing levels of needle loss, the detection accuracy showed less success with increasing numbers of defoliation classes
Summary
Boreal forest ecosystems normally are highly dynamic and resilient to a variety of changes which promotes stable development of forest stands across broad temporal and spatial scales. Disturbance interrupts successional development of forest ecosystems, affecting resources, the physical environment, population structure, and, in extreme cases, changing the direction of successional processes [1,2]. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, altering the geographical range and productivity of forests, especially at higher latitudes [7,8,9]. Due to rapid responses to elevating temperatures, pest insects can flexibly change their survival, development, reproduction, dispersal and geographic distribution [13,14,15,16]
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