Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.
Modern remote sensing provides cost-efficient spatial digital data that are more accurate than before. However, the influence of increased accuracy and cost-efficiency on simulations of forest management planning has not been evaluated. The aim of the present study was to analyse the effect of data acquisition accuracy on standwise forest inventory by comparing the accuracy and cost of traditional compartmentwise inventory methods with 2D and 3D measurements of digital aerial photographs and airborne laser scanning. Comparison was based on the expected net present value (NPV), i.e. economic losses that consisted of the inventory costs and incorrect timings of treatments. The reference data, totalling 700 ha, were measured from Central Park in the city of Helsinki, Finland. The data were simulated to final cut with a MOTTI simulator, which is a stand-level analysis tool that can be used to assess the effects of alternative forest management practices on growth and timber yield. The results showed that when inventory costs were not considered there were no significant differences between the expected NPV losses in 3D measurements of digital aerial photographs, laser scanning and the compartmentwise method. When inventory costs were taken into account, the compartmentwise method was still the most efficient inventory method in the study area. Forest inventories, however, are usually directed to larger areas when the costs per hectare of remote-sensing methods decrease. As a result of better accuracies, 3D and compartmentwise methods always produce better results than the 2D method when NPV losses are accounted. Simulations of this type are based on the accuracies and costs of the 3D data available today, assuming that the data can be used in tree-level measurements.
In many studies, adaptive cluster sampling (ACS) proved to be a powerful tool for assessing rare clustered populations that are difficult to estimate by means of conventional sampling methods. During 2002 and 2003, severe drought-caused damage was observed in the park forests of the City of Helsinki, Finland, especially in barren site pine and spruce stands. The aim of the present study was to examine sampling and measurement methods for assessing drought damage by analysing the effectiveness of ACS compared with simple random sampling (SRS). Horvitz-Thompson and Hansen-Hurwitz estimators of the ACS method were used for estimating the population mean and variance of the variable of interest. ACS was considerably more effective than SRS in assessing rare clustered populations such as those resulting from drought damage. The variances in the ACS methods were significantly smaller and the inventory efficiency in the field better than in SRS.
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/m 2 ), the overall estimation accuracies for tree-wise defoliation level varied
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