Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

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/m 2 ), the overall estimation accuracies for tree-wise defoliation level varied

Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level

“…The classification accuracies are comparable to the previous studies in identifying different types of infestations with various sensors [17,24,25,27], but due to the small sample size, it is difficult to make further conclusions about the performance. The conclusion whether this accuracy is sufficient or not, is dependent on the application.…”

“…The latter approach was of interest, since the arithmetic mean might not be the ideal feature because of the large number of shadow pixels in the window. A similar approach has also been used in [24] for identifying air-pollution-based damage in Norway spruces.…”

“…Remote sensing, such as aerial images, multi-temporal satellite images and synthetic aperture radar (SAR) datasets, Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), TerraSAR-X and light detection and ranging (LiDAR), have been applied in forest health surveys and detection of disturbances by pest insects [17,[20][21][22][23][24]. Mid-and low-resolution remote sensing data have proven to be practical for insect outbreak surveys over wide landscapes.…”

Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery – Case study in Norway Spruce-dominated stands in Southern Finland

“…For a detailed mapping of defoliation (i.e., tree or sample plots), possibilities involving both active and passive airborne sensors have been investigated. Airborne scanning light detection and ranging (LiDAR)-based methods can capture vegetation heights and thus may have some potential because LiDAR pulses seem to penetrate defoliated canopies more than they do healthy canopies (Kantola et al 2013;Kantola et al 2010;Solberg et al 2006;Vastaranta et al 2013a). However, these methods have many uncertainties, since the canopy density is also affected by factors other than defoliation.…”

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

“…These metrics can be imported into regression models as independent variables. In this way, the response is an aggregation of single tree measurements on sample plots and could be as in the case of mean tree height, stem volume, biomass or forest fuel parameters [9][10][11][12][13].…”