Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees.

The frequent outbreaks of European spruce bark beetle Ips typographus (L.) have destroyed huge amounts of Norway spruce Picea abies (L.) forests in central and Northern Europe. Identifying the risk factors and estimating the damage level is important for strategic damage control. The risk factors of forest damage by spruce bark beetles have mostly been analyzed on the landscape scale, while the in-stand risk factors have been less investigated. This study aims at exploring the local-scale risk factors in a flat area with spruce-dominated forest in southern Sweden. The investigated factors include four abiotic factors, i.e., soil wetness, solar radiation, slope gradient, and aspect, and three biotic factors, i.e., the number of deciduous trees and trees that died from attacks in previous years that remained (TreesLeft) and removed (TreesRemoved) from the forest stand. We put up 24 pheromone bags in six stands attacked by bark beetle in the previous years, resulting in different numbers of infested trees in each plot. We explored in which microenvironment a pheromone bag resulted in more colonization, the impact radius of each factor, and the necessary factors for a risk model. The environmental factors were obtained from remote sensing-based products and images. A generalized linear model (GLM) was used with the environmental factors as the explanatory variables and the damage levels as the response variables, i.e., the number of attacked trees for the plot scale, and healthy/infested for the single-tree scale. Using 50 m and 15 m radius of the environmental factors resulted in the best fit for the model at plot and individual tree scales, respectively. At those radii, the damage risk increased both at plot and individual tree level when spruce were surrounded by more deciduous trees, surrounded by dead trees that had been removed from the forest, and spruces located at the north and east slopes (315°–135° of aspect, > 2° slope). Soil wetness, solar radiation, and remaining standing dead trees in the surrounding did not significantly impact the damage level in the microenvironment of the study area. The GLM risk model yielded an overall accuracy of 0.69 in predicting individual trees being infested or healthy. Our efforts to investigate the risk factors provide a context for wall-to-wall mapping in-stand infestation risks, using remote sensing-based data.

Multispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods to detect trees under acute stress followed by tree mortality, few have tested the feasibility and accuracy of detecting trees under chronic stress. This study aims to develop methods and test how well UAV-based multispectral imagery can detect pine needle disease long before tree mortality. Multispectral images were acquired four times through the growing season in an area with pine trees infected by needle pathogens. Vegetation indices (VIs) were used to quantify the decline in vitality, which was verified by tree needle retention (%) estimated from the ground. Results showed that several VIs had strong correlations with the needle retention level and were used to identify severely defoliated trees (75% needle retention) was very low. The results from one study area also implied more defoliation observed from the UAV (top view) than from the ground (bottom view). We conclude that using UAV-based multispectral imagery can efficiently identify severely defoliated trees caused by needle-cast pathogens, thus assisting forest health monitoring.