In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forests in Europe, and detecting infested trees is crucial for damage control and informative decision-making regarding management. This study explores efficient methods of detecting infestations using multispectral drone images, focusing on how using different pixels from the crown segments influences the detection rates. Tree crowns were first segmented using marker-controlled watershed segmentation, and then two pixel-selection strategies were tested, including selecting the pixels closer to the tree tops, and selecting the bright pixels with values higher than certain percentiles of the entire crown segments. Two datasets were used from the same area, including 2021 with an epidemic outbreak and 2023 with an endemic outbreak, to present the potential differences caused by attack intensity. The results showed that, in the early stages (1 – 9 weeks of infestation), using the centermost pixels or the brightest pixels in the tree crowns had higher detectability than using all pixels. Red-edge-based VIs were more sensitive than red-green-based VIs. In the middle stage (10 – 16 weeks of infestation), using pixels from the entire tree crown, including tree tops and the low branches, showed higher detectability than using fewer pixels. For the late stages (after 19 weeks of infestation), using only the center pixel was sufficient, and there were minor differences between different VIs. The results were supported by observations from two datasets from different years, although variations in the detectability between different years and stands were also observed.

Pine wilt disease (PWD) is a destructive pine disease with a fast onset rate, high mortality rate, and high difficulty in prevention and control. Accurate and efficient monitoring is the foundation of disease prevention and control. This study aims to explore the potential of red-edge and blue-edge inflection positions from hyperspectral data in monitoring physiological changes and early detecting PWD. We obtained samples of Japanese pines and measured their needles hyperspectral data (wavelength range: 350-2500nm) and physiological parameter data, and obtained hyperspectral drone images of Chinese red pine (wavelength range: 400-1000nm). We used linear fitting algorithms to investigate the linear relationships between vegetation indices and physiological parameters and tested the sensitivity of vegetation indices for PWD early identification using linear discriminant analysis (LDA). The results showed that the indices of the blue-edge inflection position and the red-edge inflection position can reflect the changes in needle pigment content and moisture content, with the 4 point linear interpolation of the blue-edge point showing the best fit for water content. At the needle scale, linear interpolation indices of the blue-edge inflection position showed high accuracy in identifying both early and full-stage PWD. However, the accuracy of these indices decreases when using drone data. We concluded that the developed blue-edge inflection position vegetation indices can be used for PWD early identification. However, further optimization of band selection is needed to improve their application with drone data.

Detecting forest insect damage before the visible discoloration (green attacks) using remote sensing data is challenging, but important for damage control. In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forest in Europe, and some studies have been conducted on the early detection of infestations and forest vulnerabilities before attacks. This study assessed the detectability of the green attacks using multispectral drone images and examined the possibility of detecting vulnerable trees before attacks. The study used multispectral drone images covering 24 plots from 6 forest stands in southern Sweden, acquired in May (before attacks), June (green attack), August (green and yellow attack), and October 2021 (red attack). Drone images of individual-tree crowns were segmented and vegetation indices (VIs) were calculated for every single tree. Trees with the same duration of infestation were grouped for the analysis. Random Forest Classification (RF) and linear discriminant analysis (LDA) were used to build and compare models using all bands, sensitive bands, all VIs, and single VIs, respectively. Results were also compared between different ways of dividing training and testing data. When randomly dividing 90% and 10% trees for training and testing, the models could classify vulnerable trees before attacks with low accuracy. However, when training on trees in five stands, no model could predict infestations in the remaining test stand. Similarly, the models could not identify trees infested for fewer than five weeks. We conclude that the detectability of vulnerable trees before attacks and attacked trees with fewer than five weeks of infestation is very low. We noticed a considerable overfitting when using RF with more variables compared to using LDA with single VIs.

Pine wilt disease (PWD) is a devastating forest disease and has been listed as a quarantine pest in 52 countries around the world. Early identification of the affected trees and timely removal of them from the forest is crucial to control the spread. This study aims to explore the potential of hyperspectral data on early identification of PWD and exhibit the common spectral features, from early-infected tree crowns and needles, and from different species located in different regions. Two types of hyperspectral data were used and compared. One was using drone-based hyperspectral images with a spectral range of 400 – 1 000 nm and a resolution of 0.11 m. The images were analyzed at the individual-tree level. The other was using hyperspectral reflectance from sampled needles with a spectral range of 350 – 2 500 nm. It was used for the analysis at the needle level. We used linear discriminant analysis (LDA) to quantify the separability of spectral reflectance and first-derivative reflectance from the healthy and early-infected samples. The results showed that the red-edge bands were more sensitive than the other bands at both individual-tree and needle levels, and the first-derivative of red-edge bands achieved the best early recognition of the disease with 0.78, 0.72, and 0.85 accuracy at the individual-tree level for Chinese red pine and at the needle level for Japanese pine and Korean pine. We concluded that red-edge bands were the most informative bands with stable sensitivity at different scales and for different species.

In this study, we compared the classification accuracy at plot-level of three tree species in a boreal forest using a deep learning (DL) network applied to very high resolution (VHR) ALS data acquired with ~593 points/m2, or a linear discriminant analysis (LDA) method applied to dual-wavelength (DW) ALS data, acquired with ~80 points/m2.The methods were applied to the single trees, which were aggregated to the plot-level (10 m radius) where the majority class was used for assessing the accuracy. The overall best results were obtained with LDA, relying on DW data, with an overall accuracy (OA) of 82.1% (n=28 plots), while for the DL method using VHR data, the best OA=75.0% (n=28 plots). We acknowledge that the point density of DW data (used for LDA) is already relatively high, and the benefit of using additional spectral information is therefore higher in our study than the value of increasing the point density and identify the tree species from geometric properties (DL approach).We conclude that both dense mono-wavelength ALS data and DW ALS data contain enough information that tree species can be classified at the singletree level in this boreal forest test site in Sweden. With the point densities in this study, the classifications were more accurate with DW data, while the DL approach may be improved with a refined segmentation and pre-processing approach.

Detecting forest insect damage before the visible discoloration (green attacks) using remote sensing data is challenging, but important for damage control. In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forest in Europe. However, it is still debatable how early the infestations can be detected with remote sensing data. Some studies showed a spectral difference between healthy and green-attacked spruce trees at the plot level, while others showed that spectral differences existed before attacks. Therefore, a hypothesis is proposed that no spectral difference can be identified between green-attacked forests compared to healthy forests if the differences do not exist before the attacks. In this study, we tested this hypothesis using Sentinel-2 and WorldView-3 SWIR images on 24 healthy plots and 24 plots with mild, moderate, and severe attacks. In the results, the severely attacked plots did not show significant spectral differences in the Sentinel-2 images until August, and the sensitivity was found in the blue, red, red-edge, and SWIR band. Only the red band showed a significant difference between the healthy and moderately attacked plots in August, and only the blue, red, and SWIR band showed significant differences in September, October, and November. No significant differences were observed in the WorldView-3 images at the plot or individual tree level. We accepted the hypothesis that green attacks do not show spectral differences with the healthy forests when the differences do not exist before the attacks. We concluded that the SWIR bands were sensitive to attacks in the Sentinel-2 images with 10 m resolution, but not in the WorldView-3 images with 3.7 m resolution. Further studies are needed to explore the methodology of using WorldView-3 SWIR images for the early detection of forest infestation.

Since the pine wilt nematode spread and was discovered in China in 1982, it has caused severe damage to the pine forest and has caused a substantial finical loss. Detecting infected trees and removing them from the forest as early as possible is crucial to prevent its spread by the insect vector longhorn-beetles. This study aims at developing methods to detect and map infections using hyperspectral drone images. We inventoried 391 pines in middle east China and recorded them as healthy or early-, middle-, late-stage infected trees. The hyperspectral drone images were obtained with 0.11 m resolution and wavelength from 400 to 1000 nm, covering from red band to near-infrared (NIR). We used the successive projections algorithm (SPA) to select the sensitive bands and the support vector machine (SVM) algorithm to classify trees into different health statuses. The classification resulted in high accuracy during the middle and late-stage infection, while separating healthy and the early -stage infection was challenging. The developed method could map Pine wilt nematode infections and guide the sanitation felling as a crucial disease control measure.

Due to the ongoing global warming, European spruce bark beetles has become a serious threat to the spruce forests in Europe and caused serious environmental and economic issues. This study proposes a new vegetation index, Normalized Projected Red & SWIR (NPRS), for detection of spruce bark beetle attacks. 29 healthy and 24 bark beetle attacked plots in southern Sweden were used for evaluating the classification accuracy using NPRS at early-, intermediate- and late-stage attacks. The obtained kappa coefficients were 0.73, 0.80 and 0.88, respectively. It was concluded that the NPRS is a feasible method for continuous bark beetle mapping over large areas.