In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R² = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R² = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R² = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.

Yunnan pine shoot beetles (PSB), Tomicus yunnanensis and Tomicus minor have spread through southwestern China in the last five years, leading to millions of hectares of forest being damaged. Thus, there is an urgent need to develop an effective approach for accurate early warning and damage assessment of PSB outbreaks. Remote sensing is one of the most efficient methods for this purpose. Despite many studies existing on the mountain pine beetle (MPB), very little work has been undertaken on assessing PSB stress using remote sensing. The objective of this paper was to develop a spectral linear mixing model aided by radiative transfer (RT) and a new Yellow Index (YI) to simulate the reflectance of heterogeneous canopies containing damaged needles and quantitatively inverse their PSB stress. The YI, the fraction of dead needles, is a physically-explicit stress indicator that represents the plot shoots damage ratio (plot SDR). The major steps of this methods include: (1) LIBERTY2 was developed to simulate the reflectance of damaged needles using YI to linearly mix the green needle spectra with the dead needle spectra; (2) LIBERTY2 was coupled with the INFORM model to scale the needle spectra to the canopy scale; and (3) a look-up table (LUT) was created against Sentinel 2 (S2) imagery and inversed leaf chlorophyll content (LCC), green leaf area index (LAI) and plot SDR. The results show that (1) LIBERTY2 effectively simulated the reflectance spectral values on infested needles (mean relative error (MRE) = 1.4–18%), and the YI can indicate the degrees of needles damage; (2) the coupled LIBERTY2-INFORM model is suitable to estimate LAI (R² = 0.73, RMSE = 0.17 m m⁻², NRMSE = 11.41% and the index of agreement (IOA) = 0.92) and LCC (R² = 0.49, RMSE = 56.24 mg m⁻², NRMSE = 25.22% and IOA = 0.72), and is better than the original LIBERTY model (LAI: R² = 0.38, RMSE = 0.43 m m⁻², NRMSE = 28.85% and IOA = 0.68; LCC: R² = 0.34, RMSE = 76.44 mg m⁻², NRMSE = 34.23% and IOA = 0.57); and (3) the inversed YI is positively correlated with the measured plot SDR (R² = 0.40, RMSE = 0.15). We conclude that the LIBERTY2 model improved the reflectance simulation accuracy of both the needles and canopies, making it suitable for assessing PSB stress. The YI has the potential to assess PSB damage.

Pine wood nematode (PWN) is an invasive species which causes pine wilt disease (PWD), posing a significant threat to coniferous forests globally. Despite its destructive nature, strategies for the management of PWD spread lack a comprehensive understanding of the occurrence pattern of PWNs. This study investigates the outbreak timing and spread distances of PWD on a monthly scale. Two regions (A and B) in southeastern China, characterized by varying mixed ratios of coniferous and broadleaf trees, were examined. Infected trees were classified into early, middle, late, and dead stages. Monthly unmanned aerial vehicle (UAV) RGB data covering one year and three deep learning algorithms (i.e., Faster R-CNN, YOLOv5, and YOLOv8) were employed to identify the stress stages and positions of the trees. Further, each month, newly infected trees were recorded to calculate spread distances from the location of surrounding trees. The results indicate that the YOLOv5 model achieved the highest accuracy (mean average precision (mAP) = 0.58, F1 = 0.63), followed by Faster R-CNN (mAP = 0.55, F1 = 0.58) and YOLOv8 (mAP = 0.57, F1 = 0.61). Two PWD outbreak periods occurred between September–October and February of the following year, with early and middle-stage outbreaks in August and September and late and dead-tree outbreaks occurring between October and February of the following year. Over one year, the nearest spread distance for PWD-infected trees averaged 12.54 m (median: 9.24 m) for region A in September and 13.14 m (median: 10.26 m) for region B in October. This study concludes that February through August represents the optimal period for PWD control. Additionally, mixed conifer–broadleaf forests with a higher proportion of broadleaf trees prove beneficial in mitigating PWD outbreaks and reducing the number of infected trees. This work demonstrates the effectiveness of integrating monthly UAV-based imagery and deep learning algorithms for monitoring PWD outbreak times and spread distances, offering technical support for forest pest prevention and management.

Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R² = 0.7890) than at the plot scale (R² = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.