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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. [ABSTRACT FROM AUTHOR]

As a significant forest disturbance factor, pine shoot beetles (PSB) have caused widespread tree mortality in Yunnan pine forests over southwestern China. Due to significant vertical heterogeneity of tree crowns, empirical remote sensing methods or one-dimensional (1D) radiative transfer (RT) models are not qualified enough to accurately retrieve the damage level of PSB damage. Therefore, three-dimensional (3D) RT models which have the ability to describe vertically complex heterogeneous canopies are more suitable for the damaged forests. In this study, shoot dieback ratio (SDR) was used to parameterize the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model, in order to accurately estimate the PSB damage. Specifically, the vertically heterogeneous characteristics of the biophysical and optical properties within the canopy were carefully considered. First, we developed an approach to reconstruct damaged forest scenes through individual tree segmentation from UAV-lidar data and in situ measurements. Second, the simulated reflectance of forest scenes was compared with the actual hyperspectral and Sentinel-2 (S2) reflectance data to evaluate the performance of the parameterized RAPID. Then, the parameterized RAPID model was used to create a look-up table (LUT) containing the leaf chlorophyll content (C ab), effective leaf area index (LAI e), SDR, and dead needles LAI e (LAI dead , LAI e × SDR) as well as the corresponding simulated canopy reflectance. Furthermore, tree allometric equations were used to regularize the LUT with the intention to improve the estimation accuracy. Good agreement (RMSE: 0.015-0.031, index of agreement: 0.97-0.99) was found between the simulated reflectance and the remote sensing observations. The retrieval accuracy of the biochemical and biophysical parameters using S2 data yielded R2 = 0.55 (normalized RMSE (NRMSE) = 19%) for C ab , R2 = 0.70 (NRMSE = 15%) for the LAI e , R2 = 0.66 (NRMSE = 25%) for SDR, and R2 = 0.82 (NRMSE = 18%) for LAI dead. Using hyperspectral data with higher spatial and spectral resolution yielded better retrieval performance. This study demonstrated that 3D models (such as RAPID) have great potential to accurately invert SDR and LAI dead to assess PSB damage for the forests. [Display omitted] • Canopy vertical heterogeneity due to beetle damage has been addressed using 3D RT model RAPID. • A new shoot dieback indicator was invented to parameterize RAPID for pine shoot beetle damage. • 3D damaged forests were reconstructed from both lidar data and in situ measurements. • Tree allometric models were used to reduce uncertainty and improve inversion accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]