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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. [ABSTRACT FROM AUTHOR]

Remotely-sensed Leaf Area Index (LAI) is vital to describe the vegetation canopy and assess plant growth condition and healthy status. However, sufficient instant ground LAI samples are pre-required for calibration or validation, which is generally difficult to collect. We proposed a method, LAI-Mobile, to use mobile phones with low-cost fisheye lens to take fisheye photos and to invert LAIs, which may be popularized for ordinary people to generate big volume of LAI sample data. The feasibility of LAI-Mobile was tested by comparing with LAI−2200 and GF-1 satellite data (GF = high resolution) in a pest-invaded Yunnan pine forest area in Yunnan province of China. Results show significant correlation between LAI-2200 and LAI-Mobile data for forest plots with coefficient of determination (R2) = 0.706 and Root Mean Square Error (RSME) = 0.241, and GF-1 satellite images (R2 = 0.659 and RMSE = 0.268). The linear regression shows a good agreement between the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and the inverted GF-1 LAI, withR2 = 0.649, RMSE = 0.795. Despite larger uncertainty for single fisheye image than LAI-2200, LAI-mobile can provide fast and convenient method to collect large amount of LAI, which will support remote sensing inversion of LAI at large scale. [ABSTRACT FROM PUBLISHER]

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]

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]