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Outbreaks of pine shoot beetles (Tomicus spp.) have caused widespread tree mortality in Southwest China. However, the understanding of the role of climatic drivers in pine shoot beetle outbreaks is limited. This study aimed to characterize the relationships between climate variables and pine shoot beetle outbreaks in the forests of Yunnan pine (Pinus yunnanensis Franch) in Southwest China. The pine shoot beetle-infested total area from 2000 to 2017 was extracted from multi-data Landsat images and obtained from field survey plots. A temporal prediction model was developed by partial least squares regression. The results indicated that multi consecutive year droughts was the strongest predictor, as such a condition greatly reduced the tree resistance to the beetles. The beetle-infested total area increased with spring temperature, associated with a higher success rate of trunk colonization and accelerated larval development. Warmer temperatures and longer solar radiation duration promoted flight activity during the trunk transfer to the shoot period and allowed the completion of sister broods. Multi consecutive year droughts combined with the warmer temperatures and higher solar radiation duration could provide favorable conditions for shoot beetle outbreaks. Generally, identifying the climate variables that drive pine shoot beetle outbreaks could help improve current strategies for outbreak control. [ABSTRACT FROM AUTHOR]

• No study verified the factors affecting the dispersal of shoot beetles in Southwest China. • Landscape-level variables are the more important predictor, the roles of the stand-level variables are also important. • The prediction of damage severity distribution of Tomicus spp. highlights the need for forest management improvement. The outbreaks of shoot beetles (Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig) have caused widespread tree mortality in Southwest China. However, multi-scale variables explaining the shoot damage ratio (SDR) caused by shoot beetles have never been studied. The objective of this study was to determine the effects of stand-level and landscape-level variables on SDR during outbreaks of shoot beetles in Yunnan pine forests. Sixty-five plots were generated during 2015–2017 based on beetle infestation maps derived from multi-data Landsat images and field survey. Nine explanatory variables were quantified to explain the SDR severity. The forest cover was obtained from a Worldview-3 high‐resolution image. We used the forest resource planning and design survey data to develop landscape context variables. The beetle pressure variable was calculated using beetle infestation maps. The relative importance of the explanatory variables was analyzed using multi-model inference. We established that SDR was higher in the plots that were closer to roads and severely damaged forest areas in the previous year. SDR was negatively affected by edge density (ED) and forest cover, but positively affected by the mean shape index (SHAPE MN) and aspect. Landscape-level variables are probably the more important predictor, stand-level variables also had a significant effect on the shoot beetle outbreak. The prediction models including stand-level variables and landscape-level variables were built. Identifying variables that drive beetle-caused SDR contributed to the improvement of the existing strategies for outbreak control. [ABSTRACT FROM AUTHOR]

• Manipulated infestation revealed the crown spectral signature under pine shoot beetle attack. • 4 weeks' pine shoot beetle shoot-feeding attack turned most shoots from green to red. • Crown heterogenous discoloration resulted in a decline of chlorophyll and water content. • Shoot damage influenced the retrieval accuracy of physiological parameter via hyperspectral data. • 35 spectral variables were evaluated in random forest for identifying shoot damage ratio. Pine shoot beetle (PSB, Tomicus spp.) outbreaks cause widespread Yunnan pine (Pinus yunnanensis Franch) mortality in southwestern China. Early identification of PSB attacks could help forest managers mitigate the infestation before it turns into an outbreak. However, the subtle spectral changes and complex process of PSB-induced crown discoloration make the remote sensing approach difficult. This study employed a manipulated insect infestation experiment to reveal suitable monitoring indicators. Healthy Yunnan pine crowns were infested with PSB adults at different preset densities (light, moderate, and severe) using the bagging method. The crown damage parameters, physiological properties, and corresponding spectral data were systematically measured in time series. Partial least squares regression (PLSR) was used to retrieve crown chlorophyll a+b (Cab) and relative water content (RWC) via band reflectance. Random forest (RF) was used to determine the optimum spectral variables capable of capturing dynamic variations in crown shoot damage ratio (SDR). Results showed that (1) after four weeks' PSB shoot feeding attack, SDR reached saturated (26%-50%) for all damage levels, indicating a crown discoloration "switch point". (2) A continuous decline was found in Cab and RWC at both shoot and crown levels during the whole infestation process, and the crown level decrease was smaller than the shoot level due to crown heterogeneous discoloration. (3) For PLSR retrieving crown Cab and RWC, estimation accuracy of healthy crowns could reach R2 = 0.70, RMSE cv = 14.60 mg/m2 for Cab, and R2 = 0.82, RMSE cv = 1.25% for RWC, respectively. However, PSB early attack impacted these retrieval accuracies. (4) Spectral differences were evident in the visible region (530–600 nm) and near-infrared (NIR) plateau (780–1300 nm) among the preset damage levels. Significant differences in spectral variables (AMP, CI, OSAVI, RD, and SR) were observed between healthy and moderately damaged crowns. (5) In RF modeling, the importance of spectral variables for SDR estimation varied throughout the shoot feeding process. OSAVI, CI, MSI, PSRI, and SR2 were the top indicators that were suitable for identifying SDR ranging from 5% to 50%. SDR could be estimated with an accuracy of R2 = 0.71, RMSE cv = 8.92%, after 4 weeks' infestation. Simultaneously, with the RF model, 4 preset damage levels could be differentiated with an out-of-bag error of 14.94% and a kappa coefficient of 0.8835. In conclusion, this study mainly examined the physiological and spectral signatures of PSB early attacked crowns, and provided optimum spectral variables and models which will further help unmanned aerial vehicle (UAV) and satellite remote sensing identify PSB infestation at the early stages. [ABSTRACT FROM AUTHOR]

Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig have caused serious shoot damage in Yunnan pine (Pinus yunnanensis Faranch) forests in the Yunnan province of China. However, very few remote sensing studies have been conducted to detect the different shoot damage ratios of individual trees. The aim of the study was to evaluate the suitability of eight-band WorldView-3 satellite image for detecting different shoot damage ratios (e.g., "healthy", "slightly", "moderately", and "severely"). An object-based supervised classification method was used in this study. The tree crowns were delineated on a 0.3 m pan-sharpened worldview-3 image as reference data. Besides the original eight bands, normalized two-band indices were derived as spectral variables. For classifying individual trees, three classifiers—multinomial logistic regression (MLR), a stepwise linear discriminant analysis (SDA), and random forest (RF)—were evaluated and compared in this study. Results showed that SDA classifier based on all spectral variables had the highest classification accuracy (78.33%, Kappa = 0.712). Compared to original eight bands of Worldview-3, normalized two-band indices could improve the overall accuracy. Furthermore, the shoot damage ratio was a good indicator for detecting different levels of individual damaged trees. We concluded that the Worldview-3 satellite data were suitable to classify different levels of damaged trees; therefore, the best mapping results of damaged trees was predicted based on the best classification model which is very useful for forest managers to take the appropriate measures to decrease shoot beetle damage in Yunnan pine forests. [ABSTRACT FROM AUTHOR]