Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

Background:Pine wilt disease(PWD)is a major ecological concern in China that has caused severe damage to millions of Chinese pines(Pinus tabulaeformis).To control the spread of PWD,it is necessary to develop an effective approach to detect its presence in the early stage of infection.One potential solution is the use of Unmanned Airborne Vehicle(UAV)based hyperspectral images(HIs).UAV-based HIs have high spatial and spectral resolution and can gather data rapidly,potentially enabling the effective monitoring of large forests.Despite this,few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine.Method:To fill this gap,we used a Random Forest(RF)algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data(data directly collected from trees in the field).We compared relative accuracy of each of these data collection methods.We built our RF model using vegetation indices(VIs),red edge parameters(REPs),moisture indices(MIs),and their combination.Results:We report several key results.For ground data,the model that combined all parameters(OA:80.17%,Kappa:0.73)performed better than VIs(OA:75.21%,Kappa:0.66),REPs(OA:79.34%,Kappa:0.67),and MIs(OA:74.38%,Kappa:0.65)in predicting the PWD stage of individual pine tree infection.REPs had the highest accuracy(OA:80.33%,Kappa:0.58)in distinguishing trees at the early stage of PWD from healthy trees.UAV-based HI data yielded similar results:the model combined VIs,REPs and MIs(OA:74.38%,Kappa:0.66)exhibited the highest accuracy in estimating the PWD stage of sampled trees,and REPs performed best in distinguishing healthy trees from trees at early stage of PWD(OA:71.67%,Kappa:0.40).Conclusion:Overall,our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage,although its accuracy must be improved before widespread use is practical.We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data.We believe that these results can be used to improve preventative measures in the control of PWD.

Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.

Patterns of biomass,carbon,and nitrogen storage distribution dynamics after the invasion of pine forests by Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) in the three Gorges Reservoir Region

Masson pine stands infected by Pine wilt disease(PWD) in the Three Gorges Reservoir Region of central China were surveyed to quantify the immediate responses and subsequent trajectories of biomass,carbon(C),and nitrogen(N) in stand-level major ecosystem compartments.The biomasses of above-and belowground tree components,as well as of the understory,forest floor,and mineral soil(0–40 cm),were determined within each stand.C and N storage were also estimated for each ecosystem compartment.Overstory biomass decreased steadily with the extent of PWD infection.Understory biomass ranged from1.97 to 4.16 Mg ha^(-1),and the observed value for forest floor biomass was 12.89–22.59 Mg ha^(-1).The highest mean C and N concentrations were found in the stem bark and needles of Masson pine,respectively,while the lowest were found in the semi-to fully decomposed layer of the forest floor and stem wood of Masson pine,respectively.The C and N storage of aboveground trees,tree roots,and the aboveground ecosystem decreased with the extent ofPWD infection.However,the C and N contents of the understory,forest floor,and total mineral soil initially declined after PWD infection before recovering over the following several years.Those result concluded that the biomass,C,and N storage of different forest ecosystem compartments have experienced certain variations following the PWD epidemic.This is vital to understand the shifts in stand-level C and N allocation in PWD-damaged forest stands,as well as for predicting the responses of regional and global C and N cycling.

Prediction of the global potential geographical distribution of Hylurgus ligniperda using a maximum entropy model

Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of the host through its boring activity;it can also be a vector of various pathogenic fungi. This study was conducted to investigate the environmental variables limiting the distribution of H. ligniperda and the change trend of its suitable areas under climate change.Results: We used a maximum entropy model to predict the potential geographical distribution of H. ligniperda on a global scale under near current and future climatic scenarios using its occurrence data and environmental variables. The result shows that the areas surrounding the Mediterranean region, the eastern coastal areas of Asia, and the southeastern part of Oceania are highly suitable for H. ligniperda. The environmental variables with the greatest effect on the distribution of H. ligniperda were determined using the jackknife method and Pearson’s correlation analysis and included the monthly average maximum temperature in April, precipitation of driest quarter, the monthly average minimum temperature in December, precipitation of coldest quarter, mean temperature of driest quarter and mean diurnal range.Conclusions: Excessive precipitation in winter and low temperatures in spring had a great effect on the distribution of H. ligniperda. The potential geographical distribution of H. ligniperda was predicted to change under future climatic conditions compared with near current climate conditions. Highly suitable areas, moderately suitable areas and low suitable areas were predicted to increase by 59.99%, 44.43% and 22.92%, respectively, under the2081–2100 ssp245 scenario.

Current and future control of the wood-boring pest Anoplophora glabripennis

The Asian longhorn beetle (ALB) Anoplophora glabripennis is one of the most successful and most feared invasive insect species worldwide. This review covers recent research concerning the distribution of and damage caused by ALB, as well as major efforts to control and manage ALB in China. The distribution and destruction range of ALB have continued to expand over the past decade worldwide, and the number of interceptions has remained high. Detection and monitoring methods for the early discovery of ALB have diversified, with advances in semiochemical research and using satellite remote sensing in China. Ecological control of ALB in China involves planting mixtures of preferred and resistant tree species, and this practice can prevent outbreaks. In addition, strategies for chemical and biological control of ALB have achieved promising results during the last decade in China, especially the development of insecticides targeting different stages of ALB, and applying Dastarcus helophoroides and Dendrocopos major as biocontrol agents. Finally, we analyze recommendations for ALB prevention and management strategies based on native range and invasive area research. This information will hopefully help some invaded areas where the target is containment of ALB.

Spatial distribution of Holcocerus hippophaecolus (Lepidoptera: Cossidae) pupae in a seabuckthorn (Hippophae rhamnoides) stand

The seabuckthorn carpenter moth,Holcocerus hippophaecolus,which has a generation time of four years,is recently becoming one of the major pests of the seabuckthorn(Hippophae rhamnoides)in Inner Mongolia,Liaoning,Shanxi,Ningxia and Shaanxi of China(Hua et al.,1990).The larvae of the H.hippophaecolus mainly damage the stems and roots of the seabuckthorn,and the mature larvae pupate in the soil.The spatial distribution of the pupae was analyzed by using biostatistics and geostatistics in order to effectively control the insect and further study the spatial distribution of the population.Results show that most of the pupae(90%)had an eclosion time span from early June to the end of July.The sex ratio of the pupae was nearly 1:1 in the woodland samples.In addition,24.3%of the 971 trees investigated had pupae and it ranged from 0 to 4 per tree within a distance of 1.3 m from the base of the stem.90%of the pupae were aggregated within a distance of 1 m from the base of the stem.The pupae show intense spatial aggregation in the sampled woodland which had an 11.1 m spatial dependence and a 90.7%intensity in the local spatial continuity.Moreover,the population presented an intensive spotted distribution and many aggregated spots were found in the woodlands.As for the relationship between grid size and variogram of the pupae,the variations in the range,the intensity of local spatial continuity and the sill were all very low or non-existent when the grid size was 5 m,6 m or 7 m.Whereas,the value of the decisive coefficient was the biggest when the grid size was 5 m making it the ideal grid size.