利用固定翼无人机监测松材线虫病疫点枯死松树的初步研究

无人机航摄监测森林病虫害是一个新的研究热点。为探究无人机航摄在松材线虫病监测中的应用,本研究于2017年11月利用小型固定翼无人机采集了广东省河源市新丰江库区松材线虫病疫点的航摄影像,总面积1425.9 hm~2。固定翼无人机搭载了1台可见光数码相机和1台多光谱数码相机,能同时采集枯死松树的可见光和近红外的航摄影像。利用LAMapper软件对航摄图像进行空中三角测量和像素匹配,获得可见光正射影像和多光谱正射影像。使用ERDAS软件生成影像的归一化植被指数(NDVI)。然后将带有地理信息的完整影像自动导入GIS系统进行异常点识别和几何矫正,导出最终的影像数据。最后,对影像进行分析,并根据植被指数(NDVI)对图像进行分类。分析结果显示,航摄的疫点中共自动识别1486株枯死松树,并获得了其分布地图及坐标点位置。验证结果表明监测的准确率达到80%以上,坐标点精度达到2-3 m。本研究结果具有低成本、自动化、可靠、客观、高效和及时等优点,可为大面积监测松材线虫病的发生现状和流行动态、评估防控效果和灾害损失提供技术支撑。     

基于多色彩空间的YOLOv5松枯死树检测方法

【目的】针对在松枯死树监测实践中,从无人机航拍RGB影像中自动识别松枯死树漏检率高的问题,提出了一种生产应用场景下基于多色彩空间的YOLOv5松枯死树高精度自动识别新方法。【方法】利用无人机采集大面积松材线虫病发生林分的RGB图像,用Pix4Dmapper软件拼接,用LabelImg开源软件建立VOC格式的松枯死树数据集,分别用Faster R-CNN、YOLOv3、YOLOv4、YOLOv5、SSD和EfficientDet等6种基于深度学习的目标检测算法对数据集进行训练和测试,以精确率、召回率、平均准确率以及F₁分数作为评价指标筛选出最优目标检测算法;然后将采集的RGB图像转换成LAB和HSV色彩空间图像,再将这3个色彩空间的图像分别用最优目标检测算法进行训练,得到目标在每个色彩空间的边界框,使用非极大值抑制算法对这些边界框进行处理,得到最优边界框实现松枯死树自动识别。【结果】6种算法均取得良好效果,其中YOLOv5模型为最优算法,其精准率、平均查准率和F₁分数在6种算法中均最高,分别达到97.58%、82.40%和0.85。通过3个色彩空间融合后,反映漏检情况的召回率由74.54%提高到98.99%,平均准确率提升至98.39%。【结论】基于多色彩空间的YOLOv5模型能够显著提高从无人机航拍RGB影像中检测松枯死树的精度,为松枯死树监测提供了有力工具,也有助于松材线虫病的防治。     

基于多光谱卫星影像与机器学习算法的松材线虫病受害林分识别研究

松材线虫病（Pine Wilt Disease,PWD）被称为“松树癌症”,具有高传染率和高死亡率,对我国森林资源构成了严重的威胁,对我国的经济、社会和生态造成了重大损失。及时发现并清理疫木是遏制松材线虫病蔓延的有效手段,精准监测疫木是防控松材线虫病的前提,但是现阶段缺少大面积识别松材线虫病疫木的技术方法。本文旨在探索哨兵-2号与Landsat-8遥感卫星影像对受害松林的识别能力,采用随机森林（Random Forest,RF）、支持向量机（Support Vector Machine,SVM）、决策树（Decision Tree,DT）和极端梯度提升（Extreme Gradient Boosting,XGBoost）等4种机器学习算法建立了松材线虫病监测模型。结果表明：基于哨兵-2号影像数据建立的监测模型对受害松林的识别准确率高于Landsat-8遥感卫星影像,其中基于10 m分辨率的影像数据建立的监测模型识别准确率最高,随机森林、决策树、支持向量机和极端梯度提升等算法建立模型的准确率分别达到了79.3%、76.2%、78.7%和78.9%。在3种不同的影像数据集中,RF、SVM和XGBoost的准确率、Kappa系数和ROC值接近,均明显优于决策树算法。光谱特征中的绿波段、红波段、短波近红外波段和长波近红外以及植被指数中的NBRI、NGRDI、TVI、NDVI和PSSR等对松材线虫病监测模型的贡献值最高。递归消除法对特征参数的筛选效果最好,特征数量由原来50个减少至35个。本研究建立的松材线虫病受害林分监测模型为科学防控松材线虫病提供了技术支撑。     

松材线虫病发生及防控现状

松材线虫在20世纪30年代就由美国的Steiner和Buhrer作为新种报道,但直至1971年才在日本被确认是引起松树枯死的原因。目前松材线虫在世界上分布还不很普遍,但对世界松林构成严重威胁。本文介绍了松材线虫发生及防控现状,着重介绍了松材线虫在世界的分布、寄主植物、传媒昆虫及管理现状。针对我国松材线虫病的发生和防控现状,探讨了我国松材线虫病防控对策和提出展望。     

利用大面积无人机航拍影像研究松材线虫病病死树林间分布规律

本研究使用固定翼无人机拍摄4 200 ha林地,从中选取了广东省河源市和平县阳明镇、紫金县紫城镇、东源县义合镇共3个样地的3 500 ha林地的航拍影像进行分析,用以探究松材线虫Bursaphelenchus xylophilus病死树的空间分布情况,及不同立地因子对疫情的影响,为松材线虫病监测预报提供解决途径。通过Pix4Dmapper软件对航拍的图像进行拼接生成正射影像图（DOM）等成果,然后使用eCognition（易康）软件对影像成果进行分割、分类和信息提取,最后借助ArcGIS平台进行病死树数量统计并获取方位、坡向、坡度、海拔等立地因子信息。结果表明,松材线虫病死树分布均呈聚集分布。使用双对角线法、平行线法、“Z”字法、五点法等不同抽样方法调查发现,仅五点法所得平均数与总体平均数无明显差异（P＜0.05）。松材线虫病死树在不同立地因子下均有差异：主要分布在西坡、南坡和东南坡,西坡最多为25.94%,其次是南坡23.57%;主要分布在半阳坡和阳坡,半阳坡占36.54%,阳坡占34.09%;主要分布在凸坡,但随着疫情的发展,凹坡病死树数量逐渐超过凸坡;主要分布海拔区间在300～350 m和250～300 m之间,其中300～350 m的病死树占30.43%,250～300 m的病死树占21.83%。固定翼无人机作业效率高,可在较短的时间内拍摄大面积的林地,获取足够的样本用于研究分析,其成本明显低于人工调查。分析结果表明,研究区域内松材线虫病死树分布具有空间差异和地形差异。在今后的松材线虫病防治中,可以考虑结合地形因素,提高对疫情的防治效率,为防治工作提供了新的思路与方法。     

松材线虫对其携带的细菌繁殖的影响

1.引言松材线虫病（Bursaphelenchus xylophilus）是松树的一种毁灭性病害,在日本、中国、韩国和北美、尼日利亚和葡萄牙等国家蔓延,造成了巨大经济损失,其中以日本和中国受害最重.一直认为松材线虫是引起该病的唯一病原,但近十几年来的研究发现,细菌在致病过程中可能起着重要作用,相继从病木和松材线虫体上分离到能对黑松苗有致萎活性的细菌.赵博光等首次根据实验提出松材线虫病是线虫和细菌共同侵染引起的复合侵染病害的假说,并在以后的试验中得到了验证.关于松材线虫对其细菌繁殖的影响研究鲜有报道.本试验采用从感病松树上分离并鉴定了的细菌菌株中选取假单胞属7株、其它属的细菌菌株3株,     

江西省赣州市南康区松材线虫病发生特征

松材线虫病是我国南方森林面临的主要灾害之一。本文基于野外调查和高分一号(GF-1)卫星WFV影像数据,采用随机森林模型构建松材线虫病空间识别模型,探究地形、人类活动和林分因子等对病害发生的影响,监测病害空间分布,并采用空间自相关性分析评估江西省赣州市南康区松材线虫病发生特征。结果表明：构建模型对松材线虫病的识别效果良好(AUC值=0.99,总体精度=0.96),可以实现对区域松材线虫病空间分布情况的有效监测;归一化差异绿度指数(NDGI)、距高速公路的距离、归一化植被指数(NDVI)是重要的建模因子;空间自相关性分析表明,松材线虫病的发生存在明显的空间正相关性即空间聚集性特征;南康区松材线虫病高发生区集中于赤土乡、朱坊镇和十八塘乡,低发生区集中于蓉江街道附近;分析变量的边际效应发现,离高速公路远、离县道近的低海拔地段是松材线虫病易发区域。研究结果可服务于区域松材线虫病分布的快速监测,对该病害防治和管理具有一定的指导意义。     

化学通讯在松材线虫侵染和扩散中的作用

松材线虫为外来入侵种 ,由其引起的松材线虫病正在我国迅速扩散蔓延 ,造成我国部分地区松林资源的毁灭性破坏。松材线虫病的发生和流行与媒介天牛、寄主植物、共生真菌和细菌密切相关 ,松材线虫 -墨天牛 -松树 -共生微生物之间存在着广泛的化学联系 ,它们通过化学互作 ,调控松材线虫的行为 ,影响松材线虫的侵染和扩散     

松材线虫与伴生微生物的生态关系

松材线虫是重要的外来有害生物,造成松树大量死亡,带来严重的经济损失和生态破坏.松材线虫与伴生微生物存在密切的生态关系.本文综述了松材线虫伴生微生物的种类及其在松材线虫繁殖和致病过程中的重要生态作用,从微生态系统角度对松材线虫病进行探讨.伴生真菌能为松材线虫提供食物,维持松材线虫次生侵染循环,提高分散型第三龄幼虫在种群中的比例,利于松材线虫侵染扩散;伴生细菌能够提高松材线虫致病性,促进其繁殖,并菌有助于松材线虫降解松萜类物质,从而提高松材线虫的适应性.     

高度重视虫传危险性森林病害——松材线虫病

松材线虫病 ,又名松树萎蔫病 ,是松树的一种毁灭性病害 ,具有发病致死速度快、传播蔓延迅速、防治难度大等特点 ,因此被称为“松树癌症” ,在国内外均被列为重要的植物检疫对象。松树一旦感染此病 ,最快的4 0多天即可枯死 ;病害流行很快 ,一般从发病到松林毁灭只需 3～ 5年。该病目前主要分布在北美和东北亚 ,在美国、加拿大、墨西哥、日本、韩国、中国 ,葡萄牙也有分布 ,但危害程度不一 ,以日本受害最重。在北美三国 ,该病虽严重影响了木材出口 ,但并未对松林造成严重危害。而在东北亚 ,该病导致松树大量死亡 ,尤其在日本 ,该病分布全国 ,…     

Detection and classification of diseased pine trees with different levels of severity from UAV remote sensing images

The accurate detection and classification of diseased pine trees with different levels of severity is important in terms of monitoring the growth of these trees and for preventing and controlling disease within pine forests. Our method combines a DDYOLOv5 with a ResNet50 network for detecting and classifying levels of pine tree disease from remote sensing UAV images. In this approach, images are preprocessed to increase the background diversity of the training samples, and efficient channel attention (ECA) and hybrid dilated convolution (HDC) modules are introduced to DDYOLOv5 to improve the detection accuracy. The ECA modules enable the network to focus on the characteristics of diseased pine trees, and solve the problem of low detection accuracy caused by the similarities in color and texture between diseased pine trees and the complex backgrounds. The HDC modules capture the contextual information of targets at different scales; they increase the receptive field to focus on targets of different sizes, and address the difficulty of detection caused by large variations in the shapes and sizes of diseased pine trees. In addition, a low confidence threshold is adopted to reduce missed detections and a ResNet50 classification network is applied to classify the detection results into different levels of severity, in order to reduce the number of false detections and improve the classification accuracy. Our experimental results show that the proposed method improves the precision by 13.55%, the recall by 5.06% and the F1-score by 9.71% on 8 test images compared with YOLOv5. Moreover, the detection and classification results from our approach show that it outperforms classical deep learning object detection methods such as Faster R-CNN and RetinaNet.     

Using an evapo-transpiration model (ETpN) to predict the risk and expression of symptoms of pine wilt disease (PWD) across Europe

The pine wood nematode (PWN) Bursaphelenchus xylophilus is the causal agent of pine wilt disease (PWD), a xylem restricting disease of pine trees. PWN, a native of North America where it very rarely kills native pine trees, has spread internationally killing host trees in China, Japan, Korea, Taiwan and Portugal, with isolated incursions into Spain. Based on the locations where tree mortality has been recorded, it appears that pine trees growing in hot, dry conditions are more susceptible to pine wilt disease. This paper describes the ETpN model, an evapo-transpiration model (previously developed by Forest Research), which has been modified to incorporate the presence of PWN inside a tree and which predicts the regions of Europe that are likely to succumb to PWD. ETpN acts independently of the vector beetle (Monochamus spp.), predicting the likelihood of PWD on the assumption that a tree in a particular region has already been infested by the pine wood nematode. Different regions across Europe are included to investigate and demonstrate how different climates affect PWD incidence significantly. Simplified, “lite” and latency models have been developed to allow a non-specialist user to determine respectively the risk of PWD at a particular location and the likelihood of delays (latency) in expression of wilt symptoms.     

Hazard ratings of pine forests to a pine wilt disease at two spatial scales (individual trees and stands) using self-organizing map and random forest

Pine wilt disease (PWD) caused by the pine wood nematode is the most serious global threat to pine forests. Hazard ratings of trees and forests to pest attacks provide important information to efficiently identify current or future hazardous conditions. However, in spite of the importance of hazard ratings for managing PWD, there are few studies on hazard ratings in this system. In this study, we evaluated the hazard ratings of pine trees and pine stands to PWD by considering environmental factors at the level of the stand and the individual tree. Our results showed that trees with larger diameter at breast height (DBH) showed a higher risk rate than those with smaller DBH, indicating that large trees have an increased probability of exposure to vector beetles because they are tall and have a large crown volume. We also found that reduced tree vigour could be related to susceptibility to PWD. In pine stands, geographical factors showed a high correlation with the occurrence of PWD. PWD occurrence was rare at high altitudes, but was more common on steep and south-facing slopes. These patterns were consistently observed in the results from 2 computational approaches: self-organizing map (SOM) and random forest models. The combination of SOM and random forest was effective to extract ecological information from the dataset. The SOM efficiently characterized relations among variables, and the random forest model was effective at predicting ecological variables, including the hazard rating of trees to disturbances.     

Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model

Pest monitoring of forest areas is essential to pest control. The existing remote sensing satellite image methods have been widely used in detecting pine wilt disease due to their low cost and large detection range. However, most existing methods for pine wilt disease detection are based on multi-phase remote sensing satellite imagery and use manually designed features or machine learning-based algorithms. This makes these methods time-consuming and does not allow early detection of pest-infested forests and can also lead to further spread of the disease. In addition, machine learning-based algorithms can have poor detection performance and generalization ability. To address these shortcomings, this paper uses the pine forest in the Qingyuan area of Liaoning Province in China as a study area to analyze the physiological characteristics of pine pests based on the aerial photography data collected by a Quadrotor-type unmanned aerial vehicle (UAV). By combining these data with the artificial field survey data, the pest-infested areas of forest are marked in the Landsat 8 satellite remote sensing (SRS) images. Further, an end-to-end automatic pest detection framework is designed based on a multi-scale attention-UNet (MA-UNet) model and monophasic images. In addition, the detection performance of the developed model is further optimized using the data augmentation technique to extend the labeled dataset. Compared with the traditional model, the proposed model achieves a much better recall rate of 57.38% in detecting pest-infested forest areas, while the recall rates of the Support Vector Machine (SVM), UNet, attention-UNet, and MedT models are 14.38%, 49.33%, 48.02%, and 33.64%, respectively. According to the results, the proposed model can achieve timely detection and screening of pest-infested forest areas, improving forest management efficiency.     

Evolutionary change in a pine wilt system following the invasion of Japan by the pinewood nematode, <Emphasis Type="Italic">Bursaphelenchus</Emphasis><Emphasis Type="Italic">xylophilus</Emphasis>

Pinewood nematode (PWN), Bursaphelenchus xylophilus, is the causative agent of pine wilt disease (PWD) of pine trees and is transmitted by cerambycid beetles belonging to the genus Monochamus. PWN is believed to have been introduced into Japan from North America at the beginning of the 20th century. In this article, we first provide an outline of the PWD system and the range expansion of PWN in Japan and then review the literature, focusing on the virulence of PWN. Virulence is a heritable trait in PWN, with high virulence being closely related to a high rate of reproduction and within-tree dispersal. When two PWN isolates with different virulence levels are inoculated into pine seedlings, the more virulent nematodes always dominate in dead seedlings. In a laboratory setting, many more virulent nematodes board the insect vectors than avirulent ones. The age at which vectors transmit the most abundant PWNs to pine twigs changes during the course of a PWD epidemic. However, the relation between virulence and transmission of PWN remains as yet relatively unknown. Such information would enable ecologists to predict the evolution of the PWD system. In this review we also compare ecological traits between the PWN and the avirulent congener, B. mucronatus.     

Diversity of Bacteria Carried by Pinewood Nematode in USA and Phylogenetic Comparison with Isolates from Other Countries

Pine wilt disease (PWD) is native to North America and has spread to Asia and Europe. Lately, mutualistic relationship has been suggested between the pinewood nematode (PWN), Bursaphelenchus xylophilus the causal nematode agent of PWD, and bacteria. In countries where PWN occurs, nematodes from diseased trees were reported to carry bacteria from several genera. However no data exists for the United States. The objective of this study was to evaluate the diversity of the bacterial community carried by B. xylophilus, isolated from different Pinus spp. with PWD in Nebraska, United States. The bacteria carried by PWN belonged to Gammaproteobacteria (79.9%), Betaproteobacteria (11.7%), Bacilli (5.0%), Alphaproteobacteria (1.7%) and Flavobacteriia (1.7%). Strains from the genera Chryseobacterium and Pigmentiphaga were found associated with the nematode for the first time. These results were compared to results from similar studies conducted from other countries of three continents in order to assess the diversity of bacteria with associated with PWN. The isolates from the United States, Portugal and China belonged to 25 different genera and only strains from the genus Pseudomonas were found in nematodes from all countries. The strains from China were closely related to P. fluorescens and the strains isolated from Portugal and USA were phylogenetically related to P. mohnii and P. lutea. Nematodes from the different countries are associated with bacteria of different species, not supporting a relationship between PWN with a particular bacterial species. Moreover, the diversity of the bacteria carried by the pinewood nematode seems to be related to the geographic area and the Pinus species. The roles these bacteria play within the pine trees or when associated with the nematodes, might be independent of the presence of the nematode in the tree and only related on the bacteria''s relationship with the tree.     

The developmental process of xylem embolisms in pine wilt disease monitored by multipoint imaging using compact magnetic resonance imaging

In pine wilt disease (PWD), embolized tracheids arise after virulent pine wood nematodes (PWN), Bursaphelenchus xylophilus, invade the resin canal of pine tree; infected pine trees finally die from significant loss of xylem water conduction. We used a compact magnetic resonance imaging system with a U-shaped radio frequency (rf) probe coil to reveal the developmental process of the xylem dysfunction in PWD. Multiple cross-sectional slices along the stem axis were acquired to periodically monitor the total water distribution in each 1-year-old main stem of two 3-year-old Japanese black pines (Pinus thunbergii) after inoculation of PWN. During the development of PWD, a mass of embolized tracheids around the inoculation site rapidly enlarged in all directions. This phenomenon occurred before the significant decrease of water potential. Some patch-like embolisms were observed at all monitoring positions during the experimental period. Patchy embolisms in a cross-section did not expand, but the number of patches increased as time passed. When the significant decrease of water potential occurred, the xylem dysfunctional rate near the inoculation point exceeded 70%. Finally, almost the whole area of xylem was abruptly embolized in all cross-sections along the stem. This phenomenon occurred just after water conduction was mostly blocked in one of the cross-sections. Thus, it appears that the simultaneous expansion of embolized conduit clusters may be required to induce a large-scale embolism across the functional xylem. Consequently, xylem dysfunction in infected trees may be closely related to both the distribution and the number of PWN in the pine stem.     

ITS鉴定真菌病原:一例司法鉴定研究实例

本研究选择一例疑似空气污染物伤害松树的司法鉴定案作为研究实例,在现场勘察和初步形态鉴定的基础上,应用ITS序列分析鉴定了对疑似空气污染物伤害松树的病原做了分子鉴定。我们对委托鉴定的疑似空气污染物伤害松树的地及周边地区进行了勘查,现场勘查结果并不支持空气污染物伤害松树的看法。为明确松树枯死的原因,对取样病枝上的疑似病原物进行了显微镜观察和培养性状观察,根据显微镜下孢子的形态和病原菌的培养性状,初步判断导致马尾松枯死的病原可能为拟盘多毛孢属(Pestalotipsis)真菌。为进一步确定病原种类,我们应用分子生物学技术对危害马尾松的病原物进行了ITS分子鉴定。测定了疑似真菌的ITS序列,并进行了比对和系统发育分析。研究结果表明,导致松树枯死的病原菌系韦司梅拟盘多毛孢(Pestalotipsis vismiae)。在本研究案例中,利用真菌ITS序列的系统发育分析,快速、准确地鉴定了真正导致松树枯死的病原,为今后类似司法鉴定案件审理提供了证据鉴定的新手段。