A Study on the Changes of Pinus Massoniana Spatial Pattern by Pine Wood Nematode Invasion Based on Remote Sensing and GIS

2006 ◽  
Author(s):  
L. Wang ◽  
X. Zhang ◽  
Y. Luo ◽  
J. Shi ◽  
H. Huang
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Pinus Massoniana ◽  
Remote Sensing And Gis ◽  
Pine Wood Nematode ◽  
Pine Wood

scholarly journals Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing

2020 ◽  
Vol 2 (2) ◽  
pp. 294-307 ◽  
Author(s):  
Xiaoling Deng ◽  
Zejing Tong ◽  
Yubin Lan ◽  
Zixiao Huang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wood Nematode ◽  
Dead Tree ◽  
Pine Wood ◽  
Tree Detection ◽  
Pine Wilt

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.

Niche of insect borers within Pinus massoniana infected by pine wood nematode

2006 ◽  
Vol 1 (4) ◽  
pp. 460-463 ◽  
Author(s):  
Jiying Song ◽  
Youqing Luo ◽  
Juan Shi ◽  
Xiaosu Yan ◽  
Weiping Chen ◽  
...  
Keyword(s):  
Pinus Massoniana ◽  
Pine Wood Nematode ◽  
Pine Wood

scholarly journals Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

2021 ◽  
Vol 13 (22) ◽  
pp. 4682
Author(s):  
Yahao Zhang ◽  
Yuanyong Dian ◽  
Jingjing Zhou ◽  
Shoulian Peng ◽  
Yue Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Risk ◽  
Detection Rate ◽  
Risk Level ◽  
Anthropogenic Activity ◽  
Pine Wood Nematode ◽  
Subtropical Zone ◽  
Pine Wood ◽  
Risk Levels ◽  
Gis Data

Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.

Study on Karst landscape spatial pattern based on remote sensing and GIS

2009 ◽  
Author(s):  
Dan Xiao ◽  
Kangning Xiong ◽  
Yulun An ◽  
Wenzhuan Zhang
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Remote Sensing And Gis ◽  
Karst Landscape

Impact of invasion of pine wood nematode on the growth of dominant shrub Pleioblastus amarus in Pinus massoniana communities

2009 ◽  
Vol 11 (1) ◽  
pp. 61-63 ◽  
Author(s):  
Juan Shi ◽  
You-qing Luo ◽  
Hai-wei Wu ◽  
Xiao-su Yan ◽  
Ping Jiang ◽  
...  
Keyword(s):  
Pinus Massoniana ◽  
Pine Wood Nematode ◽  
Pine Wood

Direct PCR-based method for detecting Bursaphelenchus xylophilus, the pine wood nematode in wood tissue of Pinus massoniana

2010 ◽  
Vol 41 (2) ◽  
pp. 165-168 ◽  
Author(s):  
Y. Q. Hu ◽  
X. C. Kong ◽  
X. R. Wang ◽  
T. K. Zhong ◽  
X. W. Zhu ◽  
...  
Keyword(s):  
Pinus Massoniana ◽  
Bursaphelenchus Xylophilus ◽  
Pine Wood Nematode ◽  
Direct Pcr ◽  
Pine Wood ◽  
Wood Tissue
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