scholarly journals A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2083
Author(s):  
Biyao Zhang ◽  
Huichun Ye ◽  
Wei Lu ◽  
Wenjiang Huang ◽  
Bo Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Image Classification ◽  
Detection Method ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt ◽  
Pine Trees ◽  
Complex Landscape

Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green–Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilted pine trees. The results showed that the proposed method effectively distinguishes wilted pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user’s accuracy (81.2% vs. 67.7%) while maintaining the same level of producer’s accuracy (84.7% vs. 82.6%).

scholarly journals Surveillance of pine wilt disease by high resolution satellite

2022 ◽  
Author(s):  
Hongwei Zhou ◽  
Xinpei Yuan ◽  
Huanyu Zhou ◽  
Hengyu Shen ◽  
Lin Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Convolution Neural Network ◽  
Pinewood Nematode ◽  
Data Set ◽  
Pine Wilt

AbstractPine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China. This destructive disease has the characteristics of bring wide-spread, fast onset, and long incubation time. Most importantly, in China, the fatality rate in pines is as high as 100%. The key to reducing this mortality is how to quickly find the infected trees. We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool. This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite. The recognition accuracy of the test data set was 99.4%, and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees. It can provide strong technical support for the prevention and control of pine wilt disease.

scholarly journals EXTRACTION OF THE INDIVIDUAL TREE INFECTED BY PINE WILT DISEASE USING UNMANNED AERIAL VEHICLE OPTICAL IMAGERY

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 247-252
Author(s):  
X. Zhou ◽  
L. Liao ◽  
D. Cheng ◽  
X. Chen ◽  
Q. Huang
Keyword(s):  
Remote Sensing ◽  
Southern China ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Field Measurements ◽  
Wilt Disease ◽  
Individual Tree ◽  
Remote Sensing Technique ◽  
Pine Wilt ◽  
Pine Trees

Abstract. For eliminating pine trees infected pine wilt disease in southern China based on remote sensing technique, it is important to ensure the provision of timely information about individual diseased tree. It is not easy to detect and extract the diseased pine trees from conventional remote sensing techniques. This paper proposes a new approach for extracting information about individual diseased tree, without the use of satellite images and aerial hyperspectral images. Field measurements in different leaf infected stages indicates the possibility of extracting diseased trees by using only the three regular bands, red, green and blue. VEG was selected and proved to be the optimal index in 12 vegetation indices from the three visible bands. Using the adaptive local threshold selection methods, VEG grayscale image pixels could be automatically segmented into the diseased trees region. Based on mathematical morphology, the accuracy of individual tree information extraction reached 90%.

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Run Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Early Stage ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pinus Tabulaeformis ◽  
Pine Tree ◽  
Preventative Measures ◽  
Pine Wilt ◽  
Pine Trees ◽  
Airborne Vehicle

Abstract 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.

scholarly journals The Microbiome of Endophytic, Wood Colonizing Bacteria from Pine Trees as Affected by Pine Wilt Disease

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Diogo Neves Proença ◽  
Romeu Francisco ◽  
Susanne Kublik ◽  
Anne Schöler ◽  
Gisle Vestergaard ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt ◽  
Pine Trees

scholarly journals A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fengdi Li ◽  
Zhenyu Liu ◽  
Weixing Shen ◽  
Yan Wang ◽  
Yunlu Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection System ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Edge Computing ◽  
Pine Wilt

scholarly journals Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4673-4687
Author(s):  
Jixiang Zhao ◽  
Shanwei Liu ◽  
Jianhua Wan ◽  
Muhammad Yasir ◽  
Huayu Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Evidence Theory ◽  
Remote Sensing Image

scholarly journals Pine wilt disease detection in high-resolution UAV images using object-oriented classification

2021 ◽  
Author(s):  
Zhao Sun ◽  
Yifu Wang ◽  
Lei Pan ◽  
Yunhong Xie ◽  
Bo Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Digital Camera ◽  
Object Oriented ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Segmentation Algorithm ◽  
Tree Crown ◽  
Multi Scale ◽  
Pine Wilt

AbstractPine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

Volatile compounds in pine stands suffering from pine wilt disease: qualitative and quantitative evaluation

Nematology ◽  
2006 ◽  
Vol 8 (6) ◽  
pp. 869-879 ◽  
Author(s):  
Kazuyoshi Futai ◽  
Natsumi Kanzaki ◽  
Yuko Takeuchi
Keyword(s):  
Volatile Compounds ◽  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Economic Damage ◽  
Qualitative And Quantitative ◽  
Pine Wilt ◽  
Pine Trees ◽  
Quantitative Properties ◽  
Pine Stands

AbstractPine wilt disease causes ecological and economic damage in Japanese pine forests in spite of intensive effort to protect them from the pine wood nematode, Bursaphelenchus xylophilus. Pine trees infected with B. xylophilus emit a characteristic bouquet of volatile compounds bioactive to the vector beetle of the nematode, Monochamus alternatus, and potentially affecting symptom development inside the trees. To investigate the qualitative and quantitative properties of volatile compounds in the field, we profiled the volatile emissions in two Japanese black pine stands, one naturally suffering from pine wilt disease and the other artificially inoculated with B. xylophilus. In both pine stands, the emission of some terpenoids from the infected trees such as (−)-α-pinene, began to increase in summer, overlapping the oviposition season of the vector beetle, but peaked in the summer and autumn. These data suggest that the beetles may not necessarily depend on the tremendous quantity of volatiles alone when they search for suitable trees on which to oviposit.

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