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

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.

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Application of Random Forest Classification to Detect the Pine Wilt Disease from High Resolution Spectral Images

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324293 ◽

2020 ◽

Author(s):

Marian-Daniel Iordache ◽

Vasco Mantas ◽

Elsa Baltazar ◽

Nicolas Lewyckyj ◽

Niels Souverijns

Keyword(s):

High Resolution ◽

Random Forest ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Random Forest Classification ◽

Pine Wilt ◽

Forest Classification

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Surveillance of pine wilt disease by high resolution satellite

Journal of Forestry Research ◽

10.1007/s11676-021-01423-8 ◽

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.

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

Remote Sensing ◽

10.3390/rs13112083 ◽

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%).

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Effects of cavitation on the development of pine wilt disease caused by Bursaphelenchus xylophilus.

Japanese Journal of Phytopathology ◽

10.3186/jjphytopath.54.606 ◽

1988 ◽

Vol 54 (5) ◽

pp. 606-615 ◽

Cited By ~ 34

Author(s):

Keiko KURODA ◽

Toshihiro YAMADA ◽

Kazuhiko MINEO ◽

Hirotada TAMURA

Keyword(s):

Pine Wilt Disease ◽

Bursaphelenchus Xylophilus ◽

Wilt Disease ◽

Pine Wilt

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

Forest Ecosystems ◽

10.1186/s40663-021-00328-6 ◽

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.

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Dynamical aspects of pine wilt disease and control measures

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2021.110764 ◽

2021 ◽

Vol 145 ◽

pp. 110764

Author(s):

Takasar Hussain ◽

Adnan Aslam ◽

Muhammad Ozair ◽

Fatima Tasneem ◽

J.F. Gómez-Aguilar

Keyword(s):

Pine Wilt Disease ◽

Wilt Disease ◽

Control Measures ◽

Pine Wilt ◽

And Control

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Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Forests ◽

10.3390/f12060731 ◽

2021 ◽

Vol 12 (6) ◽

pp. 731

Author(s):

Zhuoqing Hao ◽

Jixia Huang ◽

Yantao Zhou ◽

Guofei Fang

Keyword(s):

River Basin ◽

Yangtze River ◽

Yangtze River Basin ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Spatiotemporal Pattern ◽

Pinewood Nematode ◽

The Yangtze River ◽

Pine Wilt ◽

The Yangtze River Basin

The Yangtze River Basin is among the river basins with the strongest strategic support and developmental power in China. As an invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus has introduced a serious obstacle to the high-quality development of the economic and ecological synchronization of the Yangtze River Basin. This study analyses the occurrence and spread of pine wilt disease (PWD) with the aim of effectively managing and controlling the spread of PWD in the Yangtze River Basin. In this study, statistical data of PWD-affected areas in the Yangtze River Basin are used to analyse the occurrence and spread of PWD in the study area using spatiotemporal visualization analysis and spatiotemporal scanning statistics technology. From 2000 to 2018, PWD in the study area showed an “increasing-decreasing-increasing” trend, and PWD increased explosively in 2018. The spatial spread of PWD showed a “jumping propagation-multi-point outbreak-point to surface spread” pattern, moving west along the river. Important clusters were concentrated in the Jiangsu-Zhejiang area from 2000 to 2015, forming a cluster including Jiangsu and Zhejiang. Then, from 2015–2018, important clusters were concentrated in Chongqing. According to the spatiotemporal scanning results, PWD showed high aggregation in the four regions of Zhejiang, Chongqing, Hubei, and Jiangxi from 2000 to 2018. In the future, management systems for the prevention and treatment of PWD, including ecological restoration programs, will require more attention.

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