Hyperspectral evidence of early-stage pine shoot beetle attack in Yunnan pine

Liu, Yu Jie; Zhan, Zhongyi; Ren, Lili; Sang-zi, ZE; Yu, Linfeng; Jiang, Qi; YouQing, Luo

doi:10.1016/j.foreco.2021.119505

Cited by 16 publications

(9 citation statements)

References 82 publications

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“…However, using only RGB data cannot be used for early PWD detection. According to previous studies ( Abdullah et al., 2019b ; Bárta et al., 2021 ; Liu et al., 2021 ; Ortiz et al., 2013 ; Yu et al., 2021d ), the red edge, NIR, and SWIR regions were considered as sensitive bands for early monitoring. For example, some studies found that healthy trees normally have higher reflectances than damaged trees in the NIR regions ( Ortiz et al., 2013 ; Liu et al., 2021 ; Yu et al., 2021d ).…”

Section: Discussionmentioning

confidence: 99%

“…The short-wave infrared (SWIR) region of the spectrum is also key for distinguishing healthy trees from those damaged by Ips typographus and Dendroctonus rufipennis (Foster et al, 2017;Abdullah et al, 2019b;Baŕta et al, 2021;Huo et al, 2021). Other studies also found that healthy trees normally have higher reflectances than damaged trees in the near infrared (NIR) bands (Liu et al, 2021;Ortiz et al, 2013;Yu et al, 2021d). The spectral features of NIR and SWIR are related to the leaf cell structure and water content (Carter and Knapp, 2001;Entcheva Campbell et al, 2004;Einzmann et al, 2021).…”

Section: Potential Spectral Bands For Pwd Early Detectionmentioning

confidence: 99%

“…Hyperspectral RS uses data from hundreds of bands and continuous wavelengths, and these bands can capture physiological changes in infested trees, which help detect early-stage pest and diseases infestations ( Cheng et al., 2010 ; Abdullah et al., 2018 ; Liu et al., 2021 ; Yu et al., 2021d ). As the advanced technology used in forest pest and diseases detection, UAV-based hyperspectral imagery (HSI) can provide highly accurate detection with flexible and efficient data acquisition ( Iordache et al., 2020 ; Li et al., 2020 ; Zhang et al., 2018 ; Lin et al., 2019 ; Lin et al., 2021 ; Yu et al., 2021a ).…”

Section: Introductionmentioning

confidence: 99%

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Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Huo²,

Huang³

et al. 2022

Front. Plant Sci.

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Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Potential Spectral Bands For Pwd Early Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Huo²,

Huang³

et al. 2022

Front. Plant Sci.

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“…Moreover, with ITCs detected with ALS data, it is possible to help the field work of the harvester by optimizing the mapping of the skit trail network [15]. On the other hand, ALS data are not so effective in describing the physiological characteristics of trees or their health status, which are better captured by spectral data, in particular, hyperspectral data [16][17][18][19]. Hyperspectral data have been used in the literature to map tree diseases and insect outbreaks [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, ALS data are not so effective in describing the physiological characteristics of trees or their health status, which are better captured by spectral data, in particular, hyperspectral data [16][17][18][19]. Hyperspectral data have been used in the literature to map tree diseases and insect outbreaks [17,18]. To combine the ITCs derived from ALS data with the spectral information of the hyperspectral imagery, the pixels of the images are usually aggregated inside each crown with different techniques [20].…”

Section: Introductionmentioning

confidence: 99%

Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data

et al. 2022

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Wood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were small.

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Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

Li,

Huo,

Zhang

2024

Computers and Electronics in Agriculture

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Hyperspectral evidence of early-stage pine shoot beetle attack in Yunnan pine

Cited by 16 publications

References 82 publications

Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data

Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

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