Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

Pan, Jie; Lin, Jizhen; Xie, Tong

doi:10.3390/rs15092281

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…The most significant changes in SPAD occur during the regreening stage, and these notable fluctuations in SPAD values likely aid in achieving good model fits. The research results (Tables 5, 6 and 8) demonstrate that the scale of hyperspectral imagery influences the accuracy of the inversion models, with varying accuracies at different scales, aligning with the findings of Pan [60] and Zhu [61] (overall inversion accuracy: ground > UAV). The study indicates that some PLSR models perform slightly worse than RF models, consistent with previous research [62,63].…”

Section: Accuracy Of Chlorophyll Content Modelssupporting

confidence: 69%

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Xu,

Dai,

Zhang

et al. 2024

Agronomy

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Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R2, RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R2, RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.

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Section: Accuracy Of Chlorophyll Content Modelssupporting

confidence: 69%

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Xu,

Dai,

Zhang

et al. 2024

Agronomy

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“…All four indices calculated using EO-1 Hyperion data could effectively monitor the defoliation in spruce-balsam fir forests infected with spruce budworm [14]. The green normalized difference vegetation index (GNDVI) performed better in the detection experiments of Masson pine disease [15]. Run Yu et al [16] concluded that a random forest algorithm incorporating REP could better classify the disease severity in diseased pine.…”

Section: Introductionmentioning

confidence: 99%

Using a Vegetation Index to Monitor the Death Process of Chinese Fir Based on Hyperspectral Data

Tang,

Zang,

Lin

et al. 2023

Forests

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Chinese fir is one of the most widely distributed and extensively planted timber species in China. Therefore, monitoring pests and diseases in Chinese fir plantations is directly related to national timber forest security and forest ecological security. This study aimed to identify appropriate vegetation indices for the early monitoring of pests and diseases in Chinese fir plantations. For this purpose, the researchers used an imaging spectrometer to capture hyperspectral images of both experimental and control groups. The experimental group consisted of Chinese fir trees with two sections of bark stripped off, while the control group consisted of healthy Chinese fir trees. The study then assessed the sensitivity of 11 vegetation indices to the physiological differences between the two groups using the Mann–Whitney U test. The results showed that both the green-to-red region spectral angle index (GRRSGI) and the red edge position index (REP) were able to monitor the difference as early as 16 days after damage. However, GRRSGI performs best in monitoring early death changes in Chinese fir trees because it is less affected by noise and is more stable. The green–red spectral area index (GRSAI) also had high stability, but the monitoring effect was slightly worse than that of GRRSGI and REP. Compared with other indices, GRRSGI and GRSAI can better exploit the advantages of hyperspectral data.

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“…There have been many studies using multispectral remote sensing techniques for PWD monitoring [9,11,12]. However, the spectral information obtained from multispectral remote sensing is not sufficient for early identification of PWD due to the subtle spectral response between healthy and early infected pine trees [13]. Through imaging or non-imaging spectral technology, hyperspectral remote sensing technology can obtain continuous spectral information of very narrow electromagnetic band features, which compensates for the shortage of multispectral remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery

Chen

Yao

et al. 2023

Remote Sensing

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The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accurate monitoring of pine wilt disease (PWD) is a prerequisite for timely and effective disaster prevention and control. UAVs can carry hyperspectral sensors for near-ground remote sensing observations, which can obtain rich spatial and spectral information and have the potential for infected tree identification. Deep learning techniques can use rich multidimensional data to mine deep features in order to achieve tasks such as classification and target identification. Therefore, we propose an improved Mask R-CNN instance segmentation method and an integrated approach combining a prototypical network classification model with an individual tree segmentation algorithm to verify the possibility of deep learning models and UAV hyperspectral imagery for identifying infected individual trees at different stages of PWD. The results showed that both methods achieved good performance for PWD identification: the overall accuracy of the improved Mask R-CNN with the screened bands as input data was 71%, and the integrated method combining prototypical network classification model with individual tree segmentation obtained an overall accuracy of 83.51% based on the screened bands data, in which the early infected pine trees were identified with an accuracy of 74.89%. This study indicates that the improved Mask R-CNN and integrated prototypical network method are effective and practical for PWD-infected individual trees identification using UAV hyperspectral data, and the proposed integrated prototypical network enables early identification of PWD, providing a new technical guidance for early monitoring and control of PWD.

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Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

Cited by 7 publications

References 40 publications

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Using a Vegetation Index to Monitor the Death Process of Chinese Fir Based on Hyperspectral Data

Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery

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