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  1. Article ; Online: Monitoring the Severity of Pantana phyllostachysae Chao on Bamboo Using Leaf Hyperspectral Data

    Xuying Huang / Zhanghua Xu / Xu Yang / Jingming Shi / Xinyu Hu / Weimin Ju

    Remote Sensing, Vol 13, Iss 4146, p

    2021  Volume 4146

    Abstract: Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge ... ...

    Abstract Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge on whether the severity of this pest could be effectively monitored by using remote sensing methods is very limited. To fill this gap, this study aimed to identify the PPC damage of moso bamboo leaves using hyperspectral data. Specifically, we investigated differences in relative chlorophyll content (RCC), leaf water content (LWC), leaf nitrogen content (LNC), and hyperspectral spectrum among healthy, damaged (mildly damage, moderately damage, severely damage), and off-year bamboo leaves. Then, the hyperspectral indices sensitive to pest damage were selected by recursive feature elimination (RFE). The PPC damage identification model was constructed using the light gradient boosting machine (LightGBM) algorithm. We designed two different scenarios, without (A) and with (B) off-year samples, to evaluate the impact of off-year leaves on identification results. The RCC, the LWC, and the LNC of damaged leaves generally showed clear declined trends with the deterioration of damaged severity. The RCC and the LNC of off-year leaves were significantly lower than those of healthy and damaged leaves, whereas the LWC of off-leaves was significantly different from that of damaged leaves. The pest infestation caused noticeable distortion of leaf spectrum, increases in red and shortwave infrared bands, and decreases in green and near-infrared bands. The magnitude of reflectance change increased with the pest severity. The reflectance of off-year leaves in visible and near-infrared regions was distinguishably higher than that of healthy and damaged leaves. The overall accuracy (OA) of the constructed model for the identification of leaves with different degrees of damage severity reached 81.51%. When off-year, healthy, and damaged leaves were lumped together, the OA of the ...
    Keywords moso bamboo ; pest ; hyperspectral data ; machine learning ; Science ; Q
    Subject code 580
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Changing Relationships between Nitrogen Content and Leaf Spectral Characteristics of Moso Bamboo Leaves under Pantana phyllostachysae Chao Stress

    Zhanghua Xu / Hui Yu / Bin Li / Zhenbang Hao / Yifan Li / Songyang Xiang / Xuying Huang / Zenglu Li / Xiaoyu Guo

    Forests, Vol 13, Iss 1752, p

    2022  Volume 1752

    Abstract: Nitrogen is an important indicator of vegetation health, but the relationship between changes in the leaf nitrogen content of Moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and leaf spectra remains unclear. We analyzed the ... ...

    Abstract Nitrogen is an important indicator of vegetation health, but the relationship between changes in the leaf nitrogen content of Moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and leaf spectra remains unclear. We analyzed the relationship between the leaf nitrogen content and leaf spectra of Moso bamboo leaves under PPC stress to investigate whether the relationship could be used to detect pests and prevent their spread. We measured the nitrogen content and leaf spectra of Moso bamboo leaves under different damage levels, identified spectral indicators that were correlated with leaf nitrogen content (by removing the envelope and first-order differentiation of the raw spectra), and estimated leaf nitrogen content from the spectral data using regression models. Leaf nitrogen content decreased with increasing pest damage, and the leaf spectral curves changed, with the “green peak” and “red valley” in the visible range disappearing and the slope of the spectral curve decreasing. The wavelength region with the strongest correlation between the nitrogen content and spectral characteristics changed significantly with increasing pest damage, and the correlation in the red-edge region gradually decreased. The fits of nitrogen-content estimation models tended to decrease and then increase with increasing pest damage and were worst among leaves in the moderate damage state (Mo). A disordered relationship between nitrogen content and spectral characteristics indicated possible PPC damage. The degree of disorder was greatest in the Mo state. This study provides theoretical support for remote sensing monitoring of PPC hazards.
    Keywords Pantana phyllostachysae Chao ; Moso bamboo ; nitrogen content ; spectral characteristics ; pest damage ; regression model ; Plant ecology ; QK900-989
    Subject code 580
    Language English
    Publishing date 2022-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Retrieving chlorophyll content and equivalent water thickness of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

    Zhanghua Xu / Anqi He / Yiwei Zhang / Zhenbang Hao / Yifan Li / Songyang Xiang / Bin Li / Lingyan Chen / Hui Yu / Wanling Shen / Xuying Huang / Xiaoyu Guo / Zenglu Li

    Forest Ecosystems, Vol 10, Iss , Pp 100108- (2023)

    2023  

    Abstract: Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative ... ...

    Abstract Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative transfer model is widely used for vegetation biochemical component content inversion. However, the presence of leaf-eating pests, such as Pantana phyllostachysae Chao (PPC), weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered. Therefore, this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables (LUTs) based on the PROSAIL framework by setting different parameter ranges according to infestation levels. Quantitative inversions of leaf area index (LAI), leaf chlorophyll content (LCC), and leaf equivalent water thickness (LEWT) were derived. The scale conversions from LCC to canopy chlorophyll content (CCC) and LEWT to canopy equivalent water thickness (CEWT) were calculated. The results showed that LAI, CCC, and CEWT were inversely related with PPC-induced stress. When applying multiple LUTs, the p-values were <0.01; the R2 values for LAI, CCC, and CEWT were 0.71, 0.68, and 0.65 with root mean square error (RMSE) (normalized RMSE, NRMSE) values of 0.38 (0.16), 17.56 μg·cm‒2 (0.20), and 0.02 cm (0.51), respectively. Compared to the values obtained for the traditional PROSAIL model, for October, R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT, respectively and RMSE decreased by 0.35 μg·cm‒2 for CCC. The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model, while establishing multiple LUTs under different pest-induced damage levels, was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.
    Keywords Moso bamboo ; Chlorophyll content ; Equivalent water thickness ; PROSAIL model ; Multiple LUTs ; Pantana phyllostachysae Chao ; Ecology ; QH540-549.5
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher KeAi Communications Co., Ltd.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

    Zhanghua Xu / Qi Zhang / Songyang Xiang / Yifan Li / Xuying Huang / Yiwei Zhang / Xin Zhou / Zenglu Li / Xiong Yao / Qiaosi Li / Xiaoyu Guo

    Forests, Vol 13, Iss 418, p

    2022  Volume 418

    Abstract: In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a ... ...

    Abstract In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient boosting tree (XGBoost) machine learning algorithms. The overall detection accuracy (OA) and Kappa coefficient of SVM, RF, and XGBoost were 81.95%, 0.733, 85.71%, 0.805, and 86.47%, 0.811, respectively. Meanwhile, the detection accuracies of SVM, RF, and XGBoost were 78.26%, 76.19%, and 80.95% for healthy, 75.00%, 83.87%, and 79.17% for mild damage, 83.33%, 86.49%, and 85.00% for moderate damage, and 82.5%, 90.91%, and 93.75% for severe damage Moso bamboo, respectively. Overall, XGBoost exhibited the best detection performance, followed by RF and SVM. Thus, the study findings provide a technical reference for the regional monitoring and control of PPC in Moso bamboo.
    Keywords UAV multispectral remote sensing ; Moso bamboo forest ; Pantana phyllostachysae Chao ; feature selection ; detection model ; Plant ecology ; QK900-989
    Subject code 629
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Molecular and computational analysis of 45 samples with a serologic weak D phenotype detected among 132,479 blood donors in northeast China

    Xu Zhang / Guiji Li / Zhuren Zhou / Chaopeng Shao / Xuying Huang / Lichun Li / Xiaofeng Li / Ying Liu / Hua Fan / Jianping Li

    Journal of Translational Medicine, Vol 17, Iss 1, Pp 1-

    2019  Volume 11

    Abstract: Abstract Background RH1 is one of the most clinically important blood group antigens in the field of transfusion and in the prevention of fetal incompatibility. The molecular analysis and characterization of serologic weak D phenotypes is essential to ... ...

    Abstract Abstract Background RH1 is one of the most clinically important blood group antigens in the field of transfusion and in the prevention of fetal incompatibility. The molecular analysis and characterization of serologic weak D phenotypes is essential to ensuring transfusion safety. Methods Blood samples from a northeastern Chinese population were randomly screened for a serologic weak D phenotype. The nucleotide sequences of all 10 exons, adjacent flanking intronic regions, and partial 5′ and 3′ untranslated regions (UTRs) were detected for RHD genes. Predicted deleterious structural changes in missense mutations of serologicl weak D phenotypes were analyzed using SIFT, PROVEAN and PolyPhen2 software. The protein structure of serologic weak D phenotypes was predicted using Swiss-PdbViewer 4.0.1. Results A serologic weak D phenotype was found in 45 individuals (0.03%) among 132,479 blood donors. Seventeen distinct RHD mutation alleles were detected, with 11 weak D, four partial D and two DEL alleles. Further analyses resulted in the identification of two novel alleles (RHD weak D 1102A and 399C). The prediction of a three-dimensional structure showed that the protein conformation was disrupted in 16 serologic weak D phenotypes. Conclusions Two novel and 15 rare RHD alleles were identified. Weak D type 15, DVI Type 3, and RHD1227A were the most prevalent D variant alleles in a northeastern Chinese population. Although the frequencies of the D variant alleles presented herein were low, their phenotypic and genotypic descriptions add to the repertoire of reported RHD alleles. Bioinformatics analysis on RhD protein can give us more interpretation of missense variants of RHD gene.
    Keywords RHD variant ; Serological weak D phenotype ; Molecular and computational analysis ; Weak D ; Partial D ; DEL ; Medicine ; R
    Subject code 572
    Language English
    Publishing date 2019-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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