Spectroscopy and Spectral Analysis, Volume. 42, Issue 9, 2726(2022)

Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox

Figures & Tables(11)
Location of geographical area of Shunchang County, Nanping City, Fujian Province, remote sensing of experimental areas and measuring points distribution (2D) (b) and (3D) (c)
Random Forest Structure
SPAD variation trend of phyllostachys pubescens leaf samples under different conditions
Leaf samples of phyllostachys pubescens (a) under different conditions and their spectral information (b)
Pearson correlation analysis of vegetation Index with different pest levels(1)—(35) represent characteristic Indexes respectively: CIgreen, CIred, NDVI705, DVI, SAVI, OSAVI, MCARI, TCARI, RVI, ARVI, GNDVI, PRI, VARI, NPCI, PRI*CI, R515/R570, mSR, VOG1, VOG2, VOG3, 469 nm, 702 nm, 760 nm, 469FDR, 469SDR, 702FDR, 702SDR, 760FDR, 760SDR, CARI, RES, REA, REDVI, RERVI, RENDVI, similarly hereinafter
Pearson correlation analysis of leaf spectral characteristics of leaf samples under different hazard levels(a): Healthy leaf sample; (b): Mild leaf sample; (c); Moderate leaf sample; (d): Severe leaf sample; (e): Off year leaf sample
SPAD detection results of phyllostachys pubescens leaves by four models(R2 and RMSE are model fitting degree and root mean square error, respectively, similarly hereinafter)(a): Multiple linear regression; (b): Ridge regression; (c): Random forest regression; (d): XGBoost regression
SPAD detection results of phyllostachys pubescens leaves under different damage conditions by four models(a): Healthy leaf—Multiple linear regression; (b): Healthy leaf—Ridge regression; (c): Healthy leaf—Random forest regression;(d): Healthy leaf—XGBoost regression; (e): Mild leaf—Multiple linear regression; (f): Mild leaf—Ridge regression;(g): Mild leaf—Random forest regression; (h): Mild leaf—XGBoost regression; (i): Moderate leaf —Multiple linear regression;(j): Moderate leaf —Ridge regression; (k): Moderate leaf —Random forest regression; (l): Moderate leaf —XGBoost regression;(m): Severe leaf —Multiple linear regression; (n): Severe leaf —Ridge regression; (o): Severe leaf —Random forest regression;(p): Severe leaf —XGBoost regression; (q): Off year leaf—Multiple linear regression; (r): Off year leaf —Ridge regression;(s): Off year leaf —Random forest regression; (t): Off year leaf —XGBoost regression;
Characteristic space of leaf spectral Index of all leaf samples and leaf samples under different pest grades
Changes of model R2 and RMSE under different regularization parameters
  • Table 1. SPAD-related vegetation Index

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    Table 1. SPAD-related vegetation Index

    Vegetation
    Index
    Full nameCalculation formulaSource
    NDVI705Normalized difference vegetation Index[705, 750]NDVI705=(R750-R705)/(R750+R705)[13]
    DVIDifference vegetation IndexDVI=R800-R670[14]
    RVIRatio vegetation IndexRVI=R800/R670[14]
    SAVISoil adjusted vegetation IndexSAVI=1.5[(R800-R670)/(R800+R670+0.5)][14]
    ARVIAtmospherically resistant vegetationAVRI=R810-(2R680-R480)/R810+2(R680-R480)
    GNDVIGreen normalized difference vegetation IndexGNDVI=(R800-R550)/(R800+R550)[15]
    MCARIModified Chlorophyll absorption ratio IndexMCARI=[(R700-R670)-0.2(R701-R550)](R700/R670)[13]
    TCARITransformed Chlorophyll absorption ratio IndexTCARI=3[(R700-R670)-0.2(R700-R550)(R700/R670)][13]
    OSAVIOptimized soil adjusted vegetation IndexOSAVI=1.16[(R800-R670)/(R800+R670+0.16)][13]
    PRIPhotochemical reflectance IndexPRI=(R570-R531)/(R570+R531)[15]
    VARIVisible atmospherically resistance IndexVARI=(R555-R680)/(R555+R680-R480)[16]
    NPCINormalized pigment Chlorophyll ratio IndexNPCI=(R680-R430)/(R680+R430)[15]
    VOG1Vogelmann Red Edge Index 1VOG1=R740/R720[17]
    VOG2Vogelmann Red Edge Index 2VOG2=(R734-R747)/(R715+R726)[17]
    VOG3Vogelmann Red Edge Index 3VOG3=(R734-R747)/(R715+R720)[17]
    PRI*CICarotenoid/Chlorophyll Ratio IndexPRI*CI=(R531-R570)/(R531+R570)*((R760/R700)-1)[18]
    R515/R570Simple ratio vegetation IndexR515/R570[19]
    CIgreenGreen Chlorophyll IndexCIgreen=(R800/R550)-1[20]
    CIredRed Chlorophyll IndexCIred=(R800/R720)-1[20]
    mSRModified red edge simple ratio IndexmSR=(R705-R445)/(R705+R445)[21]
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. Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2726

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Paper Information

Category: Research Articles

Received: Mar. 12, 2021

Accepted: Jul. 11, 2021

Published Online: Nov. 17, 2022

The Author Email:

DOI:10.3964/j.issn.1000-0593(2022)09-2726-14

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