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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 73-81     DOI: 10.6046/gtzyyg.2019.02.11
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Multi-model estimation of forest leaf area index in the Three Gorges Reservoir area
Lixin DONG1,2,3
1.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, Beijing 100081, China
2.National Satellites Meteorological Center, Beijing 100081, China
3.The Joint Center for Satellite Research and Applications, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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Abstract  

Leaf area index (LAI) is an important structural variable for quantitative study of the energy exchange characteristics of forest ecosystems. Based on field observations of LAI, 7 kinds of vegetation indexes and 5 custom vegetation indexes based on Landsat TM, LAI estimation model of different forest types were established through the model screening, in which the multiple regression model for coniferous forest and principal component analysis model for broad-leaved forest and mixed forest were used. Finally, the regional scale forest LAI distribution map was made through multiple model estimation. The accuracy of LAI is 0.829 4, 1.111 5 and 1.790 9 for coniferous forest, broad-leaved forest and mixed forest respectively. And the total R 2 is over 0.77 for all the forests. The results will provide basic data for forest ecosystem and carbon cycle studies.

Keywords forest leaf area index      vegetation index method      principal component analysis      Three Gorges Reservoir area      remote sensing     
:  TP79  
Issue Date: 23 May 2019
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Lixin DONG
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Lixin DONG. Multi-model estimation of forest leaf area index in the Three Gorges Reservoir area[J]. Remote Sensing for Land & Resources, 2019, 31(2): 73-81.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.11     OR     https://www.gtzyyg.com/EN/Y2019/V31/I2/73
Fig.1  Location of study area and distribution of the field samples
植被指数 LAI
针叶林 阔叶林 混交林 总体
VI1 0.701 94 0.396 88 0.392 77 0.668 68
NDVI 0.661 74 0.376 37 0.651 81 0.702 49
SAVI 0.602 46 0.339 23 0.295 06 0.621 81
ARVI 0.569 70 0.306 31 0.283 61 0.593 52
MSAVI 0.563 19 0.342 85 0.305 82 0.605 73
SARVI 0.544 32 0.295 41 0.282 86 0.577 90
VI2 0.418 47 0.315 30 0.355 01 0.467 11
VI5 0.402 91 -0.010 50 0.322 68 0.345 46
VI4 0.377 51 0.292 58 0.328 18 0.455 33
VI3 0.352 68 0.341 50 0.176 05 0.286 66
PVI 0.347 80 0.123 51 0.072 66 0.403 55
EVI 0.346 52 0.135 54 0.039 42 0.389 16
Tab.1  Correlation between LAI and vegetation indexes of varied forest species
类型 植被指数 回归模型 R R2 标准误差 F检验 显著性系数
针叶林 VI1 Y=-2.57-8.781X 0.702 0.493 1.387 35.938 0.000
NDVI Y=-9.116+0.058X 0.662 0.438 1.398 28.825 0.000
SAVI Y=-1.937+4.952X 0.602 0.363 1.554 4.591 0.000
ARVI Y=-0.407+5.952X 0.570 0.325 1.601 17.779 0.000
MSAVI Y=-4.264+9.213X 0.563 0.317 1.609 17.188 0.000
SARVI Y=-0.426+3.36X 0.544 0.296 1.634 15.578 0.000
阔叶林 VI1 Y=0.28-9.09X 0.397 0.157 2.359 5.795 0.022
NDVI Y=1.374+7.515X 0.367 0.135 2.390 4.834 0.035
MSAVI Y=-4.659+13.441X 0.343 0.118 2.414 4.129 0.051
SAVI Y=0.335+5.762X 0.339 0.115 2.418 4.031 0.053
混交林 NDVI Y=-16.096+0.101X 0.652 0.425 2.236 10.342 0.006
VI1 Y=-1.312-10.854X 0.393 0.154 2.711 2.554 0.132
VI2 Y=0.746-9.633X 0.355 0.126 2.756 2.019 0.177
Tab.2  Univariate linear regression models between LAI and vegetation indexes of varied forest species
Fig.2  Modeling of relationship between LAI and NDVI for needle forest
模型 线性 二次多项式 复合模型 生长模型 对数模型 三次多项式 S曲线 指数模型 双曲线模型 幂指数模型
NDVI 0.438 0.518 0.345 0.345 0.420 0.517 0.317 0.345 0.401 0.332
VI1 0.493 0.596 0.410 0.410 0.446 0.589 0.323 0.410 0.395 0.369
Tab.3  R2 of nonlinear regression models between LAI and vegetation indexes (needle forest)
类型 植被指数 回归模型 R R2 标准误差 F检验 显著性系数
针叶林 VI1 Y=8.159-3.091/X 0.628 0.395 1.515 24.111 0.000
NDVI Y=14.678-2 408.372/X 0.634 0.401 1.507 24.820 0.000
SAVI Y=6.364-3.131/X 0.482 0.233 1.706 11.209 0.000
ARVI Y=4.502-0.729/X 0.361 0.130 1.816 5.552 0.000
MSAVI Y=8.432-4.165/X 0.482 0.232 1.706 11.203 0.000
SARVI Y=3.405-0.183/X 0.215 0.406 1.902 1.790 0.000
阔叶林 VI1 Y=14.647+5.554/X 0.453 0.206 2.290 8.026 0.008
NDVI Y=27.504-4 665.65/X 0.371 0.139 2.385 4.996 0.033
MSAVI Y=18.348-9.751/X 0.345 0.119 2.412 4.192 0.049
SAVI Y=13.535-7.365/X 0.345 0.119 2.412 4.195 0.049
混交林 NDVI Y=26.721-4 455.288/X 0.633 0.401 0.633 9.374 0.008
VI1 Y=15.172+6.145/X 0.394 0.155 2.709 2.576 0.131
VI2 Y=13.317+4.006/X 0.337 0.113 2.776 1.790 0.202
Tab.4  Inverse models between LAI and vegetation indexes of varied forest species
类型 多元线性回归模型 R R2 标准
误差		 F检验 显著性
系数
针叶林 Y=-5.226-13.729VI1-3.693VI2+6.05VI4 0.803 0.644 1.193 9 35.93 0.000
阔叶林 Y=-4.884-12.882VI1+0.000 4EVI 0.523 0.273 2.227 0 5.642 0.008
混交林 Y=-16.092+0.101NDVI 0.652 0.425 2.235 8 10.342 0.006
Tab.5  Multivariate linear regression models between LAI and vegetation indexes of varied forest species
类型 C1 C2 C3 C4
针叶林 VI1 VI3 PVI EVI
阔叶林 VI1 EVI PVI NDVI
混交林 VI1 EVI PVI NDVI
Tab.6  PCA of different forest types
植被类型 主成分分量 特征值 方差贡献/% 累积贡献/%
针叶林 C1 7.319 45 61.00 61.00
C2 2.588 47 21.57 82.57
C3 0.745 39 6.21 88.78
C4 0.538 48 4.49 93.26
阔叶林 C1 7.402 98 61.69 61.69
C2 2.479 73 20.66 82.36
C3 0.907 83 7.57 89.92
C4 0.672 17 5.60 95.52
混交林 C1 6.524 52 54.37 54.37
C2 3.970 18 33.08 87.46
C3 0.752 40 6.27 93.73
C4 0.576 63 4.81 98.53
Tab.7  Contribution rate of each PCA component
类型 主成分回归模型 R R2 标准误差 F检验 显著性系数 数量
针叶林 Y=-2.760-11.095C1+0.016C2-0.063C3+0.001C4 0.766 0.587 1.306 11 12.064 0.000 39
阔叶林 Y=-14.964-10.586C1+0.001C2-0.027C3+0.056C4 0.571 0.326 2.220 30 3.383 0.022 33
混交林 Y=-15.991-6.114C1+0.001C2-0.056C3+0.092C4 0.670 0.449 2.469 35 2.239 0.131 16
Tab.8  PCA regression models between LAI and vegetation indexes of varied forest species
Fig.3  Result of LAI in Three Gorges Reservoir area
Fig.4  Comparison predicted LAI with measuremed LAI for different forest types
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