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国土资源遥感  2019, Vol. 31 Issue (2): 73-81    DOI: 10.6046/gtzyyg.2019.02.11
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
三峡库区森林叶面积指数多模型遥感估算
董立新1,2,3
1.中国遥感卫星辐射测量与定标重点开放实验室,北京 100081
2.国家卫星气象中心,北京 100081
3.中国气象局卫星应用联合研究中心,北京 100081
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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摘要 

叶面积指数(leaf area index,LAI)是定量研究森林生态系统能量交换的一个重要结构参数。本文利用野外观测LAI,以及Landsat TM计算的7种常用植被指数和5个自定义植被指数,通过筛选建立了不同森林类型的LAI估算模型,其中,针叶林采用多元逐步回归模型,阔叶林与混交林采用主成分分析模型,最终通过多个模型估算三峡库区区域尺度森林LAI。利用样地实测LAI数据进行精度验证,针叶林、阔叶林和混交林的均方根误差分别为0.829 4,1.111 5和1.790 9,判定系数R 2均达到了0.77以上。研究结果将为森林生态系统和碳循环研究提供基础数据。

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关键词 森林叶面积指数植被指数法主成分分析三峡库区遥感    
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.

Key wordsforest leaf area index    vegetation index method    principal component analysis    Three Gorges Reservoir area    remote sensing
收稿日期: 2018-03-27      出版日期: 2019-05-23
:  TP79  
基金资助:国家高分辨率对地观测重大专项项目“基于GF-5热红外数据的大气校正与农田干旱监测应用示范”(11-Y20A32-9001-15/17);国家气候变化专项“气候与CO2浓度变化对高寒植被影响遥感评估”(CCSF-14-06);公益性行业专项第三次青藏高原大气科学考查试验课题“青藏高原卫星反演产品校验外场观测试验与产品改进与资料同化研究”(GYHY201406001-01);国务院三峡办项目“三峡工程生态与环境遥感动态与实时监测”共同资助(SX2002-004)
作者简介: 董立新(1973-),男,博士,副研究员,主要从事定量遥感应用研究。Email: dlx_water@163.com。
引用本文:   
董立新. 三峡库区森林叶面积指数多模型遥感估算[J]. 国土资源遥感, 2019, 31(2): 73-81.
Lixin DONG. Multi-model estimation of forest leaf area index in the Three Gorges Reservoir area. Remote Sensing for Land & Resources, 2019, 31(2): 73-81.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.11      或      https://www.gtzyyg.com/CN/Y2019/V31/I2/73
Fig.1  研究区位置及野外样点分布示意图
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  各种植被指数与LAI的R
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  各种植被指数与LAI的一元线性回归分析
Fig.2  针叶林LAI-NDVI不同模型拟合图
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  植被指数与LAI的非线性回归模型R2(针叶林)
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  植被指数与LAI的双曲线分析模型
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  各种植被指数与LAI的多元逐步回归分析
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  不同森林类型的PCA结果
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  PCA各分量贡献率
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  各种植被指数与LAI的主成分回归模型
Fig.3  三峡库区LAI结果
Fig.4  不同类型的森林LAI观测值与预测对比
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