Please wait a minute...
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
全文: PDF(2411 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
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。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
董立新
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
ZTFLH:  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:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.11      或      http://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
[1] Chen J M, Black T A . Foliage area and architecture of clumped plant canopies from sunfleck size distributions[J]. Agricultural and Forest Meteorology, 1992,60(3-4):249-266.
doi: 10.1016/0168-1923(92)90040-B
[2] Gower S T, Kucharik C J, Norman J M . Direct and indirect estimation of leaf area index,FAPAR,and net primary production of terrestrial ecosystems[J]. Remote Sensing of Environment, 1999,70(1):29-51.
doi: 10.1016/S0034-4257(99)00056-5
[3] Chen J, Cihlar J . Retrieving leaf area index of Boreal Conifer Forest using Landsat TM images[J]. Remote Sensing of Environment, 1996,55:153-162.
doi: 10.1016/0034-4257(95)00195-6
[4] Geol N S, Rozehnal I . A High-level Language for L-systems and Its Application[M]. New York:Springer-Verlag, 1992: 231-251.
[5] Chen X X, Vierling L, Rowell E . Using LiDAR and effective LAI data to evaluate IKONOS and Landsat7 ETM+ vegetation cover estimates in a ponderosa pine forest[J]. Remote Sensing of Environment, 2004,91(1):14-26.
doi: 10.1016/j.rse.2003.11.003
[6] 朱高龙, 居为民, 范文义 , 等. 帽儿山地区森林冠层叶面积指数的地面观测与遥感反演[J]. 应用生态学报, 2010,21(8):2127-2124.
Zhu G L, Ju W M, Fan W Y , et al. Forest canopy leaf area index in Maoershan Mountain:Ground measurement and remote sensing retrieval[J]. Chinese Journal of Applied Ecology, 2010,21(8):2127-2124.
[7] 刘婧怡, 汤旭光, 常守志 , 等. 森林叶面积指数遥感反演模型构建及区域估算[J]. 遥感技术与应用, 2014,29(1):18-25.
doi: doi:10.11873/j.issn.1004\|0323.2014.1.0018
Liu J Y, Tang X G, Chang S Z , et al. Application of remote sensing to inverse the forest leaf area index and regional estimation[J]. Remote Sensing Technology and Application, 2014,29(1):18-25.
doi: doi:10.11873/j.issn.1004\|0323.2014.1.0018
[8] 韩婷婷, 习晓环, 王成 , 等. 基于TM数据的西双版纳地区森林叶面积指数反演[J]. 遥感信息, 2014,29(2):28-32.
Han T T, Xi X H, Wang C , et al. Forest leaf area index inversion based on TM data in Xishuangbanna Area[J]. Remote Sensing Information, 2014,29(2):28-32.
[9] 刘振波, 刘杰 . 森林冠层叶面积指数遥感反演——以小兴安岭五营林区为例[J]. 生态学杂志, 2015,34(7):1930-1936.
Liu Z B, Liu J . Retrieving forest canopy LAI from remote sensing data:A case study over Wuying forest in the Lesser Khingan[J]. Chinese Journal of Ecology, 2015,34(7):1930-1936.
[10] 姚雄, 余坤勇, 杨玉洁 , 等. 基于随机森林模型的林地叶面积指数遥感估算[J]. 农业机械学报, 2017,48(5):159-166.
Yao X, Yu K Y, Yang Y J , et al. Estimation of forest leaf area index based on random forest model and remote sensing data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(5):159-166.
[11] 张瀛, 孟庆岩, 武佳丽 , 等. 基于环境星CCD数据的环境植被指数及叶面积指数反演研究[J]. 光谱学与光谱分析, 2011,31(10):2789-2793.
Zhang Y, Meng Q Y, Wu J L , et al. Study of environmental vegetation index based on environment satellite CCD data and LAI inversion[J]. Spectroscopy and Spectral Analysis, 2011,31(10):2789-2793.
[12] Colombo R, Bellingeri D, Fasolini D , et al. Retrieval of leaf index in different vegetation types using high resolution satellite data[J]. Remote Sensing Environment, 2003,86:120-131.
doi: 10.1016/S0034-4257(03)00094-4
[13] Fensholt R . Earth observation of vegetation status in the Sahelian and Sudanian Weat Africa:Comparison of Terra MODIS and NOAA AVHRR satellite data[J]. International Journal of Remote Sensing, 2004,25(9):1641-1659.
doi: 10.1080/01431160310001598999
[14] Pu R L, Gong P . Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping[J]. Remote Sensing of Environment, 2004,91:212-224.
doi: 10.1016/j.rse.2004.03.006
[15] Vaesen K, Gilliams S, Nackaerets K , et al. Ground-measured spectral signatures as indicators of ground cover and leaf area index:The case of paddy rice[J]. Field Crops Research, 2001,69(1):13-25.
doi: 10.1016/S0378-4290(00)00129-5
[16] Franklin S E, Lavigne M B, Deuling M J , et al. Estimation of forest leaf area index using remote sensing and GIS data for modeling net primary production[J]. International Journal of Remote Sensing, 1997,18(16):3459-3471.
doi: 10.1080/014311697216973
[17] Kuusk A . Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model[J]. International Journal of Remote Sensing, 1998,19(15):2893-2905.
doi: 10.1080/014311698214334
[18] 陈丽, 张晓丽, 焦志敏 . 基于混合像元分解模型的森林叶面积指数反演[J]. 农业工程学报, 2013,29(13):124-129.
Chen L, Zhang X L, Jiao Z M . Reversion of leaf area index in forest based on linear mixture model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(13):124-129.
[19] Suits G H . The calculation of the directional reflectance of vevetative canopy[J]. Remote Sensing of Environment, 1972,2:117-125.
[20] Kuusk A . The hot spot effect of a uniform vegetative cover[J]. Soviet Journal of Remote Sensing, 1985,3:645-658.
[21] Li X W, Strahler A H .Geometric-optical modeling of conifer forest canopy[J].IEEE Transactions on Geoscience and Remote Sensing, 1985, GE-23(5):705-721.
doi: 10.1109/TGRS.1985.289389
[22] Li X W, Strahler A H . Geometric-optical bidirectional reflectance modeling of a coniferous forest canopy[J]. IEEE Transactions on Geoscience and Remote Sensing, 1986,24(6):906-919.
[23] Jupp D L B, Walker J, Penridge L K . Interpretation of vegetation structure in Landsat MSS imagery:A case study in disturbed semi-aric eucalypt woodland.Part2. Model-based analysis[J]. Journal of Environmental Management, 1986,23:35-57.
[24] Li X W, Strahler A H . Geometric-optical bidirectional reflectance modeling of the discrete-crown vegetation canopy:Effect of crown shape and mutual shadowing[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992,30(2):276-292.
doi: 10.1109/36.134078
[25] Li X W, Strahler A H . Modeling the gap probability of a discontinuous vegetation canopy[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988,26(2):161-170.
doi: 10.1109/36.3017
[26] Li X W, Strahler A H, Woodcock C E . A hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995,33(2):466-480.
doi: 10.1109/36.377947
[27] 吴富祯 . 测树学[M]. 北京: 中国林业出版社, 1992.
Wu F Z. Tree Measuring[M]. Beijing: China Forestry Publishing House, 1992.
[28] 冯宗炜, 王效科, 吴刚 . 中国森林生态系统的生物量和生产力[M]. 北京: 科学出版社, 1999.
Feng Z W, Wang X K, Wu G. Biomass and Productivity of Forest Ecosystems in China[M]. Beijing: Science Press, 1999.
[29] Hodgson M E, Shelley B M . Removing the topographic effect in remotely sensed imagery[J]. ERDAS Monitor, 1994,6:4-6.
[30] 张磊, 董立新, 吴炳方 , 等. 三峡水库建设前后库区10年土地覆盖变化[J]. 长江流域资源与环境, 2007,16(1):107-112.
Zhang L, Dong L X, Wu B F , et al. Land cover change before and after the construction of Three Gorges Reservoir within 10 years[J]. Resources and Environment in the Yangtze Basin, 2007,16(1):107-112.
[31] 董立新, 吴炳方, 郭振华 , 等. 三峡库区农林用地变化遥感监测及模拟预测[J]. 农业工程学报, 2009,25(s2):290-297.
Dong L X, Wu B F, Guo Z H , et al. Remote sensing monitoring and simulation prediction of agricultural and forestry land use in Three Gorges Reservoir area[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009,25(s2):290-297.
[32] 陈述彭, 童庆禧, 郭华东 , 等. 遥感信息机理研究[M]. 北京: 科学出版社, 1998.
Chen S P, Tong Q X, Guo H D , et al. Research on the Mechanism of Remote Sensing Information[M]. Beijing: Science Press, 1998.
[33] Duncan J , Stow JD A V,Franklin JJ ,et al..Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin,New Mexico[J]. International Journal of Remote Sensing, 1993,14(18):3395-3416.
doi: 10.1080/01431169308904454
[34] Rouse J W, Haas R W, Schell J A , et al. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation[R]. Greenbelt:NASA, 1974.
[35] Richardson A J, Wiegand C L . Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing, 1977,43(12):1541-1552.
[36] Huete A R . A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988,25(3):295-309.
doi: 10.1016/0034-4257(88)90106-X
[37] Purevdor J T S, Tateishi R, Ishiyama T , et al. Relationships between percent vegetation cover and vegetation indices[J]. International Journal of Remote Sensing, 1998,19(18):3519-3535.
doi: 10.1080/014311698213795
[38] Clevers J G P W . The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil-background[J]. Remote Sensing of Environment, 1989,29:25-37.
doi: 10.1016/0034-4257(89)90076-X
[39] Kaufman Y J, Tanre D . Atmospherically resistant vegetation index (ARVI) for EOS MODIS[J]. IEEE Transactions Geoscience and Remote Sensing, 1992,30(2):261-270.
doi: 10.1109/36.134076
[40] Pu R L, Gong P. Hyperspectral Remote Sensing and Its Application[M]. Beijing: Higher Education Press, 2000.
[1] 王海庆,李丽,陈玲,许文佳,杨金中,刘琼. 基于尾矿库调查的西藏自治区金属矿开采强度分析[J]. 国土资源遥感, 2019, 31(2): 218-223.
[2] 叶发茂,罗威,苏燕飞,赵旭青,肖慧,闵卫东. 卷积神经网络特征在遥感图像配准中的应用[J]. 国土资源遥感, 2019, 31(2): 32-37.
[3] 谢奇芳,姚国清,张猛. 基于Faster R-CNN的高分辨率图像目标检测技术[J]. 国土资源遥感, 2019, 31(2): 38-43.
[4] 陈震,张耘实,章远钰,桑玲玲. 高标准农田建后遥感监测方法[J]. 国土资源遥感, 2019, 31(2): 125-130.
[5] 刘英,岳辉,侯恩科. MODIS数据在陕西省干旱监测中的应用[J]. 国土资源遥感, 2019, 31(2): 172-179.
[6] 胡官兵,刘舫,党伟,杨坤,陈庆松. 遥感技术在滇西南植被覆盖区地质填图中的应用[J]. 国土资源遥感, 2019, 31(2): 224-230.
[7] 梁林林,江利明,周志伟,陈玉兴,孙亚飞. 无人机遥感影像面向对象分类的冻土热融滑塌边界提取[J]. 国土资源遥感, 2019, 31(2): 180-186.
[8] 周阳,张云生,陈斯飏,邹峥嵘,朱耀晨,赵芮雪. 基于DCNN特征的建筑物震害损毁区域检测[J]. 国土资源遥感, 2019, 31(2): 44-50.
[9] 韩衍欣,蒙继华. 面向地块的农作物遥感分类研究进展[J]. 国土资源遥感, 2019, 31(2): 1-9.
[10] 阿茹罕,何芳,王标标. 加权空-谱主成分分析的高光谱图像分类[J]. 国土资源遥感, 2019, 31(2): 17-23.
[11] 史园莉,孙中平,姜俊,高乾,孙浩,闻瑞红. 环境遥感云服务平台与高性能平台对比分析[J]. 国土资源遥感, 2019, 31(2): 240-245.
[12] 陈玲,贾佳,王海庆. 高分遥感在自然资源调查中的应用综述[J]. 国土资源遥感, 2019, 31(1): 1-7.
[13] 葛芸,江顺亮,叶发茂,姜昌龙,陈英,唐祎玲. 聚合CNN特征的遥感图像检索[J]. 国土资源遥感, 2019, 31(1): 49-57.
[14] 黄巍,黄辉先,徐建闽,刘嘉婷. 基于Canny边缘检测思想的改进遥感影像道路提取方法[J]. 国土资源遥感, 2019, 31(1): 65-70.
[15] 涂兵,张晓飞,张国云,王锦萍,周瑶. 递归滤波与KNN的高光谱遥感图像分类方法[J]. 国土资源遥感, 2019, 31(1): 22-32.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《国土资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 Email:gtzyyg@agrs.cn; gtzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发