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基于遥感的高山松连清固定样地地上生物量估测模型构建

张加龙 胥辉

张加龙, 胥辉. 基于遥感的高山松连清固定样地地上生物量估测模型构建[J]. 北京林业大学学报, 2020, 42(7): 1-11. doi: 10.12171/j.1000-1522.20190394
引用本文: 张加龙, 胥辉. 基于遥感的高山松连清固定样地地上生物量估测模型构建[J]. 北京林业大学学报, 2020, 42(7): 1-11. doi: 10.12171/j.1000-1522.20190394
Zhang Jialong, Xu Hui. Establishment of remote sensing based model to estimate the aboveground biomass of Pinus densata for permanent sample plots from national forestry inventory[J]. Journal of Beijing Forestry University, 2020, 42(7): 1-11. doi: 10.12171/j.1000-1522.20190394
Citation: Zhang Jialong, Xu Hui. Establishment of remote sensing based model to estimate the aboveground biomass of Pinus densata for permanent sample plots from national forestry inventory[J]. Journal of Beijing Forestry University, 2020, 42(7): 1-11. doi: 10.12171/j.1000-1522.20190394

基于遥感的高山松连清固定样地地上生物量估测模型构建

doi: 10.12171/j.1000-1522.20190394
基金项目: 林业公益性行业科研专项(201404309),云南省唐守正院士专家工作站(2018IC066),云南省王广兴专家工作站(2018IC100),国家自然科学基金项目(31770677、31860207)
详细信息
    作者简介:

    张加龙,博士,副教授。主要研究方向:森林生物量遥感估测。Email:jialongzhang@swfu.edu.cn 地址:650024 云南省昆明市盘龙区白龙路300号西南林业大学林学院

    责任作者:

    胥辉,博士,教授,博士生导师。主要研究方向:森林测计。Email:zyxy213@126.com 地址:同上

  • 中图分类号: S758.5

Establishment of remote sensing based model to estimate the aboveground biomass of Pinus densata for permanent sample plots from national forestry inventory

  • 摘要:   目的  研究利用遥感方法构建高山松固定样地地上生物量估测的参数模型,可以在今后前期样地的基础上直接快速、准确地估测生物量,或者开展少量的外业调查即可获取地上生物量。  方法  基于遥感因子与样地地上生物量变化量和线性混合模型提高生物量估测精度,以香格里拉市1987、1992、1997、2002、2007、2012、2017年7期国家森林资源清查固定样地和对应年份Landsat TM、OLI的Level-1数据为基础,首先对遥感数据进行预处理:包括辐射定标、大气校正、几何校正和地形校正,提取原始波段、比值因子、植被指数、图像增强信息、纹理指数、混合像元分解后的丰度、叶面积指数,计算5 ~ 30年间隔样地对应的遥感因子变化值。根据森林资源二类调查的高山松分布特征,选择地形因子作为线性混合模型的固定和随机效应,采用多元线性回归、非线性回归、地理加权回归、线性混合模型构建高山松地上生物量估测的静态模型,基于遥感光谱信息变化量构建了有树高和无树高参与的动态模型。最后对不同的建模方法和验证结果进行对比分析,选择最优结果作为估测模型并验证。  结果  (1)分析静态数据建模和验证的结果,采用样地号为固定因子、坡度等级为随机因子的线性混合模型的拟合R2最高,为0.75;但利用训练数据集和2017年数据验证,其精度都较低。(2)分析变化量数据建模和验证的结果,采用样地号为固定因子、坡度等级为随机因子、遥感因子变化量为自变量的线性混合模型拟合R2最高,为0.70,预测精度P值为(68.86 ± 11.93)%;增加平均树高变化量,拟合R2最高为0.79,预测P值为(73.39 ± 6.18)%。(3)无论是有、还是无树高参与的变化量模型其拟合和预测精度都达到80%,其预测精度达到了非参数模型预测精度。  结论  基于变化量的估测模型的拟合和预测精度较静态模型有所提高;综合遥感因子、地形因子构建的高山松地上生物量估测线性混合模型,其精度有较大提高;采用遥感因子变化量构建的高山松地上生物量估测模型,有效弥补了静态光学遥感数据估测生物量的不足,经检验可用于其他年期的估测。

     

  • 图  1  研究区及连清高山松固定样地分布图

    Figure  1.  Research area and distribution of Pinus densata of permanent sample plots from national forestry inventory

    图  2  多元回归分析拟合精度对比

    Figure  2.  Accuracy comparison of modeling with multiple regression analysis

    表  1  固定样地调查次数统计

    Table  1.   Statistics of investigation time of permanent sample plots

    样地编号 Sample plot No.162172183200212214255293298342371192306345
    调查次数 Investigation time77777777777666
    样地编号 Sample plot No.346231285165180300325365236199211232245
    调查次数 Investigation time6554444321111
    下载: 导出CSV

    表  2  获取的研究区Landsat Level-1 影像

    Table  2.   Collected images of Landsat Level-1 in the research area

    调查年份
    Investigation year
    ID日期
    Date
    云量
    Cloud cover/%
    2017 LC08_L1TP_132040_20171216_20171224_01_T1 2017−12−16 0.78
    LC08_L1TP_132041_20171216_20171224_01_T1 2017−12−16 0.45
    LC08_L1TP_131041_20171225_20180103_01_T1 2017−12−25 0.30
    2012 LT51320412011286BKT00 2011−10−13 19.52
    LT51320402011014BKT00 2011−01−14 18.15
    LT51310412011007BKT00 2011−01−07 0.22
    2007 LT51310412007060BJC00 2007−03−01 1.00
    LT51320402007003BJC01 2007−01−03 23.00
    LT51320412006288BJC00 2006−10−15 15.00
    2002 LT51310412002302BJC00 2002−10−29 0.13
    LT51320402002005BJC00 2002−01−05 0.15
    LT51320412002005BJC00 2002−01−05 2.80
    1997 LT51310411997320BKT01 1997−11−16 7.00
    LT51320401997279BKT00 1997−10−06 16.00
    LT51320411997311BKT00 1997−11−07 4.00
    1992 LT51310411991320BKT00 1991−11−16 8.05
    LT51320401991311BKT00 1991−11−07 2.44
    LT51320411991311BKT00 1991−11−07 5.24
    1987 LT51320411987364BKT00 1987−12−30 10.60
    LT51320401987364BKT00 1987−12−30 23.89
    下载: 导出CSV

    表  3  遥感光谱变量

    Table  3.   Remote sensing spectral variables

    类型 Type变量及方法 Variable and method
    原始单波段
    Original single band
    C,B1,B2,B3,B4,B5,B7
    简单比值植被指数
    Vegetation index of simple band ratio[3335]
    B43 = B4/B3,B42 = B4/B2,B54 = B5/B4,B3Albedo = B3/(B1 + B2 + B3 + B4 + B5 + B7),B437 = B4 × B3/B7
    植被指数
    Vegetation index[3637]
    NDVI = (B4 − B3)/(B4 + B3),ND32 = (B3 − B2)/(B2 + B3),ND54 = (B5 − B4)/(B5 + B4),ND53 = (B5 − B3)/(B5 + B3),ND57 = (B5 − B7)/(B5 + B7),ND452 = (B4 + B5 − B2)/(B5 + B4 + B2),DVI = B4 − B3
    图像增强
    Image enhancement[4, 38]
    主成分变换 Principal component transformation:VIS123 = B1 + B2 + B3,Albedo = B1 + B2 + B3 + B4 + B5 + B7,MID57 = B5 + B7
    纹理信息
    Texture information[39]
    均值、方差、均一性、反差、相异、熵、角二阶矩、相关性、偏斜。窗口有5 × 5和9 × 9,用R5和R9表示 Mean(ME), variance(VA), homogeneity(HO), contrast(CO), dissimilarity(DI), entropy(EN), second moment(SM), correlation(CC), skewness(SK). The window sizes are 5 × 5 and 9 × 9, represented by R5 and R9, respectively
    丰度信息
    Fraction information
    进行主成分分析,采用像元纯净指数法提取纯净端元,通过N-D可视化选取高山松、裸地、阴影端元样本,通过线性波谱分离法得到高山松丰度图 Principal component analysis is carried out, and the pure endmembers are extracted by the pixel pure index method. The endmember samples of Pinus densata, bare land and shades are selected by N-D visualization, and the fraction map of Pinus densata is obtained by linear spectral decomposition
    叶面积指数
    Leaf area index[40]
    ${\rm LAI} = (3.618 \times {\rm EVI} - 0.118)$
    注:C.海岸波段;B1、B2、B3、B4、B5和B7分别表示波段1、波段2、波段3、波段4、波段5和波段7;EVI表示植被增强指数。下同。Notes: C, coastal; B1, B2, B3, B4, B5 and B7 represent band 1, band 2, band 3, band 4, band 5 and band 7, respectively; EVI means enhanced vegetation index. The same below.
    下载: 导出CSV

    表  4  高山松分布海拔、坡度分级

    Table  4.   Elevation and slope grades of Pinus densata distribution

    海拔
    Elevation/m
    海拔等级
    Elevation
    grade
    坡度
    Slope
    degree/(°)
    坡度等级
    Slope degree
    grade
    1 500 ~ 3 000 1 0 ~ 8 1
    3 000 ~ 3 200 2 8 ~ 15 2
    3 200 ~ 3 400 3 15 ~ 25 3
    3 400 ~ 3 600 4 25 ~ 35 4
    3 600 ~ 3 800 5 35 ~ 90 5
    3 800 ~ 4 000 6
    4 000 ~ 5 520 7
    下载: 导出CSV

    表  5  1987—2012年连清样地训练集线性混合模型精度对比

    Table  5.   Accuracy comparison using permanent sample plots of training datasets for national forestry inventory from 1987 to 2012 with linear mixed model

    序号
    No.
    模型形式
    Model form
    固定效应
    Fixed effect
    随机效应
    Random effect
    拟合精度R2
    Fitting accuracy R2
    1 lnAGB = R5B4CC + ND32 + ND54 样地号 Sample plot No. 海拔等级 Elevation grade 0.69
    2 lnAGB = R5B4CC + ND32 + ND54 样地号 Sample plot No. 坡度等级 Slope degree grade 0.75
    3 lnAGB = R5B4CC + ND32 + ND54 海拔等级 Elevation grade 坡度等级 Slope degree grade 0.47
    4 lnAGB = R5B4CC + ND32 + ND54 坡度等级 Slope degree grade 海拔等级 Elevation grade 0.46
    注:AGB. 地上生物量。R5B4CC 、ND32 、 ND54为遥感光谱变量 。下同。Notes: AGB, aboveground biomass. R5B4CC , ND32 , ND54 are remote sensing spectral variables. The same below.
    下载: 导出CSV

    表  6  多元回归分析的拟合方式

    Table  6.   Modeling way of multiple regression analysis

    序号 No.方式 Mode遥感光谱变量 Remote sensing spectral variable
    1 78组训练数据集
    78 groups of training datasets
    R5B4CC,ND32,ND54
    2 78组训练数据集增加234组变化量
    Increase 234 change datasets in 78 groups of training datasets
    R5B4CC,ND32,ND54
    3 78组训练数据和变化量为5 − 100 t/hm2共232组
    78 groups of training data and changes of 5 ~ 100 t/ha, totally 232 groups
    ND452,R9B4SK,R9B1VA,B54,R5B1SM,NDVI,R9B1ME,R5B7HO,R9B4DI,R5B4SK
    4 生物量变化量为1 ~ 200 t/hm2共178组数据
    Variation of 1−200 t/ha, totally 178 groups
    R9B7SK,R5B7SK,R9B5HO,R9B4DI,B3ALBEDO,R5B3SK,R5B7SM,LAI,R5B7CO, R5B5HO, R5B1ME, DVI, R5B4ME, R9B7CO, R5B1CR, B54, R9B7VA
    5 生物量变化量为5 ~ 100 t/hm2共153组数据
    A total of 153 groups with a variation of 5−100 t/ha
    R9B4SK,R9B1SM,B57,R5B2HO,R5B2SM,R5B7HO,B7,PCA_5
    6 每5年生物量变化量为正值的69组数据中相关性强的前3个因子
    The first three correlated factors in 69 groups with positive change every
    five years[23]
    R5B2EN,R5B1EN,R5B7SK
    7 相关性强的前10个遥感因子共234组数据
    234 groups of the top10 remote sensing factors with strong correlation
    DVI,R5B7EN,R9B7SK,B3ALBEDO,R9B1EN,R5B2EN,R5B1EN,R5B7SK,R9B4SK,R9B3EN
    8 上一步数据进行逐步回归
    Stepwise regression with the data of last step
    R5B7SK,R9B7SK,DVI,B3ALBEDO,R5B2EN,R5B7EN
    9 相关性强前10遥感因子,去除AGB负、小于1、异常值共178组
    Removal of negative, less than 1, abnormal AGB values of the top 10
    remote sensing factors with strong correlation, totally 178 groups
    DVI,R5B1EN,R9B7SK,B3ALBEDO,R9B1EN,R5B2EN,R5B7SK,R5B7EN,R9B4SK,R9B3EN
    下载: 导出CSV

    表  7  线性混合模型拟合精度对比

    Table  7.   Modeling accuracy comparison with LMM

    序号
    No.
    固定效应
    Fixed effect
    随机效应
    Random effect
    拟合精度R2
    Fitting accuracy R2
    1 样地号 Sample plot No. 坡度等级 Slope degree grade 0.70
    2 样地号 Sample plot No. 海拔等级 Elevation grade 0.67
    3 样地号 Sample plot No. 坡向等级 Slope aspect grade 0.64
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-15
  • 修回日期:  2019-11-27
  • 网络出版日期:  2020-07-04
  • 刊出日期:  2020-08-14

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