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森林地上生物量遥感估算方法

田晓敏 张晓丽

田晓敏, 张晓丽. 森林地上生物量遥感估算方法[J]. 北京林业大学学报, 2021, 43(8): 137-148. doi: 10.12171/j.1000-1522.20200166
引用本文: 田晓敏, 张晓丽. 森林地上生物量遥感估算方法[J]. 北京林业大学学报, 2021, 43(8): 137-148. doi: 10.12171/j.1000-1522.20200166
Tian Xiaomin, Zhang Xiaoli. Estimation of forest aboveground biomass by remote sensing[J]. Journal of Beijing Forestry University, 2021, 43(8): 137-148. doi: 10.12171/j.1000-1522.20200166
Citation: Tian Xiaomin, Zhang Xiaoli. Estimation of forest aboveground biomass by remote sensing[J]. Journal of Beijing Forestry University, 2021, 43(8): 137-148. doi: 10.12171/j.1000-1522.20200166

森林地上生物量遥感估算方法

doi: 10.12171/j.1000-1522.20200166
基金项目: 人工林资源监测关键技术研究(2017YFD0600900)
详细信息
    作者简介:

    田晓敏,博士,讲师。主要研究方向:遥感数据在林业中的应用。Email:XM_Tian@yeah.net 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    张晓丽,教授,博士生导师。主要研究方向:森林动态监测、定量遥感研究。Email:zhang-xl@263.net 地址:同上

  • 中图分类号: S757.3

Estimation of forest aboveground biomass by remote sensing

  • 摘要: 生物量是林业和生态应用研究的重要信息,森林生态系统地上生物量估算的遥感技术引起了国内外学者的广泛关注。总结与探讨不同数据源与估算方法能够为森林地上生物量的估算提供指导。本文首先总结并探讨单传感器遥感数据,包括光学遥感、合成孔径雷达与激光雷达数据在森林地上生物量估算中的应用,以及协同使用多源遥感数据估算森林地上生物量的优势;然后论述森林地上生物量估算的传统模型估算法与机器学习估算方法(决策树法、K最近邻法、人工神经网络、支持向量机、最大熵)。多源遥感数据集成能够结合不同数据的优势,能够为森林地上生物量估算提供丰富的特征信息,结合机器学习估算方法,是提高森林地上生物量估算的准确性的发展趋势。

     

  • 表  1  不同分辨率的主要光学数据估算森林地上生物量

    Table  1.   Estimation of forest aboveground biomass by main optical data of different resolution

    分辨率分类
    Resolution classification
    传感器
    Sensor
    空间分辨率
    Spatial resolution
    研究区域
    Study area
    模型或方法
    Model or method
    精度
    Precision (R2)
    参考文献
    Reference
    低分辨率
    Low resolution
    Terra/MODIS 1 km,
    0.5 km,
    0.25 km
    美国密歇根州、明尼苏达州、威斯康星州
    Michigan, Minnesota and Wisconsin of USA
    经验模型
    Empirical model AGBev = 111 × [NDVIc10.3/(NDVIc10.3 + 0.3510.3)],
    AGBde = 233 × NIR + 2.7 × AGE − 71
    0.86 (常绿林
    evergreen forest)
    0.95 (落叶林
    deciduous forest)
    [8]
    NOAA/AVHRR 1.1 km 约旦
    Jordan
    多元线性回归模型
    Multiple linear regression model
    0.75 [17]
    中分辨率
    Medium resolution
    Landsat/TM 30 m 北京山区乔木林
    Arbor forest in mountainous area in Beijing
    多元逐步回归法
    Multiple stepwise regression
    0.87 [13]
    Landsat/ETM 30 m 鄱阳湖湿生植被
    Wetland vegetation in the Poyang Lake
    采样数据与ETM4波段数据的线性相关模型
    Linear correlation model between sampled data and ETM4 data
    0.86 [12]
    Landsat/OLI 30 m 江苏省虞山林场
    Yushan Forest Farm, Jiangsu Province
    多元逐步回归法
    Multiple stepwise regression
    0.88 [14]
    高分辨率
    High resolution
    IKONOS 4 m 法属圭亚那
    French Guiana
    多元线性回归模型
    Multiple linear regression model
    0.87 [18]
    QuickBird 2.4 m 加拿大东部的北方寒带森林
    Boreal forests of eastern Canada
    多元线性回归模型
    Multiple linear regression model
    0.84 [15]
    注:AGBev为常绿林;AGBde为落叶林;NDVIc是修正归一化植被指数,计算公式为NDVIc = NDVI × [1 − (mIR − mIRmin)/(mIRmax − mIRmin)];mIR是中红外波段;NIR是近红外波段反射率;AGE为树龄。Notes: AGBev is evergreen forests; AGBde is deciduous forests; NDVIc is the modified normalized vegetation index, the calculation formula NDVIc = NDVI × [1 − (mIR − mIRmin)/(mIRmax − mIRmin)]; mIR is the mid-infrared band; NIR is the reflectivity of the near infrared band; AGE means tree age.
    下载: 导出CSV

    表  2  主要合成孔径雷达数据估算森林地上生物量

    Table  2.   Estimation of forest aboveground biomass by main synthetic aperture radar data

    传感器
    Sensor
    波段
    Wave band
    极化方式
    Polarization
    空间分辨率
    Spatial resolution/m
    研究区域
    Study area
    模型或方法
    Model or method
    R2参考文献
    Reference documentation
    ERS-1/2
    SAR
    C VV 30 芬兰,瑞典
    Finland, Sweden
    相干性分析
    Coherence analysis
    0.82 [32]
    ENVISAT/
    ASAR
    C VV, HH,
    VH, HV
    950, 150, 30 印度杜赫瓦国家公园
    Dudhwa National Park of India
    线性回归模型
    Linear regression model
    0.86 [33]
    JERS-1
    SAR
    L HH 18 瑞典北部针叶林带
    Boreal conifer belt in northern Sweden
    线性回归模型
    Linear regression model
    0.78 [34]
    ALOS/
    PALSAR
    L HH 7 ~ 100 大兴安岭
    Daxing’anling Mountain
    简单线性模型、指数模型和加入地理因子模型
    Simple linear model, exponential model and model with geographic factors added
    0.85 [35]
    下载: 导出CSV

    表  3  主要激光雷达数据估算森林地上生物量

    Table  3.   Estimation of forest aboveground biomass by main LiDAR data

    传感器
    Sensor
    光斑直径
    Spot diameter/m
    时间范围
    Time range
    研究区域
    Study area
    模型或方法
    Model or method
    R2参考文献
    Reference
    USGS/LiDAR < 1,8 ~ 25 意大利北部前阿尔卑斯山脉
    Northern Italy in the Pre-Alps
    多元逐步回归分析
    Multiple stepwise regression analysis
    0.87 [47]
    GLAS/ICEsat 60 ~ 70 2003—2009 西伯利亚中南部
    South-central Siberia
    线性模型,无截距线性模型,对数线性模型
    Linear model, non-intercept linear model, log-linear model
    0.83 [48]
    下载: 导出CSV

    表  4  多源遥感数据估算森林地上生物量

    Table  4.   Estimation of forest aboveground biomass by multi-source remote sensing data

    数据集成
    Data integration
    数据源
    Data source
    研究区域
    Study area
    方法或模型
    Model or method
    R2参考文献
    Reference
    多源光学数据
    Multi-source optical data
    Landsat5 TM + ALOS AVNIR-2 + CBERS-02B CCD 东莞市
    Dongguan City
    多元回归分析
    Multiple regression analysis
    0.65 [49]
    光学数据 + 合成孔径雷达
    数据
    Optical data + SAR
    Landsat8 OLI + PolSAR 内蒙古大兴安岭根河实验区
    Genhe Forest Reserve, Inner Mongolia
    多元逐步回归、随机森林法、
    k-最近邻法
    Multiple stepwise regression,
    random forest, k-nearest neighbors
    0.65 [50]
    光学数据 + 激光雷达数据
    Optical data + LiDAR
    Landsat8 OLI + LiDAR 马来西亚沙巴
    Sabah, Malaysia
    多元逐步回归
    Multiple stepwise regression
    0.81 [51]
    HYDICE + LiDAR 拉塞尔瓦生物站
    La Selva Biological Station
    线性回归分析
    Linear regression analysis
    0.90 [52]
    合成孔径雷达数据 + 激光
    雷达数据
    SAR + LiDAR
    SAR + InSAR + LiDAR 美国西南部
    Southwestern United States
    线性回归分析
    Linear regression analysis
    0.85 [53]
    光学数据 + 合成孔径雷达
    数据 + 激光雷达数据
    Optical data + SAR + LiDAR
    ETM+ + QuickBird + LiDAR + SAR/InSAR 内华达山脉加利福尼亚山
    Sierra Nevada California Mountains
    多元回归分析
    Multiple regression analysis
    0.84 [54]
    下载: 导出CSV

    表  5  估算森林地上生物量的机器学习方法

    Table  5.   Machine learning methods for estimating forest aboveground biomass

    机器学习方法
    Machine learning method
    描述
    Description
    优点
    Advantage
    缺点
    Disadvantage
    参考文献
    Reference
    决策树回归
    Decision tree regression
    逼近离散值函数的树状预测模型,基本算法有随机森林和梯度提升决策树
    A tree prediction model that approximates a discrete value function. The basic algorithms include random forest and gradient boosting decision tree
    变量选择和交互式建模
    Variable selection and interactively modeling
    数据的细微变化会导致不同的树状拆分
    Minor changes of data result in a different split
    [71-73]
    k-NN估测法
    K-nearest neighbor estimation method
    通过逆距离加权方法,将某个位置处目标变量的值预测为k个邻近变量的加权平均值
    Value of a target variable at a certain location is predicted as a weighted average with k neighbors by the inverse distance weighting method
    可使用多种特征作为预测变量
    Various features can be used as predictor variables
    选择适当的变量很费时
    Selection of proper predictor variables is time-consuming
    [74-76]
    人工神经网络
    Artificial neural nets
    输出变量与输入变量的组合通过网络训练相连接的黑箱模型
    A black-box model in which output variables are connected with combinations of the input variables through network training
    有效解决数据非线性、非高斯和噪声问题
    Effectively solve nonlinearity, non-Gaussian and noise problems of data
    过度拟合训练数据
    Overfitting the training data
    [48,77-78]
    支持向量机
    Support vector machine
    用内积函数定义的非线性变换将输入空间变换到高维空间,把问题转化为线性的方式
    Mapping the input data into a higher dimensional kernel induced feature space, turning the problem to a linear manner
    解决小样本和高维问题
    Solve small sample and high-dimensional problems
    核函数的选择造成估算
    误差
    Choice of kernel function causes estimation errors
    [79-81]
    最大熵
    Maximum entropy
    根据连续或绝对环境变量的最大熵概率分布,预测目标概率分布的黑箱方法
    A black-box method in which the target probability distribution can be estimated according to the probability distribution of maximum entropy with continuous or categorical environmental variables
    对小样本数据有效
    Effective despite small sample data
    初始信息是很必要的
    Prior information is necessary
    [82-83]
    下载: 导出CSV
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  • 收稿日期:  2020-06-01
  • 修回日期:  2020-07-30
  • 网络出版日期:  2021-07-10
  • 刊出日期:  2021-08-31

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