植物生态学报 ›› 2011, Vol. 35 ›› Issue (4): 402-410.DOI: 10.3724/SP.J.1258.2011.00402 cstr: 32100.14.SP.J.1258.2011.00402
收稿日期:2010-09-15
接受日期:2010-11-26
出版日期:2011-09-15
发布日期:2011-04-13
作者简介:* E-mail: fanwy@163.com
FAN Wen-Yi*(
), ZHANG Hai-Yu, YU Ying, MAO Xue-Gang, YANG Jin-Ming
Received:2010-09-15
Accepted:2010-11-26
Online:2011-09-15
Published:2011-04-13
摘要:
森林生物量的定量估算为全球碳储量、碳循环研究提供了重要的参考依据。该研究采用黑龙江长白山地区的TM影像和133块森林资源一类清查样地的数据, 选取地学参数、遥感反演参数等71个自变量分别构建多元逐步回归模型、传统BP (back propagation)神经网络模型和基于高斯误差函数的BP神经网络改进模型(Gaussian error function, Erf-BP), 进而估算该地区的森林生物量, 并进行比较分析。结果表明, 多元逐步回归模型估测的森林生物量预测精度为75%, 均方根误差为26.87 t·m-2; 传统BP神经网络模型估测森林生物量的预测精度为80.92%, 均方根误差为21.44 t·m-2; Erf-BP估测森林生物量的预测精度为82.22%, 均方根误差为20.83 t·m-2。可见, 改进后的Erf-BP能更好地模拟生物量与各个因子之间的关系, 估算精度更高。
范文义, 张海玉, 于颖, 毛学刚, 杨金明. 三种森林生物量估测模型的比较分析. 植物生态学报, 2011, 35(4): 402-410. DOI: 10.3724/SP.J.1258.2011.00402
FAN Wen-Yi, ZHANG Hai-Yu, YU Ying, MAO Xue-Gang, YANG Jin-Ming. Comparison of three models of forest biomass estimation. Chinese Journal of Plant Ecology, 2011, 35(4): 402-410. DOI: 10.3724/SP.J.1258.2011.00402
| 树种1) Species1) | 茎生物量 Stem biomass (WS) | 枝生物量 Branch biomass (WB) | 叶生物量 Foliage biomass (WL) | 出处 Derivation | 树高 Tree height (H) |
|---|---|---|---|---|---|
| 1 | WS = 0.0145(D2H)1.006 | WB = 0.000063(D2H)1.536 | WL = 0.00104(D2H)1.148 | H = 6.6831exp(0.0515D) | |
| 2 | WS = 0.0134(D2H)1.020 | WB = 0.0105(D2H) 0.7386 | WL = 0.181D1.8415 | H = 5.7584exp(0.0499D) | |
| 3 | WS = 0.025(D2H)0.96 | WB = 0.0021(D2H)0.8181 | WL = 0.00126(D2H)0.968 | H = 6.9441exp(0.0468D) | |
| 4 | WS = 0.057D2.4753 | WB = 0.0116D2.4054 | WL = 0.0083D2.3733 | H = 6.8879exp(0.0343D) | |
| 5 | WS = 0.0238(D2H)0.936 | WB = 0.005(D2H)0.9105 | WL = 0.0036(D2H)0.897 | H = 6.8879exp(0.0343D) | |
| 6 | WS = 0.032417D2.3565 | WB = 0.20893D1.7082 | WL = 0.29156D1.2807 | Self-established | H = 6.6831exp(0.0515D) |
| 7 | WS = 0.06013(D2H)0.891 | WB = 0.00652(D2H)1.169 | WL = 0.0044(D2H)0.9919 | H = 8.4834exp(0.0277D) | |
| 8 | WS = 0.02511(D2H)0.927 | WB = 0.00957(D2H)0.974 | WL = 0.8725(D2H)0.2034 | H = 10.6exp(0.0169D) | |
| 9 | WS = 0.2286(D2H)0.6938 | WB = 0.0247(D2H)0.7378 | WL = 0.0108(D2H)0.8181 | Self-established | H = 9.285exp(0.0196D) |
| 10 | WS = 0.3274(D2H)0.7217 | WB = 0.01349(D2H)0.7197 | WL = 0.02347(D2H)0.893 | Self-established | H = 7.0338exp(0.0332D) |
| 11 | WS = 0.03146(D2H)1.032 | WB = 0.007429D2.6745 | WL = 0.002754D2.4965 | H = 8.1427exp(0.0249D) | |
| 12 | WS = 0.07936(D2H)0.901 | WB = 0.014167(D2H)0.764 | WL = 0.01086(D2H)0.847 | Self-established | H = 13.799exp(0.0138D) |
| 13 | WS = 0.14114(D2H)0.723 | WB = 0.00724(D2H)1.0225 | WL = 0.0079(D2H)0.8085 | H = 6.2635exp(0.0334D) | |
| 14 | WS = 0.03141(D2H)0.733 | WB = 0.002127D2.9504 | WL = 0.00321D2.473 5 | H = 8.1877exp(0.0219D) | |
| 15 | WS = 0.01275(D2H)1.009 | WB = 0.00824(D2H)0.975 | WL = 0.00024(D2H)0.991 | H = 7.0889exp(0.0349D) | |
| 16 | WS = 0.1193(D2H)0.8372 | WB = 0.002(D2H)1.12 | WL = 0.000015(D2H)1.47 | H = 9.8065exp(0.0246D) | |
| 17 | WS = 0.2286(D2H)0.6938 | WB = 0.0247(D2H)0.7378 | WL = 0.0108(D2H)0.8181 | H = 12.136exp(0.0133D) |
表1 主要树种生物量方程及树高方程
Table 1 Biomass and tree height functions of main tree species
| 树种1) Species1) | 茎生物量 Stem biomass (WS) | 枝生物量 Branch biomass (WB) | 叶生物量 Foliage biomass (WL) | 出处 Derivation | 树高 Tree height (H) |
|---|---|---|---|---|---|
| 1 | WS = 0.0145(D2H)1.006 | WB = 0.000063(D2H)1.536 | WL = 0.00104(D2H)1.148 | H = 6.6831exp(0.0515D) | |
| 2 | WS = 0.0134(D2H)1.020 | WB = 0.0105(D2H) 0.7386 | WL = 0.181D1.8415 | H = 5.7584exp(0.0499D) | |
| 3 | WS = 0.025(D2H)0.96 | WB = 0.0021(D2H)0.8181 | WL = 0.00126(D2H)0.968 | H = 6.9441exp(0.0468D) | |
| 4 | WS = 0.057D2.4753 | WB = 0.0116D2.4054 | WL = 0.0083D2.3733 | H = 6.8879exp(0.0343D) | |
| 5 | WS = 0.0238(D2H)0.936 | WB = 0.005(D2H)0.9105 | WL = 0.0036(D2H)0.897 | H = 6.8879exp(0.0343D) | |
| 6 | WS = 0.032417D2.3565 | WB = 0.20893D1.7082 | WL = 0.29156D1.2807 | Self-established | H = 6.6831exp(0.0515D) |
| 7 | WS = 0.06013(D2H)0.891 | WB = 0.00652(D2H)1.169 | WL = 0.0044(D2H)0.9919 | H = 8.4834exp(0.0277D) | |
| 8 | WS = 0.02511(D2H)0.927 | WB = 0.00957(D2H)0.974 | WL = 0.8725(D2H)0.2034 | H = 10.6exp(0.0169D) | |
| 9 | WS = 0.2286(D2H)0.6938 | WB = 0.0247(D2H)0.7378 | WL = 0.0108(D2H)0.8181 | Self-established | H = 9.285exp(0.0196D) |
| 10 | WS = 0.3274(D2H)0.7217 | WB = 0.01349(D2H)0.7197 | WL = 0.02347(D2H)0.893 | Self-established | H = 7.0338exp(0.0332D) |
| 11 | WS = 0.03146(D2H)1.032 | WB = 0.007429D2.6745 | WL = 0.002754D2.4965 | H = 8.1427exp(0.0249D) | |
| 12 | WS = 0.07936(D2H)0.901 | WB = 0.014167(D2H)0.764 | WL = 0.01086(D2H)0.847 | Self-established | H = 13.799exp(0.0138D) |
| 13 | WS = 0.14114(D2H)0.723 | WB = 0.00724(D2H)1.0225 | WL = 0.0079(D2H)0.8085 | H = 6.2635exp(0.0334D) | |
| 14 | WS = 0.03141(D2H)0.733 | WB = 0.002127D2.9504 | WL = 0.00321D2.473 5 | H = 8.1877exp(0.0219D) | |
| 15 | WS = 0.01275(D2H)1.009 | WB = 0.00824(D2H)0.975 | WL = 0.00024(D2H)0.991 | H = 7.0889exp(0.0349D) | |
| 16 | WS = 0.1193(D2H)0.8372 | WB = 0.002(D2H)1.12 | WL = 0.000015(D2H)1.47 | H = 9.8065exp(0.0246D) | |
| 17 | WS = 0.2286(D2H)0.6938 | WB = 0.0247(D2H)0.7378 | WL = 0.0108(D2H)0.8181 | H = 12.136exp(0.0133D) |
| 变量 Variable | 相关系数 Correlation coefficient | 变量 Variable | 相关系数 Correlation coefficient | 变量 Variable | 相关系数 Correlation coefficient | |||
|---|---|---|---|---|---|---|---|---|
| 原始波段 Original band | Band1 | -0.029 | 纹理信息 Texture | 对角线方向 diagonal direction | 相异性 Dissimilarity | Dis1 | -0.206 | |
| Band2 | -0.508** | 平均值 Mean | Mean1 | -0.354** | Dis2 | -0.342** | ||
| Band3 | -0.450** | Mean2 | -0.225* | Dis3 | -0.061 | |||
| Band4 | 0.035 | Mean3 | -0.122 | Dis4 | -0.093 | |||
| Band5 | -0.357** | Mean4 | 0.027 | Dis5 | -0.301** | |||
| Band7 | -0.459** | Mean5 | -0.383** | Dis7 | 0.038 | |||
| 波段组合 Band combination | TM73 | 0.100 | Mean7 | -0.323** | 熵 Entropy | Ent1 | -0.233* | |
| TM437 | -0.095 | 方差 Variance | Var1 | -0.250* | Ent2 | -0.342** | ||
| TM452 | 0.511** | Var2 | -0.308** | Ent3 | -0.137 | |||
| TM42 | 0.591** | Var3 | -0.122 | Ent4 | -0.136 | |||
| 差值植被指数 Difference vegetation index (DVI) | 0.121 | Var4 | -0.178 | Ent5 | -0.336** | |||
| 简单比值指数 Simple ratio (SR) | 0.482** | Var5 | -0.305** | Ent7 | -0.017 | |||
| 归一化植被指数 Normalized difference vegetation index (NDVI) | 0.419** | Var7 | -0.062 | 角二阶矩 Angular second moment | Sec1 | 0.230* | ||
| 转换型植被指数 Transformed vegetation index (TVI) | 0.415** | 均一性 Homogeneity | Hom1 | 0.206 | Sec2 | 0.340** | ||
| 垂直植被指数 Perpendicular vegetation index (PVI) | 0.071 | Hom2 | 0.342** | Sec3 | 0.137 | |||
| 红外指数 Infrared index (II) | 0.466** | Hom3 | 0.061 | Sec4 | 0.108 | |||
| 土壤调整植被指数 Soil-adjusted vegetation index (SAVI) | 0.418** | Hom4 | 0.088 | Sec5 | 0.329** | |||
| 优化的简单比值指数 Modified simple ratio (MSR) | 0.469** | Hom5 | 0.301** | Sec7 | 0.019 | |||
| 土壤调整植被指数2 Soil-adjusted vegetation index 2 (SAVI2) | 0.409** | Hom7 | -0.038 | 相关性 Correlation | Cor1 | 0.309** | ||
| 非线性植被指数 Non-linear index (NLI) | 0.330** | 对比度 Contrast | Con1 | -0.206 | Cor2 | 0.366** | ||
| 地学信息 Geographic information | 纵坐标Y Y-coordinate | -0.259* | Con2 | -0.342** | Cor3 | 0.131 | ||
| 横坐标X X-coordinate | -0.231* | Con3 | -0.061 | Cor4 | 0.143 | |||
| 高程 Elevation | 0.598** | Con4 | -0.113 | Cor5 | 0.172 | |||
| 坡向 Slope aspect | -0.280** | Con5 | -0.292** | Cor7 | 0.095 | |||
| 坡度 Slope | -0.125 | Con7 | 0.038 | 郁闭度 Closure | 0.283** | |||
表2 自变量因子与样地生物量的相关系数
Table 2 Correlation coefficients between biomass of plots and independent variables
| 变量 Variable | 相关系数 Correlation coefficient | 变量 Variable | 相关系数 Correlation coefficient | 变量 Variable | 相关系数 Correlation coefficient | |||
|---|---|---|---|---|---|---|---|---|
| 原始波段 Original band | Band1 | -0.029 | 纹理信息 Texture | 对角线方向 diagonal direction | 相异性 Dissimilarity | Dis1 | -0.206 | |
| Band2 | -0.508** | 平均值 Mean | Mean1 | -0.354** | Dis2 | -0.342** | ||
| Band3 | -0.450** | Mean2 | -0.225* | Dis3 | -0.061 | |||
| Band4 | 0.035 | Mean3 | -0.122 | Dis4 | -0.093 | |||
| Band5 | -0.357** | Mean4 | 0.027 | Dis5 | -0.301** | |||
| Band7 | -0.459** | Mean5 | -0.383** | Dis7 | 0.038 | |||
| 波段组合 Band combination | TM73 | 0.100 | Mean7 | -0.323** | 熵 Entropy | Ent1 | -0.233* | |
| TM437 | -0.095 | 方差 Variance | Var1 | -0.250* | Ent2 | -0.342** | ||
| TM452 | 0.511** | Var2 | -0.308** | Ent3 | -0.137 | |||
| TM42 | 0.591** | Var3 | -0.122 | Ent4 | -0.136 | |||
| 差值植被指数 Difference vegetation index (DVI) | 0.121 | Var4 | -0.178 | Ent5 | -0.336** | |||
| 简单比值指数 Simple ratio (SR) | 0.482** | Var5 | -0.305** | Ent7 | -0.017 | |||
| 归一化植被指数 Normalized difference vegetation index (NDVI) | 0.419** | Var7 | -0.062 | 角二阶矩 Angular second moment | Sec1 | 0.230* | ||
| 转换型植被指数 Transformed vegetation index (TVI) | 0.415** | 均一性 Homogeneity | Hom1 | 0.206 | Sec2 | 0.340** | ||
| 垂直植被指数 Perpendicular vegetation index (PVI) | 0.071 | Hom2 | 0.342** | Sec3 | 0.137 | |||
| 红外指数 Infrared index (II) | 0.466** | Hom3 | 0.061 | Sec4 | 0.108 | |||
| 土壤调整植被指数 Soil-adjusted vegetation index (SAVI) | 0.418** | Hom4 | 0.088 | Sec5 | 0.329** | |||
| 优化的简单比值指数 Modified simple ratio (MSR) | 0.469** | Hom5 | 0.301** | Sec7 | 0.019 | |||
| 土壤调整植被指数2 Soil-adjusted vegetation index 2 (SAVI2) | 0.409** | Hom7 | -0.038 | 相关性 Correlation | Cor1 | 0.309** | ||
| 非线性植被指数 Non-linear index (NLI) | 0.330** | 对比度 Contrast | Con1 | -0.206 | Cor2 | 0.366** | ||
| 地学信息 Geographic information | 纵坐标Y Y-coordinate | -0.259* | Con2 | -0.342** | Cor3 | 0.131 | ||
| 横坐标X X-coordinate | -0.231* | Con3 | -0.061 | Cor4 | 0.143 | |||
| 高程 Elevation | 0.598** | Con4 | -0.113 | Cor5 | 0.172 | |||
| 坡向 Slope aspect | -0.280** | Con5 | -0.292** | Cor7 | 0.095 | |||
| 坡度 Slope | -0.125 | Con7 | 0.038 | 郁闭度 Closure | 0.283** | |||
| r | R2 | R2adj | SE | Sig. | DW |
|---|---|---|---|---|---|
| 0.887 | 0.788 | 0.776 | 11.57 | 0.000 | 2.054 |
表3 回归模型描述
Table 3 Description of regression model
| r | R2 | R2adj | SE | Sig. | DW |
|---|---|---|---|---|---|
| 0.887 | 0.788 | 0.776 | 11.57 | 0.000 | 2.054 |
| 变量 Variable | Coef_uns | Coef_s | t0.05 | Sig. | VIF |
|---|---|---|---|---|---|
| 常数 Constant | 125.696 8 | 6.211 | 0.000 | ||
| 郁闭度 Closure | 18.692 6 | 0.123 7 | 2.402 | 0.018 | 1.099 |
| 高程 Elevation | 0.024 | 0.134 3 | 2.265 | 0.026 | 1.456 |
| X | -0.000 14 | -0.266 3 | -5.273 | 0.000 | 1.057 |
| Band3 | -0.057 5 | -0.273 1 | -4.258 | 0.000 | 1.704 |
| Cor1 | 0.295 4 | 0.539 5 | 7.903 | 0.000 | 1.931 |
表4 回归模型系数、显著性及共线性检验结果
Table 4 Results of regression model coefficients, significance and collinearity
| 变量 Variable | Coef_uns | Coef_s | t0.05 | Sig. | VIF |
|---|---|---|---|---|---|
| 常数 Constant | 125.696 8 | 6.211 | 0.000 | ||
| 郁闭度 Closure | 18.692 6 | 0.123 7 | 2.402 | 0.018 | 1.099 |
| 高程 Elevation | 0.024 | 0.134 3 | 2.265 | 0.026 | 1.456 |
| X | -0.000 14 | -0.266 3 | -5.273 | 0.000 | 1.057 |
| Band3 | -0.057 5 | -0.273 1 | -4.258 | 0.000 | 1.704 |
| Cor1 | 0.295 4 | 0.539 5 | 7.903 | 0.000 | 1.931 |
| 激活函数 Activation function | 最小训练次数 Minimum training times | 最大训练次数 Maximum training times | 平均训练次数 Average training times | 预测平方差和 Squared prediction error |
|---|---|---|---|---|
| Erf-BP | 317 | 359 | 338 | 20.83 |
| 传统BP神经网络模型 Traditonal BP neutral network model | 509 | 723 | 616 | 21.44 |
表5 传统BP神经网络模型与Erf-BP训练结果对比
Table 5 Comparisons of training results by traditional BP neutral network model and Erf-BP
| 激活函数 Activation function | 最小训练次数 Minimum training times | 最大训练次数 Maximum training times | 平均训练次数 Average training times | 预测平方差和 Squared prediction error |
|---|---|---|---|---|
| Erf-BP | 317 | 359 | 338 | 20.83 |
| 传统BP神经网络模型 Traditonal BP neutral network model | 509 | 723 | 616 | 21.44 |
| 建模方法 Modeling method | R2 | 预测精度 PRECISIONmod | 预测精度 PRECISIONpre | 均方根误差 RMSEmod (t·m-2) | 均方根误差 RMSEpre (t·m-2) |
|---|---|---|---|---|---|
| 逐步回归法 Regression | 0.788 | 76.00% | 75.00% | 18.91 | 26.87 |
| Erf-BP | 0.975 | 86.04% | 82.22% | 10.17 | 20.83 |
表6 不同建模方法的精度比较
Table 6 Comparisons of precisions by different models
| 建模方法 Modeling method | R2 | 预测精度 PRECISIONmod | 预测精度 PRECISIONpre | 均方根误差 RMSEmod (t·m-2) | 均方根误差 RMSEpre (t·m-2) |
|---|---|---|---|---|---|
| 逐步回归法 Regression | 0.788 | 76.00% | 75.00% | 18.91 | 26.87 |
| Erf-BP | 0.975 | 86.04% | 82.22% | 10.17 | 20.83 |
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