植物生态学报 ›› 2011, Vol. 35 ›› Issue (4): 402-410.DOI: 10.3724/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 |
[1] | Brown S, Sathaye J, Canell M, Kauppi PE (1996). Mitigation of carbon emission to the atmosphere by forest management. Commonwealth Forestry Review, 75, 80-91. |
[2] | Chen CG (陈传国), Zhu JF (朱俊凤) (1989). Northeast Main Forest Biomass Handbook (东北主要林木生物量手册). China Forestry Publishing House, Beijing. (in Chinese) |
[3] | Ding BY (丁宝永), Sun JH (孙继华) (1989). Studies on biological productivity and nutrient cycling of artificial forest ecosystem of Korean pine. Journal of Northeast Forestry University (东北林业大学学报), 17(Suppl. 2), 18-19. (in Chinese with English abstract) |
[4] | Efron B (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7, 1-26. |
[5] | Guo QX (国庆喜), Zhang F (张锋) (2003). Estimation of forest biomass based on remote sensing. Journal of Northeast Forestry University (东北林业大学学报), 31(2), 13-16. (in Chinese with English abstract) |
[6] | Guo ZH (郭志华), Peng SL (彭少麟), Wang BS (王伯荪) (2002). Estimating forest biomass in western Guangdong using landsat TM data. Acta Ecologica Sinica (生态学报), 22, 1832-1839. (in Chinese with English abstract) |
[7] | Hall RJ, Skakun RS, Arsenault EJ, Case BS (2006). Modeling forest stand structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225, 378-390. |
[8] | Haralick RM (1979). Statistical and structural approaches to texture. Proceeding of the IEEE, 67, 786-804. |
[9] | Holben B, Tucker CJ, Fan CJ (1980). Spectral assessment of soybean leaf area and leaf biomass. Photogrammetric Engineering and Remote Sensing, 46, 651-656. |
[10] | Ingram JC, Dawson TP, Whittaker RJ (2005). Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sensing of Environment, 94, 491-507. |
[11] | Jiang H (江洪) (1992). Study on Donglingshan Mountain Deciduous Broadleaved Forest Biomass and NPP Research (东灵山落叶阔叶林森林生物量和NPP的研究). Postdoctoral research report, Institute of Botany, Chinese Academy of Sciences, Beijing. (in Chinese with English abstract) |
[12] | Lu D, Batistella M (2005). Exploring TM image texture and its relationship with biomass estimation in Rondonia, Brazilian Amazon. Acta Amazonica, 35, 249-257. |
[13] | Luo TX (罗天祥) (1996). Patterns of Net Primary Productivity for Chinese Major Forest Types and Their Mathematical Models (中国主要森林类型生物生产力格局及其数学模型). PhD dissertation, The Commission for Integrated Survey of Natural Resources, Chinese Academy of Sciences, Beijing. 181-184. (in Chinese with English abstract) |
[14] | Pang Y (庞勇), Li ZY (李增元), Chen EX (陈尔学), Sun GQ (孙国清) (2005). Lidar remote sensing technology and its application in forestry. Scientia Silvae Sinicae (林业科学), 41(3), 129-136. (in Chinese with English abstract) |
[15] | Singh KP, Ojha P, Malik A, Jain G (2009). Partial least squares and artificial neural networks modeling for predicting chlorophenol removal from aqueous solution. Chemometrics and Intelligent Laboratory Systems, 99, 150-160. |
[16] | Tang SZ (唐守正), Zhang HR (张会儒), Xu H (胥辉) (2000). Study on establish and estimate method of compatible biomass model. Scientia Silvae Sinicae (林业科学), 36(1), 19-27. (in Chinese with English abstract) |
[17] | Wang HW (王惠文), Wu ZB (吴载斌), Meng J (孟洁) (2006). Partial Least-Squares Regression-Linear and Nonlinear Methods (偏最小二乘回归的线性与非线性方法). National Defence Industrial Press, Beijing. 57. (in Chinese) |
[18] | Xue L (薛立), Yang P (杨鹏) (2004). Summary of research on forest biomass. Journal of Fujian College of Forestry (福建林学院学报), 24, 283-288. (in Chinese with English abstract) |
[19] | Yang CJ (杨存建), Liu JY (刘纪远), Zhang ZX (张增祥) (2004). Study on the estimation of the tropical forest vegetation biomass by using remote sensing data, meteorological data and topographical data. Geography and Geo-Information Science (地理与地理信息科学), 20(6), 22-25. (in Chinese with English abstract) |
[20] | Zhang HY (张海燕), Feng TJ (冯天瑾) (2002). A study on BP networks with combined activitation functions. Journal of Ocean University of Qingdao (青岛海洋大学学报), 32, 621-626. (in Chinese with English abstract) |
[21] | Zeng WH (曾文华) (2006). Texture Information Extraction in Remote Sensing Imageries with Gray Level Co-occurrence Matrix and Wavelet Transform (基于灰度共生法和小波变换的遥感影像纹理信息提取). PhD dissertation, Northeast Normal University, Changchun. 11-14. (in Chinese with English abstract) |
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