植物生态学报 ›› 2012, Vol. 36 ›› Issue (10): 1095-1105.DOI: 10.3724/SP.J.1258.2012.01095
收稿日期:
2012-02-13
接受日期:
2012-07-23
出版日期:
2012-02-13
发布日期:
2012-09-26
通讯作者:
庞勇
作者简介:
E-mail: caf.pang@gmail.comReceived:
2012-02-13
Accepted:
2012-07-23
Online:
2012-02-13
Published:
2012-09-26
Contact:
PANG Yong
摘要:
使用小兴安岭温带森林机载遥感-地面观测同步试验获取的机载激光雷达(light detection and ranging, Lidar)点云数据和地面实测样地数据, 估测了典型森林类型的树叶、树枝、树干、地上、树根和总生物量等组分的生物量。从激光雷达数据中提取了两组变量(树冠高度变量组和植被密度变量组)作为自变量, 并采用逐步回归方法进行自变量选择。结果表明: 激光雷达数据得到的变量与森林各组分生物量有很强的相关性; 对于针叶林、阔叶林和针阔叶混交林三种不同森林类型生物量的估测结果是: 针叶林优于阔叶林, 阔叶林优于针阔叶混交林; 不区分森林类型的各组分生物量估测与地面实测值显著相关, 模型决定系数在0.6以上; 区分森林类型进行建模可以进一步提高生物量的估测精度。
庞勇, 李增元. 基于机载激光雷达的小兴安岭温带森林组分生物量反演. 植物生态学报, 2012, 36(10): 1095-1105. DOI: 10.3724/SP.J.1258.2012.01095
PANG Yong, LI Zeng-Yuan. Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China. Chinese Journal of Plant Ecology, 2012, 36(10): 1095-1105. DOI: 10.3724/SP.J.1258.2012.01095
评价指标 Evaluation index | 森林类型 Forest type | 组分生物量 Biomass component | |||||
---|---|---|---|---|---|---|---|
Wf | Wb | Ws | Wa | Wr | Wt | ||
决定系数 Coefficient of determination R2 | 阔叶林 Broad-leaved forest | 0.44 | 0.44 | 0.60 | 0.60 | 0.43 | 0.64 |
针叶林 Needle-leaved forest | 0.82 | 0.91 | 0.87 | 0.88 | 0.84 | 0.89 | |
针阔叶混交林 Needle-broad-leaved mixed forest | 0.20 | 0.42 | 0.62 | 0.61 | 0.43 | 0.63 | |
均方根误差 Root mean square error RMSE | 阔叶林 Broad-leaved forest | 1.1 | 2.0 | 17.1 | 19.3 | 4.4 | 21.5 |
针叶林 Needle-leaved forest | 1.1 | 1.4 | 17.3 | 20.0 | 2.3 | 20.2 | |
针阔叶混交林 Needle-broad-leaved mixed forest | 1.8 | 4.0 | 66.6 | 68.2 | 6.1 | 69.1 | |
相对均方根误差 Relative root mean square error rRMSE | 阔叶林 Broad-leaved forest | 0.36 | 0.23 | 0.22 | 0.21 | 0.21 | 0.19 |
针叶林 Needle-leaved forest | 0.21 | 0.15 | 0.18 | 0.18 | 0.18 | 0.16 | |
针阔叶混交林 Needle-broad-leaved mixed forest | 0.28 | 0.25 | 0.27 | 0.26 | 0.23 | 0.24 |
表1 按不同森林类型进行统计的组分生物量回归模型结果
Table 1 Results of biomass component regression models from different forest types
评价指标 Evaluation index | 森林类型 Forest type | 组分生物量 Biomass component | |||||
---|---|---|---|---|---|---|---|
Wf | Wb | Ws | Wa | Wr | Wt | ||
决定系数 Coefficient of determination R2 | 阔叶林 Broad-leaved forest | 0.44 | 0.44 | 0.60 | 0.60 | 0.43 | 0.64 |
针叶林 Needle-leaved forest | 0.82 | 0.91 | 0.87 | 0.88 | 0.84 | 0.89 | |
针阔叶混交林 Needle-broad-leaved mixed forest | 0.20 | 0.42 | 0.62 | 0.61 | 0.43 | 0.63 | |
均方根误差 Root mean square error RMSE | 阔叶林 Broad-leaved forest | 1.1 | 2.0 | 17.1 | 19.3 | 4.4 | 21.5 |
针叶林 Needle-leaved forest | 1.1 | 1.4 | 17.3 | 20.0 | 2.3 | 20.2 | |
针阔叶混交林 Needle-broad-leaved mixed forest | 1.8 | 4.0 | 66.6 | 68.2 | 6.1 | 69.1 | |
相对均方根误差 Relative root mean square error rRMSE | 阔叶林 Broad-leaved forest | 0.36 | 0.23 | 0.22 | 0.21 | 0.21 | 0.19 |
针叶林 Needle-leaved forest | 0.21 | 0.15 | 0.18 | 0.18 | 0.18 | 0.16 | |
针阔叶混交林 Needle-broad-leaved mixed forest | 0.28 | 0.25 | 0.27 | 0.26 | 0.23 | 0.24 |
图1 样地各组分生物量实测值与激光雷达估测值的对比(单位为: t·hm-2)。*, 阔叶林; ○, 针叶林; × , 针阔叶混交林; R2, 决定系数; RMSE, 均方根误差; Wa, 地上总生物量; Wb, 活枝生物量; Wf, 叶生物量; Wr, 根生物量; Ws, 树干生物量; Wt, 总生物量。
Fig. 1 Comparison of estimated biomass components from field measurements and airborne Lidar (unit: t·hm-2). *, broad-leaved forest; ○, needle-leaved forest; × , needle-broad-leaved mixed forest; R2, coefficient of determination; RMSE, root mean square error; Wa, aboveground biomass; Wb, live branch biomass; Wf, leaf biomass; Wr, root biomass; Ws, trunk biomass; Wt, total biomass.
图2 地面测量胸高断面积平均高与激光雷达75%分位数高度的对比。*, 阔叶林; ○, 针叶林; × , 针阔叶混交林; R2, 决定系数; RMSE, 均方根误差。
Fig. 2 Comparison of Lorey’s height (basal area weighted mean height) from field measurements and 75% percentile height from airborne Lidar data. *, broad-leaved forest; ○, needle-leaved forest; × , needle-broad-leaved mixed forest; R2, coefficient of determination; RMSE, root mean square error.
评价指标 Evaluation index | 森林类型 Forest type | 组分生物量 Biomass component | ||||||
---|---|---|---|---|---|---|---|---|
Wf | Wb | Ws | Wa | Wr | Wt | |||
决定系数 Coefficient of determination R2 | 阔叶林 Broad-leaved forest | 0.96 | 0.92 | 0.89 | 0.88 | 0.82 | 0.95 | |
针叶林 Needle-leaved forest | 0.97 | 0.91 | 0.97 | 0.95 | 0.85 | 0.95 | ||
针阔叶混交林 Needle-broad-leaved mixed forest | 0.82 | 0.61 | 0.79 | 0.80 | 0.79 | 0.82 | ||
均方根误差 Root mean square error RMSE | 阔叶林 Broad-leaved forest | 0.30 | 0.80 | 8.90 | 10.30 | 2.50 | 8.10 | |
针叶林 Needle-leaved forest | 0.50 | 1.30 | 8.30 | 12.20 | 2.20 | 13.30 | ||
针阔叶混交林 Needle-broad-leaved mixed forest | 0.90 | 3.30 | 49.5 | 48.90 | 3.70 | 48.90 | ||
相对均方根误差 Relative root mean square error rRMSE | 阔叶林 Broad-leaved forest | 0.10 | 0.09 | 0.11 | 0.11 | 0.12 | 0.07 | |
针叶林 Needle-leaved forest | 0.10 | 0.14 | 0.08 | 0.11 | 0.18 | 0.11 | ||
针阔叶混交林 Needle-broad-leaved mixed forest | 0.14 | 0.20 | 0.20 | 0.18 | 0.14 | 0.17 |
表2 用不同森林类型进行变量筛选、建立回归模型时的组分生物量模拟结果
Table 2 Results of biomass component regression models from variable selection and model establishment categoried by forest types
评价指标 Evaluation index | 森林类型 Forest type | 组分生物量 Biomass component | ||||||
---|---|---|---|---|---|---|---|---|
Wf | Wb | Ws | Wa | Wr | Wt | |||
决定系数 Coefficient of determination R2 | 阔叶林 Broad-leaved forest | 0.96 | 0.92 | 0.89 | 0.88 | 0.82 | 0.95 | |
针叶林 Needle-leaved forest | 0.97 | 0.91 | 0.97 | 0.95 | 0.85 | 0.95 | ||
针阔叶混交林 Needle-broad-leaved mixed forest | 0.82 | 0.61 | 0.79 | 0.80 | 0.79 | 0.82 | ||
均方根误差 Root mean square error RMSE | 阔叶林 Broad-leaved forest | 0.30 | 0.80 | 8.90 | 10.30 | 2.50 | 8.10 | |
针叶林 Needle-leaved forest | 0.50 | 1.30 | 8.30 | 12.20 | 2.20 | 13.30 | ||
针阔叶混交林 Needle-broad-leaved mixed forest | 0.90 | 3.30 | 49.5 | 48.90 | 3.70 | 48.90 | ||
相对均方根误差 Relative root mean square error rRMSE | 阔叶林 Broad-leaved forest | 0.10 | 0.09 | 0.11 | 0.11 | 0.12 | 0.07 | |
针叶林 Needle-leaved forest | 0.10 | 0.14 | 0.08 | 0.11 | 0.18 | 0.11 | ||
针阔叶混交林 Needle-broad-leaved mixed forest | 0.14 | 0.20 | 0.20 | 0.18 | 0.14 | 0.17 |
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