基于机载激光雷达的小兴安岭温带森林组分生物量反演

doi:10.3724/SP.J.1258.2012.01095

摘要/Abstract

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。

关键词: 地上生物量, 机载激光雷达, 组分生物量, 根生物量, 温带森林

Abstract:

Aims Our purpose was to demonstrate the potential of using airborne laser to estimate biomass components of temperate forest. The airborne Lidar data and field data of concomitant plots were used in a forest of the Northeastern China.
Methods A set of biomass components, i.e., leaf biomass, branch biomass, trunk biomass, aboveground biomass and belowground biomass, were calculated from field data using species-specific allometric equations. Canopy height indices and density indices were calculated from Lidar point cloud data. The height indices evaluated included maximum height of all points, mean height of all points, quadratic mean height (square root of the mean squared height of each Lidar point) as well as height percentiles. Canopy density indices were computed as the proportions of laser points above each percentile height to total number of points. Then statistical models between these biomass components from field data and Lidar indices were built. Stepwise regression was used for variable selection and the maximum coefficient of determination (R ²) improvement variable selection techniques were applied to select the ALS-derived variables to be included in the models. The least squares method was used generally and repeated until all the independent variables of the regression equation were accord with the requirements of entering models.
Important findings There were good correlations between biomass components and Lidar indices. The R ²was >0.6 for all the biomass components when we put all three types of forest (i.e., needle-leaved, broad-leaved and mixed) together. Needle-leaved forest had best estimation followed by broad-leaved and mixed forests when we built separate models for the three types of forest. This estimation capability is better when the regression models are built for different forest types.

Key words: aboveground biomass, airborne Lidar, biomass component, root biomass, temperate forest

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

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链接本文: https://www.plant-ecology.com/CN/10.3724/SP.J.1258.2012.01095

https://www.plant-ecology.com/CN/Y2012/V36/I10/1095

图/表 4

参考文献 23

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评价指标 Evaluation index	森林类型 Forest type	组分生物量 Biomass component
评价指标 Evaluation index	森林类型 Forest type	W_f	W_b	W_s	W_a	W_r	W_t
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

评价指标 Evaluation index	森林类型 Forest type	组分生物量 Biomass component
评价指标 Evaluation index	森林类型 Forest type	W_f	W_b	W_s	W_a	W_r	W_t
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

评价指标 Evaluation index	森林类型 Forest type	组分生物量 Biomass component
评价指标 Evaluation index	森林类型 Forest type	W_f	W_b	W_s	W_a	W_r	W_t
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

评价指标 Evaluation index	森林类型 Forest type	组分生物量 Biomass component
评价指标 Evaluation index	森林类型 Forest type	W_f	W_b	W_s	W_a	W_r	W_t
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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