Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China

doi:10.3724/SP.J.1258.2012.01095

Abstract

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

PANG Yong, LI Zeng-Yuan. Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China[J]. Chin J Plant Ecol, 2012, 36(10): 1095-1105.

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Figures/Tables 4

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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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