Research on Forest Biomass and Stock Volume Model Based on Stand Height and Canopy Density

doi:10.13466/j.cnki.lyzygl.2021.02.009

Abstract

Abstract:

With the rapid development of LiDAR and stereo image space aerial remote sensing technology,although China has the ability to quickly obtain the information of average stand height and canopy density,there is a lack of models to accurately estimate the biomass and volume of forest,which seriously affects the popularization and application of the technologies.In order to explore the construction of the model,the average stand height,canopy density and plant number of 52 Chinese fir sample plots from the ground survey data in Xiangxi area of Central China's Hunan Province are used to construct several inversion models of Chinese fir aboveground biomass and volume in different function forms through different independent variables,and the model is evaluated by the coefficient of determination R².The results show that the combination of factor variables based on average stand height and canopy density is closely related to aboveground biomass and volume.Among them,the above ground biomass model and volume model of Chinese fir with e as the base logarithmic variable ln(C×HH²)as the explanatory variable have the best fitting effect;the exponential function model can accurately show the relationship between the independent variable and aboveground biomass and volume.Compared with the linear function model,power function model and logarithmic function model,the exponential function model has better fitting effect.Such methods prove effective to explore the relationship among average stand height,canopy density and aboveground biomass and volume of Chinese fir.The aboveground biomass model and volume model of Chinese fir are constructed in order to provide reference for establishing the aboveground biomass and volume model for other tree species estimated by average stand height and canopy density factors.

Key words: aboveground biomass, stock volume, average stand height, canopy closure, model building

CLC Number:

S711
S758.5

WU Fayun, GAO Xianlian, ZHOU Rong, WANG Pengjie, FU Anmin. Research on Forest Biomass and Stock Volume Model Based on Stand Height and Canopy Density[J]. FOREST RESOURCES WANAGEMENT, 2021, (2): 61-67.

Figures/Tables 7

Tab.1

Tab.2

Tab.3

Tab.4

Statistics of regression results of aboveground biomass model with different variable combinations

函数类型	HD	R²	HH	R²
一次	C×HD²	0.562	C×HH²	0.626
	ln(C×HD²)	0.667	ln(C×HH²)	0.682
	log(C×HD²)	0.667	log(C×HH²)	0.682
	C²×HD²	0.415	C²×HH²	0.466
	ln(C²×HD²)	0.586	ln(C²×HH²)	0.590
	log(C²×HD²)	0.586	log(C²×HH²)	0.590
	C×HD²×N	0.428	C×HH²×N	0.408
	ln(C×HD²×N)	0.535	ln(C×HH²×N)	0.520
	log(C×HD²×N)	0.535	log(C×HH²×N)	0.520
	C²×HD²×N	0.306	C²×HH²×N	0.301
	ln(C²×HD²×N)	0.442	ln(C²×HH²×N)	0.432
	log(C²×HD²×N)	0.442	log(C²×HH²×N)	0.432
幂	C×HD²	0.843	C×HH²	0.861
	ln(C×HD²)	0.830	ln(C×HH²)	0.854
	log(C×HD²)	0.830	log(C×HH²)	0.854
	C²×HD²	0.764	C²×HH²	0.768
	ln(C²×HD²)	0.536	ln(C²×HH²)	0.679
	log(C²×HD²)	0.536	log(C²×HH²)	0.679
	C×HD²×N	0.693	C×HH²×N	0.673
	ln(C×HD²×N)	0.704	ln(C×HH²×N)	0.687
	log(C×HD²×N)	0.704	log(C×HH²×N)	0.687
	C²×HD²×N	0.587	C²×HH²×N	0.574
	ln(C²×HD²×N)	0.601	ln(C²×HH²×N)	0.591
	log(C²×HD²×N)	0.601	log(C²×HH²×N)	0.591
对数	C×HD²	0.667	C×HH²	0.682
	ln(C×HD²)	0.601	ln(C×HH²)	0.624
	log(C×HD²)	0.601	log(C×HH²)	0.624
	C²×HD²	0.586	C²×HH²	0.590
	ln(C²×HD²)	0.314	ln(C²×HH²)	0.435
	log(C²×HD²)	0.314	log(C²×HH²)	0.435
	C×HD²×N	0.535	C×HH²×N	0.520
	ln(C×HD²×N)	0.516	ln(C×HH²×N)	0.506
	log(C×HD²×N)	0.516	log(C×HH²×N)	0.506
	C²×HD²×N	0.442	C²×HH²×N	0.432
	ln(C²×HD²×N)	0.423	ln(C²×HH²×N)	0.417
	log(C²×HD²×N)	0.423	log(C²×HH²×N)	0.417
指数	C×HD²	0.581	C×HH²	0.647
	ln(C×HD²)	0.843	ln(C×HH²)	0.908
	log(C×HD²)	0.843	log(C×HH²)	0.908
	C²×HD²	0.441	C²×HH²	0.493
	ln(C²×HD²)	0.764	ln(C²×HH²)	0.768
	log(C²×HD²)	0.764	log(C²×HH²)	0.768
	C×HD²×N	0.422	C×HH²×N	0.404
	ln(C×HD²×N)	0.693	ln(C×HH²×N)	0.673
	log(C×HD²×N)	0.693	log(C×HH²×N)	0.673
	C²×HD²×N	0.316	C²×HH²×N	0.310
	ln(C²×HD²×N)	0.587	ln(C²×HH²×N)	0.574
	log(C²×HD²×N)	0.587	log(C²×HH²×N)	0.574

Tab.4

Fig.1

Tab.5

Statistics of regression results of accumulation model of different variable combinations

函数类型	HD	R²	HH	R²
一次	C×HD²	0.645	C×HH²	0.700
	ln(C×HD²)	0.697	ln(C×HH²)	0.705
	log(C×HD²)	0.697	log(C×HH²)	0.705
	C²×HD²	0.478	C²×HH²	0.523
	ln(C²×HD²)	0.601	ln(C²×HH²)	0.600
	log(C²×HD²)	0.601	log(C²×HH²)	0.600
	C×HD²×N	0.414	C×HH²×N	0.388
	ln(C×HD²×N)	0.503	ln(C×HH²×N)	0.484
	log(C×HD²×N)	0.503	log(C×HH²×N)	0.484
	C²×HD²×N	0.299	C²×HH²×N	0.288
	ln(C²×HD²×N)	0.413	ln(C²×HH²×N)	0.401
	log(C²×HD²×N)	0.413	log(C²×HH²×N)	0.401
幂	C×HD²	0.878	C×HH²	0.893
	ln(C×HD²)	0.859	ln(C×HH²)	0.880
	log(C×HD²)	0.859	log(C×HH²)	0.880
	C²×HD²	0.782	C²×HH²	0.783
	ln(C²×HD²)	0.544	ln(C²×HH²)	0.687
	log(C²×HD²)	0.544	log(C²×HH²)	0.687
	C×HD²×N	0.679	C×HH²×N	0.657
	ln(C×HD²×N)	0.690	ln(C×HH²×N)	0.671
	log(C×HD²×N)	0.690	log(C×HH²×N)	0.671
	C²×HD²×N	0.571	C²×HH²×N	0.556
	ln(C²×HD²×N)	0.585	ln(C²×HH²×N)	0.572
	log(C²×HD²×N)	0.585	log(C²×HH²×N)	0.572
对数	C×HD²	0.697	C×HH²	0.705
	ln(C×HD²)	0.616	ln(C×HH²)	0.635
	log(C×HD²)	0.616	log(C×HH²)	0.635
	C²×HD²	0.601	C²×HH²	0.600
	ln(C²×HD²)	0.308	ln(C²×HH²)	0.430
	log(C²×HD²)	0.308	log(C²×HH²)	0.430
	C×HD²×N	0.503	C×HH²×N	0.484
	ln(C×HD²×N)	0.485	ln(C×HH²×N)	0.470
	log(C×HD²×N)	0.485	log(C×HH²×N)	0.470
	C²×HD²×N	0.413	C²×HH²×N	0.401
	ln(C²×HD²×N)	0.394	ln(C²×HH²×N)	0.386
	log(C²×HD²×N)	0.394	log(C²×HH²×N)	0.386
指数	C×HD²	0.627	C×HH²	0.690
	ln(C×HD²)	0.878	ln(C×HH²)	0.906
	log(C×HD²)	0.878	log(C×HH²)	0.906
	C²×HD²	0.474	C²×HH²	0.523
	ln(C²×HD²)	0.782	ln(C²×HH²)	0.783
	log(C²×HD²)	0.782	log(C²×HH²)	0.783
	C×HD²×N	0.415	C×HH²×N	0.395
	ln(C×HD²×N)	0.679	ln(C×HH²×N)	0.657
	log(C×HD²×N)	0.679	log(C×HH²×N)	0.657
	C²×HD²×N	0.311	C²×HH²×N	0.304
	ln(C²×HD²×N)	0.571	ln(C²×HH²×N)	0.556
	log(C²×HD²×N)	0.571	log(C²×HH²×N)	0.556

Tab.5

Fig.2

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郁闭度	样地数	变量	平均值	最小值	最大值	标准差
0.2	2	优势木平均高D/m	11.35	5.20	17.50	8.70
		优势木平均高H/m	11.75	6.00	17.50	8.13
		蓄积量/m³	1.55	0.40	2.70	1.63
		地上生物量/kg	1047.63	367.14	1728.13	962.36
0.3	16	优势木平均高D/m	16.82	6.80	31.30	8.11
		优势木平均高H/m	17.71	6.80	32.20	8.54
		蓄积量/m³	4.91	0.40	17.10	5.13
		地上生物量/kg	3655.05	426.54	10442.31	2930.78
0.4	3	优势木平均高D/m	8.20	6.80	9.20	1.02
		优势木平均高H/m	8.83	7.30	9.90	1.11
		蓄积量/m³	12.37	9.70	17.10	3.36
		地上生物量/kg	1001.5059	705.6651	1262.4485	228.64
0.5	6	优势木平均高D/m	15.22	7.30	24.90	5.40
		优势木平均高H/m	16.18	8.60	25.20	5.23
		蓄积量/m³	11.38	2.10	22.20	7.75
		地上生物量/kg	5339.15	771.29	10771.98	3254.68
0.6	8	优势木平均高D/m	20.80	16.10	25.20	2.90
		优势木平均高H/m	23.20	21.10	27.10	1.98
		蓄积量/m³	7.08	2.10	12.30	3.77
		地上生物量/kg	8430.84	3015.95	12193.98	2930.74
0.7	14	优势木平均高D/m	15.44	7.90	27.80	6.29
		优势木平均高H/m	16.31	8.10	27.90	6.14
		蓄积量/m³	8.58	0.80	17.20	4.99
		地上生物量/kg	4193.21	1561.99	7759.64	2154.89
0.8	3	优势木平均高D/m	16.77	14.90	20.10	2.36
		优势木平均高H/m	17.87	15.90	20.50	1.94
		蓄积量/m³	12.00	10.70	13.00	0.96
		地上生物量/kg	7147.82	7047.85	7325.79	126.16

函数表达式	函数释义
y=ax+b	一次线性函数
y=x^α	幂函数
y=a^x	指数函数
y=log_ax	对数函数