Establishment and application of simultaneous models for estimating main stand characteristics based on Sentinel-2 data in Beijing

doi:10.13466/j.cnki.lczyyj.2025.03.013

Abstract

Abstract:

In order to explore the feasibility of establishing main stand characteristics models based on Sentinel-2 data to estimate the factors of forest patches,the ground plot measured data and spectral characteristic metrics of 1 500 forest plots in Beijing were used to develop prediction models of three major forest types through the error-in-variable simultaneous equations.The models involve eight main stand characteristics,including mean DBH,mean height,dominant height,stem number,basal area,stock volume,biomass and carbon storage.Additionally,based on the spectral characteristic parameters extracted by 25 m×25 m grid cells within the forest patches in Beijing,the eight prediction models were used to estimate main stand characteristics of all forest patches.The results showed:1)The spectral characteristic metrics of Sentinel-2 that contributed the most to the estimation of main stand characteristics were B11 and B12(short wave infrared 1 and 2 band reflectance),followed by B5(red-edge 1 band reflectance)and RVI(Ratio Vegetation Index);2)The mean prediction errors(MPEs)of eight main stand characteristics models of three major forest types were less than 10%,either self-validation or cross-validation;3)The cumulative value of stock volume in all forest patches estimated by the volume models is only -1.74% lower than that obtained by the integrated monitoring of the municipality,which was within the allowable error range of sampling survey.The eight prediction models of three major forest types can be used to estimate the main stand characteristics of forest patches in Beijing;and the prediction accuracy of the main stand characteristics models based on spectral characteristic parameters of Sentinel-2 can almost meet the technical requirements of forest resource inventory and monitoring,then can be applied in practice.

Key words: spectral characteristic metrics, main stand characteristics, error-in-variable, simultaneous models, Beijing

CLC Number:

S771

ZENG Weisheng, WEN Xuexiang, LI Xiaoyao, TAN Bingxiang, SUN Xiangnan, LIU Qiangyi, WANG Tian. Establishment and application of simultaneous models for estimating main stand characteristics based on Sentinel-2 data in Beijing[J]. Forest and Grassland Resources Research, 2025, (3): 109-118.

Figures/Tables 5

Tab.1

Tab.2

Tab.3

The evaluation indexes of simultaneous models for three main forest types

森林类型	林分因子	自检结果						交叉检验结果
森林类型	林分因子	R²	E_SE	E_TR/%	E_AS/%	E_MP/%	E_MPS/%	R²	E_SE	E_TR/%	E_AS/%	E_MP/%	E_MPS/%
针叶林	D	0.266	3.09 cm	5.05	6.27	2.51	26.90	0.257	3.11 cm	5.04	6.37	2.53	27.15
	H	0.305	1.80 m	3.09	3.70	2.67	28.54	0.302	1.80 m	3.08	3.74	2.68	28.65
	H_d	0.332	2.13 m	2.43	2.85	2.65	28.58	0.328	2.13 m	2.40	2.90	2.66	28.67
	G	0.661	3.70 m²/hm²	1.48	5.57	3.91	39.23	0.649	3.77 m²/hm²	1.24	5.57	3.98	39.57
	N	0.282	402株/hm²	3.11	7.03	4.91	49.93	0.282	403株/hm²	2.98	6.95	4.91	50.23
	V	0.639	22.20 m³/hm²	-0.84	-1.02	5.16	48.13	0.623	22.70 m³/hm²	-1.20	-0.95	5.27	48.54
	B	0.494	26.80 t/hm²	-6.27	-0.63	5.37	45.99	0.469	27.50 t/hm²	-6.60	-0.57	5.50	46.45
	C	0.520	13.10 t/hm²	-6.12	-1.19	5.26	45.24	0.495	13.40 t/hm²	-6.45	-1.11	5.40	45.74
硬阔林	D	0.407	3.00 cm	1.28	1.52	1.59	18.34	0.399	3.02 cm	1.26	1.52	1.60	18.44
	H	0.321	1.95 m	0.89	0.50	1.65	18.47	0.316	1.96 m	0.84	0.48	1.66	18.56
	H_d	0.327	2.32 m	0.41	-0.06	1.64	18.55	0.322	2.33 m	0.37	-0.08	1.64	18.63
	G	0.606	3.88 m²/hm²	7.48	6.38	3.01	31.13	0.604	3.89 m²/hm²	7.47	6.51	3.02	31.33
	N	0.425	360株/hm²	8.75	11.99	3.57	38.34	0.418	362株/hm²	8.83	12.39	3.59	38.77
	V	0.576	23.70 m³/hm²	4.62	0.22	3.86	36.95	0.574	23.70 m³/hm²	4.61	0.32	3.86	37.12
	B	0.595	28.20 t/hm²	-1.61	-1.83	3.31	32.34	0.592	28.30 t/hm²	-1.59	-1.72	3.32	32.52
	C	0.592	13.60 t/hm²	-1.54	-1.72	3.32	32.59	0.589	13.60 t/hm²	-1.52	-1.60	3.34	32.76
软阔林	D	0.408	5.75 cm	4.72	5.97	3.46	24.33	0.399	5.80 cm	4.58	5.98	3.48	24.58
	H	0.554	3.25 m	0.43	-0.73	3.01	21.92	0.545	3.29 m	0.47	-0.61	3.04	22.13
	H_d	0.528	3.54 m	0.23	-0.81	2.89	20.99	0.517	3.58 m	0.26	-0.71	2.92	21.26
	G	0.521	4.56 m²/hm²	8.18	9.46	5.11	38.38	0.507	4.63 m²/hm²	7.59	9.38	5.19	38.86
	N	0.290	198株/hm²	5.35	8.30	5.72	41.49	0.263	201株/hm²	5.06	8.47	5.82	42.18
	V	0.482	45.90 m³/hm²	-5.74	-5.49	7.04	42.07	0.450	47.30 m³/hm²	-6.34	-5.50	7.26	42.21
	B	0.307	39.30 t/hm²	-9.45	-4.20	6.78	38.06	0.265	40.40 t/hm²	-9.96	-4.31	6.98	38.23
	C	0.275	19.00 t/hm²	-10.02	-4.24	6.87	38.05	0.232	19.60 t/hm²	-10.51	-4.35	7.07	38.26

Tab.3

Fig.1

Fig.2

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统计特征	平均胸径 (D)/cm	平均高 (H)/m	优势高 (H_d)/m	株数 (N)/(株/hm²)	断面积 (G)/(m²/hm²)	蓄积量 (V)/(m³/hm²)	生物量 (B)/(t/hm²)	碳储量 (C)/(t/hm²)
平均数	14.0	8.8	10.3	771	10.8	59.0	68.8	33.2
最小值	5.0	1.9	2.0	15	0.1	0.1	0.1	0.1
最大值	44.8	27.4	28.1	4 110	43.9	435.4	338.6	163.7
标准差	6.1	3.9	4.4	598	7.8	57.5	54.9	26.5
变动系数/%	43.6	44.2	42.4	77.6	72.8	97.4	79.7	79.7

森林类型	林分因子	解释变量	联立模型(9)的参数估计值
森林类型	林分因子	解释变量	a₀~g₀	a₁~f₁	a₂~d₂	a₃~d₃
针叶林 (n=400)	D	X₂	2.262	-1.219
	H	X₃	0.756 1	-1.283
	G	X₂、X₃	8.059	-13.98	-12.15
	V	X₂、X₃	10.48	-14.67	-16.17
	H_d		-0.562 4	1.285
	B		0.437 5	1.218
	C		0.497 2
硬阔林 (n=800)	D	X₂、X₃、X₄	1.576	-3.223	1.388	0.321 0
	H	X₂、X₄	1.419	-1.218	0.213 5
	G	X₂、X₃、X₄	4.066	-5.223	-10.52	0.497 0
	V	X₂、X₃、X₄	6.424	-8.478	-10.69	0.551 5
	H_d		-0.092 6	1.214
	B		1.424	1.441
	C		0.479 6
软阔林 (n=300)	D	X₁、X₂、X₃	1.532	-1.023	-5.612	4.050
	H	X₁、X₂、X₃	0.595 5	-1.658	-3.862	2.948
	G	X₁、X₂、X₃	7.779	-18.83	-20.04	17.42
	V	X₁、X₂、X₃	11.99	-25.59	-31.71	28.86
	H_d		1.272	1.032
	B		6.004	0.849 6
	C		0.480 8