Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

doi:10.12302/j.issn.1000-2006.202010004

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2021, Vol. 45 ›› Issue (4): 49-57.doi: 10.12302/j.issn.1000-2006.202010004

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Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

ZHAO Yinghui¹^,²(), GUO Xinlong¹, ZHEN Zhen¹^,²^,^*()

1. School of Forestry, Northeast Forestry University, Harbin 150040, China
2. Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China

Received:2020-10-02 Accepted:2021-05-06 Online:2021-07-30 Published:2021-07-30
Contact: ZHEN Zhen E-mail:zyinghui0925@126.com;zhzhen@syr.edu

Abstract

Abstract:

【Objective】 We explored feature variables extracted through a combination of airborne laser scanning (ALS) and Sentinel-2A data, and investigated with the aim of identifying the best variable combination mode and estimation method for estimating the forest aboveground biomass (AGB) of natural secondary forests. 【Method】 Based on ALS data from 2015, Sentinel-2A data, and the fixed sample plots of the continuous inventory of secondary forest resources from Maoershan Forest Farm in 2016, this study extracted height features from ALS data (all the LiDAR variables, AL), several vegetation indices from Sentinel-2A (all the optical variables, AO), and then combined the two kinds of variables into new variables (COLI₁ and COLI₂). Finally, five models were constructed, including the stepwise multiple linear regression (SMLR), K-nearest neighbor (K-NN), support vector regression (SVR), random forest (RF), and stack sparse auto-encoder (SSAE) of AGB for natural secondary forests using six feature combinations (AO+AL, COLI₁, COLI₂, COLI₁+AO+AL, COLI₂+AO+AL and COLI₁+ COLI₂+AO+AL). The influence of the COLIs variable, and that of different models, on the accuracy of the AGB was investigated. 【Result】The COLIs variable could efficiently improve the accuracy of AGB estimates and, compared to the other four models, SSAE had the highest accuracy regardless of the variables. The SSAE model with the combination of optical and ALS features (COLI₁+ COLI₂+AO+AL) had the best model performance of R² which was 0.83, RMSE was 11.06 t/hm², rRMSE was 8.23%. 【Conclusion】 The combined variable COLIs can effectively improve the estimation accuracy of natural secondary forest AGB, and one way of the deep learning model (SSAE) is superior to the other prediction models in estimating the AGB of a natural secondary forest. This conclusion will help to further apply the deep learning model to draw a large-area AGB spatial distribution map and estimate the other forest parameters. In general, the SSAE model with the combination of ALS and Sentinel-2A data could estimate AGB more accurately than the other models. This finding provides a technical support for AGB estimation and carbon evaluation of natural secondary forests.

Key words: airborne laser radar (ALS), Sentinel-2A, combined optical and LiDNR index (COLIs), stack sparse auto-encoder (SSAE), natural secondary forest, aboveground biomass

CLC Number:

S758

ZHAO Yinghui, GUO Xinlong, ZHEN Zhen. Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(4): 49-57.

Figures/Tables 3

Table 1

Table 2

Table 3

Accuracy results of each model"

模型 model	变量组合 variables combination	R²	σ(RMSE)/ (t·hm^-2)	σ(rRMSE)/ %
SMLR	A_O+A_L	0.22	39.67	29.52
	$I CO L 1$	0.39	30.43	22.65
	$I CO L 2$	0.35	32.58	24.25
	$I CO L 1$ +A_O+A_L	0.47	27.39	20.38
	$I CO L 2$ +A_O+A_L	0.44	28.96	21.55
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.52	26.37	19.62
K-NN	A_O+A_L	0.37	37.28	27.74
	$I CO L 1$	0.56	23.49	17.48
	$I CO L 2$	0.52	24.91	18.54
	$I CO L 1$ +A_O+A_L	0.61	25.73	19.15
	$I CO L 2$ +A_O+A_L	0.58	27.64	20.57
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.66	26.19	19.49
SVR	A_O+A_L	0.41	34.70	25.82
	$I CO L 1$	0.60	20.34	15.14
	$I CO L 2$	0.56	23.49	17.48
	$I CO L 1$ +A_O+A_L	0.67	21.67	16.13
	$I CO L 2$ +A_O+A_L	0.65	22.52	16.76
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.72	20.98	15.61
RF	A_O+A_L	0.45	33.61	25.01
	$I CO L 1$	0.64	19.38	14.42
	$I CO L 2$	0.62	21.29	15.84
	$I CO L 1$ +A_O+A_L	0.75	17.35	12.91
	$I CO L 2$ +A_O+A_L	0.72	18.64	13.87
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.79	16.07	11.96
SSAE	A_O+A_L	0.50	31.75	23.63
	$I CO L 1$	0.78	12.71	9.46
	$I CO L 2$	0.76	15.36	11.43
	$I CO L 1$ +A_O+A_L	0.81	13.68	10.18
	$I CO L 2$ +A_O+AL	0.79	15.07	11.22
	$I CO L 1$ + $I CO L 2$ +A_O+A_L	0.83	11.06	8.23

Table 3

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林分类型 forest types	样地数 plots number	胸径/cm DBH
林分类型 forest types	样地数 plots number	最大值 max.	最小值 min.	平均值 mean
白桦林 Betula phatyphylla forest	4	14.6	13.1	13.8
阔叶混交林 broad-leaved mixed forest	134	19.8	9.2	15.1
胡桃楸林 Juglans mandshurica forest	1	12.4	12.4	12.4
落叶松林 Larix gmelinii forest	8	19.7	14.2	15.5
山杨林 Populus davidiana forest	1	17.0	17.0	17.0
柞树林 Quercus mongolica forest	4	22.4	14.0	18.0
樟子松林 Pinus sylvestris forest	1	14.1	14.1	14.1
针阔混交林 mixed broadleaf-conifer forest	7	15.0	9.4	11.6
针叶混交林 coniferous mixed forest	1	16.0	16.0	16.0

特征变量 feature variable	R²	σ(RMSE)/ (t·hm^-2)	σ(rRMSE)/ %	特征变量 feature variable	R²	σ(RMSE)/ (t·hm^-2)	σ(rRMSE)/ %
h₁₀	0.39	40.95	30.48	h_max	0.20	47.13	35.07
h₂₀	0.42	39.97	29.75	h_min	0.15	48.86	36.36
h₃₀	0.44	39.52	29.41	h_skew	0.28	44.76	33.31
h₄₀	0.44	39.47	29.37	h_kurt	0.19	47.32	35.22
h₅₀	0.43	39.84	29.65	h_std	0.21	46.78	34.81
h₆₀	0.41	40.47	30.12	h_mean	0.45	39.16	29.14
h₇₀	0.39	41.11	30.59	h_cv	0.20	47.07	35.03
h₈₀	0.35	42.24	31.44	h_var	0.21	46.86	34.87
h₉₀	0.30	43.90	32.67	h_mode	0.27	44.96	33.46
h₉₉	0.22	46.27	34.43	C	0.19	47.07	35.03

Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

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