机器学习算法在柽柳灌丛沙丘地上生物量估算中的应用

doi:10.13866/j.azr.2026.01.13

Abstract

Abstract:

Tamarix chinensis, a dominant shrub species in arid ecosystems, is characterized by high stress resistance and adaptive growth strategies. It is crucial for stabilizing sand dunes, reducing wind erosion, and facilitating ecological restoration. Consequently, the aboveground biomass (AGB) of Tamarix nebkha is an essential indicator of vegetation status and desertification control in arid regions. The research object of this study was Tamarix nebkha in the lower reaches of the Tarim River. In total, 92 spectral, vegetation, and texture indices were extracted as feature variables from Landsat 8 images. For subsequent variable selection, stepwise regression, least absolute shrinkage and selection operator (LASSO), and extreme gradient boosting (XGBoost) algorithms were employed. Random forest (RF), support vector regression (SVR), and backpropagation neural network (BPNN) models were then constructed to estimate the AGB of Tamarix nebkha. The adaptability and estimation accuracy of different feature selection methods and models were compared, and the applicability of multivariate algorithms for the estimation of Tamarix nebkha AGB were explored. The results indicated that: (1) The variable sets selected by the stepwise regression, LASSO, and XGBoost algorithms were significantly correlated with the AGB of Tamarix nebkha, and the low multicollinearity among variables (VIF<5) confirmed the effectiveness of the algorithms. (2) The models constructed using the LASSO and XGBoost algorithms were significantly more accurate than those constructed using the stepwise method. Among the tested models, the RF model developed using the LASSO algorithm had the best performance (R²=0.73, RMSE=447.63 g·m^-2). The incorporation of multivariate combinations substantially enhanced the predictive capability of the models. (3) The LASSO-based RF model estimated the mean AGB of Tamarix nebkha in the lower Tarim River region to be 1733.63 g·m^-2 and the total biomass to be approximately 1.71×10⁸ kg. The developed model exhibited high reliability and strong applicability across the regional scale. The findings of this study provide a scientific basis for optimizing the selection of remote sensing inversion methods and enhancing the accuracy of AGB estimation in desert shrub ecosystems.

Key words: Tamarix nebkha, aboveground biomass, LASSO algorithm, machine learning, remote sensing estimation

DONG Yaqing, SONG Shaoteng, GUO Xiaoqian, ZHAO Yuanjie. Application of machine learning algorithms for estimation of aboveground biomass of Tamarix nebkha[J].Arid Zone Research, 2026, 43(1): 144-155.

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简称	变量名称	简称	变量名称	简称	变量名称
ARVI	大气阻抗植被指数	NDVI	归一化植被指数	RVI	比值植被指数
B	亮度植被指数	NDVI 563	3、5和6波段的归一化差值植被指数	SAVI	土壤校正植被指数
DVI	差异植被指数	NLI	归一化差异植被指数	SAVI 2	改良土壤校正植被指数
EVI	增强型植被指数	OSAVI	优化土壤植被指数	SIPI	结构无关色素指数
G	绿度植被指数	PC1_A	主成分变换	SR	简单比值指数
GCI	绿色叶绿素指数	PC1_B	主成分变换	TVI	三维植被指数
GRVI	绿度归一化植被指数	PC1_C	主成分变换	VIRI	可见光大气抗性植被指数
MSAVI	改进型土壤调节植被指数	PRI	光合作用反射指数	VIS	差异植被指数
MSR	改进的简单比值指数	PVI	垂直植被指数	W	温度植被指数
MVI	修正植被指数	REIP	红边拐点指数

模型	参数
随机森林（RF）	决策树数量=100，最小叶子节点样本数=5
支持向量机（SVR）	核函数系数=0.8，惩罚因子=5
反向传播回归（BPNN）	学习率=0.01，隐含层神经元数=5，训练目标误差阈值=1×10^-6

模型	非标准化系数		t值	Sig.	共线性统计量
模型	B	标准误差	t值	Sig.	容差	VIF
常量	1.37	0.086	15.855	0
MSR	0.687	0.111	6.195	0	0.618	1.618
MSAVI	0.453	0.11	4.132	0	0.633	1.58
Correlation_B4	-0.399	0.144	-2.767	0.008	0.364	2.745
Correlation_B5	0.331	0.101	3.28	0.002	0.746	1.341
Correlation_B6	0.492	0.136	3.611	0.001	0.409	2.446

变量集	变量名称
L1	DVI
L2	DVI、Dissmilarity_B1
L3	DVI、Dissmilarity_B1、Correlation_B6
L4	DVI、Dissmilarity_B1、Correlation_B6、Correlation_B3
L5	DVI、Dissmilarity_B1、Correlation_B6、Correlation_B3、G
L6	DVI、Dissmilarity_B1、Correlation_B6、Correlation_B3、G、NDVI 563
L7	DVI、Dissmilarity_B1、Correlation_B6、Correlation_B3、G、NDVI 563、Homogeneity_B5

选择算法	RF模型		SVR模型		BPNN模型
选择算法	R²	RMSE	R²	RMSE	R²	RMSE
逐步法	0.64	512.51	0.7	527.37	0.64	528.74
LASSO	0.73	447.63	0.68	505.96	0.7	520.82
XGBoost	0.68	449.83	0.71	451.96	0.67	468.08

Application of machine learning algorithms for estimation of aboveground biomass of Tamarix nebkha

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