Spatiotemporal evolution and landscape pattern analysis of the invasive species Spartina alterniflora in the Yellow River Delta wetland

doi:10.17521/cjpe.2024.0160

Abstract

Abstract:

Aims In recent years, the invasion of Spartina alterniflora has caused significant damage to the structure and function of the wetland ecosystem in the Yellow River Delta Nature Reserve. Analyzing the invasion characteristics and landscape pattern changes of S. alterniflora over a long-term period holds considerable ecological importance.

Methods In this study, we use long-term Landsat optical remote sensing images as the primary data source, focusing on the wetlands of the Yellow River. Employing a support vector machine classification algorithm, we extracted wetland type information of the Yellow River Delta from 2000 to 2022. This allowed us to uncover the spatiotemporal dynamics of the wetlands. Additionally, we applied eight landscape pattern indices to examine the outbreak characteristics of S. alterniflora and conducted further analysis on its spatial pattern, evolution characteristics, and associated ecological factors.

Important findings The results revealed that: (1) The average overall accuracy of ground object extraction across 23 periods was 88.60%, with an average Kappa coefficient of 0.85. The Support Vector Machine algorithm demonstrated higher classification accuracy compared to other traditional machine learning methods, making it highly effective for analyzing wetland evolution mechanisms. (2) The long-term wetland evolution analysis showed distinct phases in the expansion of S. alterniflora. From 2002 to 2010, its expansion was slow. However, since 2010, the expansion on both sides of the Yellow River estuary entered an explosive phase. By 2012, its area had increased to 22.63 km², continuing to grow steadily, reaching 50.42 km² by 2021. The landscape pattern index indicated that S. alterniflora has a significant ecological advantage in the study area. Fragmented patches of S. alterniflora have progressively merged and connected, invading native wetland vegetation and creating a mosaic-like spatial pattern with other vegetation communities. (3) The expansion of S. alterniflora in the study area showed a high correlation with hydrothermal conditions, sunshine duration, topography, and the mudflat environment. Among these factors, the correlation between sunshine conditions, proximity to the sea, and the growth of S. alterniflora was particularly strong. These findings provide theoretical support for relevant departments in the sustainable management and utilization of wetlands in the Yellow River Delta Nature Reserve, as well as in the control and eradication of S. alterniflora.

Key words: Spartina alterniflora, long-term remote sensing monitoring, Yellow River Delta, wetland classification, evolution mechanism analysis

DUAN Jun-Cheng, WANG Zhi-Yong, GAO Wei-Cong, ZHANG Cheng-Kai, GAO Chang-Hong, LIU Xiao-Tong, LI Zhen-Jin. Spatiotemporal evolution and landscape pattern analysis of the invasive species Spartina alterniflora in the Yellow River Delta wetland[J]. Chin J Plant Ecol, 2025, 49(6): 922-938.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2024.0160

https://www.plant-ecology.com/EN/Y2025/V49/I6/922

Figures/Tables 15

References 43

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年份 Year	生产者精度 Producer accuracy (%)	用户精度 User accuracy (%)	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
2000	-	-	92.84	0.92
2001	-	-	93.20	0.91
2002	80.79	98.33	95.23	0.94
2003	94.26	94.74	91.12	0.90
2004	91.16	93.56	93.38	0.92
2005	95.57	92.41	92.96	0.89
2006	83.74	93.58	93.09	0.92
2007	91.02	91.03	92.29	0.90
2008	93.11	87.38	93.78	0.92
2009	95.48	92.41	89.16	0.86
2010	87.96	89.18	89.31	0.86
2011	87.64	85.89	80.35	0.78
2012	92.44	86.17	86.22	0.82
2013	92.88	89.08	88.36	0.85
2014	84.48	86.91	93.18	0.91
2015	79.13	87.16	85.88	0.79
2016	81.17	91.61	81.89	0.77
2017	81.46	89.43	84.05	0.80
2018	81.85	84.43	84.67	0.80
2019	92.06	88.36	88.84	0.86
2020	83.38	85.80	85.87	0.82
2021	85.74	80.25	81.25	0.77
2022	91.57	82.99	89.76	0.87
平均 Mean	87.95	89.08	88.60	0.85

年份 Year	生产者精度 Producer accuracy (%)	用户精度 User accuracy (%)	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
2000	-	-	92.84	0.92
2001	-	-	93.20	0.91
2002	80.79	98.33	95.23	0.94
2003	94.26	94.74	91.12	0.90
2004	91.16	93.56	93.38	0.92
2005	95.57	92.41	92.96	0.89
2006	83.74	93.58	93.09	0.92
2007	91.02	91.03	92.29	0.90
2008	93.11	87.38	93.78	0.92
2009	95.48	92.41	89.16	0.86
2010	87.96	89.18	89.31	0.86
2011	87.64	85.89	80.35	0.78
2012	92.44	86.17	86.22	0.82
2013	92.88	89.08	88.36	0.85
2014	84.48	86.91	93.18	0.91
2015	79.13	87.16	85.88	0.79
2016	81.17	91.61	81.89	0.77
2017	81.46	89.43	84.05	0.80
2018	81.85	84.43	84.67	0.80
2019	92.06	88.36	88.84	0.86
2020	83.38	85.80	85.87	0.82
2021	85.74	80.25	81.25	0.77
2022	91.57	82.99	89.76	0.87
平均 Mean	87.95	89.08	88.60	0.85

分类方法 Classification method	生产者精度 Producer accuracy (%)	用户精度 User accuracy (%)	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
最大似然法 Maximum Likelihood	81.63	78.07	81.96	0.77
最小距离法 Minimum Distance	59.16	62.10	72.19	0.69
神经网络 Neural Network	71.14	95.31	85.89	0.82
随机森林 Random Forest	75.54	82.88	84.25	0.81
支持向量机 Support Vector Machine	92.06	88.36	88.84	0.86

分类方法 Classification method	生产者精度 Producer accuracy (%)	用户精度 User accuracy (%)	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
最大似然法 Maximum Likelihood	81.63	78.07	81.96	0.77
最小距离法 Minimum Distance	59.16	62.10	72.19	0.69
神经网络 Neural Network	71.14	95.31	85.89	0.82
随机森林 Random Forest	75.54	82.88	84.25	0.81
支持向量机 Support Vector Machine	92.06	88.36	88.84	0.86

		2012
		互花米草 Spartina alterniflora	水体 Water	芦苇 Phragmites australis	柽柳 Tamarix chinensis	裸滩 Nude beach	碱蓬 Suaeda salsa	总和 Summation
2002	互花米草 Spartina alterniflora	0.04	0.15	0.38	0.11	0.01	0.05	0.74
	水体 Water	16.40	410.22	3.80	4.19	31.83	4.56	470.98
	芦苇 Phragmites australis	0.37	3.46	39.84	7.09	2.59	0.88	54.22
	柽柳 Tamarix chinensis	0.61	5.78	14.59	19.32	5.40	1.69	47.39
	裸滩 Nude beach	4.60	50.73	6.52	8.79	176.82	13.59	261.05
	碱蓬 Suaeda salsa	0.62	22.43	8.06	5.10	19.77	5.67	61.65
	总和 Summation	22.63	492.76	73.19	44.60	236.42	26.44	896.04