黄河三角洲湿地入侵物种互花米草时空演化与景观格局分析

doi:10.17521/cjpe.2024.0160

摘要/Abstract

摘要： 近年来, 随着入侵物种——互花米草(Spartina alterniflora)的爆发, 黄河三角洲自然保护区的湿地生态系统的结构和功能受到了严重破坏, 分析长时序互花米草入侵特征及其景观格局变化具有重要生态价值。该研究以长时序Landsat光学遥感影像为数据源, 以黄河三角洲湿地为实验区, 基于支持向量机分类算法获取了2000-2022年的黄河三角洲湿地植被类型信息, 在此基础上揭示湿地的时空动态变化, 联合8种景观格局指数, 探究互花米草的爆发特征, 分析互花米草的空间格局、演变特点和生态因子。结果表明: (1)研究获取的23个时期的地物提取结果的平均总体精度为88.60%, 平均Kappa系数为0.85。支持向量机算法具有高于其他传统机器学习方法的分类精度, 其结果可以较好地满足湿地演化机理分析需求; (2)长时序湿地演化结果表明, 互花米草的扩张呈现出明显的阶段性特征: 2002-2010年, 互花米草扩张速度缓慢; 自2010年开始, 互花米草在黄河入海口两侧的扩张进入爆发期, 到2012年面积增长至22.63 km², 2012-2021年间互花米草面积持续稳定增长, 至2021年, 互花米草的面积扩张至最大, 达到50.42 km²。景观格局指数的变化表明, 研究区内互花米草表现出较高的生态优势。破碎的互花米草斑块不断聚集连接, 逐渐入侵湿地本土植被并与其他植被群落形成了交错分布的空间格局; (3)黄河三角洲互花米草的扩张, 与水热条件、日照时间、地形与滩涂环境等多种自然因素表现出较高的相关性, 其中, 日照情况、离海距离与互花米草的生长极显著相关。研究结果可为相关部门在黄河三角洲自然保护区湿地合理利用以及互花米草治理和清除方面提供数据参考。

关键词: 互花米草, 长时序遥感监测, 黄河三角洲, 湿地分类, 演化机理分析

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

段俊丞, 王志勇, 高维聪, 张成凯, 高长宏, 刘晓彤, 李振今. 黄河三角洲湿地入侵物种互花米草时空演化与景观格局分析. 植物生态学报, 2025, 49(6): 922-938. DOI: 10.17521/cjpe.2024.0160

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. Chinese Journal of Plant Ecology, 2025, 49(6): 922-938. DOI: 10.17521/cjpe.2024.0160

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2024.0160

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

图/表 15

图1 黄河三角洲湿地互花米草研究应用的Landsat影像时间分布图。

Fig. 1 Temporal distribution of Landsat images for researchof Spartina alterniflora in the Yellow River Delta wetland.

图2 黄河三角洲湿地互花米草研究区位置图。右图为2020年的Landsat7-ETM+卫星影像。

Fig. 2 Location of the study area of Spartina alterniflora in the Yellow River Delta wetland. Right figure based on Landsat7-ETM+ remote sensing images in 2020.

图3 应用于分类的训练样本分布图(A)与应用于精度验证的验证样本分布图(B) (底图是2021年黄河三角洲湿地的Landsat遥感影像图)。

Fig. 3 Distribution of training samples applied to classification (A) and the distribution of test samples applied to accuracy verification (B) (Based on Landsat remote sensing images in the Yellow River Delta wetland in 2021).

图4 总体技术流程图。NDVI, 归一化植被指数; SVM, 支持向量机。

Fig. 4 Technical process. NDVI, normalized difference vegetation index; SVM, support vector machine.

图5 黄河三角洲自然保护区湿地地物分类结果图。

Fig. 5 Yellow River Delta Nature Reserve wetland classification results.

表1 黄河三角洲自然保护区湿地地物分类精度验证结果

Table 1 Accuracy verification results of Yellow River Delta Nature Reserve wetland classification

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

图6 黄河三角洲湿地不同分类方法的地物分类结果对比。A, 最小距离方法。B, 最大似然方法。C, 神经网络方法。D, 随机森林方法。E, 支持向量机算法。

Fig. 6 Classification results from different classification methods in the Yellow River Delta wetland. A, Minimum Distance method. B, Maximum Likelihood method. C, Neural Network method. D, Random Forest method. E, Support Vector Machine method.

表2 黄河三角洲湿地不同分类方法的分类精度对比

Table 2 Comparison of classification accuracy in the Yellow River Delta wetland using different classification methods

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

图7 2000-2022年黄河三角洲自然保护区湿地地物面积变化。

Fig. 7 Change of wetland surface area in the Yellow River Delta Nature Reserve from 2000 to 2022.

表3 2002-2012年黄河三角洲地物类型面积转移矩阵(单位: km2)

Table 3 Transfer matrix of surface feature types in the Yellow River Delta from 2002 to 2012 (unit: km2)

		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

表4 2012-2022年黄河三角洲地物类型面积转移矩阵(单位: km2)

Table 4 Transfer matrix of surface feature types in the Yellow River Delta from 2012 to 2022 (unit: km2)

		2022
		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
2012	互花米草 Spartina alterniflora	13.45	5.59	1.08	0.76	0.84	0.91	22.63
	水体 Water	10.73	440.09	8.95	1.56	28.44	3.00	492.76
	芦苇 Phragmites australis	1.15	13.31	42.89	10.94	1.79	3.11	73.19
	柽柳 Tamarix chinensis	1.28	4.10	5.83	24.25	4.66	4.47	44.60
	裸滩 Nude beach	14.59	83.46	8.28	6.73	118.20	5.16	236.42
	碱蓬 Suaeda salsa	3.32	2.46	2.49	3.84	9.20	5.12	26.44
	总和 Summation	44.52	549.01	69.52	48.09	163.13	21.77	896.04

图8 黄河三角洲湿地互花米草质心移动路线图。

Fig. 8 Spartina alterniflora centroid moving route in the Yellow River Delta wetland.

图9 2002-2022年黄河三角洲湿地互花米草扩张情况图。A, 2002-2012年的扩张情况。B, 2012-2022年的扩张情况。

Fig. 9 Spartina alterniflora expansion in the Yellow River Delta wetland, 2002-2022. A, Expansion from 2002 to 2012. B, Expansion from 2012 to 2022.

图10 2002-2022年黄河三角洲湿地互花米草景观格局指数图。A, 斑块所占景观面积比例(PLAND)与最大斑块指数(LPI)年际变化图。B, 斑块形状指数(LSI)、散布与并列指数(IJI)、聚合指数(AI)年际变化图。C, 破碎度指数(SPLIT)年际变化图。D, 分维度指数(PAFRAC)与景观多样性指数(SHDI)指数年际变化图。

Fig. 10 Landscape pattern index map of Spartina alterniflora in the Yellow River Delta wetland from 2002 to 2022. A, Annual variation of percentage of landscape (PLAND) and largest patch index (LPI). B, Interannual variation of landscape shape index (LSI), interspersion and juxtaposition index (IJI) and aggregation index (AI). C, Interannual variation of splitting index (SPLIT). D, Annual variation of perimeter-area fractal dimension (PAFRAC) and shannon’s diversity index (SHDI).

表5 各类自然因子与互花米草NDVI的相关性计算

Table 5 Relevance calculation between various natural factors and the NDVI of Spartina alterniflora

因素 Factor	温度 Temperature (℃)	降水量 Precipitation (mm)	日照时间 Sunshine duration (h)	距海岸线距离 Distance from shoreline (m)	海拔 Altitude (m)	坡度 Gradient (°)	黄河年输沙量 Annual sediment discharge (10⁸t)
相关系数 Correlation coefficient	0.346^*	0.667^*	0.760^**	0.986^**	-0.964^*	-0.720^*	0.433^*

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