洞庭湖湿地植被时空动态及其驱动力分析

doi:10.11707/j.1001-7488.LYKX20240048

摘要/Abstract

摘要：

目的: 探究洞庭湖湿地植被覆盖变化的长期时空格局及其对气候变化和人类活动的响应机制，为湿地生态系统保护提供决策依据。方法: 利用FSDAF（时空融合数据分析框架）算法融合Landsat和MODIS影像，获取洞庭湖湿地2000—2019年月尺度归一化差异植被指数（NDVI）时间序列，采用改进的STL时序分解方法分离洞庭湖湿地植被NDVI季节和趋势分量，在不同时间尺度下量化湿地植被覆盖对环境变化和人为干扰的响应。基于线性回归方法与高时空分辨率的NDVI季节和趋势分量数据对洞庭湖湿地植被进行时空动态分析，识别湿地植被在不同尺度的时空动态格局。应用基于偏相关的分析方法定量评估2000—2019年3个主要气候因子（温度、降水量和太阳辐射）和人为因素对趋势和季节性植被变化的贡献。结果: 1） 2000—2019年，洞庭湖湿地植被NDVI季节和趋势分量变化呈现出空间分异格局，但总体呈“绿化”趋势，变化率分别为4.8×10^?3 a^?1和0.4×10^?3 a^?1。2）温度和太阳辐射与植被变化存在显著正相关关系，与植被变化的季节相关性大于趋势相关性。降水量与植被变化的相关性相对较低，且与水稻的NDVI变化呈负相关关系（趋势分量偏相关系数R= -0.27；季节分量偏相关系数R= ?0.42）。3） 2000—2019年，人为因素和气候变化对洞庭湖湿地植被变化的平均相对贡献率分别为58%和42%，其中人为因素对长期和季节性湿地植被生长与恢复的相对贡献率分别为55%和62%，气候变化对长期和季节性湿地植被退化的相对贡献率分别为53%和56%。结论: 人为因素促进植被生长是洞庭湖湿地植被增绿的主要动因；气候变化对湿地生态系统构成威胁，采取合适的生态保护与修复方案仍是未来实现洞庭湖湿地生态系统可持续发展的重要手段。

关键词: 湿地植被, 气候变化, 时空融合, NDVI时间序列, 洞庭湖湿地

Abstract:

Objective: Wetland vegetation can purify the environment, regulate climate, and improve the soil, which is of great significance to the ecological security and stability of the wetland system. Wetland vegetation communities are, however, under serious threat from accelerated global warming and human disturbance. Probing the long-term spatial-temporal pattern of wetland vegetation cover change and its response to climate change and human activities are important for informing decisions on wetland protection. Method: It is difficult to collect long-term, reliable optical observations with high spatiotemporal resolution in the Dongting Lake wetland due to its location in the subtropical monsoon climate zone and its frequent cloud and rainy conditions. Firstly, this study used the flexible spatiotemporal data analysis fusion (FSDAF) algorithm to fuse Landsat and MODIS images to obtain the monthly normalized difference vegetation index (NDVI) time series during the study period (2000—2019). To quantify the response of wetland vegetation cover to environmental changes and human disturbance at different time scales, the seasonal and trend components of wetland vegetation NDVI in Dongting Lake were separated by an improved seasonal-trend decomposition procedure based on the loess (STL) time series decomposition method. Based on the linear trend analysis and NDVI seasonal and trend component data with a high spatiotemporal resolution, the spatiotemporal dynamic patterns of wetland vegetation at different scales were identified. Finally, a partial correlation-based approach was used to quantitatively assess the contributions of three major climatic factors (i.e., temperature, precipitation, and solar radiation) and anthropogenic factors to seasonal and trend vegetation changes from 2000 to 2019. Result: 1) From 2000 to 2019, the trend component and seasonal component of NDVI vegetation in Dongting Lake wetland showed spatial differentiation patterns, but overall exhibited a “greening” trend, with a change rate of 4.8×10^?3 a^?1 and 0.4×10^?3 a^?1, respectively. 2) Vegetation growth has a significant positive correlation with temperature and solar radiation, and its seasonal correlation with vegetation change is generally greater than trend correlation. The correlation between precipitation and vegetation is relatively low, and it has a negative correlation with rice (trend component R=?0.27; seasonal component R=?0.42). 3) From 2000 to 2019, human factors and climate change have driven 58% and 42% of the NDVI vegetation changes in the Dongting Lake wetland, respectively. Among them, the relative contribution rates of human factors to the long-term and seasonal growth and restoration of wetland vegetation are 55% and 62%, respectively, while the relative contribution rates of climate change to the long-term and seasonal degradation of wetland vegetation are 53% and 56%, respectively. Conclusion: Human factors have promoted the growth of wetland vegetation and are the main driving force behind the greening of Dongting Lake wetland vegetation from 2000 to 2019, while climate change poses a threat to wetland ecosystems. Adopting appropriate ecological protection and restoration measures is still an important means to achieve sustainable development of the Dongting Lake wetland ecosystem in the future. This study elucidates the response mechanism of Dongting Lake wetland vegetation to climate change and human activities, providing a scientific basis for spatial decision-making on wetland conservation.

Key words: wetland vegetation, climate change, spatiotemporal fusion, times series NDVI, Dongting Lake wetland

中图分类号:

S758

张雨田,石军南,张怀清,吴炳伦. 洞庭湖湿地植被时空动态及其驱动力分析[J]. 林业科学, 2024, 60(8): 1-13.

Yutian Zhang,Junnan Shi,Huaiqing Zhang,Binglun Wu. Spatiotemporal Patterns and Driving Forces of Vegetation Restoration and Degradation in Dongting Lake Wetland[J]. Scientia Silvae Sinicae, 2024, 60(8): 1-13.

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植被变化Vegetation cover change	C_CC	C_HA	气候贡献C_CC (%)	人为贡献C_HA (%)
恢复Restoration （i.e., S > 0）	> 0	> 0	$ \dfrac{\left\|{C}_{\mathrm{C}\mathrm{C}}\right\|}{\left\|{C}_{\mathrm{C}\mathrm{C}}\right\|+\left\|{C}_{\mathrm{H}\mathrm{A}}\right\|}\times 100 $	$ \dfrac{\left\|{C}_{\mathrm{H}\mathrm{A}}\right\|}{\left\|{C}_{\mathrm{C}\mathrm{C}}\right\|+\left\|{C}_{\mathrm{H}\mathrm{A}}\right\|}\times 100 $
	> 0	< 0	100	0
	< 0	> 0	0	100
退化Degradation （i.e., S < 0）	< 0	< 0	$ \dfrac{\left\|{C}_{\mathrm{C}\mathrm{C}}\right\|}{\left\|{C}_{\mathrm{C}\mathrm{C}}\right\|+\left\|{C}_{\mathrm{H}\mathrm{A}}\right\|}\times 100 $	$ \dfrac{\left\|{C}_{\mathrm{H}\mathrm{A}}\right\|}{\left\|{C}_{\mathrm{C}\mathrm{C}}\right\|+\left\|{C}_{\mathrm{H}\mathrm{A}}\right\|}\times 100 $
	< 0	> 0	100	0
	> 0	< 0	0	100

湿地植被类型 Wetland vegetation type	植被变化 Vegetation cover change	趋势尺度 Trend scale (%)	季节尺度 Seasonal scale (%)
芦苇Phragmites communis	恢复Restoration	82	73
芦苇Phragmites communis	退化Degradation	18	27
苔草Cyperus rotundus	恢复Restoration	46	66
苔草Cyperus rotundus	退化Degradation	54	34
水稻Oryza sativa	恢复Restoration	63	61
水稻Oryza sativa	退化Degradation	37	39
森林Forest	恢复Restoration	72	77
森林Forest	退化Degradation	28	23

湿地植被类型 Wetland vegetation type	气候因子 Climatic factors	趋势尺度 Trend scale	季节尺度 Seasonal scale
芦苇Phragmites communis	温度Temperature	0.75	0.84
	降水量Precipitation	0.23	0.48
	太阳辐射Solar radiation	0.81	0.73
苔草Cyperus rotundus	温度Temperature	0.65	0.72
	降水量Precipitation	0.64	0.83
	太阳辐射Solar radiation	0.54	0.63
水稻Oryza sativa	温度Temperature	0.68	0.81
	降水量Precipitation	?0.27	?0.42
	太阳辐射Solar radiation	0.63	0.72
森林Forest	温度Temperature	0.78	0.80
	降水量Precipitation	0.52	0.13
	太阳辐射Solar radiation	0.51	0.62

湿地植被类型 Wetland vegetation type	气候因子 Climatic factors	趋势尺度 Trend scale/a^?1	季节尺度 Seasonal scale/a^?1
芦苇Phragmites communis	温度Temperature	0.0026	0.0037
	降水量Precipitation	?0.0022	?0.0027
	太阳辐射Solar radiation	?0.0016	0.0013
苔草Cyperus rotundus	温度Temperature	0.0052	0.0038
	降水量Precipitation	?0.0011	?0.0036
	太阳辐射Solar radiation	0.0012	0.0027
水稻Oryza sativa	温度Temperature	0.0036	0.0052
	降水量Precipitation	?0.0028	?0.0053
	太阳辐射Solar radiation	?0.0003	0.0012
森林Forest	温度Temperature	0.0022	0.0016
	降水量Precipitation	?0.0034	?0.0011
	太阳辐射Solar radiation	?0.0007	0.0006

湿地植被类型 Wetland vegetation type	影响因Influencing factor	趋势尺度 Trend scale	季节尺度 Seasonal scale
芦苇 Phragmites communis	气候变化Climate change	42	47
芦苇 Phragmites communis	人类活动Human activities	58	53
苔草Cyperus rotundus	气候变化Climate change	44	53
苔草Cyperus rotundus	人类活动Human activities	56	47
水稻Oryza sativa	气候变化Climate change	37	45
水稻Oryza sativa	人类活动Human activities	63	55
森林Forest	气候变化Climate change	46	49
森林Forest	人类活动Human activities	54	51
平均值（按像元平均） Mean (by pixel)	气候变化Climate change	45	38
平均值（按像元平均） Mean (by pixel)	人类活动Human activities	55	62