黄河三角洲植被空间分布特征及其环境解释

为了解黄河三角洲地区植被空间分布与环境因子之间的关系,通过局地植被样方调查、区域遥感影像提取归一化植被指数(NDVI)及地形高度、地下水位埋深、表层土壤Cl~-含量等环境数据采集,综合样地植被与环境数据进行了除趋势对应分析(DCA)和除趋势典范对应分析(DCCA),并对区域NDVI与主要环境变量进行了单因子相关性分析和多元逐步回归分析。结果显示:DCA排序可将黄河三角洲植被分为翅碱蓬、柽柳-翅碱蓬、芦苇-柽柳、芦苇4个主要群落类型(群丛),DCCA与DCA排序图总体相似,但DCCA更清晰地表明其第一轴主要代表的是潜水Cl~-浓度等关键水盐因子,且随着水土环境系统盐分含量的减小,群落由翅碱蓬逐渐向芦苇演变。区域典型植被群落和NDVI分布格局与变化趋势受地下水位埋深和潜水Cl~-浓度2个环境因素影响较大(NDVI与2个环境变量间建立的二元回归方程R~2=0.57),而土壤Cl~-含量的植被效应实际上受地下水位埋深和潜水Cl~-浓度的影响。在区域地下水普遍浅埋条件下,地下水成为影响植被生长与分布的生态环境最敏感要素,而地下水位埋深和潜水Cl~-浓度是这一要素中的2个关键因子,尤其是后者梯度变化对天然植被分布格局起重要的控制作用。

Spatial distribution of vegetation and environmental interpretation in the Yellow River Delta

In the Yellow River Delta, the normalized difference vegetation index (NDVI) was extracted from remote sensing images, whereas environmental data (e.g., topographic height, groundwater table, surface soil Cl ^- content, and others) were collected using quadrat sampling. To understand the relationships between the spatial distribution of vegetation and environmental factors in the study area, detrended correspondence analysis (DCA) and detrended canonical correspondence analysis (DCCA) were conducted. In addition, single factor correlation analysis and multiple stepwise regression analysis were also conducted using the regional NDVI and environmental factors. Results showed that the vegetation in the Yellow River Delta could be divided into four main community types: Suaeda heteroptera, Tamarix chinensis and S. heteroptera, Phragmites australis and T. chinensis, and P. australis. DCCA and DCA ordination diagrams were similar. However, compared with DCA, DCCA clearly showed that the first axis was mainly representative of Cl^- concentration in the phreatic aquifer and key water and salt factors. With the decrease in the salinity of the groundwater and soil environmental systems, the community evolved from S. heteroptera to P. australis. The distribution of the vegetation communities and NDVI in the area were influenced by the depth of groundwater table and Cl^- concentration in the phreatic aquifer. A binary regression relationship (R² = 0.57) was found between NDVI and two environmental variables. The effect of the soil Cl^- content on vegetation was influenced by the depth of the groundwater table and the Cl^- concentration in the phreatic aquifer. Because of the shallowness of the groundwater, groundwater was sensitive to vegetation growth and distribution. The groundwater table and Cl^- concentration in the phreatic water were the two key influencing factors on groundwater. In particular, the Cl^- concentration in the phreatic water largely controlled vegetation distribution and growth. To maintain wetland ecosystem health, the relevant departments should pay attention to the increase in groundwater Cl^- concentration caused by dried-up river courses, seawater intrusion, and shoreline erosion, among other factors, which will adversely affect vegetation. Some measures could help to accelerate the sustainable development of coastal wetland vegetation, such as making full use of the ecological regulations in the lower reaches of the Yellow River.