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黄河三角洲自然保护区湿地植被生物量空间分布及其影响因素
引用本文:刘莉,韩美,刘玉斌,潘彬. 黄河三角洲自然保护区湿地植被生物量空间分布及其影响因素[J]. 生态学报, 2017, 37(13): 4346-4355
作者姓名:刘莉  韩美  刘玉斌  潘彬
作者单位:山东师范大学, 地理与环境学院, 济南 250014,山东师范大学, 地理与环境学院, 济南 250014,山东师范大学, 地理与环境学院, 济南 250014,山东师范大学, 地理与环境学院, 济南 250014
基金项目:国家自然科学基金面上项目(41371517);山东省科技攻关计划(2013GSF11706)
摘    要:以黄河三角洲自然保护区为研究区域,以野外实测湿地植被地上生物量数据、Landsat-8影像数据和土壤各因子检测数据为数据源,通过分析各遥感因子与实测植被生物量的相关关系,建立生物量模型,进行生物量的定量反演。通过研究生物量与土壤、水环境因子的关系,筛选影响生物量的关键因子,进而分析生物量的空间分布规律。结果表明:湿地植被地上生物量的干重与各遥感因子的相关性较高;以NDVI、EVI、MSAVI、DVI、RVI、Band1、Band2、Band3、Band4、Band6共10个因子作为自变量建立的反演模型最优;反演计算的生物量干重分为5个等级区,最低的1级区和最高的5级区面积较小,为82.23、72.16 km2,分别占研究区湿地植被总面积的13.35%、11.71%。生物量干重适中的2、3、4级区所占面积较大,为211.99、136.39、113.29 km2,分别占研究区湿地植被总面积的34.41%、22.14%、18.39%;在各环境因子中水深对芦苇生物量干重影响最大,土壤含水率对碱蓬生物量干重影响最大,水、盐条件是导致优势种植被生物量干重出现空间分异的主导因素;植被生物量干重呈现由陆向海减小,由黄河河道两岸向外递减的趋势。

关 键 词:黄河三角洲  湿地植被生物量  遥感  反演模型  空间分布
收稿时间:2015-08-24
修稿时间:2017-01-05

Spatial distribution of wetland vegetation biomass and its influencing factors in the Yellow River Delta Nature Reserve
LIU Li,HAN Mei,LIU Yubin and PAN Bin. Spatial distribution of wetland vegetation biomass and its influencing factors in the Yellow River Delta Nature Reserve[J]. Acta Ecologica Sinica, 2017, 37(13): 4346-4355
Authors:LIU Li  HAN Mei  LIU Yubin  PAN Bin
Affiliation:School of Geography and Environment, Shandong Normal University, Ji''nan 250014, China,School of Geography and Environment, Shandong Normal University, Ji''nan 250014, China,School of Geography and Environment, Shandong Normal University, Ji''nan 250014, China and School of Geography and Environment, Shandong Normal University, Ji''nan 250014, China
Abstract:The Yellow River Delta Nature Reserve is selected as the research area, and the wetland vegetation biomass data measured in the field, landsat-8 image data acquired from the United States Geological Survey (USGS), and soil factor test data obtained by laboratory test were used as the data sources. The wetland vegetation biomass model has been established, and the quantitative biomass inversion model has been conducted by analyzing the correlation coefficient between Landsat-8 images, vegetation indices, and biomass measured in the field. By studying the relationship between the wetland vegetation biomass and soil water environmental factors, the key factors affecting the vegetation biomass were selected, and the spatial distribution rules of wetland vegetation biomass were analyzed in the Yellow River Delta Nature Reserve. The results showed that the correlation between dry weight of wetland vegetation aboveground biomass and remote sensing factors (band and vegetation indices) is relatively higher. The optimal inversion model is established using 10 factors as independent variables, including 5 vegetation indices (normalized difference vegetation index, NDVI; environmental vulnerability index, EVI; modified soil-adjusted vegetation index, MSAVI; difference vegetation index, DVI; and ratio vegetation index, RVI) and 5 bands (Band1, Band2, Band3, Band4, and Band6). The dry weight of wetland vegetation biomass is obviously divided into five classes according to the inversion calculation in the Yellow River Delta Nature Reserve. The least dry weight of wetland vegetation biomass is categorized as Class 1, and the highest dry weight of wetland vegetation biomass is categorized as Class 5, both of which occupied small areas. Class 1 and Class 5 areas are 82.23 km2 accounting for 13.35% and 72.16 km2 accounting for 11.71% of the total area of wetland vegetation in the study area, respectively. Furthermore, the area of the other classes (Class 2, Class 3, and Class 4) is larger than Class 1 and Class 5, and their dry weight of wetland vegetation biomass is moderate. Moreover, Class 2, Class 3, and Class 4 areas are 211.99 km2 accounting for 34.41%, 136.39 km2 accounting for 22.14%, and 113.29 km2 accounting for 18.39% of the total area of wetland vegetation in the study area, respectively. Among the environmental factors that affect the wetland vegetation biomass in the Yellow River Delta Nature Reserve, water depth has the greatest effect on the dry weight of Phragmites australis biomass. In addition, soil water has the greatest effect on the dry weight of Suaeda glauca biomass. The complex interactions of river water, groundwater, and seawater led to the spatial variation of salinity. Water and salt conditions are the leading factors causing the spatial differences of the predominant dry weight of vegetation biomass in the Yellow River Delta Nature Reserve. The dry weight of vegetation biomass in the Yellow River Delta Nature Reserve tends to decrease from land to ocean from the river course of the Yellow River to both riverbanks.
Keywords:Yellow River Delta  wetland biomass  remote sensing  inversion model  spatial distribution
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