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黄河三角洲潮间带不同类型湿地景观格局变化与趋势预测
引用本文:孙万龙,孙志高,田莉萍,胡星云.黄河三角洲潮间带不同类型湿地景观格局变化与趋势预测[J].生态学报,2017,37(1):215-225.
作者姓名:孙万龙  孙志高  田莉萍  胡星云
作者单位:福建师范大学地理研究所, 福州 350007;福建师范大学湿润亚热带生态地理-过程教育部重点实验室, 福州 350007;清华大学环境学院, 北京 100084,福建师范大学地理研究所, 福州 350007;福建师范大学湿润亚热带生态地理-过程教育部重点实验室, 福州 350007,福建师范大学地理研究所, 福州 350007;福建师范大学湿润亚热带生态地理-过程教育部重点实验室, 福州 350007,福建师范大学地理研究所, 福州 350007;福建师范大学湿润亚热带生态地理-过程教育部重点实验室, 福州 350007
基金项目:国家自然科学基金资助项目(41371104);福建省“闽江学者奖励计划”资助项目
摘    要:以1979—2013年7期卫星遥感影像(Landsat TM)为数据源,结合野外实地调查,通过建立黄河三角洲潮间带湿地数据库,探讨不同类型湿地的景观格局以及自然与人为因素对景观格局变化的影响,并基于Markov模型对未来20年三角洲潮间带不同类型湿地的景观格局进行了趋势预测。结果表明,三角洲的潮间带湿地面积在1979—2013年间整体呈先降低后增加变化。其中,1979—2010年的湿地面积持续减少,由1050.28 km~2减少为575.39 km~2,减少率为45.22%;2010—2013年的湿地面积略有增加,由575.39 km~2增加为596.17 km~2,增长率为0.36%。1979—2013年,潮间带主要湿地景观类型随距海远近均呈明显带状分布,但芦苇湿地面积呈明显降低趋势(减少273.53 km~2,减少率为79.68%),盐田养殖池面积呈显著增加趋势(增长12.04km~2,增长率为1584.21%),而光滩、碱蓬湿地、碱蓬-柽柳湿地和柽柳-芦苇湿地等其它类型湿地面积整体均呈波动减少趋势。未来20年,潮间带湿地面积整体将呈降低趋势,其值将由2010年的575.39 km~2减少为2030年的546.98 km~2,减少率为6.60%。芦苇湿地面积将继续减少(减少30.16 km~2,减少率为24.12%),盐田养殖池面积将持续增长(增加3.71 km~2,增长率为38.61%),而光滩、碱蓬湿地、碱蓬-柽柳湿地和柽柳-芦苇湿地等其它类型湿地面积均将呈小幅波动变化。研究发现,尽管自然与人为驱动力的双重作用决定了1979—2013年间潮间带的湿地景观格局及其动态变化,但黄河年输沙量(x_1)、区域GDP(x_2)和水产品产量(x_3)对潮间带湿地面积变化(y)的影响更为重要(y=733.192+35.317 x_1-0.005 x_2-4.085 x_3,P=0.00010.05),其对过去30多年间潮间带湿地面积变化的解释贡献高达76.7%。随着黄河三角洲高效生态经济区国家战略的实施,为实现潮间带区域的可持续发展,潮间带湿地的保护与生态保育应给予特别重视。

关 键 词:景观格局  驱动力  潮间带湿地  Markov预测  黄河三角洲
收稿时间:2016/7/26 0:00:00
修稿时间:2016/9/1 0:00:00

Variation and prediction of different marsh landscapes in intertidal zone of the Yellow River Delta
SUN Wanlong,SUN Zhigao,TIAN Liping and HU Xingyun.Variation and prediction of different marsh landscapes in intertidal zone of the Yellow River Delta[J].Acta Ecologica Sinica,2017,37(1):215-225.
Authors:SUN Wanlong  SUN Zhigao  TIAN Liping and HU Xingyun
Institution:Institute of Geography, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Humid Sub-tropical Eco-geographical Process of the Ministry of Education, Fujian Normal University, Fuzhou 350007, China;School of Environmental, Tsinghua University, Beijing 100084, China,Institute of Geography, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Humid Sub-tropical Eco-geographical Process of the Ministry of Education, Fujian Normal University, Fuzhou 350007, China,Institute of Geography, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Humid Sub-tropical Eco-geographical Process of the Ministry of Education, Fujian Normal University, Fuzhou 350007, China and Institute of Geography, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Humid Sub-tropical Eco-geographical Process of the Ministry of Education, Fujian Normal University, Fuzhou 350007, China
Abstract:Seven Landsat TM images during 1979-2013, combined with a field survey, were used to establish a database of tidal marshes in the Yellow River Delta, to determine the characteristics of landscape pattern in tidal marshes and the impacts of natural and human factors thereon. The trends of landscape patterns in tidal marshes in the future 20 years were also forecast using a Markov model. Results showed that the tidal marsh areas generally decreased from 1050.28 to 575.39 km2 with a decrement rate of 45.22% from 1979 to 2010, and increased to 596.17 km2 during 2010 to 2013, with an increment rate of 0.36%. The main landscape pattern showed obvious zonal distribution characteristics from the land to sea, and the landscape types in a seaward direction were Phragmites australis marsh, Suaeda salsa-Tamarix chinensis-P. australis marsh, and S. salsa marsh and mudflat, respectively. In general, the areas of P. australis marsh decreased significantly, with a decrement rate of 79.68%, whereas those of salt-cultural-pond (SP) and resident district (RD) continued increasing during 1979-2013, and those of other landscape patterns showed varied fluctuations, decreasing overall. In the next 20 years, the areas of tidal marshes are predicted to show a decreasing trend, with a decrement rate of 6.60%. Particularly, the areas of P. australis marsh are predicted to decrease continuously and those of the SP and RD to increase continuously, whereas those of the other landscapes are predicted to show slight fluctuations. In this study, we found that, although the dual function of natural and human driving forces determined the dynamics of landscape patterns of tidal marshes during 1979-2013, the annual sediment load (x1), regional GDP (x2), and output of aquatic products (x3) were more critical factors affecting the landscape patterns (y) of tidal marshes (y=733.192+35.317 x1-0.005 x2-4.085x3, p=0.0001 < 0.05), which could explain 76.7% of the variations in tidal marshes over the past 30 years.
Keywords:landscape pattern  driving force  tidal marsh  markov model  Yellow River Delta
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