景观指数耦合景观格局与土壤侵蚀的有效性
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  • 英文篇名:Effectiveness of landscape metrics in coupling soil erosion with landscape pattern
  • 作者:刘宇
  • 英文作者:LIU Yu;Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences;
  • 关键词:景观指数 ; 景观格局分析 ; 土壤侵蚀 ; WATEM/SEDEM ; 景观指数有效性
  • 英文关键词:Landscape metrics;;Landscape pattern analysis;;Soil erosion;;WATEM/SEDEM;;Landscape metric effectiveness
  • 中文刊名:STXB
  • 英文刊名:Acta Ecologica Sinica
  • 机构:中国科学院地理科学与资源研究所,生态系统网络观测与模拟重点实验室;
  • 出版日期:2017-03-22 17:55
  • 出版单位:生态学报
  • 年:2017
  • 期:v.37
  • 基金:国家自然科学基金青年基金资助项目(41301032);国家自然科学基金重大资助项目(41390464)
  • 语种:中文;
  • 页:STXB201715001
  • 页数:13
  • CN:15
  • ISSN:11-2031/Q
  • 分类号:4-16
摘要
景观格局分析是景观生态学中揭示景观变化及其生态效应的主要方法,而景观指数是景观格局分析中广泛使用的工具。土壤侵蚀是土壤物质在景观中的迁移和再分配过程,受地形、植被和人类活动及其空间格局的调控。运用景观格局分析揭示景观格局变化特别是土地利用/覆被格局变化对土壤侵蚀的影响是土壤侵蚀研究中应用景观生态学原理和方法的典型。在当前的研究中,斑块-廊道-基质范式下建立的景观指数对侵蚀过程的解释能力不断受到质疑,建立筛选适用的景观指数的原则和方法十分必要。以延河流域碾庄沟小流域为例,利用WATEM/SEDEM模型模拟多个年份流域侵蚀产沙和输沙量;基于土地利用/覆被数据,利用Fragstat4.2软件,计算了相应年份流域斑块、边界密度、形状、集聚与分散和斑块类型多样性4个方面的代表性景观指数。在此基础上,分析了景观指数与流域侵蚀产沙和输沙之间的关系,讨论了景观指数在土壤侵蚀研究中的有效性,在景观和斑块类型水平上分析了景观指数表达"源"、"汇"两大类景观类型的空间格局与侵蚀产沙和输沙之间的关系的一致性。结果表明:斑块-廊道-基底范式下发展的景观指数在指示景观格局的土壤侵蚀效应时存在局限。相对而言,斑块类型尺度的景观指数更能有效表达景观格局与土壤侵蚀的关系。基于景观类型在土壤侵蚀过程中的"源"、"汇"功能,提出了在土壤侵蚀研究中筛选适用的景观指数的原则:(1)对"源"、"汇"两类景观类型,景观指数与土壤侵蚀状况表征变量的相关系数符号相反;(2)对同为"源"或"汇"景观类型的多个景观类型,景观指数与土壤侵蚀表征变量的相关系数应具有符号一致性。尽管景观指数在斑块类型水平上具有一定的有效性,但用其预测景观格局变化的侵蚀效应有很大的不确定性。因此,基于土壤侵蚀过程与景观格局的作用机制发展新型的景观指数是增强景观格局分析预测土壤侵蚀过程的能力的途径。
        Landscape pattern analysis by applying landscape metrics is a widely used approach to reveal the ecological consequences of landscape change. Soil erosion is the processes of detachment and relocation of soil materials over landscapes,which are regulated by the spatial pattern of landforms,vegetation cover,and human activities. Therefore,landscape pattern analyses to reveal the effects of landscape pattern shifts,especially the land use/cover change,on soil erosion is a good example of the practice of principles and methodology of landscape ecology. However,the capacity of landscape metrics,specifically metrics of"Fragstats"family,used to explain the relationship between the landscape pattern and soil erosion processes was problematic. Therefore,the establishment of principles to select effective metrics is necessary.In this paper,soil erosion and sediment delivery over multiple years in a loess watershed were modeled using WATEM/SEDEM. Representative landscape metrics from four categories,including patch/edge density,shape indices,metrics describing aggregation,and diversity indices were calculated using Fragstat 4. 2 based on land use/cover datasets. Thecorrelation between soil erosion and these landscape metrics were tested to evaluate their validity in coupling landscape pattern shifts with soil erosion dynamics. The consistency in revealing the relationship between soil erosion and the spatial pattern of "sink" and "source " landscape types was also evaluated. The results advocate that there are limitations of applying landscape metrics following the patch-corridor-matrix model to indicate the soil erosion status. However,at the landscape class level,landscape metrics showed greater effectiveness in linking landscape pattern with soil erosion. The following principles for selecting suitable landscape metrics for soil erosion status assessments were determined:( 1) the signs of correlation coefficients between soil erosion variables and landscape metrics should be different for sink and source types;( 2) the signs of correlation coefficients between landscape metrics for multiple sink or source types and soil erosion variables should be the same. Although landscape metrics are relatively effective in coupling landscape pattern with soil erosion at the class level,we show here that using them as predictive tools could result in considerable uncertainty. It was suggested that development of metrics that reflect the behavior of landscape patterns in soil erosion and sediment delivery could reasonably promote the predictive capacity of landscape metrics.
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