Fu Gang, Xiao Nengwen, Qi Yue, Wang Wei, Li Junsheng, Zhao Caiyun, Cao Ming, Xia Juyi
College of Water Sciences Beijing Normal University Beijing China.
State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment Chinese Research Academy of Environmental Sciences Beijing China.
Ecol Evol. 2021 Oct 12;11(21):15225-15236. doi: 10.1002/ece3.8206. eCollection 2021 Nov.
One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two-grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first-grade land use data, but also containing the internal detail information of second-grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.
生态学的核心问题之一是了解景观格局对生态过程的影响。为此，我们需要准确捕捉精细景观结构的变化，以避免丢失关于空间异质性的信息。景观格局指标（LPI）可以表征空间结构并提供一些关于景观格局的信息。然而，对景观格局指标的研究主要集中在景观格局的水平结构上，而很少有研究涉及层次景观结构不同层次之间的垂直关系。因此，对垂直层次关系的忽视可能会导致严重偏差，并降低景观格局指标的代表性和准确性。景观格局结构的层次理论可以显著减少层次信息的损失，而信息熵可以定量描述景观单元的垂直状态。因此，我们基于层次理论和信息熵建立了一种新的景观格局指标多维融合方法。在此，我们基于两级土地利用数据（其土地利用分类系统包含两个等级/层次）为常用的简单景观格局指标创建了一个通用融合公式，并通过案例研究推导了3个融合景观格局指标（FLI）。结果表明，融合方法捕捉到了精细空间结构的信息。融合景观格局指标与传统景观格局指标差异最大的区域是垂直结构最大的区域，如生态交错带，而之前垂直结构被忽视了。融合景观格局指标具有更精细的空间代表性和准确性，不仅保留了一级土地利用数据的主要趋势信息，还包含了二级土地利用数据的内部细节信息。捕捉景观格局更精细的空间信息应促进融合方法的应用，该方法应适用于更多的景观格局指标或更多维度的数据。并且融合景观格局指标准确性的提高将改进依赖更精细空间信息的生态模型。
