Wang Chaojun, Zhao Hongrui
3S Center, Tsinghua University; Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
Entropy (Basel). 2018 May 23;20(6):398. doi: 10.3390/e20060398.
Distinguishing and characterizing different landscape patterns have long been the primary concerns of quantitative landscape ecology. Information theory and entropy-related metrics have provided the deepest insights in complex system analysis, and have high relevance in landscape ecology. However, ideal methods to compare different landscape patterns from an entropy view are still lacking. The overall aim of this research is to propose a new form of spatial entropy H) in order to distinguish and characterize different landscape patterns. H is an entropy-related index based on information theory, and integrates proximity as a key spatial component into the measurement of spatial diversity. Proximity contains two aspects, i.e., total edge length and distance, and by including both aspects gives richer information about spatial pattern than metrics that only consider one aspect. Thus, H provides a novel way to study the spatial structures of landscape patterns where both the edge length and distance relationships are relevant. We compare the performances of H and other similar approaches through both simulated and real-life landscape patterns. Results show that H is more flexible and objective in distinguishing and characterizing different landscape patterns. We believe that this metric will facilitate the exploration of relationships between landscape patterns and ecological processes.
长期以来，区分和表征不同的景观格局一直是定量景观生态学的主要关注点。信息论和与熵相关的指标在复杂系统分析中提供了最深刻的见解，并且在景观生态学中具有高度相关性。然而，从熵的角度比较不同景观格局的理想方法仍然缺乏。本研究的总体目标是提出一种新的空间熵形式（H），以便区分和表征不同的景观格局。H是一个基于信息论的与熵相关的指标，并将邻近性作为关键空间成分纳入空间多样性的测量中。邻近性包含两个方面，即总边缘长度和距离，通过同时纳入这两个方面，比仅考虑一个方面的指标提供了关于空间格局的更丰富信息。因此，H提供了一种新的方法来研究边缘长度和距离关系都相关的景观格局的空间结构。我们通过模拟和实际景观格局比较了H和其他类似方法的性能。结果表明，H在区分和表征不同景观格局方面更加灵活和客观。我们相信，这个指标将有助于探索景观格局与生态过程之间的关系。
