An aggregation index (AI) to quantify spatial patterns of landscapes
An aggregation index (AI) to quantify spatial patterns of landscapes
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DOI：
10.1023/a:1008102521322
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发表时间：
2000-10-01
影响因子：
5.2
通讯作者：
Mladenoff, DJ
中科院分区：
文献类型：
--
作者：
He, HS;DeZonia, BE;Mladenoff, DJ
在景观生态研究中，经常需要测量单个地图类内空间模式的聚合水平。传染指数（CI）、形状指数（SI）和同类邻接概率（Qi）在衡量空间格局聚集性时都有一定的局限性。我们开发了一种聚合指数（AI），它是针对具体类别且独立于景观组成的。 AI 假设具有最高聚合级别 (AI =1) 的类由共享最多可能边缘的像素组成。像素不共享边缘（完全分解）的类具有最低的聚合级别（AI = 0）。 AI与SI和Qi类似，但它计算聚合的精度比后两者更精确。我们评估了人工智能在不同级别下的性能：(1) 聚合、(2) 斑块数量、(3) 空间分辨率和 (4) 不同空间尺度的真实物种分布图。在所有这些情况下，人工智能都能产生合理的结果。由于它是特定于类别的，因此它比衡量整体景观聚合的 CI 更精确。因此，人工智能提供了将类别的空间模式与特定过程关联起来的定量基础。由于AI是比率变量，地图单位不影响计算。它可以在来自相同或不同景观的类之间进行比较，甚至可以在不同分辨率下对来自相同景观的相同类进行比较。
There is often need to measure aggregation levels of spatial patterns within a single map class in landscape ecological studies. The contagion index (CI), shape index (SI), and probability of adjacency of the same class (Qi), all have certain limits when measuring aggregation of spatial patterns. We have developed an aggregation index (AI) that is class specific and independent of landscape composition. AI assumes that a class with the highest level of aggregation (AI =1) is comprised of pixels sharing the most possible edges. A class whose pixels share no edges (completely disaggregated) has the lowest level of aggregation (AI =0). AI is similar to SI and Qi, but it calculates aggregation more precisely than the latter two. We have evaluated the performance of AI under varied levels of (1) aggregation, (2) number of patches, (3) spatial resolutions, and (4) real species distribution maps at various spatial scales. AI was able to produce reasonable results under all these circumstances. Since it is class specific, it is more precise than CI, which measures overall landscape aggregation. Thus, AI provides a quantitative basis to correlate the spatial pattern of a class with a specific process. Since AI is a ratio variable, map units do not affect the calculation. It can be compared between classes from the same or different landscapes, or even the same classes from the same landscape under different resolutions.