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The aggregation index (AI) is a classical ecology calculation method, which has been widely used for measuring the aggregation level of spatial patterns within a landscape scale in landscape ecological studies. However, it has certain limitions. For instance, identical results can be obtained by AI even when the shape and number of landscape patches are totally different in two landscape units. Furthermore, the value of AI approaches to 1 if the landscape patch is large enough. To solve these problems, a logical limitation of the original AI equation was revised firstly. Secondly, an improved AI-J was developed based on the awareness of the effects of spatial distribution characteristics of patches and changing spatial scale on AI operation. Finally, the accuracy of AI and AI-J results were evaluated through a case study of city green patches in Chengdu, P. R. China. The results show that the calculated result of AI-J is more precise than that of AI and AI-J can be used to compare a certain landscape class under different spatial scales.
The aggregation index (AI) is a classical ecology calculation method, which has been widely used for measuring the aggregation level of spatial patterns within a landscape scale in landscape ecological studies. However, it has certain limitions. For instance, identical results can be obtained by AI even when the shape and number of landscape patches are totally different in two landscape units. Furthermore, the value of AI approaches to 1 if the landscape patch is large enough. To solve these problems, a logical limitation of the original AI equation was revised first. Secondly, an improved AI-J was developed based on the awareness of the effects of spatial distribution characteristics of patches and changing spatial scale on AI operation. Finally, the accuracy of AI and AI-J results were evaluated through a case study of city green patches in Chengdu, PR China. The results show that the calculated result of AI-J is more precise than that of AI and AI-J can be used to compare a ce rtain landscape class under different spatial scales.