摘要
Three types of landscape boundary (forest/pepper field, forest/cabbage field,and forest/grassland) were selected in the arid valley of upper reaches of Minjiang River,southwestern China. On the basis of vegetation diversity, the depth of edge influence (DEI) ondifferent types of landscape boundaries was estimated using principal components analysis (PCA)method and moving split-window techniques (MSWT). The results showed that in the 5 transects, PCAmethod was able to detect the edge influence depth with 3 transects, while MSWT could explain 4transects. It is concluded that PCA and MSWT both can be used to detect the depth of edge influencewithin 50 m from the edge to the interior. Similar conclusions were drawn in the forest of eachtransect with the two methods, but no similar conclusions were drawn in the pepper field of eachtransect. Although the two methods have advantages and disadvantages respectively, they are usefultools for characterizing edge dynamics. Comparing the two methods, MSWT is more successful.
Three types of landscape boundary (forest/pepper field, forest/cabbage field,and forest/grassland) were selected in the arid valley of upper reaches of Minjiang River,southwestern China. On the basis of vegetation diversity, the depth of edge influence (DEI) ondifferent types of landscape boundaries was estimated using principal components analysis (PCA)method and moving split-window techniques (MSWT). The results showed that in the 5 transects, PCAmethod was able to detect the edge influence depth with 3 transects, while MSWT could explain 4transects. It is concluded that PCA and MSWT both can be used to detect the depth of edge influencewithin 50 m from the edge to the interior. Similar conclusions were drawn in the forest of eachtransect with the two methods, but no similar conclusions were drawn in the pepper field of eachtransect. Although the two methods have advantages and disadvantages respectively, they are usefultools for characterizing edge dynamics. Comparing the two methods, MSWT is more successful.
基金
This work was financially supported by the Major State Basic Research Program of China (973 Program: 2002CB111506).
