This study seeks to isolate a select group of landscape metrics particularly well-suited for describing the Hani Terrace in southwest of China.We examined the response of 47 landscape metrics to a large range of image...This study seeks to isolate a select group of landscape metrics particularly well-suited for describing the Hani Terrace in southwest of China.We examined the response of 47 landscape metrics to a large range of imagery grain sizes.Based on a correlation analysis,the original 47 metrics were placed into 21 groups such that all metrics within a group were strongly correlated with each other with a value of more than 0.9,and were represented by a single descriptor.Using these cross-sectional metrics in the context of principal components analysis,we found that five factors explained almost 93％ of the total variation in the landscape pattern.The highest loadings for these five factors were the Splitting index （SPLIT）,Patch area distribution （AREA_CV）,Shannon＇s diversity index （SHDI）,Euclidean nearest neighbor distance distribution （ENN_AM）,and Total core area （TCA）,respectively.Considering the real landscape,we added the Patch fractal dimension distribution （FRAC_MN） as the sixth landscape pattern metric.As the scale effect of landscape pattern metrics we design to investigate how a suite of commonly use landscape metrics respond to changing grain size.Based on the anlasis,we determined that the best domain of scale to characterise the Hani Terrace pattern metrics is between 40m and 45m.Through the fractal method,we found that the characteristic scale of the Hani Terrace is the same as the scale domain of metrics,among the 40m and 45m.We suggest that the majority of the patterns in the Hani Terrace landscapes,indeed for all those in southwest China,can be described effectively with these six metrics.展开更多
基金National Public Benefit(Environmental) Research Foundation of China(201009020)National Natural Science Foundation of China(3120037641201580)
文摘This study seeks to isolate a select group of landscape metrics particularly well-suited for describing the Hani Terrace in southwest of China.We examined the response of 47 landscape metrics to a large range of imagery grain sizes.Based on a correlation analysis,the original 47 metrics were placed into 21 groups such that all metrics within a group were strongly correlated with each other with a value of more than 0.9,and were represented by a single descriptor.Using these cross-sectional metrics in the context of principal components analysis,we found that five factors explained almost 93％ of the total variation in the landscape pattern.The highest loadings for these five factors were the Splitting index （SPLIT）,Patch area distribution （AREA_CV）,Shannon＇s diversity index （SHDI）,Euclidean nearest neighbor distance distribution （ENN_AM）,and Total core area （TCA）,respectively.Considering the real landscape,we added the Patch fractal dimension distribution （FRAC_MN） as the sixth landscape pattern metric.As the scale effect of landscape pattern metrics we design to investigate how a suite of commonly use landscape metrics respond to changing grain size.Based on the anlasis,we determined that the best domain of scale to characterise the Hani Terrace pattern metrics is between 40m and 45m.Through the fractal method,we found that the characteristic scale of the Hani Terrace is the same as the scale domain of metrics,among the 40m and 45m.We suggest that the majority of the patterns in the Hani Terrace landscapes,indeed for all those in southwest China,can be described effectively with these six metrics.
