Dataset Information

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

ABSTRACT: This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p?

SUBMITTER: Wu CD

PROVIDER: S-EPMC5128799 | biostudies-other | 2016 Nov

REPOSITORIES: biostudies-other

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Publications

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

Wu Chih-Da CD Cheng Chi-Chuan CC Chang Che-Chang CC Lin Chinsu C Chang Kun-Cheng KC Chuang Yung-Chung YC

Scientific reports 20161130

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap clas ...[more]

PMID: 27901127

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Dataset Information

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

Publications

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

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