Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/9717
Title: Application of Principal Component Analysis (PCA) in Taxonomy Research to Derive Plant Functional Types for Use in Dynamics Models
Authors: Yasmin Yahya
Roslan Ismail
(UniKL MIIT)
Keywords: Principal component analysis (PCA)
cluster analysis (CA)
diversity
classification
Issue Date: Jan-2015
Publisher: ACM
Citation: Yasmin Yahya and Roslan Ismail. 2015. Application of principal component analysis (PCA) in taxonomy research to derive plant functional types for use in dynamics models. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (IMCOM '15). ACM, New York, NY, USA, , Article 14 , 6 pages. DOI=10.1145/2701126.2701166 http://doi.acm.org/10.1145/2701126.2701166
Abstract: Forest management is essential for maintaining environmental stability and ecological biodiversity. The high species diversity of tropical rainforest forests obstructs the development of forest dynamic models. A lot of tree species exist in the forest for which each type of species will have insufficient data for reliable parameter estimation. The best way to avoid bias prediction is to group the trees based on their characteristics similarity. In a tropical rain forest in Koh Kong province, Cambodia, four species groups have been classified using statistical analyses of principal component analysis (PCA) and cluster analysis. Some indices related to diameter structure, growth, mortality and recruitment of species were formed from measurement data rather than the parameter estimates of some predetermined growth regression functions.
URI: http://localhost/xmlui/handle/123456789/9717
ISSN: 978-1-4503-3377-1
Appears in Collections:Conference Paper

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