NDLI: Application of Self-Organizing Feature Map clustering and ordination to the analysis of subalpine meadows in North China

Content Provider	IEEE Xplore Digital Library
Author	Jin-Tun Zhang Min Li
Copyright Year	2010
Description	Author affiliation: Institute of Loess Plateau, Shanxi University, Taiyuan 030006, China (Min Li) \|\| College of Life Sciences, Beijing Normal University, 100875, China (Jin-Tun Zhang)
Abstract	Artificial neural network theory is a newer mathematic branch discipline. The SOFM clustering and ordination were just introduced to plant ecology recently. In this article, these two methods were applied to study subalpine meadows in the Wutai Mountains, North China. The results showed that SOFM clustering classified 78 quadrats into 8 community types, basically representing the associations of the high and cold meadows in the Wutai Mountains. This classification was meaningful in ecology. The SOFM ordination reflected ecological gradients obviously, indicating that altitude was the most important factor in affecting the growth and distribution of the meadow vegetation, and slope and aspect also had certain roles. SOFM clustering and ordination methods performed well in this application, and this study showed that the combination of these two methods was better in ecological analysis. The conservation of meadows in the Wutai Mountains needs further to strengthened.
Starting Page	1564
Ending Page	1568
File Size	255272
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583714
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	vegetation-environment relation Biological system modeling Communities Vegetation mapping Artificial neural networks Vegetation Soil Environmental factors Mountain meadow quantitative analysis SOFM artificial neural network
Content Type	Text
Resource Type	Article

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