自然资源学报 ›› 2012, Vol. 27 ›› Issue (1): 122-131.doi: 10.11849/zrzyxb.2012.01.013

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旅游影响区等级划分及旅游影响指标的分析

郭秀玲, 上官铁梁   

  1. 山西大学 环境与资源学院,太原 030006
  • 收稿日期:2011-03-15 修回日期:2011-06-25 出版日期:2012-01-20 发布日期:2012-01-20
  • 作者简介:郭秀玲(1984- ),女,山西朔州人,硕士,主要从事环境生态学研究。E-mail: xiu.ling.guo@163.com
  • 基金资助:

    山西省自然科学基金项目(2006011095)。

Grade Classification of Tourism Affected Zone and Analysis of Relevant Indicators

GUO Xiu-ling, SHANGGUAN Tie-liang   

  1. College of Environment and Resources, Shanxi University, Taiyuan 030006, China
  • Received:2011-03-15 Revised:2011-06-25 Online:2012-01-20 Published:2012-01-20

摘要: 采用TWINSPAN分类法,根据旅游对森林植被影响程度,划分为6个旅游等级影响区,反映了以游径为轴线的旅游影响水平空间格局变化规律;对12个旅游影响指标相关分析表明,有3对旅游影响指标间存在显著或极显著关系。其中剔除树桩影响系数和游径距离为极显著相关,旅游垃圾影响系数和剔除树桩影响系数、旅游垃圾影响系数和游径距离这2对指标均为显著相关;根据主成分分析的结果,有5个主成分所提供的信息量占全部信息量的81.88%,这5个主成分与12个旅游影响指标中的7个旅游影响指标关系密切。以7个旅游影响指标进行的旅游影响区等级划分与12个旅游影响指标所得结果是一致的,这就简化了旅游对植被影响评价和影响区等级划分指标的选取。

关键词: 旅游地理, 旅游影响指标选取, 主成分分析, 旅游影响等级区, TINSPAN分类

Abstract: The Luya Mountain Nature Reserve, a well-known tourism spots, is in the northern part of Luliang Mountains in Shanxi Province. It was designated as the national AAAA level scenic area in 2010. The mountain has abundant species, various vegetation types and highly diversified habitats. This thesis selected one of the typical tourist attractions in Luya Mountain Nature Reserve, namely Bingkouwa to study the impacts of tourism on forest vegetations through the quantitative ecological methods including plant classification, principal component analysis and correlation analysis. The major studying results are as follows: 1) The TWINSPAN classification divided the area into six impact zones by the extent of tourism influences, reflecting the alternations in spatial pattern by tourism activities, where the trails were used as the axis. 2) The correlation analysis revealed significant relationships among 3 out of 12 pairs of tourism indicators, namely remove stumps influence coefficient vs. trail distance, waste impact coefficient vs. remove stumps influence coefficient, and waste impact coefficient vs. trail distance. Meanwhile the H' and slope were found well correlated. 3) The PCA analysis of 12 impact indicators showed five principal components which were closely related with seven impact indicators, accounting for 81.88% of the comprehensive information. The results of tourism affected zone level division by seven tourism impact indicators and 12 tourism impact indicators were the same. This simplifies the selection for tourism impact indicators for application purpose.

Key words: tourism geography, tourism impact indicators, principal component analysis, tourism impact level zone, TWINSPAN classification

中图分类号: 

  • X36