贵州洞穴空间格局及影响因素分析

doi:10.11821/dlxb201611010

地理学报 ›› 2016, Vol. 71 ›› Issue (11): 1998-2009.doi: 10.11821/dlxb201611010

贵州洞穴空间格局及影响因素分析

张绍云^1,²(), 周忠发^1,²(), 熊康宁^1,³, 田衷珲^1,², 陈全^1,², 闫利会^1,³, 谢雅婷^1,²

1. 贵州师范大学喀斯特研究院,贵阳 550001
2. 贵州省喀斯特山地生态环境国家重点实验室培育基地,贵阳 550001
3. 国家喀斯特石漠化防治工程技术研究中心,贵阳 550001

收稿日期:2016-08-01 修回日期:2016-11-09 出版日期:2016-11-25 发布日期:2016-11-25
作者简介:
作者简介：张绍云(1992-), 男, 云南曲靖人, 硕士生, 主要从事喀斯特地貌与洞穴研究。E-mail: 724139644@.qq.com
基金资助:
国家自然科学基金项目(41361081);贵州省科技计划([2014]4004-2);贵州省重大应用基础研究项目([2014]200201)

Spatial pattern of the caves in Guizhou Province andtheir the influencing factors

Shaoyun ZHANG^1,²(), Zhongfa ZHOU^1,²(), Kangning XIONG^1,³, Zhonghui TIAN^1,², Quan CHEN^1,², Lihui YAN^1,³, Yating XIE^1,²

1. School of Karst Science, Guizhou Normal University, Guiyang, Guizhou 550001, China
2. The State Key Laboratory Incubation Base for Karst Mountain Ecology Environment of Guizhou Province, Guiyang 550001, China
3. State Engineering Technology Institute for Karst Desertification Control, Guiyang, Guizhou 550001, China

Received:2016-08-01 Revised:2016-11-09 Published:2016-11-25 Online:2016-11-25
Supported by:
National Natural Science Foundation, No.41361081;Guizhou Science and Technology Plan, No.[2014]4004-2;Guizhou Major Application of Basic Research Project, No.[2014]200201

摘要/Abstract

摘要：

以贵州水文地质图和地形图为基础,利用ArcGIS对境内近5000个洞穴洞口海拔、经纬度提取及所在地层、岩性、水系、构造划分,并运用最邻近指数、空间分析与耦合分析处理,研究了贵州全省4960个洞穴的分布特征,探讨了洞穴空间格局与地理要素之间的关系。通过对贵州洞穴进行点格局分析,得出最近邻指数为0.53,泰森多边形检验其变异系数达72.469%,符合凝聚分布模式;并采用点密度函数将全省洞穴划分为4个集聚区和1个弱影响区,即遵义—铜仁强影响区、毕节强影响区、黔西南—六盘水强影响区、贵阳—安顺—黔南强影响区和黔东南弱影响区。其中贵阳—安顺—黔南强影响区是洞穴最主要的分布区,占全省洞穴总量的36.63%,该影响区面积占全省面积的24.67%。贵州洞穴分布呈西密东疏的分布特征,形成明显的“片状”格局。在其研究背景下,地层、岩性、构造、气候、水文、海拔等因素主要制约洞穴的分布、数量、空间格局及其发育。

关键词: 喀斯特洞穴, 地理信息系统, 空间格局, 影响因素, 贵州

Abstract:

Based on the hydrogeological and topographic map of Guizhou Province, ArcGIS was used to extract the information of nearly 5000 caves in the province, including the altitude, latitude and longitude, the strata, lithology, water system, and structure division. The nearest neighbor index, spatial analysis and coupling analysis methods were used to examine the distribution of 4960 caves in Guizhou Province. The relationship between cave spatial pattern and geographical elements was discussed. Through the point pattern analysis of the caves, it is found that the nearest neighbor index is 0.53, and the variation coefficient is 72.469% tested by Thiessen polygons, which is consistent with the model of aggregate distribution. Through the dot density function method, all the caves were divided into 4 clusters, namely, Zunyi-Tongren strong influence area, Bijie strong influence area, Liupanshui-Qianxinan strong influence area, and Guiyang-Anshun-Qiannan strong influence area, and one weak influence area (Qiandongnan weak influence area). Among them, Anshun-Qiannan-Guiyang strong influence area is the most important area, accounting for 36.63% of the total number of the caves, and 24.67% of the total area of the province. The caves in Guizhou are concentrated in the west and sparsely distributed in the east, shwoing clear "flake"-shaped pattern. Results indicated that the natural factors such as strata, lithology, structure, climate, hydrology, and elevation mainly affect the distribution, quantity, spatial pattern and development of the caves.

Key words: karst cave, geographic information system, spatial pattern, influencing factors, Guizhou

张绍云, 周忠发, 熊康宁, 田衷珲, 陈全, 闫利会, 谢雅婷. 贵州洞穴空间格局及影响因素分析[J]. 地理学报, 2016, 71(11): 1998-2009.

Shaoyun ZHANG, Zhongfa ZHOU, Kangning XIONG, Zhonghui TIAN, Quan CHEN, Lihui YAN, Yating XIE. Spatial pattern of the caves in Guizhou Province andtheir the influencing factors[J]. Acta Geographica Sinica, 2016, 71(11): 1998-2009.

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贵州洞穴空间格局及影响因素分析

Spatial pattern of the caves in Guizhou Province andtheir the influencing factors

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