地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (12): 1867-1876.doi: 10.12082/dqxxkx.2019.190402

• 地球信息科学理论与方法 • 上一篇    下一篇

桂林-阳朔地区DEM地形特征与岩性相关性分析及分类研究

陈霄燕1, 潘军1,*(), 邢立新1, 蒋立军1, 孙也涵1, 仲伟敬2, 范博文1   

  1. 1. 吉林大学地球探测科学与技术学院,长春 130026
    2. 西安卫星测控中心第一活动站,渭南 714000
  • 收稿日期:2019-07-27 修回日期:2019-10-31 出版日期:2019-12-25 发布日期:2019-12-25
  • 通讯作者: 潘军 E-mail:Panj@jlu.edu.cn
  • 作者简介:陈霄燕(1994-),女,内蒙古赤峰人,硕士生,主要从事遥感与地理信息系统方面的研究。E-mail: xychen17@mails.jlu.edu.cn
  • 基金资助:
    中国地质调查局地质调查项目(3R114Z184423)

Correlation Analysis and Classification of DEM Topographic Features and Lithology in Guilin-Yangshuo, China

CHEN Xiaoyan1, PAN Jun1,*(), XING Lixin1, JIANG Lijun1, SUN Yehan1, ZHONG Weijing2, FAN Bowen1   

  1. 1. College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
    2. Unit 63771 of the Chinese people's liberation, Weinan 714000, China
  • Received:2019-07-27 Revised:2019-10-31 Online:2019-12-25 Published:2019-12-25
  • Contact: PAN Jun E-mail:Panj@jlu.edu.cn
  • Supported by:
    Geological Survey Projects of China Geological Survey(3R114Z184423)

摘要:

地形地貌是岩性解译的重要信息,地形因子作为描述DEM数字曲面几何特征的定量指标参数,可用来定量化表达不同岩性所在地区地形地貌特征。本文以桂林-阳朔地区为研究区,研究地形因子数学、地质意义,建立岩性与地形因子组合间的定量关联,进而实现岩石类型划分。本文基于ASTERGDEM提取坡度、起伏度等12个地形因子,在分析各个地形因子地质意义基础上,通过聚类分析及方差分析的多元统计分析方法,研究各岩性地形因子特性及其关联性,建立研究区岩性之间的定量差异;此外,利用因子分析方法研究岩性分类过程中的主导因素,确定适宜岩性分类方法以实现定量化岩性分类。实验结果表明:不同岩性、不同地形地貌的地形因子(组合)之间具有显著差异,基于因子分析得到的宏观地形复杂度指数(MTI)以及微观曲率指数(MCI)对岩石类型的分类精度达77.36%。研究表明,地形复杂度等地形因子可用于岩性分类,采用因子分析方法可获取反映地形地貌宏观、微观特征的定量指标,且岩性分类效果良好。

关键词: 地形因子, 岩性分类, 聚类分析, 因子分析, DEM, 宏观地形复杂度指数, 微观曲率指数, 桂林-阳朔地区

Abstract:

Topographic and geomorphological features are important for remote sensing lithologic interpretation, but there still lacks quantitative analysis of the geological correlation between topography and lithology. Digital Elevation Model (DEM) is a digital representation and simulation of topographic and geomorphological features in space. Topographic factors derived from DEM can describe the characteristics of concave and convex changes of different topographic slopes and undulations, thus quantitatively describing the characteristics of different topographic and geomorphological features. This paper focuses on studying the mathematical and geological significance of topographic factors, by establishing the quantitative correlation between lithology and topographic factors combination for classifying rock types. Based on ASTER GDEM data, this paper extracted 12 topographic factors, such as slope, profile curvature, maximum curvature, topographic relief, and elevation coefficient of variation. Based on analysis of the geological significance of each topographic factor, the characteristics and correlation of each lithologic topographic factor were studied by cluster analysis and variance analysis, and the quantitative difference between lithologies in the study area was established. In addition, the factor analysis method was used to study the dominant factors in the process of lithological classification, and to determine the appropriate lithological classification method and achieve quantitative lithological classification. Quantitative research was carried out on the two basic issues: whether topographic factors can be used for lithologic classification and how to use topographic factors to identify lithology. Experimental results show: (1) There was a significant correlation between lithology and topographic factors and the topographic factors of different lithologies could be distinguished significantly. (2) There were significant differences among the topographic factors (combinations) of different lithology and topography. The macro-topographic complexity index (MTI) and micro-curvature index (MCI) based on factor analysis had a relatively high classification accuracy of 77.36% for the rock types in the study area, highly consistent with the actual lithologic types. Our findings suggest that topographic factors such as slope and topographic complexity can be used for lithological classification, and that factor analysis can be used to obtain quantitative indicators reflecting macro-topographic complexity and micro-topographic curvature characteristics. This study can serve as a methodological reference for quantitative classification of lithology.

Key words: topographic factors, lithological classification, cluster analysis, factor analysis, DEM, macro-topographic complexity index, micro-curvature index, Guilin-Yangshuo