基于数字高程模型的中国岩溶地貌研究进展及前景分析

毕奔腾; 杨辰; 李景文; 姜建武; 周立新

doi:10.11932/karst20220211

基于数字高程模型的中国岩溶地貌研究进展及前景分析

doi: 10.11932/karst20220211

1.
中国地质科学院岩溶地质研究所/自然资源部、广西岩溶动力学重点实验室，广西桂林 541004
2.
桂林理工大学测绘地理信息学院/广西空间信息与测绘重点实验室，广西桂林 541004

基金项目: 中国地质科学院基本科研业务费（20200013）；国家自然科学基金（41961063）；中国地质调查项目（DD20221637）

详细信息

作者简介:
毕奔腾（1990－），男，助理研究员，博士研究生，研究方向：数字地形分析、地理数据建模。E-mail：bibenteng@163.com

通讯作者:
李景文（1971－），男，教授，博士生导师，研究方向：GIS理论与应用研究。E-mail：lijw2008@glite.edu.cn

中图分类号: P931.5
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出版历程
- 收稿日期: 2021-08-11
- 刊出日期: 2022-07-28

Research progress and prospect of karst geomorphology in China based on digital elevation model

1.
Institute of Karst Geology, CAGS/ Key Laboratory of Karst Dynamics, MNR & GZAR, Guilin, Guangxi 541004, China
2.
College of Geomatics and Geoinformation,Guilin University of Technology/ Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, Guangxi 541004, China

摘要

摘要: 数字高程模型（DEM）蕴含丰富的地形地貌信息，基于DEM的数字地形分析方法为岩溶地貌研究提供了科学、有效的技术手段。文章针对前人应用DEM研究中国岩溶地貌所涉及的关键技术方法，从岩溶地貌识别的尺度效应、岩溶地貌的识别与分类、岩溶地貌的形态及格局分析、岩溶区生态环境变化等方面进行了梳理和分析，提出未来应构建科学的岩溶地貌数字分类体系，对岩溶地貌进行多尺度、深层次的地形分析和定量表达，并从地形现状研究拓展到地形演变的过程与机理研究，发掘出DEM在岩溶地貌研究中更多的应用。
- 岩溶地貌 /
- 数字高程模型 /
- 数字地形分析 /
- 进展
Abstract: Digital elevation model (DEM) contains rich morphological information because the process of digital terrain modeling and extracting a series of terrain parameters based on DEM data with various spatial resolutions, known as Digital Terrain Analysis (DTA), has achieved a number of advances in topographic analysis, morphological modeling, distribution patterns and evolution patterns of karst landforms. This paper summarizes the new progress in spatial analysis of DEM based on the key technical methods of applying DEM to the previous Chinese studies on karst landform in recent years. The future research of DEM in karst geomorphology is also predicted in order to expand its application and provide technical ideas for the study of karst geomorphology from topology to the process and mechanism of topographic evolution.In this study, the applicability of the data model and the method of determining the optimal scale of analysis are discussed. Both DEM and DTA are obviously scale-dependent, and it is indisputable that the slope scales, watershed scales, and regional scales of karst topography require different resolution of DEM. Higher spatial resolution does not necessarily guarantee better automatic karst detection. Therefore, the main features of regional topography must be considered. When local terrain parameters are calculated and extracted, determining an optimal analysis window is necessary to reflect the completeness of the landform and guarantee the representativeness of calculation results.For the identification and classification of karst landforms, four effective methods for automatic extraction of karst landform units with DEM data and the advantages and disadvantages of these methods are analyzed. These methods are respectively based on cell statistics of composite factors, the extraction of terrain feature points, contour trees, and positive and negative topography. The first method is to divide the study area into a series of grids, calculate the topographic attributes of each cell grid, and construct an identification model including the establishment of peak forests, peak clusters and other karst geomorphic units. The second one is to construct a spatial relationship model of terrain feature points of different geomorphic units, and then automatically extract these units. The third one is to extract geomorphic contour lines by building a contour line recognition model for classification. The fourth one can reduce and simplify the complex and diverse terrains into positive and negative terrains to effectively highlight the morphological differences, and then divide them according to the characteristics of geomorphic units such as peak forests, peak clusters, etc.As for the morphology and pattern analysis of karst landform, the typical index parameters used to characterize the spatial pattern of karst landform are sorted out. In addition, the main applications of DEM in the ecological environment of karst area have been outlined. DEM can be used for topographic statistics, spatial pattern, spatial estimation and 3D visualization of meteorology, hydrology, soil and habitat, and then for the analysis of ecological environment changes in karst areas.However, the present classification system of karst landform is mainly focused on abstract expression and qualitative description, which can hardly quantify the detailed information of spatial structure of karst landform. This kind of system also cannot meet the demand of digitalization and intellectualization. In addition, with good recognition effect, the current methods for morphological and quantitative expression of karst landform have been widely applied to typical karst landforms such as peak forests, peaks and depressions. But for atypical karst landforms, DTA has rarely been tried.In conclusion, the prospective direction of DEM-based karst geomorphology research has been proposed in this study. First of all, a more effective quantitative classification system needs to be constructed for karst geomorphology and its topographic elements. A digital indicator system of karst geomorphology should be established to describe its spatial and geological properties in terms of spatial distribution relationships, change patterns, etc. Secondly, more comprehensive geomorphological information should be extracted from regional, watershed and slope scales to reveal the relationship between each indicator and karst development in the study on mechanism and evolution of karst development. Moreover, it is necessary to integrate remote sensing with other multi-source data to enrich the geological attributes of DEM and to delve into more information in soil, vegetation, geological structure and other fields.
- karst geomorphology /
- digital elevation model /
- digital terrain analysis /
- progress

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图 1 尺度效应的定量刻画曲线示例^[15]

Figure 1. Examples of curves for quantifying the scale effects in DTA

(a. scale effect with resolution change; b. scale effect with neighborhood size change)

下载: 全尺寸图片幻灯片

图 2 典型岩溶地貌的等高线树模型示意图

Figure 2. Contour tree model of typical karst landscape

下载: 全尺寸图片幻灯片

图 3 DEM在岩溶区生态环境中的主要应用模式

Figure 3. Main application modes of DEM in the ecological environment of karst area

下载: 全尺寸图片幻灯片

表 1 峰林、峰丛岩溶地貌的形态指数特征

Table 1. Morphological characteristics of peak forest and peak cluster

测度	形态指数	峰林、峰丛地貌与一般非岩溶地貌对比
垂向测度	高程	峰林高度较低，一般几十到两百米；峰丛高度通常在两三百米以上，最大可达六百米以上
垂向测度	起伏度	峰丛地表粗糙，高程起伏相对变化大
水平测度	邻近性	峰丛具均匀的峰洼分布，峰林零星分布
水平测度	隔离性	峰丛具孤立的洼地和互相连通的峰体
形状测度	地形表面积	峰丛具较大的地表面积，峰林具较小的地表面积
形状测度	形状指数	峰丛岩溶地区由高频率的简单形态山体构成，形状指数小；非岩溶地貌一般由低频率的复杂形态山体构成，形状指数大
垂向和水平测度	坡度	峰林、峰丛地貌较一般丘陵坡度更大，峰丛坡度一般大于30°，峰林坡度一般大于45°
特征要素	山顶点/洼地点	峰丛山顶点和洼地点密集且相间均匀分布
	鞍部点	峰林无明显鞍部点，峰丛有鞍部点且在一定范围内鞍部点围绕洼地点
	山脊线和山谷线	非岩溶地貌通常由山脊和山谷组成，山脊的海拔会逐渐下降到山谷，如果断面恰好沿着山脊线或谷底，则减少得更慢

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表 2 可用于表征岩溶地貌空间格局的典型指标参数^{[35, 39-40]}

Table 2. Typical index parameters used to characterize the spatial pattern of karst landscape

类别	量化因子	计算公式	备注	地学意义
形态统计特征	坡度	$ \beta = \arctan \sqrt {f_x^2 + f_y^2} $	式中：f_x是X方向高程变化率；f_y是Y方向高程变化率	反映地表面在该点的倾斜程度
	粗糙度	$ {\text{R}} = {S_s}/{S_p} = 1/\cos (\tan \beta ) $	计算地表的曲面面积S_s与其在水平面上的投影面积S_p之比；tanβ为DEM栅格单元坡度	反映地表的起伏变化和侵蚀程度的指标
	复合地形指数	${{CTI} } = In(\alpha /\tan \beta )$	式中：α 表示单位等高线长度的汇水面积；tanβ为该处的坡度；CTI又称地形湿度指数TWI	对径流路径长度、产流面积等的定量描述，也可反映地形的复杂性
	分形维数	$ {{\text{F}}_{\text{d}}} = - \log N(\varepsilon )/\log \varepsilon $	式中：ε为栅格格网边长大小；N（ε）为栅格总数	表征不同地貌类型下峰体形态的自组织程度
空间展布特征	形状指数	$ S = \displaystyle\sum\limits_{i = 1}^N {{W_i}} \dfrac{{{P_i}}}{{2\sqrt {\pi {A_i}} }} $	式中：N为该地区总斑块个数；W_i为第i个斑块的面积权重；P_i为第i个斑块周长；A_i为第i个斑块面积	反映地貌单元景观斑块在空间结构上的不规则程度
	邻近指数	$ PI = \displaystyle\sum {({{{a_j}} / {h_{ij}^2}})} $	式中：a_j表示斑块面积；h_ij表示斑块ij到同类型斑块的最近距离	可表征峰洼之间的隔离趋势
	莫兰指数	$ I = \dfrac{{\displaystyle\sum\limits_{i = 1}^n {\displaystyle\sum\limits_{j = 1}^m {[ {( {{x_i} - {x_m}} )( {{x_j} - {x_m}} )} ]} } }}{{\displaystyle\sum\limits_{i = 1}^n {{{( {{x_i} - {x_m}} )}^2}} }} $	式中：x_i和x_j分别为在位置i、j的测量值；x_m是所在所有i、j位置点测量值的均值；n为所有测量点的数目	反映地貌景观斑块在空间上的集聚程度
地貌发育演化特征	峰洼密度	$ D = \dfrac{{{N_p} + {N_s}}}{A} $	式中：N_p为样区内峰顶个数；N_s为样区内洼地个数；A为样区的面积	表示峰体洼地的聚集程度，侧面反映岩溶发育程度
地貌发育演化特征	面积－积分值	$ {E_i} = \dfrac{{\displaystyle\int_0^H {adh} }}{{HA}} = \displaystyle\int_0^1 {xdy} $	式中：a表示水平断面面积；h是等高线的相对高程值；H是样区的高差；A是样区的面积	通过构建不同等高线上的面积和相对高差之间的函数关系来评价地貌演化阶段和侵蚀动力差异

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