地理科学进展 ›› 2015, Vol. 34 ›› Issue (3): 330-.doi: 10.11820/dlkxjz.2015.03.008

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全月球撞击坑识别、分类及空间分布

王, 娇1,2, , 程维明1, , 周成虎1   

  1. 1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101
    2. 中国科学院大学, 北京 100049
  • 出版日期:2015-03-25 发布日期:2015-03-25
  • 作者简介:

    作者简介:王娇(1990-),女,宁夏人,博士生,主要研究方向为行星数字地貌,E-mail: wjiao@lreis.ac.cn。

  • 基金资助:
    国家自然科学基金项目(41171332)

A global inventory of lunar craters: identification, classification, and distribution

Jiao WANG1,2, Weiming CHENG1, Chenghu ZHOU1   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2015-03-25 Published:2015-03-25

摘要:

撞击坑是月球表面分布广泛的地貌单元,是研究月球的最直接窗口。本文以嫦娥一号卫星获取的遥感影像和DEM,以及国际天文学联合会(IAU)公布的撞击坑名录为基础数据源,以全月球表面撞击坑为研究对象,采用遥感图像处理与专家知识融合的目视解译法确定撞击坑的边界,识别出全月球表面直径大于500 m的撞击坑共计106030个,采用累积频率和与IAU公布撞击坑对比两种方法对目视解译的撞击坑进行精度评价,其识别的总体误差率为10.97%;按照形态特征指标,将全月球撞击坑分为六大类,对比分析了不同类的撞击坑影像及形貌差异性;对全月球撞击坑分类进行统计分析,得出了不同类型撞击坑在月球表面的数量与密度特征及空间分布情况。

关键词: 嫦娥一号, 撞击坑识别, 分类, 分布特征

Abstract:

The most widespread geomorphic units on lunar surface are craters. In this article, we use a remote sensing image and DEM model from the Cang'E-1 satellite as data source to study craters on the lunar surface. First, an integrated procedure that uses a remote sensing image, DEM and its derived products (slope, curvature) to extract the edifice of craters is applied. At a scale of 1: 2500000 edifice boundaries are manually defined by searching for breaks in slope around the base and there are a total of 106030 impact craters identified. Then we present two methods to evaluate the extraction accuracy and cumulative crater frequency. The result shows that the extraction error is less than 12 percent and the cumulative crater frequency curves from both sets of data obtained through these two methods are similar. Second, all the craters are divided into six groups including the ghost, walled plain, ring plain, crater plain, bowl-shaped, and dimple craters based on previous studies. A corresponding interpretation symbol database is established to illustrate the appearance of craters on a digital image. Finally, some statistical data on different types of craters across the lunar surface are generated. Characteristics such as number, density, and spatial distribution are calculated in ArcGIS and SPSS. We find that the bowl-shaped craters are the major type of all the craters and there is a strong inverse correlation between the number of craters and their diameter. The data provided herein is by far the most comprehensive lunar craters data so that it offers an opportunity to systematically study the impact mechanisms, impact effects, and evolutionary history of craters on lunar surface.

Key words: Chang'E-1 satellite, crater classification, detection, spatial analysis