地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (10): 1743-1755.doi: 10.12082/dqxxkx.2021.210094

• 地球信息科学理论与方法 • 上一篇    下一篇

海陆岸线多尺度矢量数据获取方法及其尺度效应评估

张应华*()   

  1. 中国科学院地理科学与资源研究所 陆地水循环及地表过程重点实验室,北京 100101
  • 收稿日期:2021-02-24 修回日期:2021-04-30 出版日期:2021-10-25 发布日期:2021-12-25
  • 作者简介:张应华(1977— ),男,河南上蔡人,博士,主要从事自然地理学研究。E-mail: zhangyinghua@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金重点项目(41730749);国家重点研发计划项目(2017YFA0604701)

Methods to Generate Different Scale Data of Coastline and Its Scale Effect Evaluation

ZHANG Yinghua*()   

  1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2021-02-24 Revised:2021-04-30 Online:2021-10-25 Published:2021-12-25
  • Contact: * ZHANG Yinghua, E-mail: zhangyinghua@igsnrr.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41730749);National Basic Research Program of China(2017YFA0604701)

摘要:

地理空间数据的多尺度表征是制图学的基石,是支撑地理数据多尺度建模分析的前提。通过对从遥感影像获取的一定尺度的地理要素矢量数据进行选择、化简、聚合或其他处理,以获取多尺度矢量数据,但多种综合处理模型和方法会导致多尺度矢量数据存在着不同程度的信息损失。本文基于地理信息系统(ArcGIS 10.6)的矢量数据制图综合功能模块,通过整合多种内嵌的自动算法和模型,结合人机协同的辅助处理方法,构建了一套系统的海陆岸线空间尺度上推方法体系,并将其应用于南美洲大陆海陆岸线矢量数据从m级空间尺度上推到30 m、250 m和1 km。基于分形理论,首次提出线矢量数据复杂度指数概念,用以表征海陆岸线地理要素特征和对比其信息精细化程度。在此基础上,对获取的30 m、250 m和1 km海陆岸线矢量数据进行信息损失评估,结果显示制图综合引起陆地和水域空间属性的改变,不同尺度表征的地理要素信息精细度存在显著差异:相比m级基础数据,30 m、250 m和1 km海陆岸线矢量数据陆地图斑数量损失分别为32.07%、90.46%和98.61%,岛屿线矢量长度信息损失分别为6.32%、49.26%和75.47%;南美洲大陆海岸线矢量数据信息精细度分别降低1.97%、25.33%和45.39%。本文构建的计算机自动综合模型和人工处理相结合的海陆岸线矢量数据空间尺度上推方法,可以实现海陆岸线矢量数据空间尺度上推获取不同尺度的线矢量数据,并描述了不同空间尺度矢量数据的信息损失状况。

关键词: 南美洲, 海岸线, 尺度, 地理信息系统, 数据融合, 算法模型, 人机协同, 矢量数据

Abstract:

Multi-scale of geospatial data is the cornerstone of cartography, and plays a key role in supporting geographic element analysis and feature recognition. Multi-scale vector data can be generated by selecting, simplifying, aggregating, or other processing of geographic element vector data of a certain scale obtained from remote sensing images. However, a variety of comprehensive processing models and methods will also lead to various levels of information loss in multi-scale vector data. The global coastline is a geographic information element with a wide coverage area, complex curves, various island combinations, and complicated structures of land and water regions. The variation of coastline vector data attributes shows different properties at different scales. For the special coastline vector data, there are multiple influencing factors, and the relationships between them are ambiguous. Therefore, it is impossible to judge the attributes of the elements only based on the combinations of a single or a small number of characteristics of the node or line elements. Meanwhile, using a single mathematical model or algorithm for simplification, the drawing effect often has a large deviation from the actual situation, and it cannot meet the drawing needs of different regions and different scales. Thus, we used Geographic Information System (ArcGIS 10.6) technology to support the automatic comprehensive function of geospatial data mapping, integrated different embedded automatic algorithms and models, and combined human-machine collaboration to build a systematic scale-up method system to achieve different scales of coastline data. Based on fractal theory, the concept of line vector data complexity index was first proposed to characterize the coastline geographic elements and to compare the degree of declination of their information. With the m-scale coastline data interpreted by manual visual interpretation, the scale-up is used to generate coastline data on the scales of 30 m, 250 m, and 1 km, respectively. The information loss assessment was performed on the obtained 30 m, 250 m and 1 km coastline vector data, and the results showed that the mapping integration caused changes in the spatial attributes of land and water. There are significant differences in the fineness of geographic element information represented by different scales. Compared with the m-scale coastline data, the loss of the number of islands on the scales of 30 m, 250 m, and 1 km is 32.07%, 90.46%, and 98.61%, respectively, the information loss of the coastline length is 6.32%, 49.26%, and 75.47%, respectively, and the information granularity of the vector data of the coastline of South America is reduced by 1.97%, 25.33%, and 45.39%, respectively. With the processes of the up-scale of the coastline, it has an increasing trend of the median, mean of the islands area and their complexity index from the m-level to 30 m, 250 m, and 1 km scales. The scale-up method constructed in this paper to combine the computer automatic synthesis model with the artificial processing of the coastline vector data has the potential to efficiently realize the scale-up of the coastline vector data, and describe the information loss of vector data at different spatial scales.

Key words: South America, coastline, different scales, geographic information system, data fusion, algorithm model, human-machine collaboration, vector data