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基于DEM的数字流域水系中面状水体自动提取方法研究
Alternative TitleAutomatic Extraction Method Research of Surface-water Based on DEM in Digital Drainage Network
陈雪莲1,2
Department信息服务与智能控制技术研究室
Thesis Advisor胡静涛
ClassificationP208.2
KeywordDem 多流向算法 面状水体提取 多特征融合 Rbf神经网络
Call NumberP208.2/C48/2013
Pages114页
Degree Discipline机械电子工程
Degree Name博士
2013-11-28
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract随着GIS、GPS和RS技术的迅猛发展,从DEM数据中识别和提取数字流域信息在水文研究领域越来越受到重视。数字高程模型(Digital Elevation Model, DEM)是表示地形表面形态变化趋势的一种数学模型,是国家地理信息的基础数据。DEM数据中包含有丰富的地形和水文信息,是GIS中进行地学分析与应用的数据基础,因此研究基于DEM数据的水文分析与提取的相关理论和方法,对于分布式水文模型的发展具有重要的理论意义。 由于地理对象的形态复杂多样,且DEM数据中存在误差,使得现有的关于数字流域的提取的研究主要集中于对线性水体的提取和研究,而针对于面状水体的提取方法非常少。传统的数字流域水系提取方法中不能包含对湖泊、水库面状水体信息,其主要原因是由于在进行DEM数据处理过程中研究人员将所有的洼地类型认定为影响水流方向存在的伪洼地,将它们用不同的处理方法进行了填平或构造成微斜坡的处理。面状水体是指湖泊、水库等由自然的地表形态形成的洼地类型,是数字流域的一个重要的组成部分,从DEM数据中识别面状水体信息具有多种生态功能和经济社会价值。目前,随着面状水体在分布式水文模型研究领域的地位不断的提升,研究者开始关注面状水体提取研究,以便使数字流域中包含有更丰富的水文信息。 本文依托国家科技重大专项课题《流域水环境风险评估与预警平台构建共性技术研究》(2009ZX07528-004)展开研究和技术攻关,以1:25万DEM数据为信息源,针对湖泊、水库等面状水体的自动提取问题,以分布式水文模型的相关理论和方法为基础,结合神经网络、模糊综合评价分析、特征融合等多种智能算法,重点对面状水体的识别和提取方法进行研究。具体的研究内容包括: 首先,提出了基于扩展邻域地表形态的多流向算法。水流方向算法是水文分析中一项重要的研究内容。在多流向算法中,确定合理的水流分配比例公式是提高多流向算法模拟效果的关键。基于多流向水流分配的基本原理,提出了一种水流分配策略随扩展邻域地表形态变化而变化的多流向算法,以便更合理地模拟地形变化对水流分配的影响。选取一块试验区域对多流向算法进行验证,实验结果表明,应用本文提出的多流向算法能够较为准确地识别出河网结构,是一种较为合理的多流向算法。 第二,分析了面状水体与伪洼地基本数据特征及二者之间的区别;论述了面状水体的DEM数据形态;研究了面状水体的数据特征,包括面状水体的面积特征、面状水体的深度特征、面状水体的潜在出水口特征、面状水体的边界特征;在此基础上结合实测数据的统计结果,建立了面状水体识别模型,并分别使用模糊综合评价法和RBF神经网络模型对面状水体识别模型进行了验证。由于DEM数据中面状水体和伪洼地难于区分,本文提出的面状水体识别模型对于提高面状水体识别的准确率具有重要的参考价值。 第三,提出了基于多特征融合的面状水体提取方法。针对基于DEM数据的数字河网提取中面状水体难以识别提取的问题,基于面状水体的多个属性特征,使用多特征融合技术进行面状水体的识别,利用扫描线的种子填充算法对面状水体的边界栅格进行搜索,从而实现以DEM数据为数据源的面状水体的自动提取。选取了一块实验区域对面状水体提取方法进行了验证,实验结果表明,本文提出的方法能够提取出与实测河网较为一致的面状水体,验证了本文方法的有效性和可行性。同时,在面状水体提取过程中采用扫描线种子填充的策略提高了算法的执行效率。 第四,实现了面状水体自动提取方法在流域水系自动提取及编码系统中的应用。介绍了流域水系自动提取及编码系统的开发背景、系统的功能结构设计和系统主要模块的界面;重点对多流向算法在水流方向生成模块、面状水体识别模型在面状水体识别模块和面状水体自动提取方法在面状水体提取模块的使用方法、实现过程和应用效果进行了分析。流域水系自动提取及编码系统已经在实际工作中进行了应用,取得的效果较为理想。 综上所述,本文提出了基于扩展邻域地表形态的多流向算法,分析了面状水体的DEM数据特征并提出了面状水体识别模型,在此基础上分别提出了基于模糊综合评价法的面状水体识别方法、基于RBF神经网络模型的面状水体识别方法和基于多特征融合的面状水体自动提取方法。基于DEM数据的面状水体自动提取可以为数字流域的面状水体自动提取研究提供一种新的研究思路和方法,同时解决其建立过程中所涉及的最主要的技术难题,从而推动这一技术在其他地区和领域的应用。
Other AbstractWith the development of information technologies such as RS, GPS and GIS, etc, the research about identifying and extracting useful information from Digital Elevation Model (DEM) by computer technology has gained great attention. DEM is a group of numbers to describe the space distribution of the ground elevation value, and reflects the distribution of the ground elevation. DEM is the core content of national fundamental geographic information database, and data foundation of GIS digital terrain analysis, including abundant geoscience information such as terrain and landform, etc. Research and grasp the theories and methods about spatial data mining based on DEM is very important to direct the application of DEM and the relevant spatial data. The existing extraction methods are mostly confined to linear water for varied shapes of geographic object and errors in DEM data. The research on extraction methods of surface-water is insufficient. The traditional extraction methods of digital valley drainage can not involve the surface-water information about lake and reservoir, chiefly because during the processing of DEM data, researchers often see all types of depression as pseudo-depression and fill them up or construct them as slight slopes by different processing methods. Surface-water refers to depression formed by natural surface configuration, such as lake and reservoir, etc and it is an important component of digital valley. Identifying the information of surface-water from DEM data has ecological function and economic and social value. As the promotion of the position of surface-water in research field, researches start paying more attention to extraction research of surface-water from DEM data to let surface-water information involved in digital valley drainage. In this paper, technical researches will be conduct based on the national science and technology major project "risk assessment of drainage basin water environment and generic technology researches of early warning platform construction” (2009ZX07528-004). Choosing 1: 250, 000 DEM data as information sources, for the automatic extraction of surface-water such as lakes, reservoirs, etc, using GIS basic theory, spatial analysis technology, theories of topography, geomorphology and hydrology as basis, combined with the processing method of DEM data, the recognition and extraction method of surface-water will be the focus of the research. Major contents are as follows: First of all, basic data characteristics and differences between surface-water and pseudo-depression are analyzed; DEM data form of surface-water is discussed; and data characteristics, including area, depth, potential outlet and boundary characteristics of surface-water are studied. On this basis and combined with the statistical results of measured data, recognition model of surface-water is established, and then the model will be verified by fuzzy comprehensive evaluation method and RBF neural network model. As it is difficult to distinguish pseudo-depression and surface-water from DEM data, the recognition model of surface-water proposed in this paper has important reference value to enhance the accuracy in surface-water recognition. Secondly, a multiple flow direction algorithm is proposed based on the extended neighborhood surface morphology. In the hydrological analysis, the algorithm of flow direction is an important content to research. In the multiple flow direction algorithm, determining the ratio of flow distribution reasonably is the key to improve its simulation results. Based on the principle of water flow distribution, this paper developed a multiple flow direction algorithm, in which strategies of flow distribution vary with the extended neighborhood surface morphology to simulate the effect of the geography change on the flow distribution reasonably. By a test area, the multiple flow direction algorithm is verified. The experimental results show that multiple flow direction algorithm proposed in this paper not only can recognize the structure of river network accurately, but also is a restively reasonable multiple flow direction algorithm. Thirdly, an extraction method with relation to surface-water combined with multiple features is proposed. Aiming at the problem that surface-water is difficult to be recognized and extracted during the process of the extraction of digital river network based on DEM, this paper realized the automatic extraction of surface-water by considering multiple attributes in the surface-water recognition model and combining the method of heuristic search. And the extraction method of surface-water is verified by a test area. The results show that the proposed method can extract surface-water feature in good agreement with practical river network, which demonstrates the effectiveness and feasibility of the method. Meanwhile, the method of heuristic search improves the efficiency of the algorithm in the extraction of surface-water process. Fourthly, the application of surface-water extraction method in the automatic extraction and coding system of drainage network system. The automatic extraction information and management platform of surface-water is designed by information technology. Through the method of system integration, the automatic extraction surface-water is combined with information management. In the same time, by application of the GIS system, the theoretical model is put into the practical work. Above all, this paper shows the characteristics of DEM data and presents the recognition model of surface-water. According to this, some useful methods are proposed, which include the multiple flow algorithm based on extended neighborhood surface patterns; the recognition method of surface-water based on the fuzzy comprehensive evaluation and the RBF neural network model, respectively; the automatic extraction surface-water method based on the fusion of multiple features. The automatic extraction surface-water method provides a new research approach for the automatic extraction surface-water in digital drainage area, meanwhile it solves the main technological problem appeared during the process of modeling. So this technology is generalized applied in other districts and fields. The automatic extraction surface-water method has theoretical significance in the development of digital drainage area. At the same time, it will play an important role in the comprehensive administration of environment in digital drainage area.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/14803
Collection信息服务与智能控制技术研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
陈雪莲. 基于DEM的数字流域水系中面状水体自动提取方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2013.
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