基于DEM的数字流域时空特征及提取研究

发布时间：2018-06-04 03:52

本文选题：数字流域 + 时空特征　；参考：《浙江大学》2014年博士论文

【摘要】：数字流域特征是对自然界流域特征的数字化表达,是流域水文模型的数据基础,对于模拟流域水文过程、保护与治理流域生态环境具有重要的作用。因此,数字流域特征提取是地理信息系统领域的一个重要问题。随着测绘技术、计算机技术、遥感技术的不断发展,基于DEM的数字流域特征提取取得了较大的进展。但由于自然界纷繁复杂,目前的研究还不能很好地反映自然界的实际流域情况,体现在：无法反映因河流分叉而形成的具有多重归属的流域情况；无法反映受时相影响而导致河流流向动态变化所形成的具有动态归属的流域情况(流域时相特征)；没有深入研究内流流域特征及其提取方法。此外,现有的数字流域特征提取方法效率较低,尤其是在海量数据处理时,效率问题甚至成为了数字流域分析的瓶颈问题。本文对数字流域分析中的洼地填平、流向分析、汇流分析、虚拟水系提取以及流域划分等问题进行了全面的研究,并重点对以下五个方面进行了深入的研究： (1)针对由河流分叉所造成的具有多重归属属性的流域,本文将这种流域定义为公共流域,并提出了公共流域的提取方法：首先对河流进行拓扑关系构建,然后对河流归属地进行地理编码,并将编码值赋予直接汇入归属地的河流；在此基础上,以被赋予编码的河流为“种子河流”,根据河流之间的拓扑关系,将编码值从下游河流传递至上游河流,确定具有多重归属属性的河流；最后结合从DEM中提取的流向信息,提取公共流域。 (2)针对河流流向动态变化所造成的具有动态归属属性的流域,本文将这种流域定义为动态流域,并提出了动态流域提取方法：将相互连通的流向动态变化的河流集合抽象为“节点”,与流向固定的河流共同构成河流网络拓扑结构；在此基础上,对河流归属地进行地理编码,并将编码值赋予直接汇入归属地的河流；根据拓扑关系,将编码值从下游河流传递至上游河流,确定具有动态归属属性的河流；最后结合从DEM中提取的流向信息,提取动态流域。 (3)针对因地表径流无法流入海洋而形成的内流流域,本文根据影响内流流域的主要因素,将内流流域分为：地形型内流流域(在DEM中以洼地的形式存在)和气候型内流流域(在DEM中不以某种特殊的形式存在,但通常包含内流河),并提出了内流流域提取方法：首先利用实测内流河对DEM进行地形约束,降低内流河所在栅格点高程,增大内流河与周围区域的高程差,使内流河所在区域形成洼地,统一气候型内流流域与地形型内流流域在DEM中的表现形式(洼地)；然后对地形约束后的DEM进行洼地填平,并识别内流流域所在平地,识别规则为：平地中包含内流河或平地中所包含的洼地面积超过设定闽值；进而将内流流域所在平地的高程恢复至洼地填平前(将平地退化为洼地),并以洼地为内流流域种子区域,结合从DEM中提取的流向信息,提取内流流域。 (4)针对数字流域特征提取中洼地与平地处理效率较低的问题,提出了快速洼地与平地处理方法：该方法在洼地填平的同时,记录水流从洼地(平地)内部以最短路径流至出口的流向,在平地增高处理中,利用该流向以水流追踪的方式快速确定平地内部栅格点与出口的距离；并且,采用距离转换计算平地内部栅格点与边界的距离；从而大幅减少了随机搜索与迭代处理,提高了处理效率。 (5)针对数字流域特征提取中汇流分析效率较低的问题,提出了快速汇流分析方法：该方法首先搜索得到流域出水口,然后以出水口为种子点,以流向的反向为搜索方向,向流域内部进行搜索,构建流域“汇流树”(汇流树的叶节点为水流源头,根节点为出水口)；最后通过对汇流树进行反向遍历计算汇流累积量。该方法对目标区域内的流域进行分而治之,缩小了每一个流域汇流分析的搜索范围,高效利用了内存,并且,通过汇流树有效避免了汇流累积量计算中对数据的随机搜索,从而提高了汇流分析的效率。本文的主要创新点包括： (1)首次提出了公共流域及其提取方法。 (2)首次提出了动态流域及其提取方法。 (3)首次对基于DEM的内流流域提取方法展开深入研究。 (4)提出了快速洼地与平地处理方法、快速汇流分析方法,提高了数字流域特征提取的效率。实验结果表明,本文所提出的方法能够有效提取公共流域、动态流域、内流流域及树状流域特征,并且提高了数字流域特征提取的效率。
[Abstract]:The characteristic of digital basin is the digital expression of the characteristics of the natural basin. It is the data base of the hydrological model of the basin. It plays an important role in simulating the hydrological process of the basin and protecting and controlling the ecological environment of the basin. Therefore, the feature extraction of digital watershed is an important problem in the field of geographic information system.
With the continuous development of Surveying and mapping technology, computer technology and remote sensing technology, the feature extraction of Digital Watershed Based on DEM has made great progress. However, because of the complex nature of nature, the current research can not well reflect the actual situation of the natural river basin, which can not reflect the multiple attribution caused by the branching of the river. River basin conditions; the dynamic attribution of river basin conditions (the characteristics of basin time facies) that can not reflect the dynamic changes in the flow direction of the river, and no in-depth study of the characteristics and extraction methods of the internal flow basin. In addition, the existing method of extracting the characteristics of the digital watershed characteristics is low, especially in the process of mass data processing. The problem has even become a bottleneck problem in digital watershed analysis.
This paper makes a comprehensive study on the problems of low-lying land filling, flow analysis, confluence analysis, virtual water system extraction and watershed division in digital basin analysis, and focuses on the following five aspects.
(1) aiming at a river basin with multiple attribution attributes, this paper defines the basin as a public basin, and puts forward the method of extracting the public basin: first, the topology of the river is constructed, then the geographical coding of the River belongs to the river, and the coding value is given to the river which is directly remitted to the home area; On the basis, the coded rivers are used as "seed rivers". According to the topological relations between rivers, the coded values are transferred from the downstream rivers to the upstream rivers, and the rivers with multiple attribution attributes are determined. Finally, a public basin is extracted from the flow information extracted from the DEM.
(2) in view of the dynamic attribution of river basins, this paper defines the watershed as a dynamic basin, and proposes a dynamic watershed extraction method, which abstracts the set of rivers which are connected to dynamic changes as "nodes", and forms a river network topology together with rivers with fixed flow direction. On this basis, geo coding is carried out on the site of the river, and the coded values are assigned to the river that directly remittance to the land. According to the topological relation, the coded values are transferred from the downstream rivers to the upstream rivers to determine the dynamic attribution of rivers. Finally, the dynamic Basin is extracted from the flow information extracted from the DEM.
(3) based on the main factors that affect the inflow of surface runoff into the ocean, this paper divides the inner stream basin into the main factors that affect the internal flow basin, including the topographic basin (in the form of a depression in DEM) and the climate type basin (not in a particular form in the DEM, but usually included in the inland river). The method of extracting the inner stream basin: first, using the measured inland river to restrain the DEM, reduce the elevation of the grid point of the inland river and increase the elevation difference between the inland river and the surrounding area, make the region of the inland river form Wa Di, unify the manifestation of the climate type internal flow basin and the topographic basin in the DEM, and then to the terrain. The restricted DEM is filled with the depression and identifies the flat land in which the internal flow basin is located. The recognition rule is that the area contained in the inland river or the flat land is more than the setting of the min value, and then the Gao Cheng in the flat land of the inner stream basin is restored to the bottomland (depressions will be degraded to the low-lying land), and the depression is the seed area of the inner flow basin. The flow basin is extracted from the flow information extracted from DEM.
(4) in view of the low efficiency of low-lying land and flat land processing in the feature extraction of digital basin, a method of fast depressions and flat ground treatment is proposed. This method is used to record the flow of water from the shortest path to the outlet flow from the low-lying land (flat ground), while the flow is traced fast in the way of water flow tracing. At the same time, the distance between the grid point and the exit of the interior is determined, and the distance between the grid points and the boundary is calculated by the distance conversion, thus the random search and iterative processing are greatly reduced, and the processing efficiency is improved.
(5) in order to solve the problem of low converge analysis efficiency in the feature extraction of digital watershed, a fast converge analysis method is proposed: this method first searches the water outlet of the basin, then takes the outlet as the seed point, and then searches the river basin with the reverse direction of the flow direction, and constructs a watershed "confluence tree" (the leaf node of the confluence tree is water). Flow source, root node is a water outlet); finally, the sink is calculated by reverse traversal of the confluence tree. This method divides the basin in the target area, reduces the search scope of the flow analysis in each basin, efficiently uses the memory, and avoids the data in the confluence accumulation calculation effectively through the confluence tree. The random search improves the efficiency of the confluence analysis.
The main innovation points of this article include:
(1) the public basin and its extraction methods have been put forward for the first time.
(2) the dynamic basin and its extraction methods are first proposed.
(3) for the first time, an in-depth study on the extraction method of DEM based inflow watershed was conducted.
(4) put forward the method of rapid depressions and leveling, fast convergence analysis method, and improve the efficiency of digital watershed feature extraction.
The experimental results show that the proposed method can effectively extract the characteristics of public basin, dynamic basin, internal flow basin and tree basin, and improve the efficiency of feature extraction of digital watershed.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：P208;P333

【参考文献】