Dissertation
Dissertation > Astronomy,Earth Sciences > Surveying and Mapping > Photogrammetry and Surveying, Mapping and Remote Sensing > Aerial Photogrammetry > Digital Mapping

Scale Characteristics and Decomposition of Drainage Gully Network Based on DEM

Author ZhangZuo
Tutor TangGuoAn;LiuXueJun
School Nanjing Normal University
Course Cartography and Geographic Information Systems
Keywords gully network scale scale decomposing lacunarity block quadrat analysis
CLC P231.5
Type PhD thesis
Year 2008
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Scale is an essential theoretic problem in geography research. In recent years, global and regional researches have been paid more and more attention as the influence of global environmental changes speeds up caused by human beings activities. There are three primary research points for both regional and global environmental analysis, which are scale characteristics, scale decomposing and scale transformation. As an independent system with closest relation to human beings, drainage basin plays an important role on national economic development, such as land management, environment controlling, and water conservancy. Hence, scale research on gully network is becoming one of the valuable research topics of geoscience, such as geomorphology, hydrology and environment.However, there’re some significant weaknesses for state-of-the-art research works. Firstly, spatial pattern of drainage networks under different analysis scales may be evaluated for the same value of the descriptive parameters; Secondly, scale characteristics of gully networks have not been taken into account by numerous existing gully ordering systems; Thirdly, only regular data have been regarded highly in current scale decomposing researches.Motivated by solving issues mentioned above, this thesis focuses on the scale characteristics of gully network. In order to solve the first problem, a multi-scale analysis model is built. This model is suitable for the gully network. It has been applied into Jiuyuangou drainage basin and a simulated gully network for investigating the gully networks’ scale characteristics. Furthermore, based on Two-term local quadrat variance (TTLQV) algorithm, a scale decomposing method is pointed out. This thesis also evaluates the question that whether we can apply scale decomposing results into gully ordering results for getting gully ordering system keeping the same scale characteristics results.The primary works and contributions of this thesis are summarized as follows:1. Three term local strip lacuanrity algorithm (3TLSL)This thesis shows that the gliding algorithm of Lacunarity has several shortcomings, such as it can only focus on the current analysis window, and is easily influenced by some local irregular values. In order to overcome these weaknesses, this thesis presents a novel algorithm named 3TLSL. Compared with gliding algorithm, 3TLSL algorithm considers the relation between current analysis window and its neighborhood. The experimental results show that 3TLSL achieves better accurately spatial pattern detecting results when compared to the gliding algorithm.2. Multi-scale analysis model for gully networkThis thesis shows that state-of-the-art algorithms of lacuanrity and blocked quadrat analysis could not handle gully network properly. In order to solve their problems, a multi-scale analysis model which is suitable for gully network has been put forward. This model adopts two-term or three-term local analysis window, and allows for gully network’s anisotropy. This model also takes the third dimension’s information into account. Therefore, it can effectively detect gully network’s pattern (for instance, multi-fractal characteristics, characteristic scale and anisotropy) which cannot or is not easy to be detected in regular scale analysis method. This model offers the foundation for the experiment design and information collection of drainage researches and supports for interpretation of gully network’s spatial pattern.3. Scale characteristics of gully network detection and interpretationThe multi-scale analysis model is applied in Jiuyuangou drainage basin’s gully network and a simulated gully network. And following works are carrying out in these networks. These works are: 1) characteristic scale detecting and its interpretation; 2) finding out the differences of scale characteristics among different directions. Furthermore, the analysis model has also been applied in the ridgeline network of Jiuyuangou drainage basin, differences of multi-scale characteristics between gully network and ridgeline network is emphasized and the coupling relation between them is also be studied.4. Scale decomposing method based on TTLQV algorithmFocusing on irregular data with unequal patch and gap size and gully network, scale decomposing method based on TTLQV algorithm is put forward. This method can effectively decompose multi-scale characteristics into several single scales and their interaction.5. Scale characteristics evaluation of gully ordering systemA regular quadrat data, simulated gully network and Xiyangou sub-watershed’s gully network are taken as the sample areas for the scale decomposing. Single scales and their interaction are separated step by step. Then multi-scale characteristics of popular gully ordering system is evaluated based on scale decomposing results. There are several conclusion can be draw from the experimental results. Firstly, the Gravelius’s gully ordering system can keep the multi-scale characteristics of fingertips branches while the Strahler’s approach can keep multi-scale characteristics of trunk part. Secondly, multi-scale characteristics of gullies in different orders are different from the whole networks’. Therefore, some proper analysis extent need to be selected according to research goal, and scale decomposing method can make help to choose a proper analysis extent.Multi-scale research of gully network is important for spatial pattern interpretation of gully network. The multi-scale analysis model can be used as reference for the similar network, such as ridgeline network. It also provides scientific foundation for understanding and forecasting drainage system’s behavior, such as drainage evolution, hydrologic forecasting and drainage management.

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