针对江汉平原区堤垸人工微地貌,综合局部地形分析、水流模拟分析以及GIS空间分析和地统计分析方法,利用SRTM DEM数据进行微地貌自动提取研究。根据人工微地貌特点,构建一种局部地形形态分析方法来对结构线位置进行标示,并提出一种流域合并的方法来进行微地貌结构线提取;进而综合上述两种方法的计算结果,提出平原区人工微地貌的组合提取方法。研究结果表明该方法可较好实现平原区垸堤人工微地貌结构线提取。
This paper presented a numerical methodology for recognition and extraction of artificial micro-geomorphology in the Jianghan Plain. In the different spatial scales of geomorphologic types, micro-geomorphology is the smallest spatial pattern and thus is more easily influenced by human activities. In this sense, artificial micro-geomorphology would show more potential in understanding the interactions between natural environment and human activities. Due to the continuous land utilization and rearrangement from historical period in the Jianghan Plain, a kind of artificial micro-geomorphology characterized by enclosed embankments on the low-lying land was formed. For decades, it played a very important role in the spatial distributions of some disasters such as flood and waterlogging. Compared with the automated extraction of drainage network and catchments boundaries from nature valleys, automated recognition of artificial micro-geomorphology skeleton lines has distinctive difficulties because of its tiny size, greater susceptibility to data error, and the different spatial structures with nature geomorphology. Despite of the progress in the automatic extraction of terrain skeleton lines, there is no auto-extracting method of artificial micro-geomorphology availablility yet, and it still dependes on the manual way to extract the skeleton lines of artificial geomorphology from the remotely sensed images and topographic maps. In this paper, a digital method for extracting the skeleton line of artificial micro-geomorphology was developed through the combination of the local topography analysis and the overland flow simulation. According to the specific artificial geomorphology features in the Jianghan Plain, a method was brought forward to identify the potential positions of the topographic skeleton lines, which is based on the evaluation of difference between the local elevations and the average elevations within moving windows. Then, an algorithm of watershed-merging based on flow simulation was developed to extract the potential skeleton lines of enclosed embankments. The algorithm can reduce the influence of the spurious pits in DEM, and generate the possible skeleton lines in a continuous way. Afterwards, based on the calculation results from the local topography analysis and the flow simulation, a new method was proposed to extract the skeleton lines of artificial micro-geomorphology by using the GIS techniques of spatial analysis and geo-statistics. The potential skeleton lines extracted from watershed-merging process were divided into segments by intersection points, and then transformed to raster format. Afterwards, raster segments of skeleton lines belonging to the proper skeleton lines of artificial micro-geomorphology were judged and fixed by using the position information calculated from local topography analysis according to their superposition degree. For the disconnected places of the extracted skeleton lines, a method was designed which could automatically extend and join the arcs on direction of the catchments borderlines through a kind of path determination algorithm. At last, the complete and closed borderlines of all the artificial geomorphologic patterns were formed through the above steps. The result shows that the aforementioned methodology is an effective attempt for the digital extraction of artificial micro-geomorphology structures, and would be helpful for the estimation of the interactions among spatial patterns of disaster, human activities and natural environment.
[1] Alcántara-Ayala I.Geomorphology, natural hazards,vulnerability and prevention of natural disasters in developing countries [J].Geomorphology,2002,47:107-124.
[2] Yin H,Li C.Human impact on floods and disasters on the Yangtze River[J].Geomorphology,2001,41:105-109.
[3] 沈荣开,王修贵,张瑜芳.涝渍兼治农田排水标准的研究[J].水利学报,2001,12: 36~39.
[4] 高玄彧.地貌形态分类的数量化研究[J].地理科学,2007,27(1):109~114.
[5] 周成虎,程维明,钱金凯,等.中国陆地1:100万数字地貌分类体系研究[J].地球信息科学学报,2009,11(6):707~724.
[6] 赵洪壮,李有利,杨景春,等.基于DEM数据的北天山地貌形态分析[J].地理科学,2009,29(3):445~449.
[7] IGBP.The Global Environmental Programmes [J].IGBP Science,2001,4:11-14.
[8] 汪小钦,王钦敏,励惠国,等.黄河三角洲土地利用/覆盖变化的微地貌区域分异[J].地理科学,2008,28(04):513~517.
[9] 苏时雨,李钜章.地貌制图[M].北京:测绘出版社,1999.
[10] Drgut L,Blaschke T.Automated classification of landform elements using object-based image analysis[J].Geomorphology,2006,81:330-344.
[11] 肖 飞,张百平,凌 峰,等.基于DEM的地貌实体单元自动提取方法[J].地理研究,2008,27(2):459~466.
[12] Iwahashi J,Pike R J.Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature[J].Geomorphology,2007,86:409-440.
[13] 程维明,周成虎,柴慧霞,等.中国陆地地貌基本形态类型定量提取与分析[J].地球信息科学学报,2009,11(6):725~736.
[14] 闾国年,钱亚东,陈钟明.基于栅格数字高程模型自动提取黄土地貌沟沿线技术研究[J].地理科学,1998,18(6):567~572.
[15] 汤国安,杨玮莹,杨 昕,等.对DEM地形定量因子挖掘中若干问题的探讨[J].测绘科学,2003,28(1):28~32.
[16] 俞 雷,刘洪斌,武 伟.基于DEM的重庆三峡库区水系提取试验研究[J].地理科学,2006,26(5):616~621.
[17] 周德民,程进强,熊立华.基于DEM的洪泛平原湿地数字水系提取研究[J].地理科学,2008,28(6):776~781.
[18] 肖建成,骆高远,姜安源.江汉平原内涝形成因素及治理刍议[J].地理学与国土研究,1996,12(3):34~39.
[19] 王学雷,吕宪国,任宪友.江汉平原湿地水系统综合评价与管理探讨[J].地理科学,2006,26(3):311~315.
[20] 刘章勇,刘百韬,李必华,等.江汉平原涝渍地成因、演替与分异规律研究[J].农业现代化研究,2003,24(1):24~28.
[21] 喻光明.江汉平原农田渍害机理研究[J].地理研究,1993,12(8):38~44.
[22] Rosen P,Hensley S,Gurrola E,et al.SRTM C-band topographic data:Quality assessments and calibration activities//IEEE Geoscience and Remote Sensing Symposium,2001,2:739-741.
[23] Smith B,Sandwell D.Accuracy and resolution of Shuttle Radar Topography Mission data[J].Geophysical Research Letters,2003,30:1467-1470.
[24] Sun G,Ranson K J,Kharuk V I,et al.Validation of surface height from Shuttle Radar Topography Mission using shuttle laser altimeter[J].Remote Sensing of Environment,2003,88:401- 411.
[25] Peucker T K,Douglas D H.Detection of surface specific points by local parallel processing of discrete terrain elevation data[J].Computer Graphics and Image Processing,1975,4(3):375-387.
[26] Toriwaki J,Fukumura T.Extraction of structural information from grey pictures[J].Computer Graphics and Image Processing,1978,8:30-51.
[27] Band L E.Topographic partition of watersheds with digital elevation models[J].Water Resources Research,1986,22:15-24.
[28] Skidmore A K.Terrain position as mapped from a gridded digital elevation model[J].International Journal of Geographical Information Systems,1990,4:33-49.
[29] Tribe A.Automated recognition of valley lines and drainage networks from grid digital elevation models:a review and a new method[J].Journal of Hydrology,1992,139:263-293.
[30] Mark D M.Automated detection of drainage networks from digital elevation models[J].Cartographica,1984,21:168-178.
[31] O'Callaghan J F,Mark D M.The extraction of drainage networks from digital elevation data[J].Computer Vision,Graphics, and Image Processing,1984,28:323-344.