基于空间形态的模糊数据集数字地貌研究
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摘要
地球表面是人类活动最为活跃的界面,地貌作为地球表层系统中的一个基本要素,它直接地影响人类活动。因此,地貌形态研究一直受到地理学家和地质学家的关注。过去三十年,数字高程模型(DEMs,Digital Elevation Models)广泛用于计算机的陆地模型数值分析,利用规则网格DEM数据,提取地貌形态参数制图,提取坡度(slope),坡向(aspect),地表凸度(convexity)和凹度(concavity),从相邻关系自动获得地貌属性。在土木工程(civil engineering),地质(geology),地貌(geomorphology),水文(hydrology),环境科学(environmental science),土地利用(landuse),土壤侵蚀(soil erosion)和植被覆盖(vegetation cover)等方面研究得到广泛应用。尤其在地貌和水文研究方面,基于DEM专题图研究方面应用更加普遍。
如何从DEM定义地貌单元进行了许多努力。根据相应的地貌过程,使用DEM可以模拟为不同的地貌形态。通过层次细分将地貌形态分成不同的地貌单元。通过坡度、坡向和曲率来描述,将地貌划分为四个层次,即地貌元素、地貌类型、地貌系统和地貌区域,其中地貌元素和地貌类型是我们研究的重点也是最重要的。
长期以来地貌研究采用野外地形测量和航空摄像测量的方法编译而来,这两种方法工作量大,研究区域受到很大的限制。因此一直停留在区域性详细地貌研究的阶段。近些年来随着航空航天事业的快速发展,及激光雷达技术的快速兴起,数据采集的手段长足进步,获得地表信息也不断丰富,例如美国航空航天局(NASA)获得的3弧秒的全球数字高程模型数据,以及日本经济产业省与美国航空航天局合作,利用ASTER立体像对制作的全球1弧秒数字高程模型(ASTER-GDEM)。为我们研究区域尺度乃至全球尺度及行星尺度的地貌形态提供了可靠的数据源。
然而随着技术的发展,获取数据能力的增强,数据表现出了高维、高信息量的特点,在数据信息提取上提出了新的挑战。本文针对这个特点,总结前人的经验,试图寻找一个统一的框架,在探索地貌形态分类的同时,有效解决模糊数据信息提取的问题。
数字计算机和地理信息系统排除了任何大小和空间分辨率区域基于地表几何形态分类的障碍。地形通过高程信息精细自动解析,通常高程数据以规则网格形式的数字高程模型来表达。
本研究采用日本经济产业省与美国航空航天局于2006年6月29日发布的1弧秒(中纬度地区约26m)分辨率的ASTER—DEM数据,提出结合地貌形态参数化与自组织神经网络映射的方法,进行地貌形态分类研究。该方法能够对海量数据进行有效的降维,而且自动获得地貌形态分类阈值,减少了人工参与的主观影响。在一定程度上突破了地貌形态参数化方法分类类型数量的限制。减少了地貌形态类型误分现象。对长春地区的地貌形态研究取得了满意的效果。
通过对长春地区的地貌分类定量研究,主要取得了以下成果:
(1)改进了地表曲面模型的拟合方法,提出了不完全二元四次方程,推导出该方程模拟的地表曲面坡度、截面曲率、最大曲率和最小曲率公式,并制作了相应的长春地区地表参数图;
(2)提出了结合地表参数化与自组织映射进行地貌形态分类,构建了长春地区地貌形态分类模型;
(3)实现地貌分类定量化,分类的数目和阈值通过自组织映射神经网络训练获得,分类方案由计算机自动确定。
通过研究实现了地貌形态分类的快速表达,减小了地貌人工解译制图的工作量,很大程度上提高了工作效率;自动获得分类阈值,不但增强了不同尺度空间信息研究的灵活性,而且提高了地貌分类的精度,为我们生态环境研究、精准农业、地质灾害评估、地质构造研究及流域划分等提供精确的背景参数;对土木工程、城市规划、自然保护区管理以及矿产普查等工作有现实的意义。
The earth surface is the most active interface of human activities. As a basic element of the earth surface system, the landscape directly influences the human activities. Therefore, research on landscape morphology has been being under the geologist’s attention. During the past three decades, the Digital Elevation models (DEMs) are widely used for computer numerical analysis of terrain. Use the regular grid DEM dataset to extract the slope, aspect, convexity, concavity and so on, and automatically get the physiognomy attributes from the neighboring relationship. It is widely applied in civil engineering, geology, geomorphology, hydrology, environmental science, landuse, soil erosion and vegetation cover. Especially in physiognomy and hydrological research, the application of the special charts based on DEM research is more common.
Many efforts have been made to examine how to define landform units from DEMs. Depending on the geomorphic processes which are considered, landforms may be modeled quite differently, we described landforms through a hierarchical subdivision of the land surface into relief units with homogenous gradient, aspect and curvature as well as form elements/facets with homogenous plan and profile curvature. Landform was described as hierarchical entities into four levels: landform elements, landform types, physiographic systems and physiographic regions, of which landform elements and types are the most important.
For a long time, landform was compiled from aerial photographs and topographic maps, however, those are time-consuming and arduous, and the region of study range is restricted. So the work has remained in the regional detailed research of stage. With the rapid development of aerospace and the booming of the laser radar technique, methods of data collection make great progress, the land surface information collected become more various, such as the shuttle radar topography mission of NASA which gets global digital elevation models as 3 arc-second, and collaboration of Ministry of Economy, Trade and industry of Japan and NASA, GDEM was produced by stereo image. Those provid us reliable data for geomorphometry from regional scale to global and planet.
However, with technological development, access to data capabilities, the data demonstrated a high-dimensional, high information content of features in the data to extract information on to us with new challenges. In this paper, the characteristics, summarized from previous experience, trying to find a unified framework, explore the landscape in the form of classification at the same time, an effective solution to the problem of fuzzy data extraction.
Digital computer and geographic information systems precludes any size and spatial resolution surface geometry based on classification of the regional barriers. Fine terrain elevation information is now automatically parse through, usually elevation data to form a regular grid digital elevation models to express.
In this study, use the ASTER-DEM data of 1 arc second (mid-latitudes of about 26m) resolution which was released on June 29. 2006 by METI and NASA, and put forward a method of integrating geomorphology parameterization and self-organizing neural network for classification research of landscape form. This method can be effective for the vast amounts of data dimensionality reduction, and automatic to get landform classification threshold, reducing the subjective impact of human involvement. To some extent, broking the limits on the number of classification of landscape form parameterization methods and also reducing the misclassification phenomenon of landform types. Changchun region geomorphic study achieved satisfactory results.
Through the quantitative geomorphological classification research of Changchun,the main research results are showed as follows:
(1) improved fitting method of surface,a partial quartic equation was proposed. Deduced formula of slope, cross-sectional curvature, maximum curvature and minimum curvature, and produced corresponding map of Changchun.
(2) Integating geomorphology parameterization and self-organizing map to construct classification model;
(3) To achieve quantitative geomorphological classification, types and thresholds are given by the self-organizing map neural network by training, classification scheme is determined by a computer program automatically.
A rapid expression of geomorphological classification has been achieved by study on the landscape, and the workload of geomorphological map artificial interpretation has been reduced, which greatly improves the work efficiency; classification threshold is computed automatically, which not only enhances the spatial information of different scales of flexibility, but also improves the classification accuracy for environmental research, precision agriculture, geological hazard assessment, geological structure and basin delineation study to provide accurate background parameters; This research has practical significance on civil engineering, urban planning, management of nature reserves and mineral surveys, etc.
如何从DEM定义地貌单元进行了许多努力。根据相应的地貌过程,使用DEM可以模拟为不同的地貌形态。通过层次细分将地貌形态分成不同的地貌单元。通过坡度、坡向和曲率来描述,将地貌划分为四个层次,即地貌元素、地貌类型、地貌系统和地貌区域,其中地貌元素和地貌类型是我们研究的重点也是最重要的。
长期以来地貌研究采用野外地形测量和航空摄像测量的方法编译而来,这两种方法工作量大,研究区域受到很大的限制。因此一直停留在区域性详细地貌研究的阶段。近些年来随着航空航天事业的快速发展,及激光雷达技术的快速兴起,数据采集的手段长足进步,获得地表信息也不断丰富,例如美国航空航天局(NASA)获得的3弧秒的全球数字高程模型数据,以及日本经济产业省与美国航空航天局合作,利用ASTER立体像对制作的全球1弧秒数字高程模型(ASTER-GDEM)。为我们研究区域尺度乃至全球尺度及行星尺度的地貌形态提供了可靠的数据源。
然而随着技术的发展,获取数据能力的增强,数据表现出了高维、高信息量的特点,在数据信息提取上提出了新的挑战。本文针对这个特点,总结前人的经验,试图寻找一个统一的框架,在探索地貌形态分类的同时,有效解决模糊数据信息提取的问题。
数字计算机和地理信息系统排除了任何大小和空间分辨率区域基于地表几何形态分类的障碍。地形通过高程信息精细自动解析,通常高程数据以规则网格形式的数字高程模型来表达。
本研究采用日本经济产业省与美国航空航天局于2006年6月29日发布的1弧秒(中纬度地区约26m)分辨率的ASTER—DEM数据,提出结合地貌形态参数化与自组织神经网络映射的方法,进行地貌形态分类研究。该方法能够对海量数据进行有效的降维,而且自动获得地貌形态分类阈值,减少了人工参与的主观影响。在一定程度上突破了地貌形态参数化方法分类类型数量的限制。减少了地貌形态类型误分现象。对长春地区的地貌形态研究取得了满意的效果。
通过对长春地区的地貌分类定量研究,主要取得了以下成果:
(1)改进了地表曲面模型的拟合方法,提出了不完全二元四次方程,推导出该方程模拟的地表曲面坡度、截面曲率、最大曲率和最小曲率公式,并制作了相应的长春地区地表参数图;
(2)提出了结合地表参数化与自组织映射进行地貌形态分类,构建了长春地区地貌形态分类模型;
(3)实现地貌分类定量化,分类的数目和阈值通过自组织映射神经网络训练获得,分类方案由计算机自动确定。
通过研究实现了地貌形态分类的快速表达,减小了地貌人工解译制图的工作量,很大程度上提高了工作效率;自动获得分类阈值,不但增强了不同尺度空间信息研究的灵活性,而且提高了地貌分类的精度,为我们生态环境研究、精准农业、地质灾害评估、地质构造研究及流域划分等提供精确的背景参数;对土木工程、城市规划、自然保护区管理以及矿产普查等工作有现实的意义。
The earth surface is the most active interface of human activities. As a basic element of the earth surface system, the landscape directly influences the human activities. Therefore, research on landscape morphology has been being under the geologist’s attention. During the past three decades, the Digital Elevation models (DEMs) are widely used for computer numerical analysis of terrain. Use the regular grid DEM dataset to extract the slope, aspect, convexity, concavity and so on, and automatically get the physiognomy attributes from the neighboring relationship. It is widely applied in civil engineering, geology, geomorphology, hydrology, environmental science, landuse, soil erosion and vegetation cover. Especially in physiognomy and hydrological research, the application of the special charts based on DEM research is more common.
Many efforts have been made to examine how to define landform units from DEMs. Depending on the geomorphic processes which are considered, landforms may be modeled quite differently, we described landforms through a hierarchical subdivision of the land surface into relief units with homogenous gradient, aspect and curvature as well as form elements/facets with homogenous plan and profile curvature. Landform was described as hierarchical entities into four levels: landform elements, landform types, physiographic systems and physiographic regions, of which landform elements and types are the most important.
For a long time, landform was compiled from aerial photographs and topographic maps, however, those are time-consuming and arduous, and the region of study range is restricted. So the work has remained in the regional detailed research of stage. With the rapid development of aerospace and the booming of the laser radar technique, methods of data collection make great progress, the land surface information collected become more various, such as the shuttle radar topography mission of NASA which gets global digital elevation models as 3 arc-second, and collaboration of Ministry of Economy, Trade and industry of Japan and NASA, GDEM was produced by stereo image. Those provid us reliable data for geomorphometry from regional scale to global and planet.
However, with technological development, access to data capabilities, the data demonstrated a high-dimensional, high information content of features in the data to extract information on to us with new challenges. In this paper, the characteristics, summarized from previous experience, trying to find a unified framework, explore the landscape in the form of classification at the same time, an effective solution to the problem of fuzzy data extraction.
Digital computer and geographic information systems precludes any size and spatial resolution surface geometry based on classification of the regional barriers. Fine terrain elevation information is now automatically parse through, usually elevation data to form a regular grid digital elevation models to express.
In this study, use the ASTER-DEM data of 1 arc second (mid-latitudes of about 26m) resolution which was released on June 29. 2006 by METI and NASA, and put forward a method of integrating geomorphology parameterization and self-organizing neural network for classification research of landscape form. This method can be effective for the vast amounts of data dimensionality reduction, and automatic to get landform classification threshold, reducing the subjective impact of human involvement. To some extent, broking the limits on the number of classification of landscape form parameterization methods and also reducing the misclassification phenomenon of landform types. Changchun region geomorphic study achieved satisfactory results.
Through the quantitative geomorphological classification research of Changchun,the main research results are showed as follows:
(1) improved fitting method of surface,a partial quartic equation was proposed. Deduced formula of slope, cross-sectional curvature, maximum curvature and minimum curvature, and produced corresponding map of Changchun.
(2) Integating geomorphology parameterization and self-organizing map to construct classification model;
(3) To achieve quantitative geomorphological classification, types and thresholds are given by the self-organizing map neural network by training, classification scheme is determined by a computer program automatically.
A rapid expression of geomorphological classification has been achieved by study on the landscape, and the workload of geomorphological map artificial interpretation has been reduced, which greatly improves the work efficiency; classification threshold is computed automatically, which not only enhances the spatial information of different scales of flexibility, but also improves the classification accuracy for environmental research, precision agriculture, geological hazard assessment, geological structure and basin delineation study to provide accurate background parameters; This research has practical significance on civil engineering, urban planning, management of nature reserves and mineral surveys, etc.
引文
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