黄土高原地貌形态图谱三维符号指标体系与应用

长期以来,地貌学界和制图学界一般是采用文字、地图和图像等方式来描述黄土高原独特的地貌形态,故往往不够形象,缺乏系统性。采用地学信息图谱和三维可视化的方法,对挖掘揭示黄土高原地貌形态特征规律是一个有效的方法。黄土高原地貌形态图谱三维符号的指标体系,是将地貌学家和制图学家对黄土高原正负地貌类型的认知结果经过系统化、标准化和科学化的归纳和提炼,从中提取出的描述黄土高原地貌三维特征的参数集合。既是对黄土高原地貌形态图谱三维立体特征的充分概括,又是三维形式建模表达的参考和依据。根据黄土高原地貌类型的分类和侵蚀特征规律,该体系分为正地貌形态指标体系、负地貌形态指标体系和坡度一侵蚀特征谱系描述指标,采用定性和定量相结合的方法来制定指标体系的具体参数。最后通过实例来验证指标体系在构造三维符号时的应用。细致精确的描述单个地貌类型的三维形态,将有助于对黄土高原地貌特征的认识和研究,为水土保持和生态环境建设提供帮助,同时也为其他地貌类型的信息图谱研究提供借鉴。     

基于DEM的黄土高原地貌分形美学图案研究

为进一步探索黄土高原千沟万壑、支离破碎的自然景观特征中蕴含的分形美学价值,本文基于黄土高原6个不同地貌类型区的数字高程模型,在分形图案设计软件Ultra Fractal中,利用多种标准化分形变换和Newton分形公式,设计并对比使用原始DEM数据提取出的地貌因子数据与不同分形变换集合得到的分形美学图案.结果 表明,对蕴...     

黄河中游黄土高原区河川基流特点及变化分析

由于气候变化和人类活动的影响。黄土高原河川基流呈显著下降趋势。对黄河径流量减少产生一定影响。采用日流量过程线切割法计算了河龙区间黄土高原主要支流的基流量。结合地形地貌、水文地质条件，分析了不同地貌类型支流基流的特点，初步分析了影响基流变化的各种因素。认为河龙区间基流变化对黄河水资源影响较大。     

黄土高原地貌类型界线划定研究进展

区分地理实体最直接有效的方式在于对其界线作出划定。目前,黄土高原地貌类型界线划定多是在分类基础上按照分类界线、自然区划界线来界定。基于不同数据源及其表达方式,本文追踪前人对黄土高原地貌类型界线划定的研究进展,从形态成因的地貌分类、数字地貌分类等分类体系中总结了黄土地貌类型界线的内涵,分析了基于自然语言和数字环境下定量描述的优缺点和存在的问题;并梳理了黄土地貌类型界线的表示方法以及基于数字地形分析技术的地貌类型定量识别及其划分方法;从地貌界线确定与分类体系的关系、地貌界线划定的理论与方法参考、地貌界线划定的尺度效应3个方面对地貌类型界线做出了讨论分析与展望,以期为黄土地貌区划的相关理论研究提供背景基础,为当地实践工作等提供理论依据和支撑。     

地貌分形理论模型的数值模拟

利用简单分形模型和分数布朗运动模型，分别对分形地貌研究中的多度域分形曲线和外营力作用下地貌剖线自仿射分形进行了模拟计算。模拟结果显示，不同机制作用下的地貌形态分维值差异不大的时候，从分维结构图上无法获得相应的多度域分维值，这时需要借助其它手段进行分析；不同起伏的地表形态可以具有相同的分维值，夷平作用并不改变由地表侵蚀作用形成的分维值，表现出外营力作用的相对稳定性，要全面描述曲线的起伏特性需要引入渡越长度。     

基于DEM的黄土地貌类型提取与制图——以黄土高原丘陵沟壑实验样区为例

以陕北绥德县刘家沟流域为实验样区 ,从地貌成因、形态和土地利用角度出发 ,分析了黄土高原丘陵沟壑区 3种典型地貌类型的区域特征 ;以不同地貌类型的区域特征为基础 ,在地理信息系统平台软件 Arc View的支持下 ,利用数字高程模型 (DEM)自动提取黄土丘陵区 3种基本地貌类型的方法和技术。研究分析结果证明 :利用 DEM自动提取地貌类型是一种快速、高效的技术方法 ,对指导退耕还林、防治水土流失和进行土地利用动态监测都具有重要意义。     

复杂网络理论在黄土高原沟谷地貌特征研究中的应用

沟谷侵蚀是塑造黄土地表侵蚀形态的主要动力,沟谷的发育过程深刻地影响着黄土地貌的发育及演化。本文在黄土高原选择6个典型地貌样区,以样区的数字高程模型为基本数据源提取沟谷系统。将沟谷系统中的沟谷节点、沟谷源点和流域出水口点作为网络节点,网络节点之间的空间拓扑关系为边,高程差为权值,构建黄土高原沟谷加权复杂网络模型。对黄土沟谷地貌的节点特征和空间结构进行定量刻画和分析,得到黄土高原不同地貌类型网络特征的空间格局及其变化,并进一步映射地貌的发育过程及演化机理。研究结果表明：① 黄土高原沟谷加权网络的点强度累积概率分布呈指数分布,相关性系数皆达到0.80以上,该网络正处于向无标度网络转化的过渡期;② 样区从南到北,沟谷特征点的点强度值呈现逐渐减小的态势,且点强度的分布具有不对称性,沟谷右侧侵蚀强度较高,点强度分布较多;③ 平均路径长度和网络结构熵值在绥德一带最大,分别为30.94、6.31,并向南北两侧呈减少的趋势,网络密度值的变化与之相反;3个指标分别从网络结构的连通性、稳定性和紧密性反映了不同沟谷地貌类型的侵蚀程度以及地貌系统的演化机理;④ 网络指标与传统地貌指标的相关性系数均超过0.85,其可以科学、准确地表达地貌形态的复杂性及地貌的发育阶段,有望作为沟谷地貌地学特征研究的参数。该方法考虑了沟谷地貌的空间拓扑关系以及系统的整体性,为复杂表面形态的地貌研究提出了一种新的思路和方法。     

黄土高原洛河流域地貌发育特征与形成演化

黄土地貌形态特征、发育机理及演化过程等一直是地学研究的热点和前沿。选取黄土高原洛河流域作为研究对象,从坡面尺度、流域尺度、区域尺度分类选取14项地貌因子,较为全面地构建了流域地貌发育形态定量参数体系;提取646个小流域为分析单元,利用主成分分析法,获得了描述流域黄土地貌形态特征的4个主因子;这4个主因子分别反映了4种地貌形态空间格局,即南北渐变型、单中心扩散型、东西过渡型、多中心扩散型。结合流域地貌类型和地貌发育程度特征,认为黄土地貌地形起伏和垂直侵蚀特征反映了研究区的总体地貌空间格局,洛河流域黄土地貌主要是在多种因素影响下,随着水系由下游向上游不断地溯源侵蚀、下切扩展,最终形成了现今形态空间分异格局。     

黄土高原沟谷地貌发育演化研究进展与展望

黄土沟谷是黄土地貌中最有活力、最具变化、最富特色的对象单元,黄土高原千沟万壑的地貌形态以及触目惊心的侵蚀状态也让区域内沟谷地貌的形成、发育及演化问题成为研究中焦点及前沿性科学问题。近年来,诸多学者采用地学测年法、特征表达法、监测模拟法力图实现对黄土沟谷发育演化进程中“过去-现代-未来”的科学认知。这些研究在相当程度上丰富了黄土沟谷发育过程的认知。本文梳理了黄土高原沟谷地貌演化相关研究的现状,并从黄土高原地貌演化、黄土沟谷发育、基于DEM的沟谷信息提取与表达等研究进行了系统的回顾、梳理与分析。此外,本文提出“黄土沟道剖面群组”概念与方法,试图从新的视角审视黄土沟谷地貌发育演化过程。沟道剖面在黄土沟谷发育演化进程中传递物质能量和累积地形动力,并通过径流节点的串联实现剖面群的连接与组合,形成独特的剖面“群组”模式;该沟道剖面群组是集黄土沟谷地貌特征与过程于一体的综合信息集成体,其三维空间结构是对黄土沟谷地貌发育演化的高度抽象与映射,并可望进一步丰富黄土高原数字地形分析理论与方法体系,为黄土高原黄土地貌成因机理与空间分异格局带来创新的认识。     

基于DEM的黄土高原地形纹理概念模型

黄土高原“千沟万壑”的地貌形态,在多尺度空间下表现出显著自相似性,具有“局部无规则,宏观有规律”的纹理特征。目前,黄土高原地形纹理的提取方法及应用已经得到初步发展,但依然缺乏在理论层面的框架体系。本文在已有学者研究成果的基础上,限定黄土高原为研究范围,明确提出黄土高原地形纹理的概念模型,即内涵、特征、分类及表达。将内涵进行扩展,除已有的宏观形态地形纹理外,提出黄土典型地貌单元（黄土塬、梁、峁等）特征组合形成的地形纹理,以及黄土坡面坡度特征形成的地形纹理;指出以数字高程模型为主的数据表达将更有利于地形纹理的量化,特别是基于DEM派生的坡面地形因子能够扩展地形纹理的特征空间,丰富地形纹理分析的数据源;明确地形纹理的基本特征,分别是区域差异性、成因复杂性和尺度依赖性;构建划分体系,以地形纹理的基元显著性、纹理成因以及可视化形态进行类别划分。本文以期构建面向黄土高原的地形纹理概念模型,推动纹理分析方法在黄土高原的应用和发展。     

Quantifying spatial scale of positive and negative terrains pattern at watershed-scale: Case in soil and water conservation region on Loess Plateau

The positive and negative terrains (P-N terrains) widely distributed across China’s Loess Plateau constitute the dual structure characteristic of loess landforms. Analysis of loess P-N terrains at the watershed scale can serve to elucidate the structural characteristics and spatial patterns of P-N terrains, which benefits a better understanding of watershed evolution and suitable scales for loess landform research. The Two-Term Local Quadrat Variance Analysis (TTLQV) is calculated as the average of the square of the difference between the block totals of all possible adjacent pairs of block size, which can be used to detect both the scale and the intensity of landscape patches (e.g., plant/animal communities and gully networks). In this study, we determined the latitudinal and longitudinal spatial scale of P-N terrain patterns within 104 uniformly distributed watersheds in our target soil and water conservation region. The results showed that TTLQV is very effective for examining the scale of P-N terrain patterns. There were apparently three types of P-N terrain pattern in latitudinal direction (i.e., Loess Tableland type, Loess Hill type, and Transitional Form between Sand and Loess type), whereas there were both lower and higher values for P-N terrain pattern scales in all loess landforms in the longitudinal direction. The P-N terrain pattern also clearly presented anisotropy, suggesting that gully networks in the main direction were well-developed while others were relatively undeveloped. In addition, the relationships between the first scales and controlling factors (i.e., gully density, nibble degree, watershed area, mean watershed slope, NDVI, precipitation, loess thickness, and loess landforms) revealed that the first scales are primarily controlled by watershed area and loess landforms. This may indicate that the current spatial pattern of P-N terrains is characterized by internal force. In selecting suitable study areas in China’ Loess Plateau, it is crucial to understand four control variables: the spatial scale of the P-N terrain pattern, the watershed area, the main direction of the watershed, and the loess landforms.     

Clustering gully profiles for investigating the spatial variation in landform formation on the Chinese Loess Plateau

The gully is the most dynamic and changeable landform unit on the Loess Plateau, and the characteristics of gully landforms are key indicators of gully evolution. Different gully profiles are connected and combined through runoff nodes. Thus, it is necessary to cluster gully profiles into a gully profile combination(GPC) to reveal the spatial variation in gully landforms throughout the Loess Plateau. First, the gradient and gully evolution index(GEI) of two sample areas in Changwu and Suide in Shaanxi Province, China are calculated and analysed based on GPC. Then, the gradient and GEI are calculated by using 90-m-resolution digital elevation model(DEM) data for the severe soil erosion area with the basin as the research unit. On this basis, the spatial variation in the development degree is analysed with Getis-Ord Gi*. The results show that the degree of gully undercutting decreases from southeast to northwest under the influence of rainfall. Due to the soil properties, the loess in the northwest is more prone to collapse, resulting in the decrease of GEI from northwest to southeast. The development degree of gullies is closely related to rivers. The strong erosive capacity of rivers leads to greater differences in gullies within the basin. At the same time, the skewness and kurtosis of the gully index in the basin are correlated; when the distribution of the gully index in the basin is less normal, the distribution of the gully index is more concentrated. These results reveal the spatial variation characteristics of the Loess Plateau based on GPC.     

Automatic recognition of loess landforms using Random Forest method

The automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. However, landform types are rather complex and gradual changes often occur in these landforms, thus increasing the difficulty in automatically recognizing and classifying landforms. In this study, small-scale watersheds, which are regarded as natural geomorphological elements, were extracted and selected as basic analysis and recognition units based on the data of SRTM DEM. In addition, datasets integrated with terrain derivatives(e.g., average slope gradient, and elevation range) and texture derivatives(e.g., slope gradient contrast and elevation variance) were constructed to quantify the topographical characteristics of watersheds. Finally, Random Forest(RF) method was employed to automatically select features and classify landforms based on their topographical characteristics. The proposed method was applied and validated in seven case areas in the Northern Shaanxi Loess Plateau for its complex andgradual changed landforms. Experimental results show that the highest recognition accuracy based on the selected derivations is 92.06%. During the recognition procedure, the contributions of terrain derivations were higher than that of texture derivations within selected derivative datasets. Loess terrace and loess mid-mountain obtained the highest accuracy among the seven typical loess landforms. However, the recognition precision of loess hill, loess hill–ridge, and loess sloping ridge is relatively low. The experiment also shows that watershed-based strategy could achieve better results than object-based strategy, and the method of RF could effectively extract and recognize the feature of landforms.     

Uncertainty of slope length derived from digital elevation models of the Loess Plateau,China

Although many studies have investigated slope gradient uncertainty derived from Digital Elevation Models（DEMs）, the research concerning slope length uncertainty is far from mature. This discrepancy affects the availability and accuracy of soil erosion as well as hydrological modeling. This study investigates the formation and distribution of existing errors and uncertainties in slope length derivation based on 5-m resolution DEMs of the Loess Plateau in the middle of China. The slope length accuracy in three different landform areas is examined to analyse algorithm effects. The experiments indicate that the accuracy of the flat test area is lower than that of the rougher areas. The value from the specific contributing area（SCA） method is greater than the cumulative slope length（CSL）, and the differences between these two methods arise from the shape of the upslope area. The variation of mean slope length derived from various DEM resolutions and landforms. The slope length accuracy decreases with increasing grid size and terrain complexity at the six test sites. A regression model is built to express the relationship of mean slope length with DEM resolution less than 85 m and terrain complexity represented by gully density. The results support the understanding of the slope length accuracy, thereby aiding in the effective evaluation of the modeling effect of surface process.     

A GIS-based modeling approach for fast assessment of soil erosion by water at regional scale,loess plateau of China

The objective of this study is to develop a unique modeling approach for fast assessment of massive soil erosion by water at a regional scale in the Loess Plateau, China. This approach relies on an understanding of both regional patterns of soil loss and its impact factors in the plateau area. Based on the regional characteristics of pre- cipitation, vegetation and land form, and with the use of Landsat TM and ground investigation data, the entire Loess Plateau was first divided into 3 380 Fundamental Asses...