Extraction and analysis of gully head of Loess Plateau in China based on digital elevation model

In China′s Loess Plateau area, gully head is the most active zone of a drainage system in gully areas. The differentiation of loess gully head follows geospatial patterns and reflects the process of the loess landform development and evolution of its drainage system to some extent. In this study, the geomorphic meaning, basic characteristics, morphological structure and the basic types of loess gully heads were systematically analysed. Then, the loess gully head′s conceptual model was established, and an extraction method based on Digital Elevation Model(DEM) for loess gully head features and elements was proposed. Through analysing the achieved statistics of loess gully head features, loess gully heads have apparently similar and different characteristics depending on the different loess landforms where they are found. The loess head characteristics reflect their growth period and evolution tendency to a certain degree, and they indirectly represent evolutionary mechanisms. In addition, the loess gully developmental stages and the evolutionary processes can be deduced by using loess gully head characteristics. This study is of great significance for development and improvement of the theoretical system for describing loess gully landforms.     

基于地形剖面的黄土塬梁峁地形各向异性奇异强度研究

黄土高原不同地貌类型对应的地形起伏变化特征各不相同,地形起伏在不同方向上的变化特征具有很大差异性,即地形各向异性。本文引入多重分形谱参数描述不同地貌类型地形各向异性的变化规律,以反映不同黄土地貌地形各向异性的局部和整体变化格局。本研究以5 m分辨率的DEM数据为基础,选取淳化、甘泉、绥德3个典型黄土塬梁峁地貌类型研究样区,对其地形各向异性的变化特征进行分析。研究表明:①3种黄土地貌类型地形各向异性变化具有显著的多重分形特征,地形各向异性变化的奇异强度由强到弱依次为淳化、绥德、甘泉;②淳化样区地形各向异性奇异强度在1.4的概率最大,在奇异强度为2.2附近又有一个小的峰值,总体以奇异强度1.4为主;③甘泉样区的奇异强度在1.8有一个小的峰值,总体以奇异强度0.7为主;④绥德样区奇异强度为0.8的概率最大,且较为均匀地分布在0.8附近,地形各向异性的概率分布呈现为对称分布。研究结果为黄土塬梁峁地形各向异性变化奇异强度为黄土塬最大,黄土峁次之,黄土梁最小。该研究可为黄土地貌的精确分类提供定量信息支持,为区域水土侵蚀评价提供地形指标。     

Planation surfaces on the Tibet Plateau, China

A planation hypothesis is proposed to explain landform evolution of the Tibet Plateau. A denudation threshold （T）, the maximum potential denudation rate for a certain type of rock, is introduced to explain the combined effects of lithology and tectonics on landform evolution. If the tectonic uplifting rate （U） is equal to or less than the threshold rate （U ≤ T）, the tectonic uplifting and terrain denudation are in dynamic equilibrium, and landforms are in a steady state. The end product should be planation surfaces whether the original landforms are flat plains or deeply dissected mountains. If U 〉 T, uplift and denudation are not able to reach a dynamic equilibrium state. The plateau surface is mostly underlain by soft rocks, such as the Mesozoic epimetamorphic argillites and Tertiary sedimentary rocks, while the mountain ranges comprise hard rocks, such as granite, gneiss and limestone. In soft rock regions, hills are low with a relative relief of mostly less than 100 m and the slopes are gentle at a gradient of 〈200. In contrast, hills can maintain steep slopes in hard rock regions. The Tibet Plateau has been under an equilibrium condition between tectonic uplifting and denudation except for the mountain ranges. The plateau might have reached the present altitudes before the Quaternary.     

Hierarchy structure characteristics analysis for the China Loess watersheds based on gully node calibration

A land surface region can be decomposed into a series of watershed units with a hierarchical organizational structure. For loess landform, the watershed is a basic spatial–structural unit that can express natural landforms, surface morphology characteristics, spatial organization and developmental evolution. In this research we adopted the concept of node calibration in the watershed structure unit, selected six complete watersheds on China Loess Plateau as the research areas to study the quantitative characteristics of the hierarchical structure in terms of watershed geomorphology based on digital elevation model(DEM) data, and then built a watershed hierarchical structure model that relies on gully structure feature points. We calculated the quantitative indices, such as elevation, flow accumulation and hypsometric integral and found there are remarkably closer linear correlation between flow accumulation and elevation with increasing gully order, and the same variation tendency of hypsometric integral also presented. The results showed that the characteristics of spatial structure become more stable, and the intensity of spatial aggregation gradually enhances with increasing gully order. In summary, from the view of gully node calibration, the China Loess watershed structure shows more significantly complex, and the developmental situation variation of the loess landforms also exhibited a fairly stable status with gully order increasing. So, the loess watershed structure and its changes constructed the complex system of the loess landform, and it has the great significance for studying the spatial pattern and evolution law of the watershed geomorphology.     

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.     

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。     

基于随机森林的黄土地貌分类研究

地貌分类在指导人类建设活动的规模与布局中有着重要的意义。然而,传统的基于数字高程模型(DEM)的地貌分类方法使用的地形因子和考虑到的地貌特征往往比较单一。本文提出了一种基于流域单元的地貌分类方法,该方法考虑了流域单元的多方面特征,包括基本地形因子统计量、地形特征点线统计量、小流域特征和纹理特征。本研究首先基于DEM进行水文分析将研究区域划分成不同的小流域。然后利用数字地形分析提取29个不同方面的特征来表征流域的形态,并基于随机森林(RF)算法进行了特征选择和参数标定。RF是一种基于决策树算法的集成分类器,能有效地处理高维数据,分类精度高。最后选择训练集小流域对RF分类器进行训练,使用训练完成的分类器对整个研究区域的地貌进行分类,研究地貌分异的规律。该实验在我国陕北黄土高原典型黄土地貌区域的地貌分类中取得了较好的结果,结果表明不同的地貌之间存在明显的区域界线,特定的地貌类型在空间上表现出明显的聚集性。通过人工判读进行验证的分类精度达到了85%,Kappa系数为0.83。     

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.     

典型地貌景观指数的多尺度效应分析 ——以山西省运城市为例

 景观指数作为定量描述景观特征的指数,具有明显的尺度效应。进行景观指数的尺度效应研究,对进一步理解格局与尺度之间的关系具有重要意义。本文以TM影像为基础,选择山西省运城市平原、丘陵、山地及综合地貌4种不同地貌类型,对7个常用景观指数进行了多尺度效应分析。结果表明:不同地形上斑块密度(PD)、景观形状指数(LSI)、结合度(COHESION)3个指数有明显的粒度效应,随粒度的增加呈现逐渐减少的变化趋势;最大斑块指数(LPI)、周长面积比分维数(PAFRAC)、景观聚集度(CONTAG)和Shannon多样性指数(SHDI)4个指数随粒度的增加几乎不发生变化。不同地貌的景观指数随幅度的变化规律比较复杂,景观形状指数(LSI)随幅度的增加呈现逐渐增加的趋势,其余6个指数在较小幅度范围内变化比较复杂,但随着幅度的增大有逐渐趋于平稳的趋势。针对研究区不同地貌类型,其景观指数在不同粒度和幅度下有较大区别,可以根据景观指数值的大小来区分地貌的复杂程度。     

基于DEM的黄土崾岘提取及其地形特征分析

崾岘是将要被切穿的鞍部,是正负地形矛盾斗争的结果,也是重要的地形控制点。典型的崾岘多位于黄土高原黄土地貌区,又称黄土崾岘,其对识别沟间地与沟谷的斗争程度有一定的指示作用。本文以黄土高原样区为例,基于1:1万DEM（5 m分辨率）和影像分辨率为0.95 m的遥感影像,利用流域边界算法和缓冲区标定,分析窗口选择5×5,实现了崾岘点位的半自动化提取。并对各崾岘点位求取坡度等地形因子,总结崾岘的空间格局和地形特征。结果显示,崾岘多分布在主流域边界和垂直于主沟道的最宽部分,地形控制作用明显。崾岘的坡度、起伏度、切割深度等值均大于鞍部值,同时,高级流域区的崾岘值大于低级流域区的崾岘值,反映出崾岘具有侵蚀程度强、表层完整性低、地表破碎度高的特点。总体而言,崾岘受沟道蚕食度高,从侧面反映了黄土地貌的发育阶段,是黄土地貌发育到中期的标志性产物。     

基于DEM小流域复杂网络的黄土高原地貌自动识别研究

地貌识别,对于人类建设,地质构造研究,环境治理等相关领域都有着重要意义。传统的基于像素单元或面向对象的地貌识别方法存在局限性。由于流域小单元具有表面形态的完整性,在地貌演化中具有明确的地理意义,基于流域小单元的地貌识别成为了该领域的一个新热点。然而,基于传统地形因子的地貌识别方法使用的因子往往较为单一或者在地学描述上存在重复性,目前尚无针对流域小单元进行空间结构描述和拓扑关系特征量化的地貌识别研究。基于此,本文基于DEM进行水文分析并通过坡谱方法解决了小流域稳定面积难以确定的问题,在黄土高原样区提取了181个稳定小流域。根据复杂网络理论和地貌学原理提出了流域加权复杂网络的概念和相应的8个定量指标用于流域空间结构的模拟和量化描述。最后采用了基于决策树的XGBoost机器学习算法进行地貌识别,实验对于黄土高原主要地貌类型的识别显现出较好的效果,Kappa系数为86.00%,总体精度达到了88.33%。对于地貌形态特征明显的地貌,复杂网络方法其顾及空间结构和拓扑特征的特性导致了其较高的识别性能,精度和召回率都在90%~100%之间。通过与前人的研究进行对比,其识别结果亦呈现出较高的精度,这些都验证了流域加权复杂网络是一种基于流域小单元地貌识别的高精度且有效的方法。     

中国“数字黄土地貌”分类体系探讨

黄土地貌属中国典型地貌之一,其分类体系是制图的基础。过去各种黄土地貌分类研究方案由于标准不统一,在实际应用中没有形成公认的地貌分类系统。本文将形态和成因有机地结合在一起,采用分层分级方法,借助于地理信息系统技术,分析并提出适用于遥感影像数据(LandsatTM和ETM)的1:100万黄土地貌分类体系,拟定出相应的编码体系,建立了典型地貌的遥感图谱。该研究为利用中高分辨率卫星遥感数据编制中国1:100万黄土地貌类型图奠定了坚实的基础,该分类方案可充分反映黄土地貌的特征,也能与全国数字地貌分类方案保持一致,同时该分层分级分类方案可满足地貌类型的扩充性。     

坡谱信息熵尺度效应及空间分异

选取陕北黄土高原48个不同地貌类型区,以其对应的1∶10000及1∶50000数字高程模型(DEM)为数据源,运用比较分析与数理统计的方法,分析了坡谱信息熵在陕北黄土高原的空间分异特征及尺度效应。结果显示,坡谱的信息熵在一定程度上可以反映地表的复杂度,坡谱信息熵和沟壑密度间有较好的幂函数关系,随着坡谱信息熵的增大,样区沟壑密度也增大。坡谱信息熵的空间分异和陕北黄土高原的黄土地貌形态在空间上的变异是相关的,可将其作为地貌类型划分的判别因子之一。     

中国1∶100万地貌图色彩系统的设计与建库

中国1:100万地貌图的色彩设计是在对中国地貌特征分析研究的基础上,通过中国地貌图物理色彩模型,根据颜色三个基本特征:即色相、亮度和饱和度的不同变化与组合,结合人们对色彩感受的物理与心理特征,对中国1:100万地貌图2 000多个类型的不同色彩进行定量分析,采用现代数字地图印刷技术的C、M、Y、K四色值来构建色彩与地貌类型之间的联系,建立完整的地貌类型色彩库。以此编制的中国1:100万地貌图不仅很好地反映了中国地貌的总体特征,也反映出不同地貌类型之间的差异,该色彩系统可为编制相关专题图提供借鉴。     

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。     

Implication of Surface Fractal Analysis to Evaluate the Relative Sensitivity of Topography to Active Tectonics,Zagros Mountains,Iran

Fractal geometry is increasingly becoming a useful tool for modeling and quantifying the complex patterns of natural phenomena. The Earth's topography is one of these phenomena that have fractal characteristics. This paper investigates the relative sensitivity of topography to active tectonics using ASTER Global Digital Elevation Model. The covering divider method was used for direct extraction of surface fractal dimension(D surf) to estimate the roughness-surface of topography with aid of geographic information system(GIS)techniques. This evaluation let us highlight the role of the geomorphic and tectonic processes on the spatial variability of fractal properties of natural landforms.Geomorphic zones can be delineated using fractal dimension mapping in which variability of surface fractal dimension reflects the roughness of the landform surface and is a measure of topography texture. Obtained results showed this method can be a quick and easy way to assess the distribution of land surface deformation in different tectonic settings. The loose alluvial deposits and irregularities derived by tectonic activity have high fractal dimensions whereas the competent formations and higher wavelength folded surfaces have lower fractal dimensions.According to the obtained results, the Kazerun Fault Zone has a crucial role in the separation of the Zagros Mountain Ranges into the different lithological,geomorphological and structural zones.     

平果岩溶堆积铝土矿地貌特征及成矿规律研究

平果岩溶堆积铝土矿是具有独特地质特征的铝土矿床,区内岩溶地貌十分发育,并且形成了多种地貌类型, 本文主要探讨各类地貌特征,研究其发展和演化的规律,分析和论述地貌与成矿的关系,为该类型矿床地质工作提供参考.     

黄土地貌类型的坡谱自动识别分析

地貌形态特征识别与分类,对生态环境、水文研究及地质构造分析等地学研究具有重要意义,已成为现代地貌学的一个研究热点。传统的统计模式识别方法精度较低,难以解决线性不可分的模式分类问题。人工方法虽然识别精度高,但因各人认知偏差导致的识别误差难以控制。人工神经网络作为一种动态信息处理系统,能有效解决线性不可分的地貌类型识别问题。坡谱是利用微观地形定量指标来反映宏观地形特征的有效方法,在地貌学研究中正受到广泛的关注。本文以陕北黄土高原8个不同地貌类型区的数字高程模型（DEM）为实验数据,以流域为分析单元,提取坡谱及其特征指标作为描述地形特征的定量因子,并通过BP神经网络的构建与学习,进行黄土地貌类型自动识别。实验结果表明,在8种地貌类型的样本数据中,第1次实验正确识别率平均值达70%;第2次和第3次实验中,去除相似度较高的峁状丘陵沟壑或峁梁状丘陵沟壑任一种地貌类型后,正确识别率平均提升为80%和85%。经Kappa系数验证,该方法能以DEM数据有效识别不同类型的黄土地貌。     

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。     

基于空间结构模式匹配的褶皱地貌类型自动识别

褶皱的自动化识别是构造解译、地貌类型划分、三维建模等工作的基础。目前,基于地形形态进行空间统计特征分析的地貌类型自动识别方法,难于满足以“对称重复”空间结构为特征的褶皱构造地貌的识别需要。本文以基于ARG的褶皱场景建模为切入点,利用形式文法定义不同褶皱类型的空间结构模式,形成了一种基于空间结构模式匹配的褶皱构造自动识别方法,进而有效地支持了褶皱地貌的自动化识别。该方法包括4个环节：（1）根据核部地层与横切剖面线提取原则,提取可能存在褶皱的场景条带;（2）基于邻接ARG建模方法对场景条带进行场景空间结构的建模与化简;（3）将化简后的邻接ARG模型表达成句子,并通过判别其能否由模式文法推导而来,实现针对不同褶皱构造模式的匹配识别;（4）基于识别出的褶皱场景进行山、谷的进一步判别,最终确定褶皱构造地貌类型。实验表明,本方法可以较为准确地识别出庐山北部山地区域的褶皱构造,并确定褶皱地貌类型。该方法基本解决了构造地貌类型的自动化识别问题,是对传统地貌形态划分方法的有效补充。