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...     

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的沟谷特征点提取与分析

沟谷特征点是反映沟谷地貌空间形态分布的重要点位,也是研究沟谷地貌演化过程与机理的关键要素。因此,对不同沟谷特征点的有效提取,是沟谷形态研究的重要基础。本文采用DEM及其在水文分析中的多种衍生数据,通过流向追踪、邻域特征判断等一系列方法,实现对径流节点、径流源点、汇流源点、潜在裂点与流域出口点的快速、准确提取。同时,对沟谷特征点进行有效分级,本文基于Strahler河流分级法建立了相应的分类标准,对沟谷特征点进行了自动分类。通过使用陕西省宜君典型样区5m分辨率的DEM数据进行实验,发现新算法计算效率高,结果准确,对潜在裂点也进行了有效探测,验证了算法的有效性。最后,对特征点提取的数据影响进行了详细分析。     

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。     

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。     

黄土高原区SRTM1 DEM高程误差校正模型构建及对比分析

对SRTM1 DEM高程误差进行校正可有效提高其应用精度。以具有典型地貌特征的黄土高原作为研究区域，以ICESat-2/ATL08陆地高程作为参考数据，引入主流机器学习算法建立SRTM1高程误差与影响因子之间的关系模型对高程值进行校正；通过分析模型性能指标、误差频数分布、校正误差空间格局以及典型剖面误差分布，以此得到不同地貌类型区的高程误差校正模型适用性。实验结果表明：在平原、风沙丘陵和黄土塬地貌区随机森林模型高程校正效果最佳，平均绝对误差分别降低0.49、0.82和1.2 m，同时校正误差在空间分布上异常值较少，低起伏度的平原和风沙丘陵地貌区典型剖面误差与原误差较为贴合；山地区支持向量机模型适用性更强，均方根误差和平均绝对误差分布降低了6.79 m和5.43 m，可大幅提升误差绝对值较小的点位频数，同时在空间格局和典型剖面验证效果最佳；黄土丘陵地貌区弹性反馈神经网络模型效果最优，均方根误差和平均绝对误差分别降低了2.3 m和2.04 m，空间分布上误差降低效果显著，典型剖面误差异常值较少；土石丘陵地貌区卷积神经网络模型效果更理想，均方根误差与平均绝对误差分别降低4.14 m和3.5 ...     

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。     

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。     

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。     

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。     

Characteristics and influencing factors of proximity distance index on the northern Shaanxi Loess Plateau in China

Topographic feature points and lines are the framework of topography, and their spatial distance relationship is an breakthrough in the study of topographical geometry, internal structure and development level. Proximity distance(PD) is an indicator to describe the distance between the gully source point(GSP) and the watershed boundary. In the upstream catchment area, PDs can be expressed by the streamline proximity distance(SPD), as well as by the horizontal proximity distance(HPD) and the vertical proximity distance(VPD) in the horizontal and vertical dimensions, respectively. The series of indicators(e.g., SPD, HPD and VPD) are important for quantifying the geomorphological development process of a loess basin because of the headward erosion of loess gullies. In this study, the digital elevation model data with 5 m resolution and a digital topographic analysis method are used for the statistical analyses of the SPD, VPD and HPD in 50 sample areas of 6 geomorphic types in the Loess Plateau of northern Shaanxi. The spatial characteristics and the influencing factors are also analysed. Results show that: 1) Central tendencies for the HPDs and the VPDs for the whole study area and the six typical loess landforms are evident. 2) Spatial patterns of the HPDs and the VPDs exhibit evident trends and zonal distributions over the whole study area. 3) The HPDs have a strong positive correlation with gully density(GD) and hypsometric integral. The VPDs also correlates with GD to an extent. Vegetation cover, mean annual precipitation and loess thickness have stronger effects on the VPD than on the HPD.     

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.     

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.     

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...     

Snake Model for the Extraction of Loess Shoulder-line from DEMs

Shoulder lines are the most important landform demarcations for geographical analysis,soil erosion modeling and land use planning in the Loess Plateau area of China.This paper proposes an automatic,effective and accurate method of determining loess shoulder line from DEMs by integrating a hydrological D8 algorithm and a snake model.The watershed boundary line is adopted as the initial contour which evolves to identify the exact position of loess shoulder-line by the guidance of an external force of snake model from DEMs.Experiments show that the method overcomes the difficulties in both threshold selection for edge detection and the disconnecting issues in former extraction approaches.The accuracy evaluation of shoulder-line maps from the two test sites of the loess plateau area show obvious improvements in the extraction.The average contour matching distance of the new method is 12.0 m on 5 m resolution DEM,and shows improvement in the accuracy and continuity.The comparisons of accuracy evaluations of the two test sites show that the snake model method performs better in the loess plain area than in the area with high gully density.