DEM分辨率对黄土侵蚀沟形态特征表达的不确定性分析

黄土侵蚀沟的地形表达是开展黄土沟谷侵蚀研究的基础工作,利用数字高程模型(DEM)定量描述侵蚀沟特征有助于研究侵蚀沟的形态变化和发育过程。基于DEM数据计算多种指标对黄土侵蚀沟特征进行描述是目前侵蚀沟研究中最为常用的方法。但是,受到格网DEM数据结构的限制,其计算结果会存在一定的不确定性。在侵蚀沟地形表达时,对形态特征的表达会受到DEM数据分辨率的影响,进而造成表达结果的不确定性。尤其在黄土高原地区,地形特征更为破碎,地形要素更为复杂,其表达结果受DEM分辨率的影响更为明显。本文以黄土高原典型样区为例,基于点云数据建立不同分辨率的DEM数据集,通过不同地形因子对侵蚀沟特征进行表达,分析DEM分辨率在黄土侵蚀沟形态特征表达时的不确定性。结果显示,分辨率的降低对主沟支沟比和纵比降等侵蚀沟形态特征因子产生了较大影响,且指标与分辨率多呈现线性变化关系。但是,随着侵蚀沟的横向扩张,DEM分辨率对其特征表达的影响逐渐被削弱。此外,在使用固定分析窗口进行侵蚀沟特征计算时,由于分辨率的降低,格网尺寸增大,其实际分析半径随之增大,使得计算范围内地表形态变化增加,导致沟谷切割深度随着分辨率的降低反而增加。同时,侵蚀沟主沟道区域受分辨率影响较小,沟头区域指标与分辨率的关系较弱。     

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...     

最优分割尺度下的多层次遥感地物分类实验分析

为了快速、准确地提取我国海岸带地区土地利用及其变化信息,选择高分辨率遥感影像作为数据源,提出了最优分割尺度下的遥感多层次地物识别分类方法。首先,通过改进的局部方差法进行最优分割尺度的确定,建立影像中各对象的方差均值与变化率随分割尺度变化曲线,确定方差均值的峰值,以变化率开始呈现下降趋势时所对应的分割值为最优分割尺度参考...     

基于DEM提取坡谱信息的不确定性

坡谱是黄土高原地学分析新方法、新思路的探索。本文基于坡谱不确定性的概念及其过程分布模型的论述,以陕北黄土高原国家1∶10000和1∶50000DEM数据为基本信息源,分析了坡谱不确定性的产生原因、主要影响因素与影响规律,阐述了坡谱提取与应用中应注意的主要问题,为正确应用坡谱进行地学分析提供重要依据。     

面向多分辨率DEM的河网相似性测度与分析

河网是地形结构的核心要素,能够有效地反映DEM对地表形态的刻画能力。实现不同分辨率条件下DEM河网相似性测度对DEM地形综合、DEM质量评估及DEM不确定性分析等研究具有重要意义。基于此,本文以黄土高原典型样区为研究区,基于5 m高精度DEM建立的多分辨率DEM数据集,构建了地形特征自适应的DEM河网自动提取方法,建立了综合拓扑关系、方位关系、距离关系及属性特征四维信息的河网相似性度量模型,并分析了河网相似性变化特征及其与地形参数的相关性。实验结果显示,河网自动提取方法的制图精度和用户精度均在90%以上,总体精度优于Passalacqua等提出的GeoNet方法,能够有效实现不同分辨率下河网较高精度提取;河网相似性度能综合反映河网空间分布特征的差异性,河网相似度与分辨率变化率之间的幂函数关系R²达0.978,且河网相似度与高程、坡度中误差分别表现为幂函数和对数函数关系,后者对数关系显著性优于前者幂函数关系。研究表明,在以河网作为DEM构建重要数据源的背景下,本文的研究成果可为DEM质量及其应用适宜性的定量评价提供基础。     

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。     

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.     

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.     

基于ASTER图像和地形因子的岩性单元分类——以新疆木垒地区为例

利用遥感图像进行岩性分类,是遥感地质应用的重要方面之一.本文运用ASTER DEM提取地形因子,并与原始的光谱图像相结合用于遥感图像的岩性单元分类.文章分析了不同尺度的地形因子对岩性单元分类的作用,并进一步分析和比较各种地形因子对岩性单元分类的作用.结果表明,在岩性单元分类过程中加入不同的地形因子可不同程度地提高岩性单...     

Review of Digital Elevation Model （DEM） Based Research on China Loess Plateau

Introduction The Loess Plateau is located in the upper and middle reaches of the Yellow River, among the western Taihang Mt, eastern Riyue-Helan Mt, northern Qinling Mt, and southern Yinsan Mt (from 100°54′to 114°33′E and 33°43′to 41°31′N; Figure 1). It covers a total area of 624,000 km2. The Loess Plateau of China has drawnworldwide attention in geographical research for its unique morphological features, abundant nature resources, most serious soil erosion, as well as its pot…     

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.     

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.     

随机森林方法支持的复杂地形区土地利用/土地覆被分类研究

随机森林方法目前已经成为遥感分类机器学习中一种有效方法,探索基于中等分辨率的Landsat卫星数据与随机森林方法相结合对复杂地形区长时间序列数据的获取及土地利用/土地覆被变化及模拟研究是非常有意义的。本文基于Landsat8OLI卫星多光谱数据,采用随机森林分类方法对青海省湟水流域复杂地形区土地利用类型进行了分类研究。针对复杂地形区域的情况,将研究区进行地理分区,根据每个分区的特点,选择相应的地形特征参数,并通过提取Landsat 8数据的光谱信息与纹理信息构建最优特征集,探索随机森林方法在复杂地形区土地利用分类的适用性。结果表明：使用Landsat8OLI数据进行随机森林分类,能较好地得到湟水流域复杂地形区域的土地利用类型结果;光谱、地形及纹理信息的结合在不同分区的表现结果不同。在脑山区光谱与地形信息结合能使随机森林分类效果最佳,总体精度达到91.33%,Kappa系数为0.886;而在浅山区与川水区综合考虑光谱、地形、纹理信息进行随机森林分类效果最佳,浅山区与川水区总体精度分别达到92.09%和87.85%,Kappa系数分别为0.902和0.859;利用随机森林算法进行优化选择纹理特征组合可以在保证分类精度的同时能够快速地提取土地利用类型信息,为复杂地形区土地利用类型的区分提供了实际可行的方法。     

面向城市DEM构建的地形要素分类及表达

地形要素是对地形在地表的空间分布特征具有控制作用的点、线或面状要素,其合理划分可为城市地形建模提供数据基础。本文在解析城市地形特征和分析现有地形图分类在城市DEM建模中不足的基础上,基于面向对象思想,以城市地形被城市道路网分割为基本原则,构建了城市地形要素分类及表达方法,并以南京市某区1:500地形图为实验数据,进行了相关验证实验与分析。实验结果表明,本文提出的地形要素分类和表达方法能够整体控制并有效表达集几何和语义信息为一体的复杂城市地形,在全局和局部地形上均有较好的建模效果。本文提出的城市地形要素分类及表达方法不但可为地形要素的分析和应用提供基础,也可为城市地形构建模拟、建设规划和分析决策等提供技术支撑。     

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。     

基于随机森林特征选择的城市绿化乔木树种分类

城市绿化在改善空气、水和土壤质量,吸收和减少二氧化碳及各种污染物,缓解城市热岛和减少雨水径流等方面发挥着重要作用。及时准确地获取树种信息是城市规划与绿化管理的先决条件,对进一步改善城市生态环境也具有重要意义。基于遥感技术,使用高空间分辨率的WorldView-2卫星影像,采用光谱、纹理、指数以及几何等多种特征相结合的面向对象方法,并通过随机森林进行特征选择,对福州大学旗山校区北部的榕树、杧果、香樟、重阳木、羊蹄甲、垂叶榕以及木棉7种主要绿化乔木进行树种分类。实地验证结果表明：通过特征选择可以减少或规避数据冗余以及休斯效应的产生,该方法可以提高现有同类型树种分类的精度,当淘汰全部特征的20%,利用34个特征（包括15个光谱特征、6个纹理特征、8个指数特征和5个几何特征）进行分类时,总精度最高,可达74.95%,Kappa系数为0.67。其中,光谱平均值的特征重要性最高,而各波段的标准差的重要性较低。WorldView-2卫星影像的4个新增波段,特别是黄光和红边波段及其构建的指数特征重要性较高,也说明这些波段在植被遥感,特别是树种分类中极具应用前景。     

基于无人机影像和面向对象随机森林算法的岩溶湿地植被识别方法研究

目前对岩溶湿地的重视程度远低于其他湿地类型,缺乏利用遥感技术进行岩溶湿地植被高精度识别的研究,但岩溶湿地同其他湿地类型一样,湿地面积退化严重,亟待需要解决。因此,本文选取受人类活动影响较大、湿地退化较为严重的广西桂林会仙喀斯特国家湿地公园的部分核心区域作为研究区,以DJI大疆御Mavic Pro无人机航摄影像为数据源,利用泛化能力强、分类精度高的面向对象随机森林算法实现了会仙岩溶湿地植被的高精度分类,探究无人机可见光影像和面向对象随机森林算法在岩溶湿地植被识别中的适用性,为无人机遥感技术应用于岩溶湿地的研究和保护提供技术参考。首先,在eCognition Developer9.0中利用多尺度迭代分割算法对影像图层进行分割;然后,基于以往在进行面向对象分类研究的经验来指导我们进行特征选择,充分考虑了影像的光谱和纹理特征、植被指数、无人机遥感数据派生的研究区数字地表模型（DSM）和几何特征;最后,在RStudio中实现了随机森林算法参数的调优、模型的构建以及分类。结果显示,面向对象随机森林算法对会仙湿地植被具有较高的识别能力,在95%置信区间内总体精度为86.75%,Kappa系数为0.83。在单一典型岩溶湿地植被识别精度中,狗牙根-白茅-水龙植被群丛的用户精度在90%以上,生产者精度高于80%,竹子-马甲子-桂花生产者精度高于80%,但是用户精度较低,仅为70.59%。     

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。     

Topographic characteristics for the geomorphologic zones in the northwestern edge of the Qinghai-Tibet Plateau

Based on geomorphologic and digital elevation model （DEM） data, the topographic characteristics of the northwestern edge of the Qinghai-Tibet Plateau are analyzed. Five representative peaks are first determined according to the topographic profile maps for the ridge and piedmont lines, and then the topographic gradient characteristics are analyzed according to the representative topographic profile acquisition method. Based on the geomorphologic database data, the regions between the ridge and the piedmont lines are divided into four geomorphologic zones; and the topographic characteristics are finally analyzed for the different geomorphologic zones regions using the DEM data. The research results show that from the piedmont to the ridge, there exist four geomorphologic zones： arid, fluvial, periglacial and glacial. The arid has the lowest elevation, topographic gradient, relief and slope characteristics. The fluvial has lower elevation and the highest topographic gradient, but with lower relief and slope characteristics. With higher elevation, the periglcial has lower topographic gradient, but the highest relief and slope characteristics. The glacial has the highest elevation with higher topographic gradient, relief and slope characteristics.