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。     

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.     

山东省东平湖省级地质公园岩溶地貌特征及保护对策

东平湖省级地质公园岩溶地貌主要发育在张夏组灰岩中,类型较多。通过对东平湖省级地质公园岩溶地貌特征的分析,提出了相应的保护对策与建议,以保障岩溶地貌景观得以长期、持续有效的开发利用与保护。     

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。     

DEM分辨率对地形纹理特征提取的影响

地形纹理是区分不同地貌形态的重要依据,DEM是地形纹理分析的重要数据。然而,DEM分辨率使地形纹理特征提取存在着不确定性问题。本文以具有显著地貌多样性与差异性的陕西省为例,选择6个不同地貌类型区为研究区,以25 m分辨率DEM数据作为信息源,构建了多尺度的地面坡度、光照模拟和粗糙度数据序列。在此基础上,引入空间灰度共生矩阵（GLCM）对地形表面纹理特征进行量化分析,以揭示数据分辨率对地形纹理特征提取的影响。研究表明：对于单一样区,在DEM及其3个派生数据中,原始高程数据和粗糙度数据的纹理参数特征值,对分辨率的变化较为敏感。对于不同的地貌类型区,二阶角矩和对比度这2个纹理参数具有最大的变异系数,表明它们对于区分不同地貌类型的能力最强;二阶角矩具有较大的尺度依赖性,随着分辨率的降低,其区分能力急剧降低,而对比度对于地貌的区分能力,则随着分辨率的降低而增强,并保持在一个较大的范围内。DEM数据的对比度对于不同地貌的区分能力,在所选4个参数中最为稳定,而粗糙度数据的二阶角矩区分不同地貌的能力,随着数据分辨率的变化而最不稳定。以上结果对于根据不同的研究对象选择适宜的DEM分辨率及地形纹理参数具有一定的指导意义。     

Landform planation index extracted from DEMs: A case study in ordos platform of China

Planation surface, a surface that is almost flat, is a kind of low-relief landforms. Planation surface is the consequence of the denudation and planation processes under a tectonic stable condition. The quantitative expression of the characteristics of planation surface plays a key role in reconstructing and describing the evolutionary process of landforms. In this study, Landform Planation Index (LPI), a new terrain derivative, was proposed to quantify the characteristics of planation surface. The LPIs were calculated based on the summit surfaces formed according to the clustering results of peaks. Ten typical areas in the Ordos Platform located in the central part of the Loess Plateau of China are chosen as the test areas for investigating their planation characteristics with the LPI. The experimental results indicate that the LPI can be effectively used to quantify the characteristics of planation surfaces. In addition, the LPI can be further used to depict the patterns of spatial differentiation in the Ordos Platform. Although the present Ordos Platform area is full of the high-density gullies, its planation characteristics is found to be well preserved. Furthermore, the characteristics of the planation surfaces can also reflect the original morphology of the Ordos Platform before the loess dusts deposition process evolved in this area. The statistical results of the LPI show that there is a gradually increasing tendency along with the increasing of slope gradient of summit surface. It indicates that the characteristics of planation surfaces vary among test areas with different landforms. These findings help to deepen the understanding of planation characteristics of the loess landform and its underlying paleotopography. Results of this study can be also served as an important theoretical reference value for revealing the evolutionary process of loess landform.     

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.     

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.     

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。     

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。     

结合Gabor小波和形态学的高分辨率图像树冠提取方法

树冠信息的遥感提取能有效辅助森林参数反演、林分长势监测、树种识别等森林调查活动。随着遥感信息自动化提取的需求不断加强,本文基于高空间分辨率遥感数据,以滁州市皇甫山林场为研究区域,设计了一种结合Gabor小波和形态学的树冠提取方法。该方法首先采用Gabor小波提取出纹理特征,其次结合K-means聚类分析方法,对PCA降维后的纹理特征向量提取出阔叶林区,最后基于形态学理论降低影像噪声,并利用前景后景标记的分水岭方法进行单木树冠提取。经过与人工解译的树冠信息结果对比发现,在郁闭度较高的阔叶林区,该自动化方法提取树冠精度较高,分割准确率A_d为79.59%,F测度达到了79.00%能有效提供精确的单木树冠信息,为林业经济调查技术的发展具有一定的实践意义。     

基于地貌特征的数字高程模型融合方法

数字高程模型（Digital Elevation Model, DEM）是一种至关重要的空间信息,广泛应用于各行各业。其中,ASTER GDEM与SRTM几乎覆盖了全球陆域,为地学研究提供了非常实用的高程数据支撑,但是由于二者传感器采集数据原理的不同,使得高程数据在不同地貌条件下的高程精度亦存在程度不一的误差。本文提出了一种新型的基于地貌特征的DEM融合方法,使得融合GDEM与SRTM后的DEM数据,消除了地貌特征的影响、显著地提高了DEM质量。该方法主要分为地理配准和高程融合2个步骤：①基于河流线对等线性地貌特征的位置数据,构建了GDEM与SRTM的水平偏移相关的误差评价函数,采用多级网格搜索法求得DEM间的水平偏移距离,实现对DEM的配准;②按照DEM高程值在不同地貌单元及边界线附近的高程变化特征,建立地貌分区的高程融合模型来融合两种地理配准后的DEM高程,尤其是实现了地貌单元边界线附近的高程平滑过渡。本文以怀柔北部地区为实验区,以1:5万地形图为参考,对2种DEM数据进行融合,统计结果表明：① 融合DEM在各地貌单元的误差均显著下降,地形表达较之融合前更加精确;② 高程差呈现正态分布,明显区别于融合前DEM不对称的多峰分布形态,说明地貌影响被有效地剔除;③ GDEM和SRTM数据的精度对坡度有较大依赖性,融合后DEM的精度在不同坡度范围下均优于GDEM和SRTM,显著降低了融合前DEM对坡度的依赖程度;④ 在不同坡向下,GDEM和SRTM的RMSE取值波动较大,融合DEM的RMSE取值在各方向表现稳定,高程精度较GDEM和SRTM有显著提高。     

图像数据融合的地貌类型识别分类与制图

计算机遥感地貌制图是利用航空像片或者卫星影像进行识别制图;另是利用DEM数据融合计算提取。对此,本文介绍了一种对区域基本地貌形态类型进行计算机自动分类的方法。它通过识别标志在影像上对地貌分布区进行数字化,把TM影像中的地貌信息和从DEM中提取出来的地貌信息结合,以划分出详细的地貌类型:如河北省沽源县的台地、河谷平原、开阔平原、丘陵、低山和中山6大类。最后,通过一定的算法进行分类成图。     

江西省都昌县山地与平原植被界线提取

由于生长环境、地带性分布规律和垂直差异,山地与平原地区的不同植被类型在遥感影像上存在“同物异谱”及“异物同谱”现象,易导致土地类型的误分。为避免此类错分,在进行土地覆被类型解译之前,应首先确定平原、山地植被的边界。本文在遥感影像聚类分析、GIS空间分析及数理统计分析技术的支持下,以江西省都昌县北部地区为研究区域,基于高分一号（GF-1）遥感卫星影像及地形要素,完成了山地植被与平原植被的界线提取。实验结果显示,本研究得到的植被界线精度高达99.47%和96.28%。与单纯基于遥感影像分类得到的植被边界成果相比,精度提高了近25%和23%。研究证明,高分辨率遥感影像与地形因子结合后,计算得到的山地平原划分界线精度有明显提高,可以满足高分辨率遥感影像土地覆被类型解译研究的需要。     

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.     

基于灰度共生矩阵的DEM地形纹理特征量化研究

 DEM的地形纹理以其表达地形表面的纯粹性与分析数据的可派生性受到越来越多关注。本文选取陕西省10个不同地貌类型区的25m分辨率DEM数据,引入空间灰度共生矩阵(GLCM)对地形表面纹理特征进行定量分析。研究表明,25m分辨率DEM数据的GLCM模型适宜分析间距是大于等于3个栅格大小。各纹理参数中,相关度可用于地形纹理的方向性量化;方差、差的方差、对比度可用于对地形纹理的周期性分析;熵、二阶角矩、逆差矩可用于对地形纹理的复杂性分析。在DEM及其派生数据中,光照模拟数据计算的各纹理参数的平均变异系数最高,表明光照模拟数据最适合于地形纹理特征的量化研究。同时本文提出了一种多参数综合的地形纹理量化方法,通过运用综合周期性和综合复杂性两个指标对不同地形区量化分析,结果表明,这两个指标对不同地形形态响应显著,可用于地形形态分类与识别研究。     

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.     

基于DEM的一体化山地特征要素提取

山顶点和山脊线等特征地形要素是构成地表地形及其起伏变化的基本框架,对地形在地表的空间分布具有控制作用。基于DEM研究山顶点、山脊线及其空间组合关系,是DEM地表形态特征研究的重要内容,也是衔接从地形特征分析向山峰等地貌学本源语言的途径之一。本文以四川盆地西南缘与青藏高原过渡地带的川西凉山山原为例,基于山峰-山脊线-控制范围一体化构建的算法策略,识别了山峰和山脊线及其等级、主山脊及其范围。结果表明,研究区内有主峰9座,次峰53座,平均高程2540 m;山脊线230条,其中主山脊9条,平均长度60 km;9大山系,近南北走向,平均控制面积1017 km^2。研究用模糊隶属度方法对算法所提取的主峰、主脉进行精度验证,隶属度介于0.98~1.00和0.37~0.57时提取的主峰、主脉基本吻合算法提取的结果。研究采用一体化山地特征要素提取方法,实现了各山地要素间紧密联系、总体结构与区域地貌特征相对吻合的目标;完成了由栅格单元向地理对象的转变;可以应用于协助地貌类型划分,协同区域地理规划等。     

基岩露头采样密度对黄土古地形重建的影响

下伏古地形对黄土地貌的形成、演化与发育具有重要的控制作用。基于下伏地层露头的采样数据,通过空间插值方法进行古地形数字模型(DEM)重建是研究黄土地貌的重要方法。其中,采样点密度是下伏地形DEM重建精度的主要影响因子。本文以1:20万绥德幅地质图所涉及区域为实验样区,研究采样点密度及样条函数方法对下伏古地形重建精度的影响。实验结果表明,在高密度样本条件下,采用规则样条插值方法进行古地形重建时,插值结果呈现显著的“龙格现象”,即多项式插值结果出现剧烈波动现象,且随着样本密度增加,样条插值结果的误差呈逐步趋缓的降低趋势,并逐步趋于稳定。同时,基于古地形DEM提取的特征点,其局部最高点和局部最低点的数目呈直线上升趋势,说明在一定程度上基于有限数据采用样条函数进行地下三维建模并不一定能获得平滑曲面。研究结果对如何选择合理的空间采样密度进行古地形DEM重建具有重要的指导意义。     

顾及多分析尺度的地形部位面向对象分类方法

地形部位是地表形态的基本单元,其分类和提取在地貌发育、数字土壤制图、景观生态制图等领域有着重要的应用。康鑫等提出的多尺度Geomorphons地形部位分类法（简称多尺度Geomorphons法）利用高程相对差异信息和地形部位多尺度特征,可避免受地形属性计算及单一分析尺度约束而误分类,然而其存在分类破碎及分析尺度域难以确定的问题。基于此,本文以多尺度Geomorphons法为基础,提出了其适宜分析尺度域确定方法,建立了以初始地形部位数据层组合的对象多尺度分割和分类方法,进而构建了顾及多分析尺度的地形部位面向对象分类方法。以陕北黄土高原区域5 m分辨率DEM为实验数据,对面向对象分类方法进行了验证与评价。实验结果表明：①均值变点法可有效解决分析尺度域难以确定的问题,实验样区适宜分析尺度域为[5×5, 33×33]栅格单元;②以0,255为二值化的地形部位数据层组合适用于多尺度分割,尺度、形状及紧致度参数组合影响分割结果,且对于实验样区存在最优分割参数;③与多尺度Geomorphons法相比,本文方法得到的地形部位分类结果完整性较好,在地表形态对应和地理认知等方面更具合理性。