论DEM地形分析中的尺度问题

DEM及其地形分析具有强烈的尺度依赖特征。本文以黄高原地区的研究为例,结合地学建模和地学模拟的需求,重点讨论DEM地形分析中的尺度问题。文中从DEM建立与应用出发,首先建立了DEM地形分析中的尺度概念体系,剖析了各类尺度之间的关系,其次讨论了尺度所引起的各种地形分析效应问题,最后探讨了DEM地形分析中的尺度转换类型和方法。     

栅格数字地形分析中的尺度问题研究方法

栅格数字高程模型（DEM）固有的尺度特征给以栅格DEM为基本输入的数字地形分析带来各种尺度问题。对栅格数字地形分析中涉及的尺度进行梳理,以分辨率和分析窗口为重点,对栅格数字地形分析中的多尺度表达、尺度效应、适宜尺度选择、尺度转换等尺度问题及其相互关系进行阐述;分别介绍各类尺度问题的现有定量研究方法,尤其对尺度效应定量刻画和适宜尺度选择方法,根据不同方法计算定量指标所利用的信息类别进行分类归纳;最后讨论了其中有待进一步开展研究的几方面工作。     

栅格数字地形分析中的尺度问题研究方法简

栅格数字高程模型（DEM）固有的尺度特征给以栅格DEM为基本输入的数字地形分析带来各种尺度问题。对栅格数字地形分析中涉及的尺度进行梳理,以分辨率和分析窗口为重点,对栅格数字地形分析中的多尺度表达、尺度效应、适宜尺度选择、尺度转换等尺度问题及其相互关系进行阐述;分别介绍各类尺度问题的现有定量研究方法,尤其对尺度效应定量刻画和适宜尺度选择方法,根据不同方法计算定量指标所利用的信息类别进行分类归纳;最后讨论了其中有待进一步开展研究的几方面工作。     

基于改进型多流向算法的地形指数空间尺度变化分析

地形指数ln(α/tanβ)是流域径流源面积和地下水水位空间分布特征的近似表征,是地形特征参数化的重要指标。基于改进后的地形指数多流向计算方法,在0.5×0.5°的区域气候模式尺度框架下,探讨了地形离散和平滑效应分别对地形指数尺度变化的影响程度;研究了地形指数在100和1000m栅格尺度上的相关性,给出两者在空间尺度上的转换关系;分析区域尺度和全球尺度下地形指数尺度转换的差异及原因。研究结果可以增强地形信息在大尺度水文模型及陆面水文过程中的适用能力。     

地形信息对确定DEM适宜分辨率的影响

分辨率会直接影响基于栅格数字高程模型（DEM）的数字地形分析结果,因此在实际应用中,需要选择适宜的DEM分辨率。目前采取的基本方法,基于某种地形信息定量刻画尺度效应曲线,从而确定DEM适宜分辨率,但对于采用不同地形信息时所产生的影响尚缺乏研究。本文针对该方法中通常采用的坡度、剖面曲率、水平曲率等3 种地形信息,每种地形信息提取时,分别使用两种不同的常用算法,在3 个不同地形特征的研究区中,逐一计算其在不同分辨率下的局部方差均值,以刻画尺度效应曲线,确定相应的DEM适宜分辨率,并进行对比分析。结果表明：① 采用剖面曲率或水平曲率所得适宜分辨率结果基本相同,但采用坡度所得出的适宜分辨率结果则有明显差别,后者所得的适宜分辨率更粗;② 采用不同地形信息时,越是在平缓地形为主的研究区,所得的适宜分辨率结果越相近,在复合地形特征的研究区所得到的适宜分辨率区间均明显较宽;③ 地形属性计算时所用的算法对适宜分辨率结果的影响不明显。     

地形对山地森林景观格局多尺度效应

以TM影像和野外调查为数据源,3S技术为研究手段,分析了泰山的森林景观结构、景观异质性动态变化以及景观格局高程的分异。选择相对高差较大的区域设计了8个地形方位,采用3种由连续样方组成的辐射状样带,在遥感影像上进行信息采集,研究地形对森林景观格局的多尺度效应。分析了10个地形因子对森林景观分异的影响。结果表明:1.在景观尺度上,地形方位、海拔、山地类型是影响森林景观镶嵌格局的控制因素,坡向、坡度是重要因素,坡形、坡位是不明显因素;2.以TM影像为信息源,从森林景观分异和梯度分析上,首先要考虑海拔坡向指数、坡形坡位指数和海拔,并立足于地形方位;3.地形主要因子间存在着稳定的显著正相关,明显地对森林分异多尺度格局具有较强的综合解释能力,不论尺度大小对森林景观分异贡献率可稳定地达到70%左右,其解释量随尺度增大而增加,但可解释性减弱。     

基于DEM的中国地形起伏度适宜计算尺度研究

基于SRTM和ASTER DEM数据,在全国范围内选取13个实验区,在渐变尺度下计算平均起伏度变化曲线的"突变点",据此确定中国地形起伏度的适宜计算尺度;结合山地界定标准计算各实验区山地面积,并采用人工解译的山地范围对计算结果进行检验。研究结果表明:1)地形起伏度适宜计算尺度与所采用的DEM数据有关,DEM分辨率越小,地形起伏度适宜计算尺度越大;2)针对同一分辨率DEM数据,中国境内的地形起伏度适宜计算尺度随地貌特征变化而变化,但总体变化幅度不大;3)针对SRTM和ASTER DEM两种常用数据源,分别选择4.72km2和3.20km2作为地形起伏度适宜计算尺度是合理的,山地界定精度达90%以上。     

地形湿度指数算法误差的定量评价

地形湿度指数(TWI)能够定量指示地形对土壤湿度空间分布的控制,是一种应用广泛的地形属性.目前基于栅格DEM的TWI计算方法结果各异,因此有必要对'TWI算法进行定量评价.对TWI算法通常是应用实际DEM数据进行评价.但实际DEM中存在的数据源误差会干扰对算法误差的评价.针对该问题,本文介绍了一种用不含数据源误差的人造...     

山区地形开阔度的分布式模型

 地形开阔度是影响山地辐射平衡及其分量的重要地形因子,是山区散射辐射、地形反射辐射等计算的重要参数。在复杂的地形条件下,地形开阔度的计算很难用数学公式描述。 利用数字高程模型（DEM）,全面考虑了坡地自身遮蔽和周围地形相互遮蔽的影响,提出了山区地形开阔度的分布式模型和算法。以1 km×1 km分辨率的DEM数据作为地形的综合反映,计算了起伏地形下中国地形开阔度的空间分布。同时,利用100 m和1 km两个分辨率的DEM数据,从不同DEM分辨率和不同地貌类型两个方面探讨了地形开阔度的空间尺度效应,阐明了区域地形开阔度随地形地貌和空间分辨率的变化规律。所提供的山地开阔度的数据可作为基础地理数据供相关研究应用。     

基于DEM的地形单元多样性指数及其算法

在阐述地形信息表达研究进展的基础上,提出基于DEM地形单元多样性指数的概念和算法。地形单元多样性指数综合了高程、坡度、坡位、坡向、汇流量和水域信息等要素。其算法集成地形位置指数和地形湿度指数算法,采用图层叠加分析,设定分类、分级指标,进行重分类组合,划分地形单元类型,利用窗口分析法计算地形单元多样性指数。以四川省为试验区,利用精度为100 m的DEM数据和水域分布数据进行模拟计算,地形单元划分为13种典型类型,统计窗口半径设为900 m,计算出的多样性指数值小于0.5的区域仅占总面积的11%,大于0.75的区域达57%,符合实验区地形特征,并对算法进行了可行性分析和验证。结果表明,该算法提取的地形单元多样性指数可以有效反映地表形态的多样性特征及其变化。该研究结果为进一步探讨基于DEM地表形态信息的概念体系,以及从微观到宏观的地形信息空间分析研究创造了条件。     

Computation of the third‐order partial derivatives from a digital elevation model

Loci of extreme curvature of the topographic surface may be defined by the derivation function (T) depending on the first‐, second‐, and third‐order partial derivatives of elevation. The loci may partially describe ridge and thalweg lines. The first‐ and second‐order partial derivatives are commonly calculated from a digital elevation model (DEM) by fitting the second‐order polynomial to a 3×3 window. This approach cannot be used to compute the third‐order partial derivatives and T. We deduced formulae to estimate the first‐, second‐, and third‐order partial derivatives from a DEM fitting the third‐order polynomial to a 5×5 window. The polynomial is approximated to elevation values of the window. This leads to a local denoising that may enhance calculations. Under the same grid size of a DEM and root mean square error (RMSE) of elevation, calculation of the second‐order partial derivatives by the method developed results in significantly lower RMSE of the derivatives than that using the second‐order polynomial and the 3×3 window. An RMSE expression for the derivation function is deduced. The method proposed can be applied to derive any local topographic variable, such as slope gradient, aspect, curvatures, and T. Treatment of a DEM by the method developed demonstrated that T mapping may not substitute regional logistic algorithms to detect ridge/thalweg networks. However, the third‐order partial derivatives of elevation can be used in digital terrain analysis, particularly, in landform classifications.     

面向地貌学本源的DEM增值理论框架与构建方法

数字高程模型（DEM）在表达地貌形态、认知地表过程、揭示地学机理等研究中发挥着基础性的作用,是重要的地理空间数据模型,广泛地应用于地学分析与建模中。但是,传统DEM具有属性单一的天然缺陷,难以支撑面向地学过程与机理挖掘的地球系统科学研究。亟待在传统DEM的基础上实现其数据模型的增值,服务于新地貌学研究范式和新对地观测技术背景下的数字地形建模与分析。立足于以上问题,本文构建了DEM增值的理论框架,主要包括DEM增值的概念、内涵、内容、类别、不同增值类别之间的相互关系,以及此理论框架的研究意义和应用范畴。提出了DEM增值的构建方法,包含：① 强调地上地下一体化、时间空间相耦合的DEM空间维度和时间维度增值方法;② 重视地下、地表和地上物质构成,形态属性耦合的物质属性和形态属性增值方法;③ 顾及自然过程、人工作用的地物对象、地貌形态的地物要素和形体要素增值方法。最后,分别以数字阶地模型、数字坡地模型和数字流域模型为例,阐释DEM在面向地貌学本源问题时的不同增值方法及应用场景。期望通过对DEM进行维度、属性和要素3个层面的增值,实现现代对地观测技术背景下数字高程模型表达方法的突破,并支撑知识驱动的数字地貌问题分析。     

Geomorphometric feature analysis using morphometric parameterization and artificial neural networks

This paper presents a semi-automatic method using an unsupervised neural network to analyze geomorphometric features as landform elements. The Shuttle Radar Topography Mission (SRTM) provided detailed digital elevation models (DEMs) for all land masses between 60°N and 57°S. Exploiting these data for recognition and extraction of geomorphometric features is a challenging task. Results obtained with two methods, Wood's morphometric parameterization and the Self Organizing Map (SOM), are presented in this paper.Four morphometric parameters (slope, minimum curvature, maximum curvature and cross-sectional curvature) were derived by fitting a bivariate quadratic surface with a window size of 5 by 5 to the SRTM DEM. These parameters were then used as input to the two methods. Wood's morphometric parameterization provides point-based features (peak, pit and pass), line-based features (channel and ridge) and area-based features (planar). Since point-based features are defined as having a very small slope when their neighbors are considered, two tolerance values (slope tolerance and curvature tolerance) are introduced. Selection of suitable values for the tolerance parameters is crucial for obtaining useful results.The SOM as an unsupervised neural network algorithm is employed for the classification of the same morphometric parameters into ten classes characterized by morphometric position (crest, channel, ridge and plan area) subdivided by slope ranges. These terrain features are generic landform element and can be used to improve mapping and modeling of soils, vegetation, and land use, as well as ecological, hydrological and geomorphological features. These landform elements are the smallest homogeneous divisions of the land surface at the given resolution. The result showed that the SOM is an efficient scalable tool for analyzing geomorphometric features as meaningful landform elements, and uses the full potential of morphometric characteristics.     

基于ASTER GDEM数据喀斯特区域地貌类型划分与分析

以30 m分辨率ASTER GDEM数据为基础,通过GIS空间分析和非监督分类的方法进行地貌基本类型的自动划分。研究结果表明:①ASTERGDEM数据能够满足1∶10万比例尺下喀斯特区域的地表形态表达;②以流域为单位提取地形因子符合地貌发育的基本规律,提取的地形因子能客观的反应地表真实形态;③采用非监督分类法能够有效的实现1∶10万比例尺下地貌基本形态的定量化、自动化分类。     

Measuring the significance of a divide to local drainage patterns

This article presents a framework for estimating a new topographic attribute derived from digital elevation models (DEMs) called maximum branch length (B _max). Branch length is defined as the distance travelled along a flow path initiated at one grid cell to the confluence with the flow path passing through a second cell. B _max is the longest branch length measured for a grid cell and its eight neighbours. The index provides a physically meaningful method for assessing the relative significance of drainage divides to the dispersion of materials and energy across a landscape, that is, it is a measure of ‘divide size’. B _max is particularly useful for studying divide network structure, for mapping drainage divides, and in landform classification applications. Sensitivity analyses were performed to evaluate the robustness of estimates of B _max to the algorithm used to estimate flow lengths and the prevalence of edge effects resulting from inadequate DEM extent. The findings suggest that the index is insensitive to the specific flow algorithm used but that edge effects can result in significant underestimation along major divides. Edge contamination can, however, be avoided by using an appropriately extensive DEM.     

Landform classification from a digital elevation model and satellite imagery

The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.     

DEM resolution dependencies of terrain attributes across a landscape

This paper documents resolution dependencies in terrain analysis and describes how they vary across landform location. Six terrain attributes were evaluated as a function of DEM resolution—slope, plan curvature, profile curvature, north–south slope orientation, east–west slope orientation, and topographic wetness index. The research highlights the effect of varying spatial resolution through a spatial sampling/resampling scheme while maintaining sets of indexed sample points at various resolutions. Tested sample points therefore coincide exactly between two directly compared resolutions in terms of their location and elevation value. An unsupervised landform classification procedure based on statistical clustering algorithms was employed to define landform classes in a reproducible manner. Correlation and regression analyses identified sensitive and consistent responses for each attribute as resolution was changed, although the tested terrain attributes responded in characteristically different ways. These responses displayed distinguishable patterns among various landform classes, a conclusion that was further verified by a series of two‐sample, two‐tailed t‐tests.     

面向地貌学本源的数字地形分析研究进展与展望

数字地形分析（DTA）是地理信息科学（GIS）研究的热点。但是,当前基于数字高程模型（DEM）的数字地形分析在地貌学研究中存在重形态轻机理、重现象轻过程、重地上轻地下等问题,急需从单一的地貌形态分析,迈向面向成因、过程与机理等地貌学本源问题的研究转变。据此,本文系统梳理了面向地貌学本源的数字地形分析的相关研究现状,并从地貌学本源认识、地貌形态建模、地形因子提取、以及其他地形分析方法等研究进行了系统的回顾、梳理与分析。研究表明,基于DEM的数字地形分析虽具有地貌特征分析的潜力与优势,但是,数字地形分析存在数据表达与分析模式上的先天缺陷,亟待通过基础理论与关键技术的突破,实现理论与方法的创新发展,实现从“坡面”走向“区域”,从“形态”走向“过程”,从“地形”走向“地貌”。而当今地球系统科学的研究发展态势也到了数字地形分析研究从重视地貌形态走向揭示地貌学本源的关键阶段。因此,本文从DEM数据模型增值、地形因子及其地形空间关系、以及宏观地形分析等侧面展望了面向地貌学本源的数字地形分析研究。当今基于DEM的数字地形分析研究,正像当前的GIS是否能够真正支撑地理学发展一样,已经处于一个非常关键的十字路口。面向地貌学本源的数字地形分析研究思路可望成为地理信息科学领域理论与方法创新的一次重要探索与实践。