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Geomorphometric feature analysis using morphometric parameterization and artificial neural networks 总被引：3，自引：0，他引：3
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. 相似文献
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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。 相似文献
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This paper presents an automated classification system of landform elements based on object-oriented image analysis. First, several data layers are produced from Digital Terrain Models (DTM): elevation, profile curvature, plan curvature and slope gradient. Second, relatively homogenous objects are delineated at several levels through image segmentation. These object primatives are classified as landform elements using a relative classification model, built both on the surface shape and on the altitudinal position of objects. So far, slope aspect was not used in classification. The classification has nine classes: peaks and toe slopes (defined by the altitudinal position or the degree of dominance), steep slopes and flat/gentle slopes (defined by slope gradients), shoulders and negative contacts (defined by profile curvatures), head slopes, side slopes and nose slopes (defined by plan curvatures). Classes are defined using flexible fuzzy membership functions. Results are visually analyzed by draping them over DTMs. Specific fuzzy classification options were used to obtain an assessment of output accuracy. Two implementations of the methodology are compared using (1) Romanian datasets and (2) Berchtesgaden National Park, Germany. The methodology has proven to be reproducible; readily adaptable for diverse landscapes and datasets; and useful in respect to providing additional information for geomorphological and landscape studies. A major advantage of this new methodology is its transferability, given that it uses only relative values and relative positions to neighboring objects. The methodology introduced in this paper can be used for almost any application where relationships between topographic features and other components of landscapes are to be assessed. 相似文献
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地形元素（如山脊、沟谷等）是地表形态类型基本单元,通过地形元素的不同空间组合可形成更高级别的地貌类型。现有的地形元素提取方法大多依靠地形属性计算,难以克服地形元素的空间相关性表达与局部地形属性计算存在不对应的矛盾,Jasiewicz和Stepinski提出的Geomorphons方法——基于高程相对差异信息进行地形元素分类,可避免这一问题,但Geomorphons方法本质上是在单一分析尺度上选择地形特征点用于判别,易受局部地形起伏的影响而造成误分类。针对这一问题,设计出一种多分析尺度下综合判别的地形元素分类方法。应用结果表明：相比Geomorphons方法,利用该方法得到的地形元素的分类结果更为合理。 相似文献
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Drainage basin object-based method for regional-scale landform classification: a case study of loess area in China 总被引：1，自引：0，他引：1
Landform classification is one of the most important procedures in recognizing and dividing earth surface landforms. However, topographical homogeneity and differences in regional-scale landforms are often ignored by traditional pixel- and object-based landform classification methods based on digital elevation models (DEMs). In this work, a drainage basin object-based method for classifying regional-scale landforms is proposed. Drainage basins with least critical areas are first delineated from DEMs. Then, terrain derivatives of mean elevation, mean slope, drainage density, drainage depth, and terrain texture are employed to characterize the morphology of the drainage basins. Finally, a decision tree based on the topographical characteristics of the drainage basins is constructed and employed to determine the landform classification law. The experiment is validated in the landform classification of regional-scale loess areas in China. Results show that clear boundaries exist in different landforms at the regional scale. Landform type in a specific region shows significant topographical homogeneity under its specific regional geomorphological process. Classification accuracies are 87.3 and 86.3% for the field investigation and model validation, respectively. Spatial patterns of classified landforms and their proximity to sediment sources and other factors can be regarded as indicators of the evolutionary process of loess landform formation. 相似文献
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John B. Lindsay Jan Seibert 《International journal of geographical information science》2013,27(7):1453-1468
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. 相似文献
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Digital elevation and remote sensing data sets contain different, yet complementary, information related to geomorphological features. Digital elevation models (DEMs) represent the topography, or land form, whereas remote sensing data record the reflectance/emittance, or spectral, characteristics of surfaces. Computer analysis of integrated digital data sets can be exploited for geomorphological classification using automated methods developed in the remote sensing community. In the present study, geomorphological classification in a moderate- to high-relief area dominated by slope processes in southwest Yukon Territory, Canada, is performed with a combined set of geomorphometric and spectral variables in a linear discriminant analysis. An automated method was developed to find the boundaries of geomorphological objects and to extract the objects as groups of aggregated pixels. The geomorphological objects selected are slope units, with the boundaries being breaks of slope on two-dimensional downslope profiles. Each slope unit is described by variables summarizing the shape, topographic, and spectral characteristics of the aggregated group of pixels. Overall discrimination accuracy of 90% is achieved for the aggregated slope units in ten classes. 相似文献
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Marcello Antonio Ventura Gorini Guilherme Lucio Abelha Mota 《International journal of geographical information science》2016,30(8):1644-1666
A method is presented to explicitly incorporate spatial and scale vagueness – double vagueness – into geomorphometric analyses. Known limitations of usual practices include using a single fixed set of crisp thresholds for morphometric classification and the imposition of a single arbitrary number of scales of analysis to the entire digital elevation model (DEM). Among the advantages of the proposed method are: fuzzification of morphometric classification rules, scale-dependent adaptive fuzzy set parametrization and an objective definition of maximum scale of analysis on a cell-by-cell basis. The method was applied to several DEMs ranging from the ocean floor to surface landscapes of both Earth and Mars. The result was evaluated with respect to modal morphometric features and to characteristic scales, suggesting a more robust method for deriving both morphometric classifications and terrain attributes. We argue that the method would be preferable to any single-scale crisp approach, at least in the context of preliminary hands-off morphometric analyses of DEMs. 相似文献
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基于DEM的地貌实体单元自动提取方法 总被引：17，自引：4，他引：13
我国传统地貌基本形态类型分类强调地貌单元的完整性,界线划分沿地貌实体边界而非规则统计单元,目前尚缺乏地貌实体单元的有效自动提取方法。针对这一难点,本文提出一种基于DEM的地貌实体单元数字提取方法。利用坡度分级,并搜索相邻栅格单元、计算坡度级别内相互连通栅格的面积,建立坡度、面积阈值综合判别规则进行山地平原的自动划分;利用地形倒置、水文淹没分析,将山体划分的二维判别规则扩展到实际三维地形中,并结合地形结构线提取算法进行山体界线自动提取、确定山地地貌实体单元。结果表明,该方法符合我国传统地貌分类体系,能够较好实现山地/平原的自动划分和山体界线的数字提取。 相似文献
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Argyros Argyridis Demetre Argialas 《International journal of geographical information science》2013,27(11):2153-2169
ABSTRACTCoastal mapping provides information for coastal zone protection. Land foreshore-border mapping is important, since it is related to land and property management, raising the need for an automated foreshore delineation framework. This research develops a multi-scale GEographic Object-Based Image Analysis method for foreshore identification from single-date very high-resolution imagery and DTM. Foreshore interpretation criteria were provided by the Greek Cadastral Office; however, they defined the foreshore-borders in an ill-defined manner. Thus, an ontology was designed and implemented to formalize the implicit spectral, geometric, and spatial relationships described in the interpretation criteria, and employ them during identification. The final ontology was delivered after three design phases: specification, conceptualization, and knowledge formalization. Imagery preprocessing through anisotropic morphological levelings reduced undesired spatial detail and noise. A three-level segmentation and ontological classification approach was developed to compute proper objects and assign them into semantic categories. Class definitions were created through fuzzy restrictions, representing value ranges of the employed properties. Evaluation of the extracted foreshore area with human-interpreted reference data showed a 77.9% quality, which was satisfactory. Evaluation by comparing the extracted foreshore border to the reference data showed an average deviation of 2.4 m, which is within Greek Cadastral Office specifications. 相似文献
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Hossein Saadat Robert Bonnell Forood Sharifi Guy Mehuys Mohammad Namdar Sasan Ale-Ebrahim 《Geomorphology》2008,100(3-4):453-464
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. 相似文献
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The detailed topographic information contained in light detection and ranging (LiDAR) digital elevation models (DEMs) can present significant challenges for modelling surface drainage patterns. These data frequently represent anthropogenic infrastructure, such as road embankments and drainage ditches. While LiDAR DEMs can improve estimates of catchment boundaries and surface flow paths, modelling efforts are often confounded difficulties associated with incomplete representation of infrastructure. The inability of DEMs to represent embankment underpasses (e.g. bridges, culverts) and the problems with existing automated techniques for dealing with these problems can lead to unsatisfactory results. This is often dealt with by manually modifying LiDAR DEMs to incorporate the effects of embankment underpasses. This paper presents a new DEM pre-processing algorithm for removing the artefact dams created by infrastructure in sites of embankment underpasses as well as enforcing flow along drainage ditches. The application of the new algorithm to a large LiDAR DEM of a site in Southwestern Ontario, Canada, demonstrated that the least-cost breaching method used by the algorithm could reliably enforce drainage pathways while minimizing the impact to the original DEM. 相似文献
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Four scaling regimes are observed in Digital Elevation Models (DEMs) when the average local slope is calculated for pixels grouped according to the values of the contributing area. Threshold criteria, proposed by various researchers to identify the extent of the channel network, are examined relative to the slope-area scaling diagram. The scaling response observed in DEMs is reproduced with a landscape evolution and channel network growth model originally developed by Willgoose et al. (1989). The threshold criterion proposed in this model is a useful tool to separate two different slope-area scaling regimes observed in DEM data. 相似文献
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流域水系自动提取在西苕溪流域的应用 总被引：7，自引：0，他引：7
论述了如何基于栅格DEM自动提取流域自然水系的原理、方法和流程.以西苕溪中上游流域为例,根据DEM精度、上游集水区面积阈值和下垫面地形的不同,对所提取水系进行了比较.针对在平均地形坡度小于3°的平坦区域所提取水系与实际河网偏差较大的问题,提出了利用主干河道和平原水系数字化作为约束条件生成河网的新方法. 相似文献
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基于Landsat 8 OLI数据与面向对象分类的昆嵛山地区土地覆盖信息提取 总被引：1，自引：0，他引：1
利用2015年Landsat 8 OLI遥感影像和DEM作为分类数据源,结合野外调查数据,采用面向对象的分类方法对昆嵛山地区土地覆盖信息进行提取,并对分类结果进行精度评价与比较分析。研究表明：面向对象分类方法提取的各地类连续且边界清晰,分类效果与实际情况基本吻合。昆嵛山地区占主导地位的土地覆盖类型是针叶林,面积为1 546.81 km2。研究区土地覆盖分类的总体精度和Kappa系数分别为91.5%和0.88,其中针叶林、草地、水体和建设用地的生产者精度均达到87%以上。相对于监督分类方法,本研究提出的土地覆盖信息提取方法的总体分类精度和Kappa系数分别提高14.7%和0.17。基于面向对象的中分辨率遥感影像,能够获取较高精度的土地覆盖信息,为大范围土地覆盖分类研究提供方法参考。 相似文献
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Daming Wang Shawn W. Laffan Yu Liu Lun Wu 《International journal of geographical information science》2013,27(2):305-326
We describe a method of morphometric characterisation of landform from digital elevation models (DEMs). The method is implemented first by classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single‐scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi‐scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi‐scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R 2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single‐ and multi‐scale terrain indices perform well when characterising the different geomorphometric features. 相似文献
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《地理学报(英文版)》2017,(11)
Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology.In this paper,we outline an automated method for mapping lunar landforms that is based on digital terrain analysis.An iterative self-organizing(ISO)cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters.These parameters divide landforms into a number of spatially extended,topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties.To illustrate the applicability of our approach,we apply it to three representative test sites on the Moon,automatically presenting our results as a thematic landform map.We also quantitatively evaluated this approach using a series of confusion matrices,achieving overall accuracies as high as 83.34% and Kappa coefficients(K)as high as 0.77.An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies. 相似文献
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I. V. Florinsky A. N. Pankratov 《International journal of geographical information science》2016,30(12):2506-2528
There are three major mathematical problems in digital terrain analysis: (1) interpolation of digital elevation models (DEMs); (2) DEM generalization and denoising; and (3) computation of morphometric variables through calculating partial derivatives of elevation. Traditionally, these three problems are solved separately by means of procedures implemented in different methods and algorithms. In this article, we present a universal spectral analytical method based on high-order orthogonal expansions using the Chebyshev polynomials of the first kind with the subsequent Fejér summation. The method is intended for the processing of regularly spaced DEMs within a single framework including DEM global approximation, denoising, generalization, as well as calculating the partial derivatives of elevation and local morphometric variables.
The method is exemplified by a portion of the Great Rift Valley and central Kenyan highlands. A DEM of this territory (the matrix 480 × 481 with a grid spacing of 30″) was extracted from the global DEM SRTM30_PLUS. We evaluated various sets of expansion coefficients (up to 7000) to approximate and reconstruct DEMs with and without the Fejér summation. Digital models of horizontal and vertical curvatures were computed using the first and second partial derivatives of elevation derived from the reconstructed DEMs. To evaluate the approximation accuracy, digital models of residuals (differences between the reconstructed DEMs and the initial one) were calculated. The test results demonstrated that the method is characterized by a good performance (i.e., a distinct monotonic convergence of the approximation) and a high speed of data processing. The method can become an effective alternative to common techniques of DEM processing. 相似文献