Multiscale Digital Elevation Models Research Articles

A heuristic DEM generalization by combining catchments

Multi-scale digital elevation model (DEM) is an important content of digital terrain analysis. To generate multi-scale DEM, this study reports a heuristic DEM generalization method. The key of DEM generalization is to maintain the main topographic features and remove the minor topographic features. Our method takes catchment as generalizing object, not single grid pixel as generalizing object. In this way, DEM simplification can be achieved on the basis of maintaining the main terrain characteristics. Compared with the resample method, VIP method and low pass filter method, the proposed method can preserve the main topographic features well when the minor topographic features are deleted. Our main contribution lies in: taking terrain information rather than raw data as the generalizing object, which improves the rationality of the generalizing results; and the heuristic method proposed in this paper has good operability and high automation. Highlights A heuristic DEM generalization method by combining catchments is developed; The proposed DEM simplification can maintain the main terrain characteristics; The heuristic method proposed in this paper has good operability and high automation;

An integrated method for DEM simplification with terrain structural features and smooth morphology preserved

ABSTRACT As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method performs well in identifying terrain structural edges, while it tends to discard the smooth morphology of a terrain surface. In addition, the filter that aims to reduce noise on a surface may over-smooth the terrain structural edges. Therefore, to preserve both the terrain structural edges and smooth morphology, we propose to combine the techniques of statistical filtering and structural analysis. Specifically, all the critical elevation points and structural edges are first detected from the DEM surface by using the structural analysis method. Then, the iterative guided normal filter is used to smooth the generalized DEM with the guidance of the structure of the original surface. After this process, the terrain structure is retained in the smooth surface of the DEM. The experimental results with a real-world dataset show that our method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations.

A simplification method for grid-based DEM using topological hierarchies

Terrain simplification is the basis of constructing multi-scale digital elevation models. However, a definite corresponding relationship between the scales and the hierarchy of terrain skeleton lines cannot be established easily. In this paper, a novel method is designed based on multi-resolution Morse complex. First, a universal improved algorithm for the original terrain Morse complex is proposed to prevent artificial intersections. Second, significant points are adaptively selected in the hierarchical skeleton lines through topological simplification. Finally, an experimental companion is created. Results indicate that the proposed simplification method can not only retain the main terrain structural characteristics, but also offer better simplification precision.

An improved ANUDEM method combining topographic correction and DEM interpolation

Void filling and anomaly replacement in conjunction with auxiliary sources of data have been widely used to improve the quality of existing problematic high-resolution digital elevation models. However, the traditional interpolation methods used for this purpose have always failed to eliminate the discrepancies between different data-sets. In this paper, an improved ANUDEM method is presented for DEM interpolation, which incorporates the idea of topographic correction using high correlation of topological structure between contour lines (CLs) from multi-scale digital elevation models (DEM). Firstly, the terrain topological structure is extracted from the CLs of a low-resolution DEM. The topographic surface correction is then undertaken based on the extracted structure, which recovers the topographic information of the sharp depressions and eminences to fit the high-resolution representation. Finally, the breaklines of the terrain surface are distilled and integrated into the denser DEM generation. The experiments undertaken confirmed the superiority of the proposed method over the other DEM interpolation methods. It is shown that the proposed method can provide results with a higher accuracy, as well as a better visual quality.

Fusion of multi-scale DEMs using a regularized super-resolution method

The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.

Landform classification via fuzzy classification of morphometric parameters computed from digital elevation models: case study on Zagros Mountains

In this study, we investigate the use of morphometric parameters and fuzzy membership functions to perform landform classification for different case areas of Zagros Mountains from digital elevation models (DEMs). First, multiscale DEMs with scales of 5 to 45 cells are generated using the lifting scheme. The maximum curvature for the scale of five cells has the lowest standard deviation, and hence, is determined to be the characteristic scale. Data layers are produced from the DEM of this scale for slope, minimum and maximum curvatures, and plan and profile curvatures. The fuzzy membership rules for these data layers are used to determine the landform classes. Comparison of the results of landform classification using the fuzzy classification method and topographic position index (TPI) with the geology map of the study area show that the fuzzy classification method provides higher accuracy (81 %) as compared to TPI (42 %). This is because for the fuzzy classification method, sloping areas are separated into sloping and non-sloping areas, and the membership functions are defined to prevent landforms belonging to the sloping areas from being classified in the non-sloping areas and vice versa.

Computation of spatial significance of mountain objects extracted from multiscale digital elevation models

The derivation of spatial significance is an important aspect of geospatial analysis and hence, various methods have been proposed to compute the spatial significance of entities based on spatial distances with other entities within the cluster. This paper is aimed at studying the spatial significance of mountain objects extracted from multiscale digital elevation models (DEMs). At each scale, the value of spatial significance index SSI of a mountain object is the minimum number of morphological dilation iterations required to occupy all the other mountain objects in the terrain. The mountain object with the lowest value of SSI is the spatially most significant mountain object, indicating that it has the shortest distance to the other mountain objects. It is observed that as the area of the mountain objects reduce with increasing scale, the distances between the mountain objects increase, resulting in increasing values of SSI. The results obtained indicate that the strategic location of a mountain object at the centre of the terrain is more important than its size in determining its reach to other mountain objects and thus, its spatial significance.

Scale compatibility analysis in geographic process research: A case study of a meteorological simulation in Hong Kong

Scale is a fundamental concept in geography. Although multiscale data, models and other products are considered when studying geography, scale mismatching can produce adverse results. Thus, scale compatibility is becoming crucial for decoding dynamic and interactive geographic processes. This paper presents a definition of scale compatibility for studying geographic processes and partitions this concept into four levels: multiple processes, dimensional, type and component. These four levels operate differently, as described in detail to support the implementation of scale compatibility. Applying the procedure to assess scale compatibility, a meteorological simulation case study in Hong Kong was investigated with regard to the use of multiscale digital elevation model (DEM) data and the Weather Research and Forecasting (WRF) model. This case study considered spatial dimension, measurement scale type and component level. The experiments showed that using DEM data and a model with different resolution and grid spacing, respectively, affected the dynamic simulation capacity, even accounting for 38% of the mean absolute error (MAE) for temperature. Furthermore, our 3 and 30 arc sec resolution DEM data are relatively more compatible with the WRF model of 1 km grid spacing. This case study not only helps to improve meteorological simulations by considering scale compatibility as an issue but also explains the significance and implementation of scale compatibility in geographic processes research.

Scale matching of multiscale digital elevation model (DEM) data and the Weather Research and Forecasting (WRF) model: a case study of meteorological simulation in Hong Kong

It is becoming easier to combine geographical data and dynamic models to provide information for problem solving and geographical cognition. However, the scale dependencies of the data, model, and process can confuse the results. This study extends traditional scale research in static geographical patterns to dynamic processes and focuses on the combined scale effect of multiscale geographical data and dynamic models. The capacity for topographical expression under the combined scale effect was investigated by taking multiscale topographical data and meteorological processes in Hong Kong as a case study. A meteorological simulation of the combined scale effect was evaluated against data from Hong Kong Observatory stations. The experiments showed that (1) a digital elevation model (DEM) using 3 arc sec data with a 1 km resolution Weather Research and Forecasting (WRF) model gives better topographical expression and meteorological reproduction in Hong Kong; (2) a fine-scale model is sensitive to the resolution of the DEM data, whereas a coarse-scale model is less sensitive to it; (3) better topographical expression alone does not improve weather process simulation; and (4) uncertainty arising from a scale mismatch between the DEM data and the dynamic model may account for 38 % of the variance in certain meteorological variables (e.g., temperature). This case study gives a clear explanation of the significance and implementation of scale matching for multiscale geographical data and dynamic models.

Computing uncertainty of physiographic features extracted from multiscale digital elevation models

In this paper, it is proposed that the mapping of uncertainties of the three predominant physiographic features of terrains, which are mountain, basins and piedmont slopes, using variation in the spatial resolution over which these landforms are defined, can be performed with fuzzy classification. The proposed methodology allows for the generation of fuzzy certainty maps which assign high levels of uncertainty to regions with high levels of change across scales. This paper demonstrates that fuzzy certainty maps provide a better quantification of landform character than Boolean landform maps alone. In terms of sensitivity to noise, the methodology is able to identify narrow bridges, and spurious landforms, and assign these errors with low certainty values. However, it is unable to identify spurious modifications to landform shape, with these errors being assigned high certainty values. Ground truth maps are required to identify these errors.

Multi-Scale DEM Generalization Processing in Different Landform Areas

As a special kind of image, the grid-based digital elevation model (DEM) is directly related with the landform and topography feature. In the real word, there are various landforms, and the results of different methods for DEM processing vary greatly. However, the discussion of using different methods in different landform areas in DEM generalization is needed. Numerous methods have been proposed previously for DEM generalization without taking topographic factors into consideration, and the setting of threshold value of most algorithms is very subjective. Therefore, we investigate the DEM generalization, using two methods (z-tolerance method and compound method) in different landform areas and propose a new way for threshold values setting in generalization. The results show that the method we proposed is feasible and the compound method offers a better performance over z-tolerance method in most conditions, especially when comparing their performances of keeping streamline shape.

Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi

Analysis of surface textures of physiographic features extracted from multiscale digital elevation models via grey level co-occurrence matrix This paper is aimed at employing grey level co-occurrence matrix (GLCM) to analyse the surface textures of physiographic features extracted from multiscale digital elevation models (DEMs). Four GLCM parameters, energy, contrast, autocorrelation and entropy, are computed for horizontal (0°), vertical (90°) and diagonal (45 and 135°) cell pair orientations. For the respective DEMs and physiographic features, varying patterns are observed in the plots of the GLCM parameters due to varying surface profiles and the changes that occur over the scales. Due to the smoothing of the terrain during multiscaling, the features have increasing values of energy and entropy, and decreasing values of contrast and entropy, indicating decreasing roughness. Mountains have the highest roughness as compared to the other features over the scales, while basins have the lowest roughness. For each parameter, similar trends are observed in the plots for the four different cell pair orientations, indicating similar trends of change of surface texture in the different orientations over the scales. However, varying values are observed for the different orientations, depending on textural uniformity in the corresponding orientations. The results obtained demonstrate that GLCM can be an appropriate tool for classifying landforms from multiscale DEMs based on the different texture characteristics of the landforms.

Analysis of surface textures of physiographic features extracted from multiscale digital elevation models via grey level co-occurrence matrix

Analysis of surface textures of physiographic features extracted from multiscale digital elevation models via grey level co-occurrence matrix

COMPARISON OF MULTI-SCALE DIGITAL ELEVATION MODELS FOR DEFINING WATERWAYS AND CATCHMENTS OVER LARGE AREAS

Abstract. Digital Elevation Models (DEMs) allow for the efficient and consistent creation of waterways and catchment boundaries over large areas. Studies of waterway delineation from DEMs are usually undertaken over small or single catchment areas due to the nature of the problems being investigated. Improvements in Geographic Information Systems (GIS) techniques, software, hardware and data allow for analysis of larger data sets and also facilitate a consistent tool for the creation and analysis of waterways over extensive areas. However, rarely are they developed over large regional areas because of the lack of available raw data sets and the amount of work required to create the underlying DEMs. This paper examines definition of waterways and catchments over an area of approximately 25,000 km2 to establish the optimal DEM scale required for waterway delineation over large regional projects. The comparative study analysed multi-scale DEMs over two test areas (Wivenhoe catchment, 543 km2 and a detailed 13 km2 within the Wivenhoe catchment) including various data types, scales, quality, and variable catchment input parameters. Historic and available DEM data was compared to high resolution Lidar based DEMs to assess variations in the formation of stream networks. The results identified that, particularly in areas of high elevation change, DEMs at 20 m cell size created from broad scale 1:25,000 data (combined with more detailed data or manual delineation in flat areas) are adequate for the creation of waterways and catchments at a regional scale.

A method for semi‐automated objective quantification of linear bedforms from multi‐scale digital elevation models

ABSTRACTThe increasing availability of large, detailed digital representations of the Earth's surface demands the application of objective and quantitative analyses. Given recent advances in the understanding of the mechanisms of formation of linear bedform features from a range of environments, objective measurement of their wavelength, orientation, crest and trough positions, height and asymmetry is highly desirable. These parameters are also of use when determining observation‐based parameters for use in many applications such as numerical modelling, surface classification and sediment transport pathway analysis. Here, we (i) adapt and extend extant techniques to provide a suite of semi‐automatic tools which calculate crest orientation, wavelength, height, asymmetry direction and asymmetry ratios of bedforms, and then (ii) undertake sensitivity tests on synthetic data, increasingly complex seabeds and a very large‐scale (39 000 km2) aeolian dune system. The automated results are compared with traditional, manually derived, measurements at each stage. This new approach successfully analyses different types of topographic data (from aeolian and marine environments) from a range of sources, with tens of millions of data points being processed in a semi‐automated and objective manner within minutes rather than hours or days. The results from these analyses show there is significant variability in all measurable parameters in what might otherwise be considered uniform bedform fields. For example, the dunes of the Rub' al Khali on the Arabian peninsula are shown to exhibit deviations in dimensions from global trends. Morphological and dune asymmetry analysis of the Rub' al Khali suggests parts of the sand sea may be adjusting to a changed wind regime from that during their formation 100 to 10 ka BP. Copyright © 2012 John Wiley & Sons, Ltd.

Spatial statistics of surface roughness change derived from multi-scale digital elevation models

Surface roughness is an important variable of geomorphologic research and hydrological modeling, and widely used in the Earth and planetary science. It plays an important role in determining how a ground object will interact with its environment. Quantification of surface-roughness variability using Digital elevation model (DEM) were applied to study area of Beichuan County, one of the most destructed areas during the Wenchuan earthquake (7.9 Mw at 14:28:01.42 CST on May 12, 2008). In this paper, four types of DEM were chosen for multi-scale assessment of the surface roughness. Three algorithms were employed, which were Root mean square of elevation, standard deviation of elevation, standard deviation of curvature profile. An overall trend of inclined to be flat from pre-earthquake to post-earthquake could be examined through the three measure methods, which indicate that Wenchuan earthquake indeed had effects on changing the surface roughness. The method of Root mean square of elevation shows the most obvious results in depicting the change before and after the earthquake.

A method for computation of surface roughness of digital elevation model terrains via multiscale analysis

In this paper, an algorithm to compute surface roughness of digital elevation model (DEM) terrains via multiscale analysis is proposed. The algorithm employs the lifting scheme to generate multiscale DEMs. At each scale, the areas of pixels that are modified are computed. Granulometric analysis is employed to compute the average area of curvature regions in the terrain, and the average roughness of the terrain due the distribution of curvature regions. The selected case studies of the algorithm implementation demonstrated that the proposed algorithm provides a surface roughness parameter that is realistic with respect to the amplitudes and frequencies of the terrain, invariant with respect to rotation and translation, and has intuitive meaning. The algorithm allows for a good quantification of a region’s convexity/concavity over varying scales, distinguishing between shallow and deep incisions of valleys and ridges of the terrain, and hence, provides an accurate surface roughness parameter.

Characterization of Areas of Pixels Modified During the Generation of Multiscale Digital Elevation Models

Characterization of Areas of Pixels Modified During the Generation of Multiscale Digital Elevation Models

Characterization of the Size Distribution of Mountains Extracted from Multiscale Digital Elevation Models

Characterization of the Size Distribution of Mountains Extracted from Multiscale Digital Elevation Models

Analysis of Geophysical Networks Derived From Multiscale Digital Elevation Models: A Morphological Approach

We provide a simple and elegant framework based on morphological transformations to generate multiscale digital elevation models (DEMs) and to extract topologically significant multiscale geophysical networks. These terrain features at multiple scales are collectively useful in deriving scaling laws, which exhibit several significant terrain characteristics. We present results derived from a part of Cameron Highlands DEM.