Input Digital Elevation Model Research Articles

Geomorphic flood hazard mapping: from floodplain delineation to flood hazard characterization

ABSTRACTRecent studies show how geomorphic descriptors, retrieved from digital elevation models (DEMs), can be used for flood hazard mapping. As they strictly depend on the accuracy of the input DEMs and reference flood hazard maps used for training, DEM-based flood hazard models may display severe inconsistencies. Our study shows the application of two advanced DEM-based models to a large study area, and presents two main innovative points. First, the delicate tasks of appropriately selecting the input DEM and flood hazard map are specifically addressed with newly defined methods. Second, the ability of DEM-based models to exploit their natural features to enhance flood hazard mapping over the study region is investigated. Our results show (a) the benefits of considering multiple geomorphic descriptors, (b) the potential of DEM-based models for completing the information of imperfect reference flood hazard maps, and (c) the advantages of continuous representation of hazard over binary flood maps.

Atmosphere-aware photoclinometry for pixel-wise 3D topographic mapping of Mars

High-resolution topographic mapping is essential for scientific investigations and operational exploration of planets, such as Mars. Photoclinometry, which uses light scattered from a surface to reconstruct 3D topography, can retrieve subtle topographic details from monocular images. However, its performance is affected by the atmosphere of Mars, which alters surface reflectance mechanisms due to the scattering and absorption by aerosols. Therefore, specific treatments accounting for these atmospheric effects are needed to enable photoclinometric mapping of Mars. This paper presents a novel photoclinometric approach incorporating a radiative-transfer model of atmospheric scattering effects for pixel-wise 3D reconstruction of the Martian surface. The approach requires a high-resolution image, a corresponding coarse-resolution digital elevation model (DEM), and information of the optical depth as inputs. A radiative transfer model adapted to the Martian atmosphere is used to account for the atmospheric effects. The approach also allows directly adopting optical depth estimates from a global database (e.g., the Mars Climate Database). The approach was validated using different types of Mars orbiter images collected by cameras on-board the Mars Reconnaissance Orbiter and the Tianwen-1 Orbiter. The results indicate that the approach can achieve a geometric accuracy (in terms of root-mean-squared error (RMSE) of the elevation) of approximately 2 pixels of the image resolution and significantly enhances topographic details. In addition, we evaluated the approach using different settings for the optical depths and spatial resolutions of the input DEMs. The results show that overestimating the optical depth leads to overestimation of topographic amplitudes (e.g., deeper craters). On the other hand, underestimating the optical depth by the same amount as overestimation leads to a smaller increase in the RMSE. Moreover, coarsening the resolution of an input DEM increases the RMSE of the photoclinometric results. Nevertheless, photoclinometry improves both the RMSE and the resolution of the input DEM. This new approach serves as an effective means for applying photoclinometry for pixel-wise 3D topographic mapping of the Martian surface. This will facilitate exploitation of the large number of high-resolution monocular images of Mars in 3D topographic mapping of the planet.

Large-scale Terrain Authoring through Interactive Erosion Simulation

Large-scale terrains are essential in the definition of virtual worlds. Given the diversity of landforms and the geomorphological complexity, there is a need for authoring techniques offering hydrological consistency without sacrificing user control. In this article, we bridge the gap between large-scale erosion simulation and authoring into an efficient framework. We set aside modeling in the elevation domain in favour of the uplift domain and compute emerging reliefs by simulating the stream power erosion. Our simulation relies on a fast yet accurate approximation of drainage area and flow routing to compute the erosion interactively, which allows for incremental authoring. Our model provides landscape artists with tools for shaping mountain ranges and valleys, such as copy-and-paste operations; warping for imitating folds and faults; and point and curve elevation constraints to precisely sculpt ridges or carve river networks. It also lends itself to inverse procedural modeling by reconstructing the uplift from an input digital elevation model and allows hydrologically consistent blending between terrain patches.

A Morphing-Based Method for Paleotopographic Reconstruction of the Transverse Canyon

The transverse canyon is a V-shaped, fluvial-genetic canyon, a secondary valley formed by transverse drainage crossing a tectonically uplifted mountain. Paleotopography of the transverse canyon is vital to drainage connection and river capture, offering insight into the processes that link large-scale river systems, analyzing paleodrainage patterns, and recreating headward erosion. Notably, modern paleotopographic reconstruction methods are usually limited to reconstructions of paleotopography in vast sedimentary basins and denuded hills in orogenic belts. When applied to transverse canyons, a specific secondary valley found in tiny locations, these techniques are difficult, expensive, and ineffective. This paper proposes an automated method for reconstructing the paleotopography of the transverse canyon using the digital elevation model (DEM) and river. (1) Restore the ridgeline above the transverse canyon based on the ridgelines of the mountains on both sides; (2) create a buffer zone based on the river centerline with unequal buffer distances on each side; (3) construct a mesh surface by interpolating transition curves from the morphing method, using the three-edge type; (4) apply a spatial interpolation method to the elevation points on the mesh surface to construct the DEM above the transverse canyon and stitch it to the input DEM to obtain the paleotopographic DEM; (5) calculate the spatial attributes. The objective of this study is to reconstruct the paleotopography of eight typical transverse canyons in the comb-like fold belt of northern Chongqing. As part of the paleotopographic reconstruction of the transverse canyon, we address the effects of dislocated mountains, erosion gullies, and different morphing techniques, as well as the applicability of the proposed method to reconstructing other secondary valleys. In conclusion, we reconstruct paleotopographic DEMs of transverse canyons to replicate headward erosion processes, assess paleodrainage patterns, and build three-dimensional solid models.

An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcán Copahue (Argentina & Chile)

Lahars are debris flows originating at volcanoes with very erosive and destructive capabilities. Lahars constitute a serious hazard for the communities close to the Volcán Copahue, lying at the Argentina-Chile border. This study presents a comprehensive analysis of the sensitivity of the geographic information system (GIS) assisted model LAHARZ to the spatial resolution and quality of the digital elevation models (DEMs) for mapping lahar inundation areas within the context of Volcán Copahue. In addition, critical lahar volumes required to reach the nearby settlements are investigated, following a literature review to characterize plausible triggering mechanisms. We compared lahar inundation areas at the four main catchments of Volcán Copahue resulting from five DEMs: three 30-m spatial resolution datasets (SRTM, ASTER and AW3D30), the 12-m WorldDEM™, and a 5-m DEM derived from SPOT-7 data. Our sensitivity analysis shows that DEM spatial resolution and quality precipitate into the LAHARZ results. The interaction between the spatial resolution and the channel confinement constitutes the main control of the output geometries. Higher-spatial resolution DEMs (i.e. smaller pixel size) better depict channels and better define confined channels zones resulting in narrower output areas and longer runouts. For the same volume, the inundation areas obtained using the 5- and 12-m pixel DEMs are narrower and 2–3 km longer than when derived from any of the 30-m DEMs. At unconfined channels, LAHARZ is less sensitive to the spatial resolution and inundation areas tend to show ragged edges regardless of the input DEM. The quality of the DEM controls the inundation trajectories. These results highlight the importance of evaluating topographic models for LAHARZ simulations before the implementation of the model for hazard mapping. Our results suggest that lahars of volumes of 105–106 m3 would reach the nearest villages within a radius of 15 km from the volcano. More work is required to improve the knowledge on lahar deposits and in turn hazard assessment.

A Neural Network-Based Fusion Approach for Improvement of SAR Interferometry-Based Digital Elevation Models in Plain and Hilly Regions of India

Interferometry Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique for studying the earth’s surface topography and deformations; it is used to generate high-quality Digital Elevation Models (DEMs). DEMs are a crucial and primary input to various topographical quantification and modelling applications. The quality of input DEMs can be further improved using fusion methods, which combine multi-sensor or multi-temporal datasets intelligently to retrieve the best information from the input data. This research study is based on developing a Neural Network-based fusion approach for improving InSAR-based DEMs in plain and hilly terrain parts of India. The study areas comprise relatively plain terrain from Ghaziabad and hilly terrain of Dehradun and their surrounding regions. The training dataset consists of DEM elevations and derived topographic attributes like slope, aspect, topographic position index (TPI), terrain ruggedness index (TRI), and vector roughness measure (VRM) in different land use land cover classes of the study areas. The spaceborne altimetry ICESat-2 ATL08 photon data are used as a reference elevation. A Feed Forward Neural Network with a backpropagation algorithm is trained based on the prepared training samples. The trained model produces fused DEMs by learning the relationship between the input and target samples; this is used to predict elevations for the test areas. The accuracy of results from the models is assessed with TanDEM-X 90 m DEM. The fused DEMs show significant improvement in terms of RMSE (Root Mean Square Error) over the input DEMs with an improvement factor of 94.65% in plain areas and 82.62% in hilly areas. The study concludes that the ANN with its universal approximation property can significantly improve the fused DEM.

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

ANN-Based DEM Fusion and DEM Improvement Frameworks in Regions of Assam and Meghalaya using Remote Sensing Datasets

The Digital Elevation Models (DEMs) are a key and primary input to a large number of modelling processes such as disaster risk monitoring, flood modelling, hydrology, geology, geomorphology, climatology, and environmental study applications. The DEM serves as an important source of topographic information representing the continuous surface of the earth in 3 dimensions with x, y and z coordinates of any point in a grided raster form or a vector TIN form. This study is based on developing a new method using the universal approximation capability of neural networks for the fusion and improvement of L-band and X-band SAR (Synthetic Aperture Radar) DEMs in the complex terrain of Assam and Meghalaya states of the Indian geographic region using the DEM fusion technique. The high-spatial-resolution ALOS PALSAR RTC HR 12.5 m DEM products are used in a fusion framework designed with neural network models. The network adaptively learns the terrain information to produce fused DEM products. The neural network models generate the relationship between the input elevation information from ALOS PALSAR DEMs and precise reference elevation from ICESat-2 spaceborne altimetry as target data. The training and testing data samples are prepared and filtered by checking the correct range of elevation from the toposheets of this region. Different models are used to separately train for the relatively plain valley portion of the Assam region and mountainous portions of the highland Shillong plateau. The obtained fused DEMs from the developed neural net structure are assessed for their quality and accuracy by estimating the RMSE parameter. The fused DEM attains an RMSE value of 7 meters for the complete region which is a significant improvement over the input DEM RMSE of approximately 11 meters. The plain area points and mountainous region points are assessed separately to analyze the predictions from neural nets in the two types of terrains observed in this study site. Moreover, TanDEM-X 90 m DEM is improved using the neural network modeling in the geometric distortions affected areas, which shows an improvement of around 33% overall at the study site. The assessment of plain and mountainous region points for near-ground points shows an improvement of 47% and 55% respectively. The fusion framework designed using the neural network models is an effective and efficient method for obtaining fused DEMs as well as for the improvement of the existing DEMs for complex terrain.

Multiresolution land surface segmentation and generalization of DEM: Tentative searching for the optimal settings of detecting elementary forms

AbstractBefore applying a land surface segmentation algorithm, generalizing the input digital elevation model (DEM) can help optimize the multiresolution land surface segmentation. We tested an indicator of optimal generalization of DEM (the index K0 (Gnn)) and the setting of the scale parameter (SP) in the multiresolution segmentation by geographical object‐based image analysis (GEOBIA). The concept of elementary forms was used as a theoretical base. We tested the utility of the estimation of scale parameter (ESP2 tool) for the estimation of SP. The DEM was generalized by approximating land surface with a fourth‐order polynomial using the least‐squares method for various sizes of a moving window. Altitude, slope, aspect, normal slope line (profile) curvature, and contour line (plan) curvature were used as the input variables of segmentation. The results from the three areas of interest differing in morphology show a clear dependence of local variance (LV) on the average segmentation area (ASA) and generalization level. The relationship between LV and ASA can be effectively used to determine the most suitable SP, however, the ESP2 tool is not ideal to define the LV–ASA relationship. The relationship better defines the nested hierarchy of land surface in case the DEM is generalized for the optimal generalization level (i.e., windows size). The optimum DEM generalization for detecting elementary forms of a particular geomorphological hierarchical level was achieved by using the moving window size for which the index K0 (Gnn) reaches the local maximum.

A novel framework for seamless mosaic of Cartosat-1 DEM scenes

Digital elevation model (DEM) is used as a source of elevation data in wide range of applications. The applications involving larger area than an individual DEM scene often need a mosaic of DEM scenes. A simple stitching exhibits seams along the borders of DEM scenes in the mosaic output. The presence of seam lines varies with the number of scenes in an overlapping region and their overlap extents. The conventional feathering-based blend method, by virtue of the method itself, considers only two input scenes in an instance of the mosaic process. This method requires a suitable ordering of the input DEM scenes, especially in the overlapping regions containing more than two scenes. In this paper, we propose a framework that automatically handles the multiple overlapping regions and creates a seamless mosaic of Cartosat-1 DEM scenes for a given region of interest. We use a tree data structure to arrange input DEM scenes hierarchically based on their metadata. A traversal algorithm is devised to establish an order of precedence among the DEM scenes. This determines a unique input sequence of DEM scenes during mosaic process. In addition, two variants of feathering-based blend method are formulated to handle all possible cases of the overlapping regions. The experiments have been conducted on Cartosat-1 DEM scenes over 1°×1° region of interest. We analyze and compare our proposed methodology with average method and feathering-based blend method. We demonstrate that our framework exhibits a smooth transition of elevation values in the overlapping regions of the DEM mosaic output.

Modeling landslide failure surfaces by polynomial surface fitting

Estimating landslide volume based on pre- and post-event elevation models without detailed field investigations is still a major challenge due to hidden failure surfaces. A MATLAB model named “SLIPFITTER” was developed to model failure surfaces using polynomial surface to fit the geometry of exposed scarps extracted from Digital Elevation Models (DEMs). To test our model, a series of analog experiments were designed to acquire data for model testing in a controlled environment. The test indicates the only option to simulate failure surfaces with only remotely sensed data, is to assume that the failure surface was outcropping at the toe of slopes. It is not possible to model landslides that do not reach the bottom of slopes without survey data. Using the assumption, the model was tested on real landslides, showing its capability of modeling for multiple landslides over an area in a single simulation, and the possibility to predict potential failure surfaces from a reported crack. The major limitations of the model include the high requirement for the input DEM quality, the incapability to consider underlying bedrock structures, and the difficulty of simulating landslides with complex movements.

Inundation Exposure Assessment for Majuro Atoll, Republic of the Marshall Islands Using A High-Accuracy Digital Elevation Model

Majuro Atoll in the central Pacific has high coastal vulnerability due to low-lying islands, rising sea level, high wave events, eroding shorelines, a dense population center, and limited freshwater resources. Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, coastal elevation data (with accuracy information) are critical for assessments of inundation exposure. Previous research has demonstrated the importance of using high-accuracy elevation data and rigorously accounting for uncertainty in inundation assessments. A quantitative analysis of inundation exposure was conducted for Majuro Atoll, including accounting for the cumulative vertical uncertainty from the input digital elevation model (DEM) and datum transformation. The project employed a recently produced and validated DEM derived from structure-from-motion processing of very-high-resolution aerial imagery. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying lands (disconnected hydrologically from the ocean) were mapped and characterized for three inundation levels using deterministic and probabilistic methods. At the highest water level modeled (3.75 ft, or 1.143 m), more than 34% of the atoll study area is likely to be exposed to inundation (68% chance or greater), while more than 20% of the atoll is extremely likely to be exposed (95% chance or greater). The study demonstrates the substantial value of a high-accuracy DEM for assessing inundation exposure of low-relief islands and the enhanced information from accounting for vertical uncertainty.

Variable DEM generalization using local entropy for terrain representation through scale

ABSTRACTAn automated method of variable digital elevation model (DEM) smoothing is presented. Using variably sized kernels to perform filtering, the method is driven by the entropy of local z-values in the DEM, i.e. the amount of information necessary to convey the elevation variety in the neighborhood of each pixel. This paper presents the method in service of low-pass filtering in order to smooth the raster, though other neighborhood-based filters could be implemented as well. When used in smoothing, the method successfully retains detail in areas of higher relief variation and suppresses it in areas of lower variation, thereby retaining more salient features like ridges, peaks, or incised valleys, while diminishing flatter ones. Varying the neighborhood size with which entropy calculations are made allows for filtering through continuous map scale, enabling multi-scale representation. The method also includes a simple correction for smoothed pixels such that their z-value range reflects that of the input DEM, thereby ensuring that subsequent products such as generated contour lines remain within correct ranges. Several illustrations are given of the method's results.

Generation of high-quality digital elevation models by assimilation of remote sensing-based DEMs

Advanced spaceborne remote sensing techniques are currently available for the generation of high-resolution digital elevation models (DEMs) aiding in the advancement of Earth’s topographic modeling. DEM assimilation using data-driven models is a technique that can effectively overcome the limitations of individual DEMs generated from different techniques and sources of remote sensing data. The assimilation of DEMs generated from optical stereo pairs (Cartosat-1) and InSAR pair (ALOS PALSAR-1) was done for improving the DEM quality. Further, by using data assimilation techniques, the openly accessible DEM products derived from remote sensing datasets were used for generation of improved high-quality DEMs. The feature level fusion and the Kalman optimal interpolation techniques were used for the assimilation of these DEMs. Three sites, namely Dehradun, Jaipur, and Kendrapara, were selected on basis of topographic conditions, i.e., rugged, moderate, and gentle. RMSEs calculated for Dehradun and Jaipur have shown significant improvement in assimilated DEMs. Assimilation of CartoDEM V3 R1 and ALOS PALSAR RTC HR DEMs at the Jaipur site has resulted in highly improved RMSE in elevation of 1.22 m using the Kalman optimum interpolation model. However, in case of the plain region of Kendrapara, the CartoDEM V3 R1 data are found to have the highest accuracy of 2.00 m, indicating importance of selective use of input DEMs and assimilation methods.

Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)

Namibia is a dry and low populated country highly dependent on agriculture, with many areas experiencing land degradation accelerated by climate change. One of the most obvious and damaging manifestations of these degradation processes are gullies, which lead to great economic losses while accelerating desertification. The development of standardized methods to detect and monitor the evolution of gully-affected areas is crucial to plan prevention and remediation strategies. With the aim of developing solutions applicable at a regional or even national scale, fully automated satellite-based remote sensing methods are explored in this research. For this purpose, three different algorithms are applied to a Digital Elevation Model (DEM) generated from the TanDEM-X satellite mission to extract gullies from their geomorphological characteristics: (i) Inverted Morphological Reconstruction (IMR), (ii) Smoothing Moving Polynomial Fitting (SMPF) and (iii) Multi Profile Curvature Analysis (MPCA). These algorithms are adapted or newly developed to identify gullies at the pixel level (12 m) in our study site in the Krumhuk Farm. The results of the three methods are benchmarked with ground truth; specific scenarios are observed to better understand the performance of each method. Results show that MPCA is the most reliable method to identify gullies, achieving an overall accuracy of approximately 0.80 with values of Cohen Kappa close to 0.35. The performance of these parameters improves when detecting large gullies (>30 m width and >3 m depth) achieving Total Accuracies (TA) near to 0.90, Cohen Kappa above 0.5, and User Accuracy (UA) and Producer Accuracy (PA) over 0.50 for the gully class. Small gullies (<12 m wide and <2 m deep) are usually neglected in the classification results due to spatial resolution constraints within the input DEM. In addition, IMR generates accurate results for UA in the gully class (0.94). The MPCA method developed here is a promising tool for the identification of large gullies considering extensive study areas. Nevertheless, further development is needed to improve the accuracy of the algorithms, as well as to derive geomorphological gully parameters (e.g., perimeter and volume) instead of pixel-level classification.

Automatic Co-Registration of Digital Elevation Models Based on Centroids of Subwatersheds

This paper proposes a new method for automatic co-registration of digital elevation models (DEMs) based on centroids of subwatersheds. Subwatersheds are stable physical features, making their centroids more reliable and accurate as control points (CPs) than the other features. In the present method, subwatersheds are delineated from DEMs using hydrological analysis procedures. Modified invariant moments are employed to measure the similarity of subwatersheds for determining the correspondences between the reference and input. Centroids of matched subwatersheds are then derived as CP candidates, where the root-mean-square error is applied to eliminate mismatches using a global consistency check method. The established CP pairs are used to estimate parameters of a 3-D conformal transformation model, which is employed to rectify the input DEM. The accuracy of CP detection was assessed using two DEM subsets of different terrains. The results of six tests showed that the maximum shift and rotation errors were about 2 m (1/45 pixel) and 0.006°. The developed approach was used to co-register the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM to the Shuttle Radar Topography Mission DEM at three locations. The results revealed that both the mean absolute error and the standard deviation of the elevation differences were reduced for all the tests after co-registration, showing the good performance of the proposed method. Comparisons have also been made against previous works, which suggested that our results were consistent with the previous studies.

Sensitivity of a Floodplain Hydrodynamic Model to Satellite-Based DEM Scale and Accuracy: Case Study—The Atchafalaya Basin

The hydrodynamics of low-lying riverine floodplains and wetlands play a critical role in hydrology and ecosystem processes. Because small topographic features affect floodplain storage and flow velocity, a hydrodynamic model setup of these regions imposes more stringent requirements on the input Digital Elevation Model (DEM) compared to upland regions with comparatively high slopes. This current study provides a systematic approach to evaluate the required relative vertical accuracy and spatial resolution of current and future satellite-based altimeters within the context of DEM requirements for 2-D floodplain hydrodynamic models. A case study is presented for the Atchafalaya Basin with a model domain of 1190 km2. The approach analyzes the sensitivity of modeled floodplain water elevation and velocity to typical satellite-based DEM grid-box scale and vertical error, using a previously calibrated version of the physically-based flood inundation model (LISFLOOD-ACC). Results indicate a trade-off relationship between DEM relative vertical error and grid-box size. Higher resolution models are the most sensitive to vertical accuracy, but the impact diminishes at coarser resolutions because of spatial averaging. The results provide guidance to engineers and scientists when defining the observation scales of future altimetry missions such as the Surface Water and Ocean Topography (SWOT) mission from the perspective of numerical modeling requirements for large floodplains of O[103] km2 and greater.

Constructing a Comprehensive Tool for Deriving Drainage Network using Semi-Open Source Tools and Comparision on Different DEM Data Sources

Abstract. Digital elevation model (DEM) provides elevation information in raster format for an area which help in analysis as these phenomena are gravity depended. Hydrological study requires creation of drainage network map. DEM is the primary input for this process. Generally 6 or more processes are required to be completed to get the drainage network. These are available in form of hydrology tools in the ArcGIS software. These processes are manual and time consuming which increases the chances of human error. An automated process is constructed in this paper which aims to create a tool that can handle multiple files in an intelligent manner. The input DEM plays a significant role in hydrological studies. This study attempts to do a comparative study to analyse the variation in the drainage network and the intermediate products with the change in the input DEM. A tool is created using ArcPy site package in Python programming language to integrate all required hydrology tools. The script is then used to create a tool in ArcGIS 10 which takes location as an input parameter and perform the process on all the DEM files inside the directory. The tool creates separate directory for every DEM file and thus reduces the chances of file mismanagement. The proposed tool is tested on two different datasets namely ASTER GDEM and Cartosat DEM. The tool runs efficiently on both the datasets and thus provides results to compare the drainage pattern produced by these different data sources.

A geomorphology-based approach for digital elevation model fusion – case study in Danang city, Vietnam

Abstract. Global digital elevation models (DEM) are considered a source of vital spatial information and find wide use in several applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) and Shuttle Radar Topographic Mission (SRTM) DEM offer almost global coverage and provide elevation data for geospatial analysis. However, GDEM and SRTM still contain some height errors that affect the quality of elevation data significantly. This study aims to examine methods to improve the resolution as well as accuracy of available free DEMs by data fusion techniques and evaluating the results with a high-quality reference DEM. The DEM fusion method is based on the accuracy assessment of each global DEM and geomorphological characteristics of the study area. Land cover units were also considered to correct the elevation of GDEM and SRTM with respect to the bare-earth surface. The weighted averaging method was used to fuse the input DEMs based on a landform classification map. According to the landform types, the different weights were used for GDEM and SRTM. Finally, a denoising algorithm (Sun et al., 2007) was applied to filter the output-fused DEM. This fused DEM shows excellent correlation to the reference DEM, having a correlation coefficient R2 = 0.9986, and the accuracy was also improved from a root mean square error (RMSE) of 14.9 m in GDEM and 14.8 m in SRTM to 11.6 m in the fused DEM. The results of terrain-related parameters extracted from this fused DEM such as slope, curvature, terrain roughness index and normal vector of topographic surface are also very comparable to reference data.

Utilization of a comparison of curvatures for land surface segmentation

Abstract Utilization of a new geomorphometric variable for land surface segmentation — the angle of absolute curvatures — is a main goal of the paper. The angle of absolute curvatures is defined as the difference between the orientation of maximal curvature (field independent) and the orientation of the greater of the profile or the tangential curvature. Land-forms separated by three types of borders (A, B, C) can be delimited from the field of angles of absolute curvatures. Borders of A type are connected with a local extreme of slope. Borders of B and C type are connected with a change to the priority of either profile or tangential curvature, as shown in computation, respectively. Fields of altitude, slope, profile curvature, tangential curvature and rotor curvature are reflected by an algorithm. Distinct borders in the field of the angles of absolute curvatures are connected with a sudden change of value and with zero isolines in the previously mentioned fields. Spatially closed entities generated by this proposed algorithm are considered to be a variant of the elementary forms of the land surface. The quality of information generated by this algorithm depends on the size of the grid mesh of the input digital elevation model. The algorithm in its current state is suitable for locating the borders of some elementary forms in the first stage of geomorphology mapping.