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Filling SRTM voids: The delta surface fill method
Abstract
This article presents a new technique for filling voids in SRTM DEM data. Tests prove the increased effectiveness of DSF in filling SRTM voids compared to the F&F technique, especially at the problematic void interfaces. DSF gives better results by both visual and quantitative measures. Because of these performance improvements, DSF is now in use by NGA and its contractors in SRTM void filling. In addition to operations on SRTM datasets, DSF has application to other DEM-level void filling endeavors. Numerous combinations of elevation data can be used in the parent and fill surface roles.
... To overcome these limitations, researchers have explored the use of auxiliary data sources, such as integrating multiple DEMs or utilizing remote sensing imagery. Techniques like fill-and-feather, delta surface fill, and moving window erosion have been developed to fuse data from various sources [4,6]. However, these approaches may falter in areas with unreliable auxiliary data or fail to capture the detailed features inherent in DSMs. ...
... Factors like water bodies, dense vegetation, low reflectivity surfaces, and complex terrain can impede the collection of accurate elevation data. For example, radar-based missions like the Shuttle Radar Topography Mission (SRTM) are prone to issues like shadowing and layover in steep or rugged terrains, leading to data gaps [6]. Additionally, atmospheric conditions and instrument limitations can contribute to missing data in DEMs. ...
... The Fill and Feather (FF) [4] technique replaces missing data with values from an auxiliary DEM and applies smoothing at the edges to ensure seamless transitions. The Delta Surface Fill (DSF) [6] method creates a delta surface by computing the difference between the DEM and a resampled auxiliary surface, which is then used to adjust the fill surface for smooth integration. These methods rely heavily on the availability of high-quality auxiliary DEMs. Figure 2. Summary of the proposed architecture for DSM void filling. ...
Digital Surface Models (DSMs) are essential for accurately representing Earth's topography in geospatial analyses. DSMs capture detailed elevations of natural and manmade features, crucial for applications like urban planning, vegetation studies, and 3D reconstruction. However, DSMs derived from stereo satellite imagery often contain voids or missing data due to occlusions, shadows, and lowsignal areas. Previous studies have primarily focused on void filling for digital elevation models (DEMs) and Digital Terrain Models (DTMs), employing methods such as inverse distance weighting (IDW), kriging, and spline interpolation. While effective for simpler terrains, these approaches often fail to handle the intricate structures present in DSMs. To overcome these limitations, we introduce Dfilled, a guided DSM void filling method that leverages optical remote sensing images through edge-enhancing diffusion. Dfilled repurposes deep anisotropic diffusion models, which originally designed for super-resolution tasks, to inpaint DSMs. Additionally, we utilize Perlin noise to create inpainting masks that mimic natural void patterns in DSMs. Experimental evaluations demonstrate that Dfilled surpasses traditional interpolation methods and deep learning approaches in DSM void filling tasks. Both quantitative and qualitative assessments highlight the method's ability to manage complex features and deliver accurate, visually coherent results.
... To apply this weighting to the final WSI, a 1 is added to the slope percentage (slope/100) and multiplied by the ∆pw to generate the ∆ws coefficient that then adds the MR (c) to create the WSI surface. WSI = ∆ws{(∆pw(1 + (degree slope/100))) + c} (2) The WSI approach was inspired by the Delta Surface Fill Method by Grohman, Kroenung, and Strebeck (2006) because of its simplicity [16]. Adding progressive and slope weighting provides for the alignment of the interpolated data between two disparate data sources. ...
... Remote Sens. 2024,16, 3418 ...
... Remote Sens. 2024, 16 As higher-quality data becomes available, this dynamic approach allows the opportunity to introduce additional variables to adapt the method to meet future requirements. Using the resulting Bit-pack data layer to implement multiple blending interpolation methods between disparate inputs is a predictable method to produce consistent outcomes that can improve transitions between disparate bathymetric data and enhance the TBDEM product. ...
Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.
... There are lots of different methods with which to fill InSAR-DEM voids. SRTM, an earlier nearly global DEM with uniform global quality using bistatic InSAR technology, has brought about a wealth of void-filling research [13][14][15][16]. A common idea is to perform interpolation, relying on the elevations around the voids to calculate the missing elevation, and proceed layer-by-layer until all voids are filled. ...
... According to the method characteristics, the selection of points used for interpolation will greatly affect the filling results. The elevations around the voids are not always reliable, resulting in discontinuity in the interface area between the voids and non-void areas [13]. To smooth the results, some scholars have proposed to first reconstruct a smooth plane in the voids. ...
... The selection of auxiliary data is a key factor, such as extracting valley lines from Landsat sensor imagery [19], night time ASTER thermal imagery data [20], and shadow maps from multispectral images [21,22].In fact, these auxiliary data are not homogeneous with those of DEMs, and are not as simple and direct as DEM. External DEMs in the same area are the most commonly used auxiliary data [13,[23][24][25]; the classic one is the fill and feather (F&F) method [15]. The void is replaced directly with external data, and the boundary bias around it is eliminated through feathering, which has the potential to blur edges and has no theoretical basis. ...
Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks.
... In such cases, especially occurring in the automatically generated DSMs from stereo satellite images with a large stereo angle, some holes may cover large areas or even mask some small objects. As a result, a complementary method has been frequently reported in the literature through which an auxiliary available DSM is utilized as a reference to fill the gaps (Dowding et al. 2004, Grohman et al., 2006, Luedeling et al, 2007, Ling et al, 2007, Krauß et al, 2009, Hoja et al, 2009, Krauß et al, 2012, Tran et al, 2014). Filling the holes in a digital elevation model by filling in data from another source is also sometimes called " fusion " in the literature, or at least seen as one step of fusion (Schindler et al, 2010). ...
... Filling the holes in a digital elevation model by filling in data from another source is also sometimes called " fusion " in the literature, or at least seen as one step of fusion (Schindler et al, 2010). Amongst all reported methods in this concept, one can find three main approaches including fill and feather (Dowding et al. 2004), delta surface fill (Grohman et al., 2006), and moving window applying erosion technique (Karkee et al., 2008). ...
... Since 2000, DEM from Shuttle Radar Topography Mission (SRTM) has being widely used as the main reference for elevation data in many 3D geospatial analysis processes. Primary SRTM DEM has many small and large holes due to the complex nature of IFSAR Technology; dielectric constant (Luedeling et al., 2007 ) beside other sources like shadowing (Grohman et al., 2006). The majority of researches on the problem of DSM void filling are dedicated to the filling of holes in SRTM DEM. ...
Digital Surface Models (DSM) derived from stereo-pair satellite images are the main sources for many Geo-Informatics applications like 3D change detection, object classification and recognition. However since occlusion especially in urban scenes result in some deficiencies in the stereo matching phase, these DSMs contain some voids. In order to fill the voids a range of algorithms have been proposed, mainly including interpolation alone or along with auxiliary DSM. In this paper an algorithm for void filling in DSM from stereo satellite images has been developed. Unlike common previous approaches we didn’t use any external DSM to fill the voids. Our proposed algorithm uses only the original images and the unfilled DSM itself. First a neighborhood around every void in the unfilled DSM and its corresponding area in multispectral image is defined. Then it is analysed to extract both spectral and geometric texture and accordingly to assign labels to each cell in the voids. This step contains three phases comprising shadow detection, height thresholding and image segmentation. Thus every cell in void has a label and is filled by the median value of its co-labelled neighbors. The results for datasets from WorldView-2 and IKONOS are shown and discussed.
... If an external reference DEM is available for the considered region, all different types of gaps are filled by ingesting the reference into the missing areas. In this case, we implemented a stable method based on the delta surface fill interpolation [19]. The principle is that the considered area is composed by the bounding box of the gap to be filled, plus an adequate margin to anchor the reference to the input DEM (see Figure 6(1a)). ...
... The delta surface fill method replaces a DEM gap with an appropriate reference source, which is adjusted to the input DEM values found at the void interface [19]. Specifically, the delta surface is defined as the difference between the DEM to be edited and the resampled external reference on the same grid and vertical datum, computed over a larger area with respect to the considered gap. ...
The spaceborne mission TanDEM-X successfully acquired and processed a global Digital Elevation Model (DEM) from interferometric bistatic SAR data at X band. The product has been delivered in 2016 and is characterized by an unprecedented vertical accuracy. It is provided at 12 m, 30 m, and 90 m sampling and can be accessed by the scientific community via a standard announcement of opportunity process and the submission of a scientific proposal. The 90 m version is freely available for scientific purposes. The DEM is unedited, which means that it is the pure result of the interferometric SAR processing and subsequent mosaicking. Residual gaps, resulting, e.g., from unprocessable data, are still present and water surfaces appear noisy. This paper reports on the algorithms developed at DLR's Microwaves and Radar Institute for a fully automatic editing of the global TanDEM-X DEM comprising gap filling and water editing. The result is a new global gap-free DEM product at 30 m sampling, which can be used for a large variety of scientific applications. It also serves as a reference for processing the upcoming TanDEM-X Change DEM layer.
... The crux of the void-filling problem is a reliable source of auxiliary data and the method used to eliminate the bias between the datasets (Ling et al., 2007;Karkee et al., 2008). The main methods used to deal with the vertical bias in multi-source DEM fusion include fill and feather (FF) (Dowding et al., 2004), DSF (Grohman et al., 2006), and TIN-DSF (Luedeling et al., 2007). The FF algorithm simply uses a constant to approximate the bias, which ignores the varying terrain surface within the void regions. ...
... Comparatively, the DSF method uses the non-void values in the surface to compute the difference surface between the input data, referring to a ''delta surface". The voids in the delta surface are then filled using a geostatistical interpolation method, considering the local surface trend (Grohman et al., 2006). However, this method is only optimal for small voids. ...
The absence of a high-quality seamless global digital elevation model (DEM) dataset has been a challenge for the Earth-related research fields. Recently, the 1-arc-second Shuttle Radar Topography Mission (SRTM-1) data have been released globally, covering over 80% of the Earth’s land surface (60°N–56°S). However, voids and anomalies still exist in some tiles, which has prevented the SRTM-1 dataset from being directly used without further processing. In this paper, we propose a method to generate a seamless DEM dataset blending SRTM-1, ASTER GDEM v2, and ICESat laser altimetry data. The ASTER GDEM v2 data are used as the elevation source for the SRTM void filling. To get a reliable filling source, ICESat GLAS points are incorporated to enhance the accuracy of the ASTER data within the void regions, using an artificial neural network (ANN) model. After correction, the voids in the SRTM-1 data are filled with the corrected ASTER GDEM values. The triangular irregular network based delta surface fill (DSF) method is then employed to eliminate the vertical bias between them. Finally, an adaptive outlier filter is applied to all the data tiles. The final result is a seamless global DEM dataset. ICESat points collected from 2003 to 2009 were used to validate the effectiveness of the proposed method, and to assess the vertical accuracy of the global DEM products in China. Furthermore, channel networks in the Yangtze River Basin were also extracted for the data assessment.
... To address the staircase effect at the seams during traditional void filling, the most straightforward strategy is filling and feathering, but this may result in the loss of terrain information. The delta surface fill (DSF) method [12], [13] and its improved versions, such as the TIN-based delta surface approach (TIN-DSF) [14] and hierarchical delta surface interpolation method [15], significantly reduce systemic vertical discrepancies across different DEMs. However, due to the complex geometric shapes of voids and resolution differences, these methods cannot address the seams. ...
Digital elevation models (DEMs) are indispensable in the fields of photogrammetry and remote sensing. During generating DEMs from multi-view satellite images, non-values often occur in areas with cloud cover or water bodies, resulting in void areas. Existing interpolation or fusion methods for filling DEMs often struggle with the challenge of ensuring smooth elevation transitions at the seams, while deep-learning-based methods are encumbered by substantial memory and computational demands. To address these issues, this study proposes a simple adaptive DEM void filling method based on raster distance transform to reconstruct a complete topographic surface, using publicly available global DEMs as auxiliary data. Firstly, an initial weight grid is created by performing distance transform on the mask of void areas. Then, the buffer zone and weight map for DEM fusion are adaptively determined by setting appropriate threshold in the initial weight grid. Finally, the target DEM is seamlessly fused with the auxiliary DEM according to the weight map. Comparative experiments conducted across different topographic terrains demonstrate the effectiveness of the proposed method in seamless DEM void filling. It achieves a substantial reduction in root mean square error of elevation differences at seams from 4.27 m to 1.47 m, significantly mitigating the staircase effect in visual. And the simulation experiments also prove that this method has a higher terrain fidelity.
... Digital elevation models (DEM) since the global dissemination of SRTM data in 2005 have come a long way. It was plagued with voids at the beginning, and studies showed how they could effectively be patched (Grohmann et al. 2006;Ling et al. 2007;Gallant and Read, 2009). Announcement of the first version of Aster GDEM in 2009 provided additional support for further strengthening SRTM's later versions (Reuter et al. 2007;Altunel, 2018). ...
There are many ways one can decide if an engineering related undertaking would be feasible and productive when the topography is thoroughly and precisely investigated before it takes shape. Forestry is just one profession that proper planning is of the essence when it comes to the logging phase of the entire production process. Logging in Türkiye is primarily handled over an ever-growing forest-road network. Although the specialized equipment e.g. yarders, tractor-winches, are also put into the works, their share and production capacity is limited and confined to certain parts of the country. Thus, timber production primarily revolves around direct tractor-skidding throughout the forest floor, taking the felled log from the stump to the nearest road. Here, topography is the real constraint in production method decision-making. Topographic maps have long been used to extract topographic parameters. However, Türkiye recently announced the completion of first national high-resolution digital elevation model, 5 m DEM. High precision, which would be achieved utilizing this DEM, reemphasized the importance of slope and topographic roughness in primary transport planning. In this study, we calculated the amount of slope and topographic roughness acreages in two forest planning units based on elevation differences. Both yielded enough extreme surface acreages, which would question the expansion of road building and justify the adoption of specialized equipment.
... Traditional interpolation methods, such as inverse distance weighting (IDW), bilinear interpolation, nearest-neighbor interpolation (NNI), and bicubic interpolation [11][12][13][14][15] were widely used in the early work, but these methods are susceptible to terrain relief, resulting in less stable accuracy [16,17]. The approach of fusing multiple data sources to construct a high-resolution DEM is also frequently utilized [18][19][20]. ...
High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques are currently low-cost and feasible methods used for obtaining high-resolution DEMs. Single-image super-resolution (SISR) techniques have become popular in DEM SR in recent years. However, DEM super-resolution has not yet utilized reference-based image super-resolution (RefSR) techniques. In this paper, we propose a terrain self-similarity-based transformer (SSTrans) to generate super-resolution DEMs. It is a reference-based image super-resolution method that automatically acquires reference images using terrain self-similarity. To verify the proposed model, we conducted experiments on four distinct types of terrain and compared them to the results from the bicubic, SRGAN, and SRCNN approaches. The experimental results show that the SSTrans method performs well in all four terrains and has outstanding advantages in complex and uneven surface terrains.
... In the early work, the traditional interpolation methods, such as inverse distance weighted interpolation (IDW) [16] and Kriging [17], have been widely used. These methods are effective in flat and low-lying terrestrial areas, but are easily impacted by topographical undulations [18]. For this reason, some scholars introduced the concept of image processing to fill the DEM voids. ...
The digital elevation model (DEM) acquired through photogrammetry or LiDAR usually exposes voids due to phenomena such as instrumentation artifact, ground occlusion, etc. For this reason, this paper proposes a multiattention generative adversarial network model to fill the voids. In this model, a multiscale feature fusion generation network is proposed to initially fill the voids, and then a multiattention filling network is proposed to recover the detailed features of the terrain surrounding the void area, and the channel-spatial cropping attention mechanism module is proposed as an enhancement of the network. Spectral normalization is added to each convolution layer in the discriminator network. Finally, the training of the model by a combined loss function, including reconstruction loss and adversarial loss, is optimized. Three groups of experiments with four different types of terrains, hillsides, valleys, ridges and hills, are conducted for validation of the proposed model. The experimental results show that (1) the structural similarity surrounding terrestrial voids in the three types of terrains (i.e., hillside, valley, and ridge) can reach 80–90%, which implies that the DEM accuracy can be improved by at least 10% relative to the traditional interpolation methods (i.e., Kriging, IDW, and Spline), and can reach 57.4%, while other deep learning models (i.e., CE, GL and CR) only reach 43.2%, 17.1% and 11.4% in the hilly areas, respectively. Therefore, it can be concluded that the structural similarity surrounding the terrestrial voids filled using the model proposed in this paper can reach 60–90% upon the types of terrain, such as hillside, valley, ridge, and hill.
... The resulting DSM has 1 meter resolution, which is approximately two times of the original image resolution. In the end, the delta surface fill algorithm (Grohman et al., 2006) is performed to fill the unmatched pixels with SRTM heights and generate the final DSM. It has to be noted that the whole DSM generation procedure is fully automatic without any manual processing. ...
... The third choice, therefore, was to use PRISM ALOS World 3D-30m (AW3D30) to fill voids. Finally, where voids still existed because none of the three ancillary data sets were available or acceptable, an advanced interpolation method was used (Grohman, 2006). An example of the before and after results of the corrected GDEM is shown in Figure 4. ...
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 14-channel imaging instrument operating on NASA’s Terra satellite since 1999. ASTER’s visible–near infrared (VNIR) instrument, with three bands and a 15 m Instantaneous field of view (IFOV), is accompanied by an additional VNIR band using a second, backward-looking telescope. Collecting along-track stereo pairs, the geometry produces a base-to-height ratio of 0.6. In 2009, the ASTER Science Team released Version 1 of the global DEM (GDEM) based on stereo correlation of 1.2 million ASTER scenes. The DEM has 1 arc-second latitude and longitude postings (∼30 m) and employed cloud masking to avoid cloud-contaminated pixels. The GDEM covers all of the Earth’s land surface from 83 degrees north to 83 degrees south latitude. Version 2 was released in 2011, with notable improvements in coverage and accuracy. In 2019, the final, Version 3, was released; again improving on coverage and removing almost all artifacts. Th GDEM is a unique, global high spatial resolution digital elevation dataset available to all users at no cost. In addition, a second unique dataset was produced and released. The raster-based ASTER Global Water Body Dataset (ASTWBD) identifies the presence of permanent water bodies, and marks them as ocean, lake, or river. An accompanying DEM file indicates the elevation for each water pixel. To date, over 110+ million 1×1 degree GDEM tiles have been distributed.
... The resulting DSM has 1 meter resolution, which is approximately two times of the original image resolution. In the end, the delta surface fill algorithm (Grohman et al., 2006) is performed to fill the unmatched pixels with SRTM heights and generate the final DSM. It has to be noted that the whole DSM generation procedure is fully automatic without any manual processing. ...
GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and −5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered.
... Spatial interpolation methods are commonly employed to fill voids, e.g., kriging, spline, and inversed distance weighted (Chen and Yue, 2010). More specialised void filling algorithms include the Delta surface fill (DSF) method (Grohman et al., 2006) and generative adversarial networks (Gavriil et al., 2019;Qiu et al., 2019). Differences between the DEM and the auxiliary data would need to be resolved before void filling proceeds. ...
The remote sensing community has identified data fusion as one of the key challenging topics of the 21st century. The subject of image fusion in two-dimensional (2D) space has been covered in several published reviews. However, the special case of 2.5D/3D Digital Elevation Model (DEM) fusion has not been addressed till date. DEM fusion is a key application of data fusion in remote sensing. It takes advantage of the complementary characteristics of multi-source DEMs to deliver a more complete, accurate, and reliable elevation dataset. Although several methods for fusing DEMs have been developed, the absence of a well-rounded review has limited their proliferation among researchers and end-users. Combining knowledge from multiple studies is often required to inform a holistic perspective and guide further research. In response, this paper provides a systematic review of DEM fusion: the pre-processing workflow, methods and applications, enhanced with a meta-analysis. Through the discussion and comparative analysis, unresolved challenges and open issues are identified, and future directions for research are proposed. This review is a timely solution and an invaluable source of information for researchers within the fields of remote sensing and spatial information science, and the data fusion community at large.
... Where voids exist in any of the source DEMs, they should be filled using auxiliary data to improve the significance of the fused DEM. Spatial interpolation methods are commonly employed to fill voids, e.g., kriging, spline, inversed distance weighted Chen and Yue, 2010More specialised void filling (VF) algorithms include the Delta surface fill (DSF) method (Grohman et al., 2006), generative adversarial networks (Gavriil et al., 2019;Qiu et al., 2019). Differences between the DEM and the auxiliary data would need to be resolved before VF proceeds. ...
The remote sensing community has identified data fusion as one of the key challenging topics of the 21st century. The subject of image fusion in two-dimensional (2D) space has been covered in several published reviews. However, the special case of 2.5D/3D Digital Elevation Model (DEM) fusion has not been addressed till date. DEM fusion is a key application of data fusion in remote sensing. It takes advantage of the complementary characteristics of multi-source DEMs to deliver a more complete, accurate and reliable elevation dataset. Although several methods for fusing DEMs have been developed, the absence of a well-rounded review has limited their proliferation among researchers and end-users. It is often required to combine knowledge from multiple studies to inform a holistic perspective and guide further research. In response, this paper provides a systematic review of DEM fusion: the pre-processing workflow, methods and applications, enhanced with a meta-analysis. Through the discussion and comparative analysis, unresolved challenges and open issues were identified, and future directions for research were proposed. This review is a timely solution and an invaluable source of information for researchers within the fields of remote sensing and spatial information science, and the data fusion community at large.
... Several interpolation methods, such as kriging, spline, and inverse distance weighting (IDW), have been extensively adopted in geographical research as a feasible technique for void filling and have achieved good performance in specific cases (Dong et al., 2020;Reuter et al., 2007). Nevertheless, interpolation methods are significantly influenced by the terrain characteristics around the voids and tend to generate discontinuous boundaries at joints caused by a sparsity of observations (Dong et al., 2020;Grohman et al., 2006). Thus, several interpolationbased methods (Arun, 2013;Heritage et al., 2009;Ling et al., 2007;Reuter et al., 2007) that integrate topographic cues (such as sampling points and feature lines) have been proposed to generate suitable terrain representations over incomplete regions. ...
Digital elevation models (DEMs) contain some of the most important data for providing terrain information and supporting environmental analyses. However, the applications of DEMs are significantly limited by data voids, which are commonly found in regions with rugged terrain. We propose a novel deep learning-based strategy called a topographic knowledge-constrained conditional generative adversarial network (TKCGAN) to fill data voids in DEMs. Shuttle Radar Topography Mission (SRTM) data with spatial resolutions of 3 and 1 arc-seconds are used in experiments to demonstrate the applicability of the TKCGAN. Qualitative topographic knowledge of valleys and ridges is transformed into new loss functions that can be applied in deep learning-based algorithms and constrain the training process. The results show that the TKCGAN outperforms other common methods in filling voids and improves the elevation and surface slope accuracy of the reconstruction results. The performance of the TKCGAN is stable in the test areas and reduces the error in the regions with medium and high surface slopes. Furthermore, the analysis of profiles indicates that the TKCGAN achieves better performance according to a visual inspection and quantitative comparison. In addition, the proposed strategy can be applied to DEMs with different resolutions. This work is an endeavour to transform topographic knowledge into computer-processable rules and benefits future research related to terrain reconstruction and modelling.
... Consequently, sharp edges at building borders are smoothed, which negatively affects the results of post-processing steps, such as the building detection. Some studies utilised auxiliary DSMs for this purpose [9], while others, utilised extra information data to support the process e.g., spectral data. For example, since the steep slopes of the DSM cannot be recovered by an interpolation procedure, the regions significantly below the first estimation of the DSM are subsequently analyzed and processed with a more appropriate value of smoothness parameter [10]. ...
... Kriging is a statistically sound method for void filling [9]. Other approaches address the removal of striping artifacts caused by the DEM generation process [36], void filling based on an external DEM as a delta surface [33], [37], and vegetation offset correction [36], [38], [39]. ...
The global digital elevation model (DEM) produced by the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) mission is an interferometric elevation model with unprecedented quality, accuracy, and coverage. It represents an unedited surface model and artifacts inherent to the interferometric Synthetic Aperture Radar (SAR) acquisition and processing technique are still present. The most prominent artifacts are water bodies appearing with a rough surface in the DEM. Additionally, outliers, voids, and larger data gaps may be present in the dataset. Therefore, DEM editing is crucial for many applications including hydrology or ortho-rectification of remote sensing data. Depending on the field of application different techniques of quality enhancement are required. This paper provides for the first time a comprehensive description of a semi-automatic framework especially developed for the TanDEM-X dataset to edit the high-resolution 12 m DEM with focus on water areas, outlier handling, and void filling. The default configuration parameters of the workflow can thereby be adapted interactively for challenging areas where appropriate. A quality assessment of the edited DEM is done by statistical measures, visual methods as well as by an artifact evaluation.
... The grid spacing is reduced into 1 arc-sec of the AW3D30 products after all filling process are completed. We applied the method of "delta surface fill" (DSF) (Grohman et al., 2006) which fills the voids with smoothing the height gaps at boundaries between the original and the filling data without any change in the original data. The adaptive filtering process, which eliminates blunders in original AW3D DSM, is applied as well after the filtering parameters, i.e., thresholds of height difference from reference DSMs, number of stacks in AW3D, and minimum distance from nearest cloud masks, were calibrated for new input datasets of the REMA DSM. ...
In 2016, the first processing of the semi-global digital surface models (DSMs) utilizing all the archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) was successfully completed. The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However, the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of semiglobal coverage, mostly due to cloud covers. In 2020, we completed the filling process by using other open-access digital elevation models (DEMs) such as Shuttle Radar Topography Mission (SRTM) DEM, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., except for Antarctica. In this paper, we report on the filling process of the remaining voids in Antarctica by using other open-access DEMs such as Reference Elevation Model of Antarctica (REMA) DSM, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X, TDX) 90m DEM, and ASTER GDEM to complete the void-free semi-global AW3D30 datasets.
... In a recent study in Australia, Jarihani et al., (Zwally et al., 2002), vegetation height (Simard et al., 2011), MODIS-derived forest canopy density and climate regionalization maps (Peel et al., 2007, Broxton et al., 2014. Sampson et al., (2015) reduced SRTM sensor noise irregularities, urban landscape and vegetation canopy elevation overestimations using a moving window filtering technique (Gallant, 2011 using a combined Delta surface filling (Grohman et al., 2006) and adaptive DEM noise smoothing (Gallant, 2011) methodology, resulting in minimised error in comparison to raw SRTM and ASTER GDEM2 (Robinson et al., 2014). ...
Floods are one of the most devastating disasters known to man, caused by both natural and anthropogenic factors. The trend of flood events is continuously rising, increasing the exposure of the vulnerable populace in both developed and especially developing regions. Floods occur unexpectedly in some circumstances with little or no warning, and in other cases, aggravate rapidly, thereby leaving little time to plan, respond and recover. As such, hydrological data is needed before, during and after the flooding to ensure effective and integrated flood management. Though hydrological data collection in developed countries has been somewhat well established over long periods, the situation is different in the developing world. Developing regions are plagued with challenges that include inadequate ground monitoring networks attributed to deteriorating infrastructure, organizational deficiencies, lack of technical capacity, location inaccessibility and the huge financial implication of data collection at local and transboundary scales. These limitations, therefore, result in flawed flood management decisions and aggravate exposure of the most vulnerable people. Nigeria, the case study for this thesis, experienced unprecedented flooding in 2012 that led to the displacement of 3,871,53 persons, destruction of infrastructure, disruption of socio-economic activities valued at 16.9 billion US Dollars (1.4% GDP) and sadly the loss of 363 lives. This flood event revealed the weakness in the nation’s flood management system, which has been linked to poor data availability. This flood event motivated this study, which aims to assess these data gaps and explore alternative data sources and approaches, with the hope of improving flood management and decision making upon recurrence. This study adopts an integrated approach that applies open-access geospatial technology to curb data and financial limitations that hinder effective flood management in developing regions, to enhance disaster preparedness, response and recovery where resources are limited. To estimate flood magnitudes and return periods needed for planning purposes, the gaps in hydrological data that contribute to poor estimates and consequently ineffective flood management decisions for the Niger-South River Basin of Nigeria were filled using Radar Altimetry (RA) and Multiple Imputation (MI) approaches. This reduced uncertainty associated with missing data, especially at locations where virtual altimetry stations exist. This study revealed that the size and consistency of the gap within hydrological time series significantly influences the imputation approach to be adopted. Flood estimates derived from data filled using both RA and MI approaches were similar for consecutive gaps (1-3 years) in the time series, while wide (inconsecutive) gaps (> 3 years) caused by gauging station discontinuity and damage benefited the most from the RA infilling approach. The 2012 flood event was also quantified as a 1-in-100year flood, suggesting that if flood management measures had been implemented based on this information, the impact of that event would have been considerably mitigated. Other than gaps within hydrological time series, in other cases hydrological data could be totally unavailable or limited in duration to enable satisfactory estimation of flood magnitudes and return periods, due to finance and logistical limitations in several developing and remote regions. In such cases, Regional Flood Frequency Analysis (RFFA) is recommended, to collate and leverage data from gauging stations in proximity to the area of interest. In this study, RFFA was implemented using the open-access International Centre for Integrated Water Resources Management–Regional Analysis of Frequency Tool (ICI-RAFT), which enables the inclusion of climate variability effect into flood frequency estimation at locations where the assumption of hydrological stationarity is not viable. The Madden-Julian Oscillation was identified as the dominant flood influencing climate mechanism, with its effect increasing with return period. Similar to other studies, climate variability inclusive regional flood estimates were less than those derived from direct techniques at various locations, and higher in others. Also, the maximum historical flood experienced in the region was less than the 1-in-100-year flood event recommended for flood management. The 2012 flood in the Niger-South river basin of Nigeria was recreated in the CAESAR-LISFLOOD hydrodynamic model, combining open-access and third-party Digital Elevation Model (DEM), altimetry, bathymetry, aerial photo and hydrological data. The model was calibrated/validated in three sub-domains against in situ water level, overflight photos, Synthetic Aperture Radar (SAR) (TerraSAR-X, Radarsat2, CosmoSkyMed) and optical (MODIS) satellite images where available, to access model performance for a range of geomorphological and data variability. Improved data availability within constricted river channel areas resulted in better inundation extent and water level reconstruction, with the F-statistic reducing from 0.808 to 0.187 downstream into the vegetation dominating delta where data unavailability is pronounced. Overflight photos helped improve the model to reality capture ratio in the vegetation dominated delta and highlighted the deficiencies in SAR data for delineating flooding in the delta. Furthermore, the 2012 flood was within the confine of a 1-in-100-year flood for the sub-domain with maximum data availability, suggesting that in retrospect the 2012 flood event could have been managed effectively if flood management plans were implemented based on a 1-in-100-year flood. During flooding, fast-paced response is required. However, logistical challenges can hinder access to remote areas to collect the necessary data needed to inform real-time decisions. Thus, this adopts an integrated approach that combines crowd-sourcing and MODIS flood maps for near-real-time monitoring during the peak flood season of 2015. The results highlighted the merits and demerits of both approaches, and demonstrate the need for an integrated approach that leverages the strength of both methods to enhance flood capture at macro and micro scales. Crowd-sourcing also provided an option for demographic and risk perception data collection, which was evaluated against a government risk perception map and revealed the weaknesses in the government flood models caused by sparse/coarse data application and model uncertainty. The C4.5 decision tree algorithm was applied to integrate multiple open-access geospatial data to improve SAR image flood detection efficiency and the outputs were further applied in flood model validation. This approach resulted in F-Statistic improvement from 0.187 to 0.365 and reduced the CAESAR-LISFLOOD model overall bias from 3.432 to 0.699. Coarse data resolution, vegetation density, obsolete/non-existent river bathymetry, wetlands, ponds, uncontrolled dredging and illegal sand mining, were identified as the factors that contribute to flood model and map uncertainties in the delta region, hence the low accuracy depicted, despite the improvements that were achieved. Managing floods requires the coordination of efforts before, during and after flooding to ensure optimal mitigation in the event of an occurrence. In this study, and integrated flood modelling and mapping approach is undertaken, combining multiple open-access data using freely available tools to curb the effects of data and resources deficiency on hydrological, hydrodynamic and inundation mapping processes and outcomes in developing countries. This approach if adopted and implemented on a large-scale would improve flood preparedness, response and recovery in data sparse regions and ensure floods are managed sustainably with limited resources.
... For the void-filling we applied the method of "delta surface fill" (DSF) (Grohman et al., 2006) which fills the voids with smoothing the height gaps at boundaries between the original and the filling data without any change in the original data. In the preliminary operation of the void-filling, we detected many obvious artifacts in the AW3D30 especially on areas around voids in the north polar areas. ...
In 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of global coverage, mostly due to cloud covers. In this paper we introduce the updates of AW3D30 filling the voids with other open-access DSMs such as Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., through inter-comparisons among these datasets.
... Although the SGM method is robust in illumination changes and accurate on the boundaries of an object, some holes, due mainly to occlusion, texture loss and a low contrast region, might still occur. These holes can be filled by either SRTM data or spectral data using interpolation methods [181,36]. Remaining holes which cannot be filled by DSM and SRTM DSM are filled using B-spline interpolation, which results in the gradual height changes in the building boundaries. ...
Automatic three-dimensional (3D) building model reconstruction using remote sensing data is crucial in applications which require large-scale and frequent building model updates, such as disaster monitoring and urban management, to avoid huge manual efforts and costs. Recent advances in the availability of very high-resolution satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for 3D building model reconstructions. In this dissertation, a novel multistage hybrid automatic 3D building model reconstruction approach is proposed which reconstructs building models in level of details 2 (LOD2) based on digital surface model (DSM) data generated from the very high-resolution stereo imagery of the WorldView-2 satellite. This approach uses DSM data in combination with orthorectified panchromatic (PAN) and pan-sharpened data of multispectral satellite imagery to overcome the drawbacks of DSM data, such as blurred building boundaries. In the first stage, the rough building boundaries in the DSM-based building masks are refined by classifying the geometrical features of the corresponding PAN images. The refined boundaries are then simplified in the second stage through a parametrization procedure which represents the boundaries by a set of line segments. The main orientations of buildings are then determined, and the line segments are regularized accordingly. The regularized line segments are then connected to each other based on a rule-based method to form polygonal building boundaries. In the third stage, a novel technique is proposed to decompose the building polygons into a number of rectangles under the assumption that buildings are usually composed of rectangular structures. In the fourth stage, a roof model library is defined, which includes flat, gable, half-hip, hip, pyramid and mansard roofs. These primitive roof types are then assigned to the rectangles based on a deep learning-based classification method. In the fifth stage, a novel approach is developed to reconstruct watertight parameterized 3D building models based on the results of the previous stages and normalized DSM (nDSM) of satellite imagery. In the final stage, a novel approach is proposed to optimize building parameters based on an exhaustive search, so that the two-dimensional (2D) distance between the 3D building models and the building boundaries (obtained from building masks and PAN image) as well as the 3D normal distance between the 3D building models and the 3D point clouds (obtained from nDSM) are minimized. In the final stage, a new approach is proposed to optimize building parameters based on an exhaustive search, so that the 2D distance between the two-dimensional (2D) and the building boundaries obtained from building masks and PAN images and normal distance between 3D building models and 3D point clouds derived from nDSM are minimized. Different parts of the building blocks are then merged through a newly proposed intersection and merging process. All corresponding experiments were conducted on four areas of the city of Munich including 208 buildings and the results were evaluated qualitatively and quantitatively. According to the results, the proposed approach can accurately reconstruct 3D models of buildings, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by the limited number of primitive roof model types required and by performing automatic parameter initializations. In addition, the proposed boundary refinement method improves the DSM-based building masks specified by 8% in area accuracy. Furthermore, the ridge line directions and roof types were detected accurately for most of the buildings. The combination of the first three stages improved the accuracy of the building boundaries by 70% in comparison to using line segments extracted from building masks without refinement. Moreover, the proposed optimization approach can achieve in most cases the best combinations of 2D and 3D geometrical parameters of roof models. Finally, the intersection and merging process could successfully merge different parts of the complex building models.
... Note that after masking GDEM V2, all of the filler elevation models had voids (GDEM V2, SRTM, and PRISM AW3D30), either inherently, or from masking, or both. The void fill routine is a modified version of the Delta Surface Fill method of Grohman [11]. The published method was modified in order to (1) use filler DEMs with voids, (2) reduce the spread of errors from void edges into the void fill, and (3) improve processing speed. ...
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 14-channel imaging instrument operating on NASA’s Terra satellite since 1999. ASTER’s visible–near infrared (VNIR) instrument, with three bands and a 15 m Instantaneous field of view (IFOV), is accompanied by an additional band using a second, backward-looking telescope. Collecting along-track stereo pairs, the geometry produces a base-to-height ratio of 0.6. In August 2019, the ASTER Science Team released Version 3 of the global DEM (GDEM) based on stereo correlation of 1.8 million ASTER scenes. The DEM has 1 arc-second latitude and longitude postings (~30 m) and employed cloud masking to avoid cloud-contaminated pixels. Custom software was developed to reduce or eliminate artifacts found in earlier GDEM versions, and to fill holes due to the masking. Each 1×1 degree GDEM tile was manually inspected to verify the completeness of the anomaly removal, which was generally excellent except across some large ice sheets. The GDEM covers all of the Earth’s land surface from 83 degrees north to 83 degrees south latitude. This is a unique, global high spatial resolution digital elevation dataset available to all users at no cost. In addition, a second unique dataset was produced and released. The raster-based ASTER Global Water Body Dataset (ASTWBD) identifies the presence of permanent water bodies, and marks them as ocean, lake, or river. An accompanying DEM file indicates the elevation for each water pixel. To date, over 100 million 1×1 degree GDEM tiles have been distributed.
... Vertical biases in the void areas in different elevation models can be eliminated by using the fill and feather (FF) technique, in which a constant is added and a feather process is applied to mitigate the abrupt change at the void edge. The delta surface fill (DSF) method proposed by Grohman et al. [21] constructs the delta surface by computing the difference in the points bordering the voids from both the original DEM data and auxiliary data, and it can account for the vertical bias of the alternative surface. Luedeling et al. [22] proposed a more promising method based on triangular irregular networks (TINs), which was an improvement of the DSF method and could better handle the varying altitude biases between different DEM datasets. ...
Digital elevation models (DEMs) are an important information source for spatial modeling. However, data voids, which commonly exist in regions with rugged topography, result in incomplete DEM products, and thus significantly degrade DEM data quality. Interpolation methods are commonly used to fill voids of small sizes. For large-scale voids, multi-source fusion is an effective solution. Nevertheless, high-quality auxiliary source information is always difficult to retrieve in rugged mountainous areas. Thus, the void filling task is still a challenge. In this paper, we proposed a method based on a deep convolutional generative adversarial network (DCGAN) to address the problem of DEM void filling. A terrain texture generation model (TTGM) was constructed based on the DCGAN framework. Elevation, terrain slope, and relief degree composed the samples in the training set to better depict the terrain textural features of the DEM data. Moreover, the resize-convolution was utilized to replace the traditional deconvolution process to overcome the staircase in the generated data. The TTGM was trained on non-void SRTM (Shuttle Radar Topography Mission) 1-arc-second data patches in mountainous regions collected across the globe. Then, information neighboring the voids was involved in order to infer the latent encoding for the missing areas approximated to the distribution of training data. This was implemented with a loss function composed of pixel-wise, contextual, and perceptual constraints during the reconstruction process. The most appropriate fill surface generated by the TTGM was then employed to fill the voids, and Poisson blending was performed as a postprocessing step. Two models with different input sizes (64 × 64 and 128 × 128 pixels) were trained, so the proposed method can efficiently adapt to different sizes of voids. The experimental results indicate that the proposed method can obtain results with good visual perception and reconstruction accuracy, and is superior to classical interpolation methods.
... This can cause noise, artifacts, and imperfections (e.g., gaps) particularly at building edges [3,5] and can consequently affect the accuracy of automatic building reconstruction approaches. Dealing with the gaps and imperfections of DSM data, a number of previous works [6][7][8][9] applied interpolation methods; however, the results are still unsatisfactory due to the interpolation deficiencies such as blurring the building boundaries, which can impose imperfections to the building masks derived from the DSM data. In order to improve building masks, a number of previous research works proposed approaches based on various combinations of DSM data and very high-resolution (VHR) satellite images [1,3,10,11]. ...
Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as disaster monitoring and urban management. Digital Surface Models (DSMs) generated from stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting in rough building shape representations. To handle 3-D building model reconstruction using such low-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs together with orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satellite imagery. The algorithm consists of multiple steps including building boundary extraction and decomposition, image-based roof type classification, and initial roof parameter computation which are prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM (nDSM) and to select the best one, a parameter optimization method based on exhaustive search is used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building block are intersected to reconstruct the 3-D model of connecting roofs. All corresponding experiments are conducted on a dataset including four different areas of Munich city containing 208 buildings with different degrees of complexity. The results are evaluated both qualitatively and quantitatively. According to the results, the proposed approach can reliably reconstruct 3-D building models, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by limiting the number of primitive roof types and by performing automatic parameter initialization.
... This can cause noise, artifacts, and imperfections (e.g., gaps) particularly at building edges [3,5] and can consequently affect the accuracy of automatic building reconstruction approaches. Dealing with the gaps and imperfections of DSM data, a number of previous works [6][7][8][9] applied interpolation methods; however, the results are still unsatisfactory due to the interpolation deficiencies such as blurring the building boundaries, which can impose imperfections to the building masks derived from the DSM data. In order to improve building masks, a number of previous research works proposed approaches based on various combinations of DSM data and very high-resolution (VHR) satellite images [1,3,10,11]. ...
Recent advances in the availability of very high-resolution (VHR) satellite data together withefficient data acquisition and large area coverage have led to an upward trend in their applicationsfor automatic 3-D building model reconstruction which require large-scale and frequent updates,such as disaster monitoring and urban management. Digital Surface Models (DSMs) generatedfrom stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting inrough building shape representations. To handle 3-D building model reconstruction using suchlow-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs togetherwith orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satelliteimagery. The algorithm consists of multiple steps including building boundary extraction anddecomposition, image-based roof type classification, and initial roof parameter computation whichare prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM(nDSM) and to select the best one, a parameter optimization method based on exhaustive searchis used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building blockare intersected to reconstruct the 3-D model of connecting roofs. All corresponding experimentsare conducted on a dataset including four different areas of Munich city containing 208 buildingswith different degrees of complexity. The results are evaluated both qualitatively and quantitatively.According to the results, the proposed approach can reliably reconstruct 3-D building models, eventhe complex ones with several inner yards and multiple orientations. Furthermore, the proposedapproach provides a high level of automation by limiting the number of primitive roof types and byperforming automatic parameter initialization.
... The aforementioned interpolation methods showed difficulties in handling large holes. This problem could be eased by using an auxiliary DSM [10][11][12][13] or physical properties of the surface [14,15], or by classifying the types of holes [16][17][18]. However, a priori knowledge of surfaces to be interpolated may not be available, especially for densely gridded DSMs. ...
A digital surface model (DSM) is an important geospatial infrastructure used in various fields. In this paper, we deal with how to improve the quality of DSMs generated from stereo image matching. During stereo image matching, there are outliers due to mismatches, and non-matching regions due to match failure. Such outliers and non-matching regions have to be corrected accurately and efficiently for high-quality DSM generation. This process has been performed by applying a local distribution model, such as inverse distance weight (IDW), or by forming a triangulated irregular network (TIN). However, if the area of non-matching regions is large, it is not trivial to interpolate elevation values using neighboring cells. In this study, we proposed a new DSM interpolation method using a 3D mesh model, which is more robust to outliers and large holes. We compared mesh-based DSM with IDW-based DSM and analyzed the characteristics of each. The accuracy of the mesh-based DSM was a 2.80 m root mean square error (RMSE), while that for the IDW-based DSM was 3.22 m. While the mesh-based DSM successfully removed empty grid cells and outliers, the IDW-based DSM had sharper object boundaries. Because of the nature of surface reconstruction, object boundaries appeared smoother on the mesh-based DSM. We further propose a method of integrating the two DSMs. The integrated DSM maintains the sharpness of object boundaries without significant accuracy degradation. The contribution of this paper is the use of 3D mesh models (which have mainly been used for 3D visualization) for efficient removal of outliers and non-matching regions without a priori knowledge of surface types.
... The calculation of TPI and SDE both require the definition of circular regions as neighborhoods. Furthermore, it is important to note that the TPI is very sensitive to the scale of the neighborhood, so the choice of scale is critical [68]. Appropriate scales can classify both small-scale features (such as valleys or fine ridge lines) and major topographic features (such as major valleys or mountains). ...
The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.
... The calculation of TPI and SDE both require the definition of circular regions as neighborhoods. Furthermore, it is important to note that the TPI is very sensitive to the scale of the neighborhood, so the choice of scale is critical [61]. Appropriate scales can classify both small-scale features (such as valleys or fine ridge lines) and major topographic features (such as major valleys or mountains). ...
Digital Elevation Models (DEMs) are widely used in geographic and environmental studies. In the current work, the fusion of multi-source DEMs is investigated to improve the overall accuracy of public domain DEMs. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods – the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD) and nonlinear adaptive multi-scale decomposition (N-AMD) - are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D – 30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the ice cloud and land elevation satellite (ICESat) global land surface altimetry data as a reference. The vertical accuracy of the fused DEM shows significant improvements of 29.6% and 19.3% in a mountainous region and 27.4% and 15.5% in a low-relief region, compared to the SRTM-1 and AW3D30 respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.
... This problem can be solved by adding a very small random variation to the coordinates of the points prior to interpolation. One point that must be taken into account is the presence of voids in SRTM data; these voids can be successfully filled by other techniques like the Delta-Surface method of Grohman et al. (2006). One drawback of our method is that voids larger than the distance to the most distant point in the neighbourhood will not be filled, and will remain as artifacts in the resultant surface. ...
The Shuttle Radar Topography Mission (SRTM), was flow on Space Shuttle Endeavour in February 2000, with the objective of acquire a digital elevation model of all land between 60º north latitude and 56º south latitude, using Interferometric Synthetic Aperture Radar (InSAR) techniques. SRTM data is distributed at horizontal resolution of 1 arc-second (aprox. 30m) for areas within the USA and at 3 arc-second (aprox. 90m) resolution for the rest of the world. A resolution of 90m can be considered suitable for small or medium-scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it won't increase the level of detail of the original DEM, but it will lead to a surface where there is coherence of angular properties (i.e., slope, aspect) between neighbouring pixels, an important characteristic when dealing with terrain analysis. This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3'' to 1''. We present for a test area in western USA, which include different adjustment schemes (changes in nugget effect value and in the interpolation radius) as well as comparisons with the original 1'' model of the area, with the National Elevation Dataset DEMs, and with other interpolation methods (splines and IDW).The basic concepts for using kriging to resample terrain data are: 1) to work only with the immediate neighbourhood of the predicted point, due the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; 2) add a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and 3) use a small value of nugget effect, to avoid smoothing that can obliterate terrain features.Drainages derived from the surfaces interpolated by kriging and by splines have good agreement with streams derived from the 1'' NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1'' surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline-interpolated surface still presented some noise and linear artifacts, which were removed by kriging.
... In many applications using DEM as an important data layer, for example hydrological model applications, the poor quality of the DEM is partially due to artefacts and vegetation canopy (Callow and Smettem, 2009;Callow et al., 2007;Nardi et al., 2006;Valeriano et al., 2006). Methods have been proposed to fill voids (Grohman et al., 2006;Jarvis et al., 2008;Reuter et al., 2007) and remove vegetation canopy (Baugh et al., 2013;Gallant et al., 2012;O'Loughlin et al., 2016;Yamazaki et al., 2012). Even after these processing steps there remain many errors and missing data in the quasi-global DEM products. ...
... Achieving seamless transitions is also crucial when filling missing data in DEMs with a DEM from different source. The transition zone between the auxilary and the main DEM is typically interpolated using inverse distance weighting (IDW) [14], or estimated as a local average of neighboring elevation pixels [15]. However, depending on the complexity of landscape features the transition zone can be much smoother than its surrounding and become a visible artifact. ...
Background
New technologies for terrain reconstruction have increased the availability of topographic data at a broad range of resolutions and spatial extents. The existing digital elevation models (DEMs) can now be updated at a low cost in selected study areas with newer, often higher resolution data using unmanned aerial systems (UAS) or terrestrial sensors. However, differences in spatial coverage and levels of detail often create discontinuities along the newly mapped area boundaries and subsequently lead to artifacts in results of DEM analyses or models of landscape processes. Methods
To generate a seamless updated DEM, we propose a generalized approach to DEM fusion with a smooth transition while preserving important topographic features. The transition is controlled by distance-based weighted averaging along the DEMs’ blending overlap with spatially variable width based on elevation differences. ResultsWe demonstrate the method on two case studies exploring the effects of DEM fusion on water flow modeling in the context of precision agriculture. In the first case study, we update a lidar-based DEM with a fused set of two digital surface models (DSMs) derived from imagery acquired by UAS. In the second application, developed for a tangible geospatial interface, we fuse a georeferenced, physical sand model continuously scanned by a Kinect sensor with a lidar-based DEM of the surrounding watershed in order to computationally simulate and test methods for controlling storm water flow. Conclusions
The results of our experiments demonstrate the importance of seamless, robust fusion for realistic simulation of water flow patterns using multiple high-resolution DEMs.
... One point that must be taken into account is the presence of voids in SRTM data; these voids can be successfully filled by other techniques such as the Delta-Surface method of Grohman et al. (2006). One drawback of our method is that voids larger than the distance to the most distant point in the neighbourhood will not be filled, and will remain as artifacts in the resultant surface. ...
Digital terrain analysis, or geomorphometry, is the practice of ground-surface quantification, through the application of techniques in Earth sciences, mathematics, engineering and computer science.
The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is currently the only near-global data available to perform local to regional-scale landform analysis. The production of a DEM using a single technique, radar interferometry, means that there is consistency in quality, availability and scale.
This work focuses upon the study of landforms in central-eastern Brazil by means of morphometric characterization of DEMs and integration between morphometric, thermochronologic and geophysical data, as well as an evaluation of the validity of use of flat surfaces in regional stratigraphic correlations.
Morphological profiles and aspect maps show a N-S trend of the major landforms of central-eastern Brazil, while smaller landforms have a NW-SE organization, better observed in the southeast region, but visible throughout the study area.
The spatial distribution of thermochronological data in Brazil is highly heterogeneous, with samples clustered in the south-southeast and northeast regions. Data from the southeast region does not show continental break-up as the main cooling event, which is identified by the large number of samples with fission-track ages (FT) between 60 and 80 Ma and that can be seen as a regional uplift event (followed by intense denudation), given the elevation range of the samples in this time span. There is a subtle trend of older FT ages from the coast towards the interior.
The trend of older FT ages as distance from the coast increases is better represented in data from the northeast region. Samples with ages around 100 Ma can be related to the continental break-up.
Morphometric analyses did not allowed the identification of vast erosional planation surfaces in the study area. Areas with a very smooth topography are related to alluvial plains (São Francisco, Araguaia and Tocantins rivers), Cretaceous sedimentary basins (Chapadão Ocidental da Bahia, Chapada do Araripe, Bauru Basin) and Paleozoic basins with Cretaceous cover (Parecis Basin) where the surface follows the subhorizontal bedding. In shield regions, several low-relief areas can be identified, although they are small in extent.
The large extent of low-lying surfaces associated with Cretaceous sedimentary basins has lead several authors to suggest that these -- presently isolated -- surfaces were continuous. This, added to inferences of "summit levels" based on the apparent levelling of hilltops in crystalline terrains, has also lead several authors to suggest that the planation surfaces extend close to the Atlantic shore.
The integration of morphometric, thermochronologic and geophysical data does not support the validity of use of flat surfaces in regional stratigraphic correlations. However, correlation between distinct morphological levels, at a local scale, is suitable.
The morphometric techniques used in this study are valid not only for the analysis of topographic surfaces, but also of buried surfaces and their palaeogeography. The availability of remote sensing generated elevation data allows the application of digital terrain analysis from local to global scales, with a range of applications, not only in the study of terrestrial landforms, but also of other planetary bodies.
The paper describes the Editing of the TanDEM-X DEM in 30 m resolution. The editing is performed with an automated appraoch developed in order to facilitate the processing of the ChangeDEM acquisitions for the TanDEM-X DEM 2020. The editing comprises the filling of gaps and the flattening of water bodies.
The digital elevation model (DEM) of Antarctica is a fundamental dataset for a wide range of glaciological applications, from fieldwork planning to ice sheet dynamics analysis. The DEM data at high spatial resolution provides a more detailed depiction of the topography. The reference elevation model of Antarctica (REMA) mosaic is a recently released high-resolution Antarctic DEM product with a high absolute vertical accuracy of 1 m or less. The REMA mosaic was generated using an optical photogrammetry technique and can reflect the surface elevation of the ice sheet. However, the REMA mosaic has a large number of data voids of various spatial sizes, which affects its wide glaciological application. Therefore, we propose the generation of a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100) with the voids filled by combining multi-source DEMs. In this study, the best-fit fill DEMs were automatically selected between almost all available circum-Antarctica or regional DEMs. Targeting numerous voids in large areas of the 100-m REMA mosaic, we propose a novel hierarchical delta surface method for interpolating the created delta surface to achieve an efficient, unbiased, and continuous filled surface. High-precision laser altimetry data were used to evaluate the vertical accuracy of the REMA mosaic. To eliminate the influence of the temporal surface elevation change and penetration depth between the DEM and laser altimetry data, we developed a new local contrast evaluation method by calculating the vertical accuracy of the equal-area buffer as a control to that of the center void. The experimental results with the Ice, Cloud and Land elevation Satellite-2 (ICESat-2) laser altimetry data validate that the filled 100-m REMA mosaic of voids can reach an elevation accuracy similar to that of the original REMA dataset for the west and east Antarctic ice sheet as well as the entire continent. In the Antarctic Peninsula area, the vertical accuracy of the filled REMA mosaic by the proposed method is superior to that of the officially released version of the Polar Geospatial Center. This is the first time that a gapless, seamless, and accurate 100-m REMA mosaic has been released which has the potential to be extensively used in glaciological applications. The proposed methods can also be used in void-filling of different types of DEM products and their elevation accuracy evaluations.
Monitoring of hydrological hazards (e.g., drought, flooding, etc.) is largely limited by the lack of appropriate modeling systems and high‐quality input data, especially in less developed countries and data‐scarce regions. The fundamental data set for global drought monitoring and flood modeling is the digital elevation model (DEM), and hydrographic variables that are usually derived from DEMs. This chapter summarizes the currently available global DEMs and hydrographic data sets for drought monitoring and flood modeling, describes the recently released high‐quality data sets, and discusses key challenges in producing a high‐quality baseline DEM as well as the upscaling of various hydrographic layers. Specifically, the newly developed MultiError‐Removed Improved‐Terrain DEM provides globally consistent high‐accuracy terrain elevation data, although with several minor caveats. The TanDEM‐X data opens a new era of timely monitoring dynamic topographic changes of the Earth's surface with high accuracy. We further introduced a few commonly used algorithms for hydrological processing of DEMs and upscaling drainage networks, and discussed their advantages and limitations. The hierarchically dominant river tracing algorithm, with proved high capability in preserving river network information defined in a high‐resolution DEM, is suggested for application to a recently updated high‐quality DEM for the derivation of upscaled hydrographic data sets for hydrological modeling at various spatial resolutions. We end the chapter by discussing the necessity of next‐generation hydrologically consistent full‐hydrography data sets for development of a better monitoring system for hazards warning, guidance, and risk assessment at the global scale.
Much of geomorphology depends on accurate topographic data. Satellites have transformed our ability to capture such data from planetary surfaces over the last 2 decades. Truly global datasets were only first released in the late 1990s; in 1997, a 5 arcminute dataset (roughly 10-km-wide pixels at the equator) was released, followed in the same year by a 30 arcsecond dataset (GTOPO30; roughly 1-km-wide pixels at the equator). The widespread use of global topographic datasets for geomorphic applications began with release of the Shuttle Radar Topography Mission's (SRTM) 3 arcsecond product in 2004 (roughly 90 m wide at the equator). SRTM data allowed, for example, the extraction of river profiles, slope maps at the hillslope scale, and floodplains. In the last decade, improvements in both the resolution and quality of topographic data have accelerated rapidly. Global 30 m data was released in 2009 (ASTER), followed by commercial 5 m (ALOS World 3D) and 12 m (TanDEM-X DEM) products in 2016. This chapter gives an overview of the history of satellite-derived topographic data products. It discusses the different instruments used and techniques available for generating topographic data from space. It then reviews the accuracy and availability of topographic datasets and discusses the implications for geomorphic research. Finally, it discusses the potential for future satellite missions to improve global topographic information.
The original data produced by the Shuttle Radar Topography Mission (SRTM) tend to have an abundance of voids in mountainous areas where the elevation measurements are missing. In this paper, deep learning models are investigated for restoring SRTM data. To this end, we explore generative adversarial nets, which represent one state-of-the-art family of deep learning models. A conditional generative adversarial network (CGAN) is introduced as the baseline method for filling voids in incomplete SRTM data. The problem regarding shadow violation that possibly arises from the CGAN restored data is investigated. To address this deficiency, shadow geometric constraints based on shadow maps of satellite images are devised. In addition, a shadow constrained conditional generative adversarial network (SCGAN), which incorporates the shadow geometric constraints into the CGAN, is developed. Training the SCGAN model requires both the remote sensing observations (i.e., the original incomplete SRTM data and satellite images) and the ground truth data (i.e., the complete SRTM data, which are manually refined from the incomplete SRTM data with the reference of in-situ measurements). The integration of the multi-source training data enables the SCGAN model to be characterized by comprehensive information including both mountain shape variation and mountain shadow geometry. Experimental results validate the superiority of the SCGAN over the comparison methods, i.e., the interpolation, the convolutional neural network (CNN) and the baseline CGAN, in SRTM data restoration.
This IAG-IDEMS bibliography contains selected papers on global DEMs (digital elevation models), bathymetry and ice products, global composite models (combinations of elevations, bathymetry and ice), and underlying modelling techniques. The listed papers are meant to give an overview on the main characteristics of modern DEM products such as from the SRTM, ASTER, ALOS/PRISM and TanDEM-X missions. Some of the studies assess the quality of the DEM data, e.g., in terms of DEM accuracy and possible caveats (e.g., artefacts, shifts, voids) relevant for geoscience applications. In some cases, the research papers focus on more than one global DEM (e.g. validation studies). The literature is subdivided into the groups: Part A-Overview Papers Part B-Global DEMs 1. SRTM-based DEMs (V2.1, V4.1, V3, MERIT), 30-90 m resolution 2. ASTER-GDEM1/2/3 DEMs, 30 m resolution 3. TanDEM-X DEMs, 12-90 m resolution 4. ALOS World 3D (AW3D) DEM, 5-30 m resolution 5. DEM validation and assessment studies 6. DEM-derived global products Part C-Global Bathymetry and Ice Models 7. Global bathymetry and digital depth models (DDM) 8. Models of Ice Sheets of Greenland/Antarctica Part D-Combined or composite global relief models Part E-Methodologies, issues (e.g., fusion, void-filling, artefacts) If you have suggestions for other papers that could be a good addition to this literature compilation, please feel free to contact me via Christian Hirt, TU Munich, 08 th May 2019
Updated in December 2018. New references in blue for a faster overview This IAG-IDEMS bibliography contains selected papers on global DEMs (digital elevation models), bathymetry and ice products, global composite models (combinations of elevations, bathymetry and ice), and underlying modelling techniques. The listed papers are meant to give an overview on the main characteristics of modern DEM products such as from the SRTM, ASTER, ALOS/PRISM and TanDEM-X missions. Some of the studies assess the quality of the DEM data, e.g., in terms of DEM accuracy and possible caveats (e.g., artefacts, shifts, voids) relevant for geoscience applications. In some cases, the research papers focus on more than one global DEM (e.g. validation studies). The literature is subdivided into the groups: Part A-Overview Papers Part B-Global DEMs 1. SRTM-based DEMs (V2.1, V4.1, V3, MERIT), 30-90 m resolution 2. ASTER-GDEM1/2/3 DEMs, 30 m resolution 3. TanDEM-X DEMs, 12-90 m resolution 4. ALOS World 3D (AW3D) DEM, 5-30 m resolution Part C-Global Bathymetry and Ice Models 5. Global bathymetry and digital depth models (DDM) 6. Models of Ice Sheets of Greenland/Antarctica Part D-Combined or composite global relief models Part E-Methodologies, issues (e.g., fusion, void-filling, artefacts) If you have suggestions for other papers that could be a good addition to this literature compilation, please feel free to contact me via Christian Hirt, TU Munich, 14 th December 2018
An increased demand for energy in the World, as well as gradually more strict regulations in
environmental protection have resulted in a more extensive usage of wind power and an increase in
construction of energy plants that use wind to generate electricity. Construction of wind power
plants is a complex process that lasts for years and includes many tasks like planning and
implementing various analyses. One of the key analyses in the process of constructing a wind power
plant is the analysis of wind estimation that is used to determine the amount of wind on a location
and the profitability of the construction. The process of wind power estimation is also a complex
process that includes many actions and the simulations performed based on meteorological and
topographic parameters (altitude, roughness and obstacles) may be conducted in various physical
models, like the widely known linear BZ model used by the WAsP program, as well as the
increasingly used CFD models. This research applies the WAsP program in the area of the existing
wind power plant “Danilo” near Šibenik, Croatia to determine the degree of influence of
topographic parameters on wind estimation. The used timescale of meteorological data is December
1, 2015 to December 1, 2016, during which there were no errors on the measuring mast. Most of the
research focuses on altitude data that approximate the real relief around the wind power plant. They
have been extracted from 6 different global DEMs, topographic maps scaled 1:25.000 and Digital
model of altitudes by the National geodetic administration of the Republic of Croatia. The contour
lines singled out from these sources have been organized info 4 categories with different
equidistances – 2.5, 5, 10 and 20 meters. The simulations have been conducted within the
researched area with a radius of 5, 10, 15 and 20 kilometers around each turbine of the “Danilo”
wind power plant. Through the testing of various areas and altitude data a total of 136 combinations
have been acquired. It has been confirmed that the estimated average power generation is lower than
the real power generation by 12 to 15%, depending on the source of altitude data. The most precise
estimation was based on EUDEM model, while the estimation with the highest deviation was the
one based on topographic map. It has been found that within the tested areas the best estimation is
made for the area within the 10-kilometer radius around the wind power plant. Through combining
the models with higher and lower resolution it has been determined that the models with higher
resolution provide better estimations. Furthermore, in the research of contour line equidistances it
has been determined that the contour lines with an equidistance of 20 meters give the best estimate
in three out of six used models.
Despite post-processing efforts by space agencies and research institutions, contemporary global digital elevation models (DEMs) may contain artefacts, i.e., erroneous features that do not exist in the actual terrain, such as spikes, holes and line errors. The goal of the present paper is to illuminate the artefact issue of current global DEM data sets that might be an obstacle for any geoscience study using terrain information. We introduce the Maximum Slope Approach (MSA) as a technique that uses terrain slopes as indicator to detect and localize spurious artefacts. The MSA relies on the strong sensitivity of terrain slopes for sudden steps in the DEM that is a direct feature of larger artefacts. In a numerical case study, the MSA is applied for globally complete screening of two SRTM-based 3 arc-second DEMs, the SRTM v4.1 and the MERIT-DEM. Based on 0.1°x 0.1° sub-divisions and a 5 m/m slope threshold, 1,341 artefacts were detected in SRTM v4.1 vs. 108 in MERIT. Most artefacts spatially correlate with SRTM voids (and thus with the void-filling) and not with the SRTM-measured elevations. The strong contrast in artefact frequency (factor ~12) is attributed to the SRTM v4.1 hole filling. Our study shows that over parts of the Himalaya Mountains the SRTM v4.1 data set is contaminated by step artefacts where the use of this DEM cannot be recommended. Some caution should be exercised, e.g., over parts of the Andes and Rocky Mountains. The same holds true for derived global products that depend on SRTM v4.1, such as gravity maps. Primarily over the major mountain ranges, the MERIT model contains artefacts, too, but in smaller numbers. As a conclusion, globally complete artefact screening is recommended prior to the public release of any DEM data set. However, such a quality check should also be considered by users before using DEM data. MSA-based artefact screening is not only limited to DEMs, but can be applied as quality assurance measure to other gridded data sets such as digital bathymetric models or gridded physical quantities such as gravity or magnetics.
This paper provides some recommendations concerning the use of version 4.1 of the near-global 3 arcsec Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) distributed by the Consortium for Spatial Information (CGIAR-CSI). This product is considered by most users to be a void-filled version of the finished grade NASA SRTM DEM. However, in non-void areas, these DEMs can exhibit relative geolocation shifts and spatially correlated elevation differences up to tens of meters, the location and extent of which depends on the geographical location and on the download mirror of the version 4.1 product. Such differences are found to be partly due to changes introduced by NASA SRTM version 2.1, with respect to NASA SRTM version 2.0, on which CGIAR-CSI version 4.1 is based, and partly to processing and/or annotation errors affecting the CGIAR-CSI version 4.1 DEMs.
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