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Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∼30 m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61 to 1.12 m, and in forests from 5.15 to 2.88 m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.

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.

Digital elevation models (DEMs) are widely used in geoscience. The quality of a DEM is a primary requirement for many applications and is affected during the different processing steps, from the collection of elevations to the interpolation implemented for resampling, and it is locally influenced by the landcover and the terrain slope. The quality must meet the user’s requirements, which only make sense if the nominal terrain and the relevant resolution have been explicitly specified. The aim of this article is to review the main quality assessment methods, which may be separated into two approaches, namely, with or without reference data, called external and internal quality assessment, respectively. The errors and artifacts are described. The methods to detect and quantify them are reviewed and discussed. Different product levels are considered, i.e., from point cloud to grid surface model and to derived topographic features, as well as the case of global DEMs. Finally, the issue of DEM quality is considered from the producer and user perspectives.

The determination of mapping units, including grid, slope, unique condition, administrative division, and watershed units, is a very important modeling basis for landslide assessments. Among these mapping units, the slope unit has been paid a lot of attention because it can effectively reflect the physical relationships between landslides and the fundamental topographic elements especially in mountainous areas. Although some methods have been proposed for slope unit extraction, effectively and automatically extracting slope units remains a difficult and urgent problem that seriously restricts the use of slope units. To overcome this problem, the innovative multi-scale segmentation (MSS) method is proposed for extracting slope units. Thus, first, the terrain aspect and shaded relief images obtained from the digital elevation model with certain weights are used as the data sources of the MSS method. Second, the scale, shape, and compactness parameters of the MSS method are properly determined according to the improved trial-and-error method. Third, the initial slope units generated by the MSS method with appropriate parameters are automatically optimized through vector analysis in GIS. Finally, reasonable slope units are obtained and the extraction performance is discussed. The Chongyi County and Wanzhou District in China are selected as study areas. The conventional hydrological method is also adopted to extract slope units for qualitative and quantitative comparisons. It can be concluded that the MSS method can accurately and automatically extract the slope units for landslide assessments in hilly and mountainous areas and performs better than the hydrological method.

The Earth’s terrain is linked to many physical processes, and gaining the most accurate representation is key to work in many sectors from engineering to natural hazards modeling and ecology. Existing global digital elevation models (DEMs) are widely used, however often suffer from systematic biases caused by trees, buildings and instrumentation error, ultimately limiting their effectiveness. We present here, FathomDEM, a new global 30 m DEM produced using a novel application of a hybrid vision transformer model. This model removes surface artifacts from a global radar DEM, Copernicus DEM, aligning it more closely with true topography. In addition to improving on other global DEMs, FathomDEM also has reduced error compared to coastal-focussed DEMs such as the recent DeltaDTM. This demonstrates its impressive capacity to perform for specific landscapes, while being trained globally to model a wide range of terrain types. FathomDEM has been tested on the downstream task of flood modeling, showing increased accuracy compared to those run with the previous best global DEM, FABDEM, approaching the performance of LiDAR based flood modeling. This improvement is attributed to FathomDEM’s smaller error and substantial reduction in artifacts. This shows the suitability of FathomDEM for applied tasks and strengthens our evaluation compared to one based on vertical error alone.

There are a limited number of studies addressing the forest status, its extent, location, type and composition over a larger area at the regional or national levels. The dense time series and a wide swath of Sentinel-2 data are a good basis for forest mapping and tree species identification over a large area. This study presents the results of the classification of the forest/non-forest cover, forest type (broadleaf and coniferous) and the identification of eight tree species (beech, oak, alder, birch, spruce, pine, fir, and larch) using the multi-temporal Sentinel-2 data in combination with topographic information. The study was conducted over the large mountain area located in southern Poland. The Random Forest classifier was used to first derive a forest/non-forest map. Second, the forest was classified into broadleaf and coniferous. Finally, the tree species classification was carried out following two approaches: (i) Non-stratified, where all species were classified together within the forest mask and (ii) stratified, where the broadleaf and coniferous tree species were classified separately within the forest type masks. The overall accuracy for the forest/non-forest cover reached 98.3% and declined slightly to 94.8% for the classification of the forest type. The use of the topographic information did not increase the accuracy of either result. The role of the topographic variables increased significantly in the process of tree species delineation. By combining the topographic information (in particular, digital elevation model) with the multi-temporal Sentinel-2 data, the classification of eight tree species improved from 75.6% to 81.7% (approach 1). A further increase in accuracy to 89.5% for broadleaf and 82% for coniferous species was observed following the stratified approach number 2. The highest overall accuracy (above 85%) was obtained for beech, oak, birch, alder, and larch. The study confirmed the potential of the multi-temporal Sentinel-2 data for accurate delineation of the forest cover, forest type, and tree species at the regional scale.

Fine‐scale seasonal snow depth observations can improve estimates of snow water equivalent at critical times of year. Airborne lidar is the current gold standard for snow depth measurement, but it involves high costs and relatively limited coverage. Using very‐high‐resolution satellite stereo images from WorldView‐2, WorldView‐3, and Pléiades‐HR 1A/1B, we produced a six‐year time series (2017–2022) of spatially continuous digital elevation models for an 874 km2 study area over Grand Mesa, Colorado. We generated high‐resolution stereo snow depth maps that capture intra‐ and interannual variability and span multiple anomalous years (58%–158% of median peak SNOTEL snow depth). Comparisons with near‐contemporaneous airborne lidar snow depth measurements showed good agreement, with median offset of −0.13 m, precision of 0.19 m and accuracy of 0.31 m. Our results suggest that satellite stereo can provide snow depth observations with the spatiotemporal coverage needed to improve operational forecast models and inform adaptive management strategies. Plain Language Summary Detailed observations of snow depth can help us better understand how much water is stored as snow during important times of the year. We used high‐resolution images from commercial satellites to create detailed maps of snow‐covered surfaces for a study site in Colorado. Using a technique called stereo photogrammetry, we created precise three‐dimensional models of surface elevation from these images. By subtracting a snow‐free summer ground surface model from the winter snow surface models, we estimated snow depth over large areas and multiple years. Our satellite snow depth estimates agreed with snow depth measurements from airborne lidar and field campaigns. This satellite stereo approach helps us understand how mountain snow depth varies from year to year, providing valuable information to improve models and decisions for water resources management. Key Points Satellite stereo photogrammetry offers repeat, spatially continuous, high‐resolution snow depth measurements over large areas Stereo snow depth measurements are within ∼0.13–0.33 m of near‐contemporaneous airborne lidar and in situ measurements Stereo snow depth captures detailed intra‐ and interannual snow depth variability in low and high snow years

Several global digital elevation models (DEMs) have been developed in the last two decades. The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original version of the TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved edited version, the Copernicus DEM, in three major European mountain ranges (the Alps, the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning data as a reference. In addition, to evaluate the applicability of data acquisition characteristics (coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect, altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from 16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively. The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees showed that acquisition characteristics provided as auxiliary data are useful for locating problematic sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM were filled using DEMs from different time periods and, hence, users performing multitemporal analysis or requiring data from a specific time period in the mountain environment should be wary when using TanDEM-X and its derivations. We suggest that when filling problematic sites using alternative DEMs, more attention should be paid to the period of their collection to minimize the temporal displacement in the final products.

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.