The geomorphological and sedimentological legacy of the historical Lake Lorsch within the Weschnitz floodplain (northeastern Upper Rhine Graben, Germany)

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

Generating high-resolution DEMs in mountainous regions using ICESat-2/ATLAS photons

High-resolution (HR) digital elevation models (DEMs) have been found to be critical for many applications, as they provide accurate basic geodata, as well as more information and accurate results. However, despite the importance of HR DEM, many areas across the world, particularly in developing countries, lack access to them. Thus, researchers inspired by the success of super resolution (SR) on image enhancement, especially the use of deep learning (DL) approaches, instead of using high-precision equipment to obtain HR DEMs, have recently presented and are discussing the concept of DEM SR. This paper provides a review of such a DEM SR technique. It first explains the basic idea of SR, then describes DEM SR, and finally, a review of DEM SR algorithms proposed in the literature is presented, describing the main approaches and some of the shortcomings. This review shall provide the geoscientific community with information on an emerging alternative technique for acquiring HR DEM that is more cost-effective and can contribute to open data, which is widely recognised as the key engine for achieving the Sustainable Development Goals (SDGs).

Abstract. High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such as roads and overpasses, can form barriers that incorrectly alter flow accumulation models, and hinder the extraction of accurate surface water drainage networks. This study tests a deep learning approach to identify the intersections of roads and stream valleys, whereby valley channels can be burned through road embankments in a HR DEM for subsequent flow accumulation modeling, and proper natural drainage network extraction.

Global flood hazard models have recently become a reality thanks to the release of open access global digital elevation models, the development of simplified and highly efficient flow algorithms, and the steady increase in computational power. In this commentary we argue that although the availability of open access global terrain data has been critical in enabling the development of such models, the relatively poor resolution and precision of these data now limit significantly our ability to estimate flood inundation and risk for the majority of the planet’s surface. The difficulty of deriving an accurate ‘bare-earth’ terrain model due to the interaction of vegetation and urban structures with the satellite-based remote sensors means that global terrain data are often poorest in the areas where people, property (and thus vulnerability) are most concentrated. Furthermore, the current generation of open access global terrain models are over a decade old and many large floodplains, particularly those in developing countries, have undergone significant change in this time. There is therefore a pressing need for a new generation of high resolution and high vertical precision open access global digital elevation models to allow significantly improved global flood hazard models to be developed.

Flood models predict inundation extents, and can be an important source of information for flood risk studies. Accurate flood models require high resolution and high accuracy digital elevation models (DEM); current global DEMs do not capture the topographic details in floodplains, and this often leads to inaccurate prediction of flood extents by flood models. Flood extents obtained from remotely sensed data provide indirect information about topography. Here, we attempt to use this information along with model predictions to produce better floodplain topography. The algorithm we describe is a two-step process: first, we reduce the noise along the observed flood boundaries for all particles. Then, the model predictions from these modified DEMs are assimilated with observations using a particle batch smoother. We implemented the algorithm for a synthetic test case. For the nominal case, we observed a significant improvement in accuracy in terms of RMSE (35% reduction), bias (20%) and standard deviation (40%). We conducted sensitivity analysis by using priors of varying bias (0.5 m, 1 m, 2 m) and standard deviation (1 m, 2 m, 4 m). The bias reduced to ~0.5 m or below in all the cases: the reduction in bias varied from 11% to 76%. The standard deviation of errors in the final estimate was almost half of the prior: the reduction varied from 40% to 49%. The reduction in RMSE ranged between 35% and 67%. For the case with 2 m bias and 4 m standard deviation (SRTM-like error levels), bias went down to 0.48 m (76% reduction), and standard deviation reduced to 2.24 m (44% reduction). Flood inundation maps produced from the final estimate DEMs also improved on its prior. For the 2 m bias cases, true positive rate (TPR) for peak inundation went from ~30% to more than 57% in all three cases. The algorithm produces promising results, and this type of analysis can be performed in data-poor floodplains where high resolution DEMs do not exist.

Glaciers retreating due to climate change have significant impacts both locally and globally. An essential part of understanding their evolution are mass balance measurements. Although the surface mass balance of glaciers is well known, non-surface components, more specifically basal and internal melt, are not well understood as they are inherently difficult to observe. Local maxima in basal melt on alpine glaciers are believed to result in the formation of large subglacial cavities, potentially leading to so called “collapse features”. The ice loss caused by these collapse features is likely to impact the retreat dynamics of glaciers. Using a parameterized model based on a complete consideration of factors of sub- and englacial energy exchange, basal melt for all 1400 Swiss glaciers was estimated. The model operates with data sets on surface mass balance and glacier geometry, as well as with simplified considerations of the relevant processes. Our model considers energy advection through ice-marginal streams and subglacial air flow, potential energy release from melt water, friction-induced heat release, geothermal heat flux and dissipation of heat uptake by surface melt water. Field observations were used to constrain some of the parameters. Additionally, high-resolution aerial imagery and digital elevation models (DEMs) were used to perform a geostatistical analysis to better understand glacio-hydrological relationships and processes. Besides modelling glacier-wide basal melt, we analyzed the spatial and temporal dynamics of individual collapse features on a selected group of glaciers in the Swiss Alps, using high resolution DEMs. The model results indicate that the advection of energy through ice-marginal streams and potential-energy release from melt water are the primary contributors to basal melt for Swiss glaciers. The relevance of the modelled components importantly varies between glaciers and depends on glacier size and topography among other factors. At the Swiss-wide scale, total basal melt is modelled to be in the range of a few millimeters to several tens of centimeters water equivalent per year (total mass balance of Swiss glaciers is on average -1 meter water equivalent per year). These results suggest that for some glaciers, basal melt is both a relevant fraction of total mass balance, as well as large enough to be measured using high-resolution in situ GNSS observations. The analysis of glacier collapse features yielded an average life span of 3.5 years and volumes of non-surface ice loss ranging from a few thousand to more than 175’000 m3. These findings, along with the model results, emphasize the substantial role of basal melt in both local retreat dynamics and total glacier-wide mass balance.

Due to the increasing impacts of global climate change in recent years, nations around the world have been grappling with frequent natural disasters. Taiwan, situated on the Pacific Rim seismic belt, is shaped by active orogeny, resulting in its rugged terrain. The island has experienced numerous typhoons, extreme rainfall, and complex hydrological conditions, making its mountainous areas particularly vulnerable to natural disasters. The accumulation of soil and sediment further alters the landscape of its watersheds, putting both infrastructure and residents at significant risk. This study therefore focuses on the monitoring and maintenance of slopes in Taiwan’s watershed areas.Since the inspection of mountain roads is limited by terrain and vegetation, this study utilizes the high-resolution Digital Elevation Model (DEM) for geomorphometric analysis to precisely target landslide hot spots, and Unmanned Aerial Vehicles (UAVs) to observe more detailed topographical features. Meanwhile, the Normalized Difference Vegetation Index (NDVI) is used to interpret landslide and vegetation restoration status, while Multi-Temporal InSAR (MTInSAR) is employed to detect topographical changes and observe post-disaster alterations.Taking the section between Chinhe and Fuxing (92K to 99K) of Taiwan Provincial Highway 20 as a case study, this highway serves as a critical horizontal transportation hub. Following the impact of Typhoon Morakot in 2009, the region has experienced highly unstable and complex hydrological conditions, resulting in persistent damage to its roads and bridges. This study primarily employs high-resolution LiDAR DEM to analyze pre- and post-disaster changes in terrain and river channels. Then the NDVI interpretation, derived from SPOT satellite imagery, reveals that the crown of the original landslide area has been actively developing, leading to the movement of rocks and debris. The MTInSAR results further corroborate this interpretation, confirming that the crown area of Yushui River remains prone to landslides, with new slide events and significant sediment accumulation in downstream areas.In summary of the analysis and on-site data, the primary disaster-prone factors are the meandering of the Lanong River and the accumulation of soil and sand, leading to extreme instability in the alluvial fans on both banks. After multiple landslides, the damage mechanism is analyzed, revealing that the region is highly susceptible to tectonic activity. The initial results facilitate the overall slope stability evaluation and provide relevant agencies with governance and maintenance recommendations to enhance road safety.Keyword：High-resolution Digital Elevation Model, DEM、Normalized Difference Vegetation Index,NDVI、Multi Temporal InSAR, MT-InSAR.

SUMMARY Computation of gravimetric terrain corrections (TCs) is a numerical challenge, especially when using very high-resolution (say, ∼30 m or less) digital elevation models (DEMs). TC computations can use spatial or/and spectral techniques: Spatial domain methods are more exact but can be very time-consuming; the discrete/fast Fourier transform (D/FFT) implementation of a binomial expansion is efficient, but fails to achieve a convergent solution for terrain slopes >45°. We show that this condition must be satisfied for each and every computation-roving point pair in the whole integration domain, not just at or near the computation points. A combination of spatial and spectral methods has been advocated by some through dividing the integration domain into inner and outer zones, where the TC is computed from the superposition of analytical mass-prism integration and the D/FFT. However, there remain two unresolved issues with this combined approach: (1) deciding upon a radius that best separates the inner and outer zones and (2) analytical mass-prism integration in the inner zone remains time-consuming, particularly for high-resolution DEMs. This paper provides a solution by proposing: (1) three methods to define the radius separating the inner and outer zones and (2) a numerical solution for near-zone TC computations based on the trapezoidal and Simpson's rules that is sufficiently accurate w.r.t. the exact analytical solution, but which can reduce the computation time by almost 50 per cent.

Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilized to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM), because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms is however slow, because large amounts of non-local terrain data have to be processes. We propose a new and more efficient method, which is based on a high-performance ray tracing library. By applying terrain simplification to remote topography, this allows the application of the new algorithms also with very high-resolution (< 5 m) DEM data, which otherwise would induce an excessive memory footprint. The topographic horizon algorithm is accompanied by a SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex one. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (1–25 km).

Abstract. Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilised to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM) because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms, however, is slow because large amounts of non-local terrain data have to be processed. We propose a new and more efficient method, which is based on a high-performance ray-tracing library. The new algorithm can speed up horizon calculation by 2 orders of magnitude relative to a conventional approach. By applying terrain simplification to remote topography, the ray-tracing-based algorithm can also be applied with very high-resolution (&lt;5 m) DEM data, which would otherwise induce an excessive memory footprint. The topographic horizon algorithm is accompanied by an SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex terrain. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from the literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (1–25 km).

<strong class="journal-contentHeaderColor">Abstract.</strong> Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilised to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM) because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms, however, is slow because large amounts of non-local terrain data have to be processed. We propose a new and more efficient method, which is based on a high-performance ray-tracing library. The new algorithm can speed up horizon calculation by 2 orders of magnitude relative to a conventional approach. By applying terrain simplification to remote topography, the ray-tracing-based algorithm can also be applied with very high-resolution (<span class="inline-formula">&lt;5</span> m) DEM data, which would otherwise induce an excessive memory footprint. The topographic horizon algorithm is accompanied by an SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex terrain. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from the literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (<span class="inline-formula">1</span>–<span class="inline-formula">25</span> km).

High-resolution Digital Elevation Model (DEM) data provides essential information for pluvial flood simulation. Although the increased accessibility and quality of publicly available DEM datasets can facilitate geospatial analysis at various scales, existing DEM datasets with global coverage mostly lack sufficient spatial resolution for pluvial flood simulations, which require detailed topographic information to be included in the simulation. Simulating flood scenarios with low-resolution DEMs (&gt;30m) can result in substantial deviations from real cases. This issue becomes even more severe for flood-prone areas in data-scarce developing countries.Image super-resolution is a technique for reconstructing low-resolution information into high-resolution data. Various deep-learning models have been employed for this task, primarily focusing on generating high-resolution natural-colour images. However, the effects of these deep learning models on enhancing the resolution of DEM data have not been extensively investigated. One of the state-of-the-art super-resolution models, the Residual Channel Attention Network (RCAN), has gained popularity due to its accuracy and efficiency. Leveraging publicly available low-resolution global DEM data and high-resolution regional DEM data, this study assesses the performance of RCAN models in a DEM super-resolution task. The experimental results suggest that, compared to conventional interpolation methods, the tested RCAN model exhibits superior performance in constructing high-resolution DEM data. The generated super-resolution DEM data were then tested in pluvial flood simulations and achieved substantially higher realism in modelling floodwater distribution. The proposed method for constructing super-resolution DEMs opens up the possibility of simulating flooding at hyper-resolution globally.

In this study, local government digital spatial data are used to describe urban geometry and analyse spatial variations of the urban climate within the central areas of Göteborg, Sweden. A high‐resolution raster digital elevation model (1 m pixel resolution) consisting of building structures and ground heights is derived from a local government geo‐database, as well as land use patterns and artificial heat sources. Parameters such as the sky view factor (SVF) and daily averages of solar radiation are calculated. Results obtained from the model are compared with intra‐urban air temperature variations which are derived from mobile measurements, as well as surface temperature variations derived from thermal infrared images. Results show that high‐resolution digital elevation models in raster format are very useful sources of data for the investigation of intra‐urban temperature variations. Results also show that the areal mean of SVF correlates with intra‐urban air temperature variations to a higher degree than SVF that is taken from a point source location. The correlation between the modelled SVF and surface temperature is high during both spring and winter. Adding information about daily averages of global radiation for the spring measurement causes the correlation between SVF and surface temperature variations to increase. Copyright © 2007 Royal Meteorological Society

Georegistration is the assignment of 3-D coordinates to the pixels of an image. It is necessary for aligning imagery with map data, fusing images from different sensors, and geolocating moving objects. Georegistration requires recovery of the exterior camera orientation or pose - that is, position and attitude - for every image in the motion imagery sequence, and often the recovery of the intrinsic camera parameters of focal length, sensor pixel aspect ratio, and radial distortion. We have developed a real- time, automated solution to this problem. It is based on the registration of actual images to predicted images from a high-resolution digital elevation model (DEM). Beginning from an initial camera estimate for the first image, the algorithm iterates on the process of forming a predicted image, registering it to the actual image, and then refining the camera estimate based on the registration results. The resulting camera model forms the initial camera estimate of the next image. In this way, the camera model of each image is recovered as well as the platform motion, and the resulting camera models are used with the DEM to stabilize and georegister the motion imagery. The algorithm works with a wide variety of DEMs, including high-resolution LIDAR and low-resolution USGS DEMs. Implemented on a dual quad-core PC, the software georegisters one gigapixel of imagery per second. Results are presented for wide area motion imagery, full motion video, and thermal infrared video, along with applications to surveillance and border security.