Surface Digital Elevation Models Research Articles

Preparation Geodatabase of Sedimentological and Geological Properties by Using GIS Technology: A Case Study Basrah Province, Southern Part of Iraq

This study uses a combination of remote sensing and geographic information systems (GIS) to create a geodatabase file of geological features in the southern Iraq (Basrah region, as a case study). The geodatabase includes information about sedimentology, topography, structure, and a surface digital elevation model (topographic model). ETM+ Satellite Imagery group with a resolution of 28.5m was the primary data for which the Basrah map was created. Collected sediment data from literature and analysis in this study were inputted to plot geospatial sediment maps of the study area. The sediment maps were created using the SEDCLASS program developed by USGS, which uses of Shepard's and Folk's Schemes to classify the sediments. This geodatabase is a vital, essential step forward that offers a dynamic for future regional development. There is a crucial need to research and create models related to geo-environmental impacts.

A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms

Due to the effects of radio frequency interference and the limitations of algorithms under specific conditions, most of the currently available microwave-based soil moisture (SM) products are spatially discontinuous and have coarse spatial resolution, whereas optical observations also reveal various data gaps due to cloud contamination. Hence, the prediction of SM over invalid pixels and disaggregation from coarse to high scales are two main processes for obtaining SM at fine spatiotemporal resolution (e.g., daily/1-km). In the present study, two methods with respect to disaggregation-first or prediction-first were investigated from the synergetic use of the widely recognized European Space Agency-Climate Change Initiative (ESA-CCI) SM product and Moderate Resolution Imaging Spectroradiometer (MODIS) images over the Tibetan Plateau (TP) region. Specifically, the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and the generalized regression neural network (GRNN) were implemented in the disaggregation and prediction, respectively. In DisPATCh, spatially complete land surface temperature (LST), normalized difference vegetation index (NDVI) and digital elevation model (DEM) were provided as essential inputs to downscale the microwave-based ESA-CCI to a spatial resolution of 1 km, whereas MODIS-derived LST, NDVI, land surface albedo and DEM were considered in the GRNN prediction. Following the two methods, the daily/1-km SM dataset over a period of three years was finally estimated. Assessments with ground in-situ SM measurements over the TP region reveal an acceptable accuracy with unbiased root mean square errors of ∼ 0.06 m3/m3, indicating the potential to obtain operational daily/1-km spatially continuous SM products in future developments.

Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model

Flood depth is one of the important attributes in damage assessment especially in areas where agro-based activities takes place. Assessing impacted region turns out to be exceptionally difficult in the case of low-lying catchments with arable lands, which inundates due to extreme precipitation and flood modelling becomes impossible. Under these circumstances, SAR derived flood maps are valuable in removing different constraints related to flood modelling with high degree of profundity. Depth estimation from non-contact based methods requires, flood boundary as a primary input. However, detecting flood depth in emerging vegetation poses a complex challenge in terms of boundary estimation due to composite signatures that are manifested on SAR data. In this paper, a new approach is proposed to extract flood boundaries by fusing SAR data of two different frequencies, which are sensitive to water level changes. In the first step, wavelet fusion is applied to combine L- and C- band SAR data followed by Otsu’s segmentation method to extract varying levels of flood boundaries. In the second step, SRTM Digital Elevation Model (DEM) with 30m horizontal resolution is used on each boundary for statistical analysis based on which water surface levels are extracted. In the final step, depth levels are calculated from the water surface elevation and DEM. Floodwater Depth Estimation tool (FwDET) derived depth measurements are used to calibrate the statistical thresholds for derived flood depths. The study carried out on 2016 Assam flood event shows maximum flood depth of 1.56 meters in the selected study area and the results are verified with evidence-based ground truth collections which showed RMSE error of 0.25 meters from the measured values.

Mapping Unmanned Aerial System Data onto Building Information Modeling Parameters for Highway Construction Progress Monitoring

The transportation infrastructure management sector lacks automated procedures that can help it find and resolve the performance deviations. The objective of this research is to illustrate the mapping of Unmanned Aerial System (UAS) collected photogrammetric data to building information modeling (BIM) parameters, and their application for automated construction progress monitoring and the generation of as-built models. The goal is to support project managers to estimate project progress during highway construction. As a part of ongoing work, this paper takes into account 4D (3D + time) data that is acquired from 3D surface digital elevation models, point clouds, LiDAR data, and orthographic photos. It maps these 4D data onto BIM parameters to create as-built models of the project at different intervals. A comparison between as-planned and as-built models using the earned value management method is employed to develop metrics that can be used for indicating cost and schedule deviations during construction. The mapping methodology introduced in this paper is illustrated using an ongoing highway construction project case study. The main contribution of this paper is the organization, processing, and integration of UAS data with BIM data structures and project management workflows. The research outcomes will assist project managers in an easy and quick identification of potential performance problems and support the project management decision-making process.

Development of a subglacial lake monitored with radio-echo sounding: case study from the eastern Skaftá cauldron in the Vatnajökull ice cap, Iceland

Abstract. We present repeated radio-echo sounding (RES, 5 MHz) on a profile grid over the eastern Skaftá cauldron (ESC) in Vatnajökull ice cap, Iceland. The ESC is a ∼ 3 km wide and 50–150 m deep ice cauldron created and maintained by subglacial geothermal activity of ∼ 1 GW. Beneath the cauldron and 200–400 m thick ice, water accumulates in a subglacial lake and is released semi-regularly in jökulhlaups. The RES record consists of annual surveys conducted at the beginning of every summer during the period 2014–2020. Comparison of the RES surveys reveals variable lake area (0.5–4.1 km2) and enables traced reflections from the lake roof to be distinguished from bedrock reflections. This allows construction of a digital elevation model (DEM) of the bedrock in the area, further constrained by two borehole measurements at the cauldron centre. It also allows creation of lake thickness maps and an estimate of lake volume at the time of each survey, which we compare with lowering patterns and released water volumes obtained from pre- and post-jökulhlaup surface DEMs. The estimated lake volume was 250 GL (gigalitres = 106 m3) in June 2015, but 320 ± 20 GL drained from the ESC in October 2015. In June 2018, RES profiles revealed a lake volume of 185 GL, while 220 ± 30 GL were released in a jökulhlaup in August 2018. Considering the water accumulation over the periods between RES surveys and jökulhlaups, this indicates 10 %–20 % uncertainty in the RES-derived volumes at times when significant jökulhlaups may be expected.

Hourly surface meltwater routing for a Greenlandic supraglacial catchment across hillslopes and through a dense topological channel network

Abstract. Recent work has identified complex perennial supraglacial stream and river networks in areas of the Greenland Ice Sheet (GrIS) ablation zone. Current surface mass balance (SMB) models appear to overestimate meltwater runoff in these networks compared to in-channel measurements of supraglacial discharge. Here, we constrain SMB models using the hillslope river routing model (HRR), a spatially explicit flow routing model used in terrestrial hydrology, in a 63 km2 supraglacial river catchment in southwest Greenland. HRR conserves water mass and momentum and explicitly accounts for hillslope routing (i.e., flow over ice and/or firn on the GrIS), and we produce hourly flows for nearly 10 000 channels given inputs of an ice surface digital elevation model (DEM), a remotely sensed supraglacial channel network, SMB-modeled runoff, and an in situ discharge dataset used for calibration. Model calibration yields a Nash–Sutcliffe efficiency as high as 0.92 and physically realistic parameters. We confirm earlier assertions that SMB runoff exceeds the conserved mass of water measured in this catchment (by 12 %–59 %) and that large channels do not dewater overnight despite a diurnal shutdown of SMB runoff production. We further test hillslope routing and network density controls on channel discharge and conclude that explicitly including hillslope flow and routing runoff through a realistic fine-channel network (as opposed to excluding hillslope flow and using a coarse-channel network) produces the most accurate results. Modeling complex surface water processes is thus both possible and necessary to accurately simulate the timing and magnitude of supraglacial channel flows, and we highlight a need for additional in situ discharge datasets to better calibrate and apply this method elsewhere on the ice sheet.

Comprehensive Evaluation of the ICESat-2 ATL08 Terrain Product

Current spaceborne lidar Ice, Cloud, and Land Elevation Satellite (ICESat)-2 provides ATL08 product for global terrain height, whose quality properties are yet to be fully understood. This article performs a comprehensive evaluation on its quality by using 3-D elevation program (3DEP) digital elevation model (DEM) and hundreds of survey marks of two counties in the USA. The evaluation is carried out in terms of data specification, survey marks, land cover, season and time (day or night) of acquisition, incidence angle, and terrain slope. The ATL08 height errors are further modeled as a function of laser incidence angle and canopy coverage. It is found out the height from ATL08 product lies between the 3DEP DEM and true ground surface. The uncertainty of ATL08 height is 0.2 m for plain terrain, and 2 m for mountainous terrain where the majority of ATL08 segments are not useful for terrain extraction. The terrain height gets more underestimated by ATL08 products in regions with large terrain slope or incidence angle and more overestimated where the terrain is covered by dense canopy. Furthermore, seasonal variation of terrain height error can be as high as 0.8 m, while the impact of acquisition time is less than 0.3 m. We expect these findings to be informative for the utilization of ATL08 terrain product by worldwide users, and for the improvement of future ATL08 product.

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

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

Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

Study on subsidence monitoring technology using terrestrial 3D laser scanning without a target in a mining area: an example of Wangjiata coal mine, China

When using a Terrestrial 3D laser scanning (TLS) to monitor the mining subsidence in a mining area, the conventional methods are to set up the public target manually, or to set up the ground control network in advance. However, these have the disadvantages of high labor intensity and low efficiency. In order to overcome the shortcomings of the conventional methods, this paper presents the subsidence monitoring method using TLS without a target in the mining area. Feature points are used to splice the multi-station point clouds in the same phase, and a few station coordinates are used to uniformly transform the multiphase data into a geodetic coordinate system. Afterwards, the surface digital elevation model (DEM) is established by using the point clouds after coordinate transformation, and the dynamic subsidence value and subsidence basin during the period of observation are obtained by DEM subtraction. Compared to traditional methods, this method simplifies the field measurement process, reduces the labor intensity, and transfers the main work to the internal industry. It is suitable for surface subsidence monitoring with a harsh external environment and complicated topographic conditions. Surface deformation values are important basic data for mining area subsidence parameters acquisition, mining area environmental assessment, land reclamation, and ecological restoration. As a consequence, it is of great value to popularize and apply in mining area for subsidence monitoring.

Unmanned Aerial Vehicle (UAV) Photogrammetry Technology for Dynamic Mining Subsidence Monitoring and Parameter Inversion: A Case Study in China

This study mainly presents the method for monitoring the surface dynamic subsidence basin (SDSB) caused by underground coal mining and obtaining parameters of mining subsidence (PMS) in the short term by using an unmanned aerial vehicle (UAV) Photogrammetry Technology. The basic ideas and methods are proposed; that is, the two-stage surface digital elevation model (DEM) is obtained in the short term by UAV; The SDSB is obtained through two phases of DEM subtraction; Based on the dynamic inversion method established in this study, the PMS was obtained. The UAV method was used to monitor the Wangjiata coal mine in Inner Mongolia of China three times in three months to obtain the three phases of DEM; we obtained the two phases SDSB by the three phases of DEM subtraction. The accuracy of DEM and SDSB were 118 mm and 121 mm respectively, Although the accuracy cannot fulfill the requirements of mining subsidence, the PMS was obtained by dynamic inversion method of full subsidence basin fitting, which has better resistance to errors; hence, the obtained PMS are reliable. Based on the engineering application, this study concludes that as a new technology, UAV photogrammetry technology can obtain the SDSB of coal mining areas in a short period with reliable PMS. Meanwhile, it has the advantages of low cost, flexible maneuverability, and so on, and overcomes the shortages of traditional observation stations, which has long observation time and high labor intensity, needs to bury fixed measuring points and easily lose them, and just has a small amount of “spot-like” observation data that cannot reflect the deformation characteristics of the whole subsidence basin. Furthermore, it is feasible to apply this method to mining subsidence monitoring and parameter inversion, and it has a great application prospect for the mining area purposes.

Soil erosion topographic factor (LS): Accuracy calculated from different data sources

The availability and precision of topographic data determines the reliability of the calculated slope length and slope gradient (LS) factor, which limits the use of the universal soil loss equation (USLE) and its adapted versions in region-scale soil erosion risk assessment. One of the common complications is the effectiveness of topographic data with different resolutions or the compatibility of topographic data originated from different sources. In this study, the topographic data from five common sources, 5-m digital elevation model (DEM) based on 1:10,000 topographic maps (5-m topo DEM of 1:10,000), 25-m DEM derived from 1:50,000 topographic maps (25-m topo DEM of 1:50,000), 30-m ASTER GDEM, 30-m and 90-m SRTM DEMs, were individually applied to calculate the LS factors of five catchments with distinct terrain characteristics. Our results show that the computation accuracy of the LS factors in the five study catchments decreased with greater grid sizes derived from the five topographic data sources. Compared to the most precision 5-m topo DEM of 1:10,000, the relative computation error of the mean LS factors was less than 10% when calculated from the 25-m topo DEM of 1:50,000, and that was less than 25% from the 30-m ASTER and 30-m SRTM DEMs. For scenarios in gently rolling areas, the 90-m SRTM DEM with the relative computation error less than 15% could be recommended when there are noises on the open sources DEMs surface. Therefore, depending on the requirements of data accuracy, different data sources can be applied individually or in combined to obtain the optimal predictions of the LS factors. However, such recommendations on data sources proposed in this study appeared to be more applicable for regions with complex terrains. Further studies over a range of terrain features and spatial scales are required to validate the effectiveness of different topographic data sources in calculating the LS factor.

Latest Geodetic Changes of Austre Lovénbreen and Pedersenbreen, Svalbard

Geodetic mass changes in the Svalbard glaciers Austre Lovénbreen and Pedersenbreen were studied via high-precision real-time kinematic (RTK)-global positioning system (GPS) measurements from 2013 to 2015. To evaluate the elevation changes of the two Svalbard glaciers, more than 10,000 GPS records for each glacier surface were collected every year from 2013 to 2015. The results of several widely used interpolation methods (i.e., inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), natural neighbor (NN), spline interpolation, and Topo to Raster (TTR) interpolation) were compared. Considering the smoothness and accuracy of the glacier surface, NN interpolation was selected as the most suitable interpolation method to generate a surface digital elevation model (DEM). In addition, we compared two procedures for calculating elevation changes: using DEMs generated from the direct interpolation of the RTK-GPS points and using the elevation bias of crossover points from the RTK-GPS tracks in different years. Then, the geodetic mass balances were calculated by converting the elevation changes to their water equivalents. Comparing the geodetic mass balances calculated with and without considering snow depth revealed that ignoring the effect of snow depth, which differs greatly over a short time interval, might lead to bias in mass balance investigation. In summary, there was a positive correlation between the geodetic mass balance and the corresponding elevation. The mass loss increased with decreasing elevation, and the mean annual gradients of the geodetic mass balance along the elevation of Austre Lovénbreen and Pedersenbreen in 2013–2015 were approximately 2.60‰ and 2.35‰, respectively. The gradients at the glacier snouts were three times larger than those over the whole glaciers. Additionally, some mass gain occurred in certain high-elevation regions. Compared with a 2019 DEM generated from unmanned aerial vehicle measurement, the glacier snout areas presented an accelerating thinning situation in 2015–2019.

Bundle adjustment of satellite images based on an equivalent geometric sensor model with digital elevation model

High-precision georeferencing of satellite stereo images becomes a key issue in multi-source data integration, geometric processing, and analysis for many remote sensing applications. Using existing geographic data, including readily available public data, as geometric reference for georeferencing satellite imagery is an effective and feasible way to improve the positional accuracy and reduce the costs and workforce restrictions on ground control points (GCPs) acquisition. This study proposes a novel bundle adjustment approach of push broom satellite imagery based on an equivalent geometric sensor model (EGSM). As an extension of the collinearity equations, EGSM is equivalent to the rigorous geometric sensor model, whose interior and exterior orientation parameters have clear geometric interpretations. The initial values of EGSM’s parameters can be completely recovered from the RPCs and optimized within the block adjustment without using any metadata of the satellite imagery. Furthermore, a publicly accessible digital elevation model (DEM) is used as constraints in the bundle adjustment to improve the direct georeferencing accuracies without GCPs. A set of pseudo observation equations is incorporated into the EGSM associated with appropriate weights based on the estimated variance of height differences between the tie points and the DEM surface. The performance of the proposed approach was evaluated by using 143 stereo scenes of Chinese Ziyuan-3 (ZY-3) images and the global DEM data SRTM GL3, ASTER GDEM, and AW3D30. The experimental results revealed that the proposed approach could improve not only vertical accuracies but also horizontal accuracies of satellite stereo images. The geopositional accuracy after adjustment is dependent on the quality and accuracy of the reference DEM itself. With AW3D30 as exclusive controls, the horizontal and vertical root mean square errors (RMSEs) of the experimental images are reduced from 17.3 m and 2.6 m to 2.5 m and 1.5 m, respectively. The horizontal accuracy corresponds to about 1.2 pixels in the image space, and the vertical accuracy is about 0.6 pixels with the base-height ratio of ZY-3 stereo images being 0.81. The generazation ability of the approach was further proved by the experiments of WorldView-3 stereo images.

Effectiveness of geometric quality control using a distance evaluation method

ABSTRACT Research studies have shown that when the error of a digital elevation model (DEM) is accepted as random, one of the two solutions is generally used. One is the employment of a benchmark, and the other is measuring the vertical distance to evaluate the vertical error. For the first solution, a benchmark or adequate random sample may be unavailable. The second solution consists of measuring only the vertical distance from a point of the DEM to the surface of the reference DEM. This solution provides only a biased vertical error. In this paper, a perpendicular distance evaluation method (PDEM) is proposed. This approach allows estimating the vertical error, and under certain irregular terrain conditions, the horizontal error. Simulations are presented in detail, starting from a real reference DEM, with 100 000 points. The results confirm that, the more irregular the terrain is, the better the horizontal error results, and that the evaluation of the vertical error is not biased.

Geomorphological mapping based on DEMs and GIS: A review

Abstract. Geomorphology is a scientific discipline dealing with the characteristics, origin, and evolution of landforms. It utilizes topographic data such as spot height information, contour lines on topographic maps, and DEMs (Digital Elevation Models). Topographic data were traditionally obtained by ground surveying, but introduction of aerial photogrammetry in the early 20th century enabled more efficient data acquisition based on remote sensing. In recent years, active remote sensing methods including airborne and terrestrial laser scanning and applications of satellite radar have also been employed, and aerial photogrammetry has become easier and popular thanks to drones and a new photogrammetric method, SfM (Structure from Motion). The resultant topographic data especially raster DEMs are combined with GIS (Geographic Information Systems) to obtain derivatives such as slope and aspect as well as to conduct efficient geomorphological mapping. Resultant maps can depict various topographic characteristics based on surface height and DEM derivatives, and applications of advanced algorithms and some heuristic reasoning permit semi-automated landform classification. This quantitative approach differs from traditional and more qualitative methods to produce landform classification maps using visual interpretation of analogue aerial photographs and topographic maps as well as field observations.For scientific purposes, landforms need to be classified based on not only shape characteristics but also formation processes and ages. Among them, DEMs only represent shape characteristics, and understanding formation processes and ages usually require other data such as properties of surficial deposits observed in the field. However, numerous geomorphological studies indicate relationships between shapes and forming-processes of landforms, and even ages of landforms affect shapes such as a wider distribution of dissected elements within older landforms. Recent introduction of artificial intelligence in geomorphology including machine learning and deep learning may permit us to better understand the relationships of shapes with processes and ages. Establishing such relationships, however, is still highly challenging, and at this moment most geomorphologists think landform classification maps based on the traditional methods are more usable than those from the DEM-based methods. Nevertheless, researchers of some other fields such as civil engineering more appreciate the DEM-based methods because they can be conducted without deep geomorphological knowledge. Therefore, the methods should be developed for interdisciplinary understanding. This paper reviews and discusses such complex situations of geomorphological mapping today in relation to historical development of methodology.

Accurate coastal DEM generation by merging ASTER GDEM and ICESat/GLAS data over Mertz Glacier, Antarctica

Mertz Glacier (MG) calved in February 2010 and a 70-years' calving cycle of MG was reported recently because of the shallow Mertz Bank. To better investigate the calving process, a high accurate surface Digital Elevation Model (DEM) with accurate date marker over MG is urgently required for numerical modeling, especially over the ice tongue. However it is quite challenging to generate an accurate DEM over high relief and steep slope regions. To solve this problem, a new method for accurate coastal DEM production by merging ASTER GDEM and ICESat/GLAS data is designed, which can effectively discriminate accurate elevation data from ASTER GDEM. Then, a DEM corresponding to the end of 2002 over MG is generated, which has an accuracy of 0.99±17.50m. This DEM is several times better in accuracy than the original ASTER GDEM (accuracy: −8.17±54.31m) and two new characteristics of ASTER GDEM have been found through analysis. First, ASTER GDEM has elevation bias (varying from −23m to 28m), spatially correlated to ASTER ground tracks, which was most probably caused by uncertainty of attitude measurements of ASTER. Second, ASTER GDEM grids with stacking number ≥4 can be effectively adjusted by ground control points so as to improve elevation accuracy. Additionally, this DEM has the best accuracy by comparing with other DEMs (RAMP DEM: 47.71±91.61m, ICESat/GLAS DEM: −1.01±30.33, Bamber DEM: −1.07±33.04m, Bedmap-2 DEM: 9.19±48.34m, and Cryosat-2 DEM: −5.42±32.02m). The high performance of our DEM in accuracy indicates that our method is effective and has a potential to be widely used to improve existing ASTER GDEM along Antarctic coast.

DEM Retrieval From Airborne LiDAR Point Clouds in Mountain Areas via Deep Neural Networks

Airborne light detection and ranging (LiDAR) remote sensing enables accurate estimation and monitoring of terrain and vegetation, and digital surface model (DSM) and digital elevation model (DEM) are vital analytical tools to achieve this estimation and monitoring. Among them, DSM can be directly acquired from airborne LiDAR point clouds; nevertheless, for the production of DEM, point clouds representing a surface of ground objects should be accurately filtered out at first. In some mountain forest areas, due to the limited penetration of airborne LiDAR, ground points sustain a serious lack, which results in the difficulty in producing accurate DEMs. To reduce the intricacy and subjectivity caused by the manual supplement to ground points, this letter proposes a new DEM retrieval method from airborne LiDAR point clouds in mountain areas based on deep neural networks (DNNs). With a DNN model trained by accurate DEMs and DSMs, DEM retrieval becomes much easier by inputting their DSM into this model for prediction. Experiments on Fujian and Hainan mountain data sets demonstrate the effectiveness of this supervised method.

A high-fidelity multiresolution digital elevation model for Earth systems

Abstract. The impact of topography on Earth systems variability is well recognised. As numerical simulations evolved to incorporate broader scales and finer processes, accurately assimilating or transforming the topography to produce more exact land–atmosphere–ocean interactions, has proven to be quite challenging. Numerical schemes of Earth systems often use empirical parameterisation at sub-grid scale with downscaling to express topographic endogenous processes, or rely on insecure point interpolation to induce topographic forcing, which creates bias and input uncertainties. Digital elevation model (DEM) generalisation provides more sophisticated systematic topographic transformation, but existing methods are often difficult to be incorporated because of unwarranted grid quality. Meanwhile, approaches over discrete sets often employ heuristic approximation, which are generally not best performed. Based on DEM generalisation, this article proposes a high-fidelity multiresolution DEM with guaranteed grid quality for Earth systems. The generalised DEM surface is initially approximated as a triangulated irregular network (TIN) via selected feature points and possible input features. The TIN surface is then optimised through an energy-minimised centroidal Voronoi tessellation (CVT). By devising a robust discrete curvature as density function and exact geometry clipping as energy reference, the developed curvature CVT (cCVT) converges, the generalised surface evolves to a further approximation to the original DEM surface, and the points with the dual triangles become spatially equalised with the curvature distribution, exhibiting a quasi-uniform high-quality and adaptive variable resolution. The cCVT model was then evaluated on real lidar-derived DEM datasets and compared to the classical heuristic model. The experimental results show that the cCVT multiresolution model outperforms classical heuristic DEM generalisations in terms of both surface approximation precision and surface morphology retention.

Slope-Independent Landscape Roughness Attribute Provided by Measurement of Local Contour Line Density

Various digital treatments have been proposed to describe the digital elevation model (DEM) surface roughness, depending on the geomorphologic purpose. Some of these treatments use the drainage network pattern directly or combine the former binary information with DEM data. Others take into account local characteristics of the DEM’s surface, such as the deviation of the perpendicular issued from each pixel or the computation of the surface curvature. The aim of the present study is to propose new geomorphic algorithms based on contour line density to calculate the DEM surface roughness. The contour line density is calculated within a moving window of n × n pixels, and the contour lines are extracted according to a given range of hypsometric intervals. The value of this total length inside the moving window reflects the local irregularities encountered in the studied zone. Meanwhile the slope-independent roughness parameter (SIRP) measures the total length of a limited number of contour lines inside a moving window, extracted according to the total hypsometric difference between the upper and lower altitudes encountered in the window. While the first calculation emphasizes the general features of the studied DEM in terms of its local surface roughness, the SIRP provides a roughness measurement independent of the slope. In the Sierra Norte de Puebla region (Central Mexico) where numerous landslides have occurred recently, slope instability is closely related with the smooth zones extracted by use of the SIRP attribute.