Digital Elevation Model Method Research Articles

Focusing the view: Improved methods for assessing viewshed impacts of onshore wind turbines

Onshore wind turbine capacity continues to grow and will only accelerate, though siting can be challenging given community opposition. We apply the hedonic valuation method with residential property sales data to assess nearby residents’ willingness to pay to avoid having views of turbines from their property. In doing so, we aim to improve methods of assessing viewshed impacts for turbines and other amenities and disamenities that have a visual component. Our recommended viewshed approach uses a Digital Surface Model (DSM), which accounts for trees and buildings that obstruct views. For comparison, we also create viewsheds based on bare-earth Digital Elevation Model (DEM), which has been more typically used other studies. Using data from New England, USA, we use a difference-in-differences identification strategy with treatment defined by the visibility of a wind turbine, while also controlling for proximity-based treatment effects. The results suggest that property values decline by 2.2%–2.5% when a wind turbine is visible, with larger impacts in urban and coastal areas. DEM methods misclassify viewshed for about 75% of properties, when compared to the DSM-based viewshed, and the resulting DEM-based valuation estimates are attenuated.

Research on Modeling Method of Digital Elevation Model for Dense Matching Point Cloud Images in Complex Environments

Research on Modeling Method of Digital Elevation Model for Dense Matching Point Cloud Images in Complex Environments

Decadal Changes in Glacier Area, Surface Elevation and Mass Balance for 2000–2020 in the Eastern Tanggula Mountains Using Optical Images and TanDEM-X Radar Data

The response of lake-terminating glaciers to climate change is complex, and their rapid changes are often closely linked to glacial-lake outburst floods. However, the eastern Tanggula Mountains, which are the only area where lake-terminating glaciers are found within the Tibetan Plateau, have received little attention to date. In this study, to address this gap, we generated updated glacier boundaries and estimated the interdecadal area changes for 2000–2020 based on the interpretation of Landsat-5/8 and Sentinel-2 images. In addition, based on the method of digital elevation model (DEM) differencing, we quantified the changes in glacier thickness and mass balance using TanDEM-X radar data and SRTM DEM over almost the same periods. The final results show that the glaciers in the eastern Tanggula Mountains, as a whole, have experienced accelerated area shrinkage (with a rate of area loss increasing from −0.34 ± 0.83 km2 a−1 to −0.93 ± 0.81 km2 a−1 for 2000–2013 and 2013–2020, respectively) and accelerated ice thinning (changing from −0.19 ± 0.05 m a−1 and −0.53 ± 0.08 m a−1 for 2000−2012 and 2012–2020, respectively). Furthermore, the region-wide glacier mass balance was −0.16 ± 0.04 m w.e. a−1 and −0.45 ± 0.07 m w.e. a−1 for these two sub-periods, corresponding to a 1.8 times acceleration of mass loss rate. The average mass balance during 2000–2020 was −0.23 ± 0.04 m w.e. a−1, which is equivalent to a rate of mass loss of −0.04 Gt a−1. More specifically, within the region, the lake-terminating glaciers have exhibited more significant acceleration of area loss and mass loss, compared to the land-terminating glaciers. However, interestingly, the average thinning rate of the lake-terminating glaciers is always lower than that of the land-terminating glaciers over all study periods, which is in contrast with previous findings in other high mountain areas (e.g., the Himalaya Mountains). Field study and proglacial lakes monitoring suggest that the local topography plays a vital role in the evolution of the glacial lakes in this region, which further affects the glacier changes. Furthermore, the present status of the glacier changes in this region can be attributed to the long-term increase in air temperature. Our findings provide a comprehensive overview of the current state of glacier changes across the eastern Tanggula Mountains and will help to improve the understanding of the heterogeneous response of glaciers to climate change.

An Extracting DEM Method Based on The Sub-apertures Difference in CSAR Mode

ABSTRACT This paper proposes a method based on the circular track synthetic aperture radar (CSAR) mode to directly obtain the digital elevation model (DEM) information of the target area through single-circle observation data. This paper improves and innovates the existing methods by using sub-aperture correlation. The circular motion trajectory of the entire radar observation platform is divided into multiple overlapping sub-aperture arcs. Each sub-aperture is projected in the height direction to observe the position deviation of the sub-aperture images more intuitively at different heights. Then, a new calculation method is used to calculate the correlation coefficient between sub-apertures, which enhances the accuracy of extracting DEM results. Finally, the measured data of the Ku band recorded by us are used to verify the effectiveness and accuracy of the algorithm proposed in this paper.

RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks

The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area.

Identification of soil piping-related depressions using an airborne LiDAR DEM: Role of land use changes

The detection of soil piping features is still a challenging issue that requires further studies, especially at larger scale. It seems that field mapping as a time-consuming method can be supported by the new technologies. This study has exploited the freely available airborne LiDAR (Light Detection and Ranging) digital elevation model (DEM) to explore piping-related depressions. The aim was to test the possibility of digital detection of piping forms using sink-fill method and different derivatives of airborne LiDAR DEM (slope, Topographic Position Index, Topographic Ruggedness Index, Positive and Negative Openness Index) in order of its utility in regional scale studies. As geological structure, soils and climate are similar in the whole study area, the analysis was extended to include the changes in land use/land cover (LULC) in the last two centuries (1862–2017). The results were verified by the detailed geomorphological mapping in the field. The study was conducted in a small catchment in the Bieszczady Mts. (SE Poland) covered by forests and grasslands/pastures, where in the past transition from pastures to forests was observed. The findings have revealed the great impact of LULC changes on soil piping, thus also on the possibility of digital mapping which is often based only on topographic information. The success rate in recognition was satisfactory on grasslands/pastures (76% for individual forms and 80% for piping systems). Areas covered by forests since the mid-19th century are unlikely places to develop piping forms (just 2 forms found) in the study area, whereas places where transition from pastures to forest has been observed may be prone to soil piping. However, the digital identification of piping forms in areas with rough topography is difficult (a success rate of 45% for individual forms and 50% for piping systems). The digital mapping of piping forms cannot rely only on the topographic information but should be always accompanied by an analysis of factors controlling piping erosion.

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications.

Regular Grid DEM Data Processing Based on Hadoop

Digital Elevation Model (DEM) has been widely used in various sectors. But its data structure is special, and the amount of data is large. When it comes to big data or large-scale data simulation, the data processing of stand-alone mode often fails to meet the needs of users. This paper focuses on the Hadoop cloud computing platform for large data parallel processing and its built-in data types is limited, study the organization method of the regular grid DEM in the cloud computing platform, and designs the data types that applicable to the regular grid DEM processing in MapReduce framework. Finally, realize the parallelization processing of DEM and use non-source flood analysis as an example to verify the feasibility of data types.

Baseline Estimation Using DEM Prior Knowledge and Capability Analysis for Gaofen-3 Repeat-Pass SAR Interferometry

In this paper, the study on the digital elevation model (DEM) reconstruction using Gaofen-3 satellite repeat-pass synthetic aperture radar (SAR) interferometry is presented. The coherence of the single-look complex (SLC) images and residuals of interferogram provide a powerful guarantee for interferometric processing for Gaofen-3. Baseline estimation is the key factor to generate accurate DEM with SAR interferometry. This paper uses the coarse orbit data and the precise orbit data of Gaofen-3 to carry on the baseline estimation and uses the prior coarse DEM information to refine the baseline estimation. Then, the topographic height is reconstructed with more precise spatial baseline. An experiment is conducted to verify the conclusion, and some mountain and flatten region are chosen as the study area. The results show that the DEM errors based on the prior DEM method are rapidly converged compared with the ones based on the orbit data method. Therefore, benefiting from the precise orbit data of the Gaofen-3 satellite, the accuracy of the baseline estimation can be improved further by using the method based on prior DEM information; thus, higher accuracy of DEM can be achieved so that Gaofen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, and high correlation, which has a good repeat-pass SAR interferometry application prospect.

Effect of Different Areal Precipitation Estimation Methods on the Accuracy of a Reservoir Runoff Inflow Forecast Model

Areal precipitation estimation directly affects the accuracy of reservoir runoff inflow forecasts and flood dispatching decision-making. Because of the heterogeneous spatial and temporal precipitation distributions in large basins, inadequate precipitation stations normally have a negative impact on forecast accuracy. Using the Panjia-kou reservoir runoff inflow forecast as the research subject, this paper adopts the Thiessen polygon block, square grid computing, and DEM (digital elevation model) methods to estimate average regional areal precipitation. Based on the estimation, a model for the Panjia-kou reservoir runoff forecast is developed. The results indicate that different areal precipitation estimation methods have significantly different effects on the accuracy of the reservoir runoff inflow forecast. When the average regional precipitation estimation from the DEM method is used as an input to the model, the simulation results are accurate and are much better than those from the other two average regional precipitation estimation methods.

Hydrological Evaluation of Flow Diversion Terraces Using Downhill-Slope Calculation Method for High Resolution and Accuracy DEMs

Slope is an important topographical parameter in hydrological models. Dramatic changes in slope are produced due to micro variations of topography in flow diversion terrace (FDT) systems. Assessing the effectiveness of FDT on soil conservation yields different results using different slope calculation methods with different resolution and accuracy digital elevation models (DEMs). In this study, the ArcGIS built-in average-neighborhood-slope (ANS) method and a downhill-slope (DHS) method were used to calculate slopes for a small agricultural watershed in New Brunswick, Canada. Six DEMs were used, i.e., 1, 5 and 10 m resolution DEMs generated with conventional photogrammetric techniques, and 1, 5, and 10 m resolution DEMs derived from light detection and ranging (LiDAR) data. Calculated slopes were summarized for the whole watershed, a patch of FDT systems, and grassed flow channels in front of embankments in the patch. Results indicated that the DHS method produced smaller slopes than the ANS method with LiDAR-1m DEM along grasses flow channels. Furthermore, the mean L-factor derived from the DHS method was smaller than that derived from the ANS in terraces and grassed flow channels for the LiDAR-1m DEM. Finally, soil loss estimated from the DHS method using RUSLE was more consistent with measurement for the watershed than that from the ANS method when the LiDAR-1m DEM was used. The DHS method can provide accurate slopes along grassed flow channels and can be used to assess FDT systems using USLE-based models with high resolution and accuracy DEMs.

An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment

Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions.

Temporary road transport route optimization based on ArcGIS platform conditions

Based on the ArcGIS software platform, the method of digital elevation model (DEM) is established by using the irregular triangulation (TIN) of the “three links and one leveling” project of the Guquan converter station. The calculation principle and the realization steps are discussed. In the clear fill area, site formation and earth temporary transport route design applications. Research shows that the simulation of ground simulation through ArcGIS can be more rapid and efficient land leveling, design optimization route.

Research on the Method of Building Dem by Contours

Digital elevation model (DEM), as an important product of digital photogrammetry and digital surveying and mapping, it plays an increasingly important role in the field of surveying and mapping and geographic information system. In this paper, it introduces the current generation methods of DEM by contour, and analyzes those interpolation methods that commonly are used to build DEM, and then describes the advantages and disadvantages of different building DEM methods systematically. Focusing on the DEM that represented by Grid and TIN, it compares the optimization methods of interpolation contour data systematically, and makes the contour interpolating DEM reflect the surface condition of the real nature more effectively.

Improved Envisat Altimetry Ice Sheet Elevation Change Data Processing Algorithms Using Repeat-Track Analysis

Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms< 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.

Metamodel for Mathematical Modelling Surfaces of Celestial Bodies on the Base of Radiolocation Data

The paper proposes for the mathematical modelling surfaces of celestial bodies by the given radar data use the models, produced from the geometrical metamodel G. The metamodel G consists of the corresponding to the dimensions of a space geometrical objects (points, lines, surfaces), the mathematical methods of interpolation, interlinations, interflatation, and the set of rules for producing mathematical models. Using the metamodelling approach allows us to consider from a unique point of view the different methods for the modelling surfaces of bodies and also to develop a computer tool which accelerates and simplify the modelling process, starting from the problem specification and finishing visualization and interpretation of the obtained solution. The correctness of the derived from the metamodel G the set of the models M1, M2 ... MN results from the correspondence of the structure of the model objects of G to the structure of experimental data, recorded in the process of radiolocation as values of the functions in the points, and traces of the functions on the given lines and surfaces. Widely used in cartography Digital Elevation Model (DEM), can be also produced from G by using for the description of the bodies the surfaces in the form of triangles. Using the metamodel G allows us to integrate the DEM method for the data specification with setting data in points and on lines (which are the basic objects of the metamodel G) and so apply more precise (comparatively with the classical interpolation) methods of interlination and interflatation of functions. Another advantage of the proposed metamodelling approach is a possibility of development of complex geometrical models by composition of the basic elements of the metamodel. As an example, the paper proposes a new method for the reconstruction of the surface of a celestial body by the data, given on the system of strips – interstripation (form the inter – in between – of the strips).

Using DTM for automatic plotting of catchments

The authors suggest the method for digital elevation models (DEMs) compiling aimed to plotting the boundaries of the drainage catchments for the lowland relief (West Siberia as an example). The proposed method allows taking into account the local contemporary depressions – traps of the suspended sediment yield and surface runoff. In the result the higher precision of the basins boundaries pattern is obtained. In this method additional information from the topographic maps is used. The DEM compiled with the use of the suggested method helps to significantly increase the accuracy of the West Siberian catchments’ automatic plotting.

Correction of Reflectivity in the Presence of Partial Beam Blockage over a Mountainous Region Using X-Band Dual Polarization Radar

AbstractTwo approaches to correcting the partial beam blockage of radar reflectivity in mountainous areas were evaluated using X-band dual polarization radar data from the Hakone mountain region, Kanto, Japan. The comparatively simple digital elevation model (DEM) method calculates the power loss in the received signal based on the geometrical relationship between radar beams and a DEM. The second approach, the modified DEM method, attempts to account for unknown power losses related to ground clutter, hardware calibration errors, etc. Comparison between ground data and reflectivity data corrected by both methods suggests that the DEM method alone was insufficient to correct beam blockage problems but that the modified DEM data were in generally good agreement with the ground data. The authors also estimated 10-min rainfall amounts using reflectivity corrected by the modified DEM method and compared these with data from a network of rain gauges in the mountainous region. In general, the results show good agreement between radar estimates and rain gauge measurements. On the basis of their results, the authors conclude that the modified DEM method offers a suitable solution to the problem of beam blockage in mountainous regions, provided that the beam blockage rate is less than 80%.

Interpolation Method of DEM based on Joint Singular Value Decomposition

Currently, a DEM (Digital Elevation Model) of high density range has been able to be created by spread of airborne laser scanner. However, when the DEM is created, the points of the DEM can not be interpolated accurately in some cases. Therefore, we propose a method of accurately interpolating the points of the DEM. The proposed method is performed by using joint singular value decomposition. Through the experiments using the DEM of 5m mesh, we demonstrate the effectiveness of our interpolation method.

Lowlands Mapping in Forest Guinea

This article presents a lowland mapping method for the Forested Guinea (Guinée Forestière) using a 30 m resolution Digital Elevation Model (DEM) that is currently the best option to analyze large forested areas. This low cost DEM method applies surface topography analysis processes to better discriminate areas with stagnant and/or accumulated water. The main index selected is the immediate proximity of flat areas to drainage network, the former with slope gradients not exceeding 5% (2.86°). The mapped lowlands potential cover a surface of 4516 km2, i.e., 10% of the total area of the region with hydro-agricultural potential.