Conventional Digital Elevation Models Research Articles

On the topographic entity-oriented digital elevation model construction method for urban area land surface

Human activity transforms a land surface into a complex surface where artificial and natural landforms coexist and continuous and emergent landforms merge. In this background, the problems of conventional digital elevation models (DEMs), such as morphological distortion, complicated updates, and lack of information, are increasingly prominent. This study proposes a new idea of DEM construction based on the concept of geographic ontology. First, landforms with common features are abstracted into a certain type of topographic entity based on their morphologies and semantics. For each type of topographic entity, a DEM was constructed independently based on the available elevation information and other information about the semantics and spatial relationships. Second, individual DEMs were merged into a complete DEM following certain rules. A 1 km2 area located in the suburb of Nanjing, Jiangsu Province, China, was selected as the experimental area. The effectiveness of the model construction method proposed in this study was verified. The results show that the DEM constructed according to the idea of this study has a significantly better performance than the conventional DEMs. The constructed DEM in this study can well represent ground objects, such as slopes, farmland, and ditches. In particular, the constructed DEM ensures the morphological accuracy of the ground objects.

RESEARCH ON A DEM COREGISTRATION METHOD BASED ON THE SAR IMAGING GEOMETRY

Abstract. Due to the systematic error, especially the horizontal deviation that exists in the multi-source, multi-temporal DEMs (Digital Elevation Models), a method for high precision coregistration is needed. This paper presents a new fast DEM coregistration method based on a given SAR (Synthetic Aperture Radar) imaging geometry to overcome the divergence and time-consuming problem of the conventional DEM coregistration method. First, intensity images are simulated for two DEMs under the given SAR imaging geometry. 2D (Two-dimensional) offsets are estimated in the frequency domain using the intensity cross-correlation operation in the FFT (Fast Fourier Transform) tool, which can greatly accelerate the calculation process. Next, the transformation function between two DEMs is achieved via the robust least-square fitting of 2D polynomial operation. Accordingly, two DEMs can be precisely coregistered. Last, two DEMs, i.e., one high-resolution LiDAR (Light Detection and Ranging) DEM and one low-resolution SRTM (Shutter Radar Topography Mission) DEM, covering the Yangjiao landslide region of Chongqing are taken as an example to test the new method. The results indicate that, in most cases, this new method can achieve not only a result as much as 80 times faster than the minimum elevation difference (Least Z-difference, LZD) DEM registration method, but also more accurate and more reliable results.

RADARSAT-2-based digital elevation models derived from InSAR for high latitudes of northern Canada

The accuracy of digital elevation models (DEMs) plays an important role in many terrain-related applications, particular in high northern latitudes where there is uncertainty in DEMs. Using the interferometric synthetic aperture radar techniques, this study examined how different RADARSAT-2 beam modes can be used to generate DEMs with high accuracy. Using a conventional interferometry method, the Spotlight DEM shows the highest accuracy among all studied DEM products, with the root-mean-square error (RMSE) ranging from 13.9 to 17.4 m, followed by the F0W3 DEM and U26W2 DEM. The error sources in DEM generation due to uncertainty in perpendicular baseline and atmospheric delay are likely more important than the random phase noise caused by volume scattering and environmental changes during synthetic aperture radar (SAR) acquisitions. The small baselines subset (SBAS) method did not significantly improve DEM quality due to the limitation of the number of SAR images in this study. The integration of both Spotlight conventional DEMs and SBAS DEM considerably improved results yielding high-quality DEMs for the study area, with an RMSE of 9.7 m. Further studies are necessary to quantitatively evaluate the effects of surface motion as well as the orbital and atmospheric errors on the DEM accuracy. The Slave River Delta in the Northwest Territories of Canada was used as a test case.

DEM orientation based on local feature correspondence with global DEMs

Thanks to rational polynomial coefficients (RPCs), which are provided by vendors to end users, digital elevation models (DEMs) can be simply derived from satellite stereo images. However, DEMs are influenced by systematic errors in the rational function model (RFM), known as RPC biases. Global DEMs (GDEMs), such as the Shuttle Radar Topography Mission (SRTM), which is the most inexpensive solution, can be applied to improve the accuracy of the relative RFM-derived DEMs. In this article, an automatic and robust local feature-based DEM matching and orientation approach is proposed in order to improve the accuracy of the relative RFM-derived DEMs without the use of ground control points (GCPs). The proposed approach consists of four main steps: (1) combined local feature extraction; (2) computation of the distinctive order-based self-similarity (DOBSS) descriptor; (3) a feature correspondence and local consistency checking process; and (4) a relative RFM-derived DEM orientation process using three-dimensional (3D) transformation models, including 3D rigid, 3D similarity and 3D affine transformations. This technique can avoid the sensitivity of conventional 3D DEM matching methods to initial values, monotonous areas and local distortions. Experimental results on two CARTOSAT-1 derived DEMs demonstrate the superior performance of the proposed DEM matching method over state-of-the-art methods, including SIFT, DAISY, LIOP, LBP, and BRISK descriptors, in terms of the number of correct matches (NCM) and DEM orientation accuracy. The results also show that the proposed method is able to significantly improve the geometric accuracy of the relative RFM-derived DEMs.

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas

Terrestrial laser scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), has been frequently applied to build bare-earth digital elevation models (DEMs) for high-accuracy geomorphology studies. The point clouds acquired from TLS often achieve a spatial resolution at fingerprint (e.g., 3cm×3cm) to handprint (e.g., 10cm×10cm) level. A downsampling process has to be applied to decimate the massive point clouds and obtain manageable DEMs. It is well known that downsampling can result in aliasing that causes different signal components to become indistinguishable when the signal is reconstructed from the datasets with a lower sampling rate. Conventional DEMs are mainly the results of upsampling of sparse elevation measurements from land surveying, satellite remote sensing, and aerial photography. As a consequence, the effects of aliasing caused by downsampling have not been fully investigated in the open literature of DEMs. This study aims to investigate the spatial aliasing problem of regridding dense TLS data. The TLS data collected from the beach and dune area near Freeport, Texas in the summer of 2015 are used for this study. The core idea of the anti-aliasing procedure is to apply a low-pass spatial filter prior to conducting downsampling. This article describes the successful use of a fourth-order Butterworth low-pass spatial filter employed in the Generic Mapping Tools (GMT) software package as an anti-aliasing filter. The filter can be applied as an isotropic filter with a single cutoff wavelength or as an anisotropic filter with two different cutoff wavelengths in the X and Y directions. The cutoff wavelength for the isotropic filter is recommended to be three times the grid size of the target DEM.

Improving merge methods for grid-based digital elevation models

Digital Elevation Models (DEMs) are used to represent the terrain in applications such as, for example, overland flow modelling or viewshed analysis. DEMs generated from digitising contour lines or obtained by LiDAR or satellite data are now widely available. However, in some cases, the area of study is covered by more than one of the available elevation data sets. In these cases the relevant DEMs may need to be merged. The merged DEM must retain the most accurate elevation information available while generating consistent slopes and aspects. In this paper we present a thorough analysis of three conventional grid-based DEM merging methods that are available in commercial GIS software. These methods are evaluated for their applicability in merging DEMs and, based on evaluation results, a method for improving the merging of grid-based DEMs is proposed. DEMs generated by the proposed method, called MBlend, showed significant improvements when compared to DEMs produced by the three conventional methods in terms of elevation, slope and aspect accuracy, ensuring also smooth elevation transitions between the original DEMs. The results produced by the improved method are highly relevant different applications in terrain analysis, e.g., visibility, or spotting irregularities in landforms and for modelling terrain phenomena, such as overland flow.

Watershed delineation using hydrographic features and a DEM in plain river network region

Watershed delineation is a required step when conducting any spatially distributed hydrological modelling. Automated approaches are often proposed to delineate a watershed based on a river network extracted from the digital elevation model (DEM) using the deterministic eight-neighbour (D8) method. However, a realistic river network cannot be derived from conventional DEM processing methods for a large flat area with a complex network of rivers, lakes, reservoirs, and polders, referred to as a plain river network region (PRNR). In this study, a new approach, which uses both hydrographic features and DEM, has been developed to address the problems of watershed delineation in PRNR. It extracts the river nodes and determines the flow directions of the river network based on a vector-based hydrographic feature data model. The river network, lakes, reservoirs, and polders are then used to modify the flow directions of grid cells determined by D8 approach. The watershed is eventually delineated into four types of catchments including lakes, reservoirs, polders, and overland catchments based on the flow direction matrix and the location of river nodes. Multiple flow directions of grid cells are represented using a multi-direction encoding method, and multiple outflows of catchments are also reflected in the topology of catchments. The proposed approach is applied to the western Taihu watershed in China. Comparisons between the results obtained from the D8 approach, the ‘stream burning’ approach, and those from the proposed approach clearly demonstrate an improvement of the new approach over the conventional approaches. This approach will benefit the development of distributed hydrological models in PRNR for the consideration of different types and multiple inlets and outlets of catchments. Copyright © 2015 John Wiley & Sons, Ltd.

Radargrammetry for Digital Elevation Model Generation Using Envisat Reprocessed Image and Simulation Image

The digital elevation model (DEM) is one of the most important sources used for Earth surface analysis because of its various applications and its usability in subsequent studies. A DEM can be generated with interferometric synthetic aperture radar (InSAR) and radargrammetry techniques from synthetic aperture radar (SAR) images with orbital separation between them as the baseline. The on-board radar imaging system records both phase and intensity information of the backscattered signals. Radargrammetry is based on the disparity between two intensity images which is less affected by temporal and atmospheric decorrelation while InSAR uses the phase differences between two images. However, SAR intensity images may have low spatial resolution compared to optical images that are used in the photogrammetric DEM generation and the original SAR image is degraded by speckle noise. This leads to low accuracy of radargrammetric DEMs. It is necessary to develop methods focusing on original SAR images to improve the accuracy of radargrammetric DEM generation. In this letter, we propose that the single look complex (SLC) SAR image reprocessing method can be utilized to improve the image quality of radargrammetric DEMs. Simulated noise-free SAR images are used to generate radargrammetric DEMs for the purpose of comparison. Three SLC SAR images are selected for image quality improvement in each track. Other images in the same track are matched and processed to one reference image. The SAR simulation images are generated based on the reference image in each track. Therefore, the products in the same track have the same meta-information. Each DEM product is compared with conventional radargrammetric DEMs. This study shows that DEMs generated from re-processed SAR images are more accurate than radargrammetric DEMs from single-pair SAR images. Also, the accuracy of radargrammetric DEM produced with the proposed method is close to DEMs generated with noise-free simulated SAR images.

Using GIS and a digital elevation model to assess the effectiveness of variable grade flow diversion terraces in reducing soil erosion in northwestern New Brunswick, Canada

AbstractFlow diversion terraces (FDT) are commonly used beneficial management practice (BMP) for soil conservation on sloped terrain susceptible to water erosion. A simple GIS‐based soil erosion model was designed to assess the effectiveness of the FDT system under different climatic, topographic, and soil conditions at a sub‐basin level. The model was used to estimate the soil conservation support practice factor (P‐factor), which inherently considered two major outcomes with its implementation, namely (1) reduced slope length, and (2) sediment deposition in terraced channels. A benchmark site, the agriculture‐dominated watershed in northwestern New Brunswick (NB), was selected to test the performance of the model and estimated P‐factors. The estimated P‐factors ranged from 0·38–1·0 for soil conservation planning objectives and ranged from 0·001 to 0·45 in sediment yield calculations for water‐quality assessment. The model estimated that the average annual sediment yield was 773 kg ha−1 yr −1 compared with a measured value of 641 kg ha−1 yr−1. The P‐factors estimated in this study were comparable with predicted values obtained with the revised universal soil loss equation (RUSLE2). The P‐factors from this study have the potential to be directly used as input in hydrological models, such as the soil and water assessment tool (SWAT), or in soil conservation planning where only conventional digital elevation models (DEMs) are available. Copyright © 2009 John Wiley & Sons, Ltd.

Impacts of Accuracy and Resolution of Conventional and LiDAR Based DEMs on Parameters Used in Hydrologic Modeling

Digital elevation model (DEM) is often used for hydrologic modeling, land use planning, engineering design and environmental protection. Research is required to assess the need of updating existing conventional DEM using higher resolution and more accurate DEMs, including light detection and ranging (LiDAR) DEM. The objective of this study was to evaluate effects of DEM accuracy and resolution on hydrologic parameters and modeling in an agriculture-dominated watershed. DEMs compared included 1 m and 10 m LiDAR based DEMs, and a conventional 10 m DEM obtained with aerial photogrammetry method. Hydrologic parameters assessed included elevation, sub-basin area and boundaries, drainage networks, slope and slope length. DEM derived hydrological parameters were used to estimate soil loss in Black Brook Watershed, New Brunswick using Revised Universal Soil Loss Equation (RUSLE). Results indicated that DEM resolution had substantial influence on the sub-basins boundaries, sub-basin area, and distribution of water flow lines. Field investigation confirmed that most of the water flow lines derived from 1 m LiDAR based DEM were accurate and a number of flow diversion terraces (FDT) failures had been identified with help of LiDAR 1 m DEM. Both conventional and LiDAR based 10 m DEM could not identify the impacts of soil conservation structures such as diversion terraces. The RUSLE predicted soil loss using 1 m LiDAR based DEM was considered to be better because both conventional and LiDAR based 10 m DEMs could not reflect the impact of FDTs on reducing soil loss.

Topographic modelling of soil moisture conditions: a comparison and verification of two models

SummaryTopography, as captured by a digital elevation model (DEM), can be used to model soil moisture conditions because water tends to flow and accumulate in response to gradients in gravitational potential energy. A widely used topographic index, the soil wetness index (SWI), was compared with a new algorithm that produces a cartographic depth‐to‐water (DTW) index based on distance to surface water and slope. Both models reflect the tendency for soil to be saturated. A 1 m resolution Light Detection and Ranging (LiDAR) DEM and a 10 m conventional photogrammetric DEM were used and results were compared with field‐mapped wet soil areas for a 193 ha watershed in Alberta, Canada, for verification. The DTW model was closer to field‐mapped conditions. Values of Kmatch90 (areal correspondence, smaller values indicating better performance) were 7.8% and 12.3% for the LiDAR and conventional DEM DTW models, respectively, and 88.5% and 86.7% for the SWI models. The two indices were poorly correlated spatially. Both DEMs were found to be useful for modelling soil moisture conditions using the DTW model, but the LiDAR DEM produced the better results. All major wet areas and flow connectivity were reproduced and a threshold value of 1.5 m DTW accounted for 71% of the observed wet areas. The poor performance of the SWI model is probably because of its over‐dependence on flow accumulation. Incorporation of a flow accumulation algorithm that replicates the effects of dispersed flow showed some improvement in the SWI model for the conventional DEM but it still failed to replicate the full areal extent of wet areas. Local downslope topography and hydrologic conditions seemed to be more important in determining soil moisture conditions than is taken account of by the SWI. The DTW model has potential for application in distributed hydrologic modelling, precision forestry and agriculture and implementation of environmental soil management practices.

Improving hydrological information acquisition from DEM processing in floodplains

AbstractExtraction of hydrological information from digital elevation models (DEMs) is a required step when conducting any spatially distributed hydrological modelling. In particular, automated methods are proposed to extract the drainage structure from the DEM. However, a realistic river network is not always derived from conventional DEM processing methods. Indeed, inaccuracy occurs in flat areas corresponding to floodplains. In these areas, additional sources of information are required to extract the correct drainage direction from the DEM. In this study, it is demonstrated that traditional approaches of DEM‐preprocessing such as the commonly known ‘stream burning’ fail to provide correct maps of drainage directions and catchment areas when the extension of flat areas is large. A new method is proposed to take advantage of available imagery data. This method is based on a ‘double DEM burning’ process: DEM is first burned in the main rivers using the channel network, and then in the floodplain using the spatial distribution of floodplains provided by classified satellite images. In this sense, the method has been referred to as the floodplain burning approach (or simply FB approach). Spatial distribution of floodplains is derived from a multitemporal SAR image classification. A system of equations is used to vary the elevation offset required to be ‘burnt’ in each cell representing the floodplain, according to the minimal distance from the channel network, which is calculated by a distance transformation. The FB approach was applied to a sub‐basin located within the larger Amazon River basin. The region is characterized by large floodplain extensions. Basin delineation maps derived from the new method were compared with those obtained from the traditional stream burning method and highlighted more realistic results. Copyright © 2008 John Wiley & Sons, Ltd.

Incorporating ancillary data to refine anthropogenically modified overland flow paths

A study carried out in the Oldman River watershed in southern Alberta, Canada, to delineate potential surface water pollution sources revealed that the use of conventional raster-based digital elevation models (DEMs) and flow-direction algorithms resulted in the delineation of incorrect watershed boundaries. The inaccuracies resulted from the resolution of the available DEM, which is too coarse to represent the presence of anthropogenically modified terrain features, such as roads, ditches and canals, which can significantly alter overland flow paths. This observation prompted the development of the computer model RIDEM (Rural Infrastructure Digital Elevation Model). By incorporating ancillary data to refine anthropogenically modified overland flow paths into the grid-based watershed delineation process, the scale of runoff flow paths created with RIDEM is reduced below the scale defined by the representation of terrain with the DEM. RIDEM also resolves the inability of grid-based flow-direction algorithms to account for the split-flow patterns that occur in irrigated landscapes. The model outputs include a corrected flow-direction matrix, gross watershed boundaries, and road-induced dead drainages. The watershed areas derived with RIDEM differed by up to 49% from watersheds derived using a conventional DEM with a 20 m grid cell size. However, results of watershed boundaries could not be verified, because highly accurate watershed boundaries do not exist for our study area. Copyright © 2005 John Wiley & Sons, Ltd.

Lidar Elevation Data for Surface Hydrologic Modeling: Resolution and Representation Issues

This paper is concerned with the application of high spatial resolution elevation data derived from light detection and ranging technologies (lidar) to surface hydrologic modeling. In recent years, airborne lidar technology has been employed to develop high accuracy digital elevation models (DEMs) with horizontal resolution on the order of a few meters. As with any spatial data product there are limits to the lidar's practical use that vary with the intended application. This paper considers potential issues and challenges for the use of lidar-derived DEMs in surface hydrologic modeling applications, such as characterizing flow direction and power, identifying sub-basins in a watershed, and calculating upstream contributing area and other variables. We compare results using conventional 30m DEMs and 6m lidar for a high relief study area and a low relief study area. Results are more comparable between these data sources, regardless of hydrologic operation, for the high relief area, while the similarity of results in the low relief area is dependent upon the particular operation. Post-processing on the lidar data successfully removed such flow obstacles as bridges that might have artificially impeded surface flow. An exploration of the effect of spatial resolution on results suggests that cell size is a more significant factor than production method.

Automated analysis and classification of landforms using high-resolution digital elevation data: applications and issues

This paper describes recent efforts to use fine spatial resolution digital elevation model (DEM) data, including, but not limited to, lidar DEM data, for automated analysis and classification of geomorphic and hydrologic terrain features. The example applications presented are based on a 3-m horizontal resolution lidar DEM for a 6 km by 8 km agricultural watershed in Alberta and on a similar 5-m horizontal resolution conventional DEM for two forested areas of 14 km by 12 km in the Cariboo Forest Region of British Columbia. The applications illustrate efforts to produce meaningful classifications of ecological and landform spatial entities and to automatically extract hydrological spatial entities required for input into the WEPP water erosion model. Fine spatial resolution DEMs present some unique problems for which solutions are still lacking or are insufficient. Errors of apparently minor extent or degree can seriously affect some forms of analysis, especially analyses that involve hydrological calculations of paths of surface water flow. A need is recognized for improved methods and tools for interpolating and editing fine spatial resolution DEMs to remove or reduce localized errors.

Digital elevation map generation using VHF-band SAR data in forested areas

The paper investigates digital elevation model (DEM) generation based on data from the ultra wideband coherent all radio band sensing (CARABAS) very high frequency (VRF)-band synthetic aperture radar (SAR). The results show excellent capability to penetrate forest areas, i.e., the generated DEMs are found to be close to the true ground height. A conventional DEM, based on stereo photography and surveying, and additional phase differential Global Positioning System (GPS) measurements have been used for comparison. The results in heavily vegetated areas (stem volume up to 600 m/sup 3//ha) show a mean height difference of less than 1.5 m and a root-mean-square (rms) error of less than 1.0 in compared to the conventional DEM. Stable backscattering properties allows us to use large baselines in order to obtain high height sensitivity. However, the amount of poor data due to low coherence increases with the increase of the baseline. The optimum baseline which balances these two effects is found to correspond to an incidence angle difference of 4/spl deg/-8/spl deg/.

Using satellite remote sensing for DEM extraction in complex mountainous terrain: Landscape analysis of the Makalu Barun National Park of eastern Nepal

The design and management of national parks and other protected areas requires a broad base of physiographic and geo-ecological information about the landscape. This paper evaluates the effectiveness of satellite remote sensing for photogrammetric stereo-mapping and digital elevation model (DEM) extraction within remote mountainous terrain. As a case study, a landscape analysis of the Makalu Barun National Park and Conservation Area of east Nepal (27.5° N, 87.0° E) was examined. The study area is a highly complex and rugged mountain landscape, with extreme topographic relief and an elevation gradient spanning more than 8300 m. A DEM extracted from stereo SPOT imagery resulted in a median disagreement of 58 m when compared to a DEM generated from a conventionally digitized GIS dataset of topographic contours (scale=1:250 000). Visual comparison of the two DEMs showed substantial agreement at the landscape scale, while larger scale comparison of 100 m contours revealed some localized differences. The SPOT extracted DEM provided equal or better basis for orthorectification of satellite imagery when compared to the conventional DEM. Derivative landscape analysis outputs, such as hydrological modelling, drainage networks and watershed boundaries, compared well with results based upon the conventional dataset. Intermediate map products useful for field research and mapping included production of an orthorectified satellite base-map image. Additionally, a fused multisensor high resolution image of the study area, combining Landsat Thematic Mapper (TM) and SPOT imagery at 10 m resolution, was orthorectified to produce a false-colour satellite image map highlighting the spectral discrimination between land cover classes.

DEM Interpolation Using Inverse Spectrum Constraints

ABSTRACT Generation of a Digital Elevation Model (DEM) in remote sensing is an important application. The process of DEM generation often requires interpolation. This paper introduces Claerbout's interpolation algorithm that uses spectral information, which is widely used in geophysical applications, and examines the applicability of the method to DEM interpolation. The interpolation process can be explained in two steps. The first step is for finding spectral information from the known data and the second step is finding missing data so as to follow the spectral trend found in the previous step. The interpolation algorithm was tested for a real DEM data and compared with other conventional DEM interpolation method such as kriging method.