Raster Digital Elevation Models Research Articles

Uncertainty in primary and secondary topographic attributes caused by digital elevation model spatial resolution

Digital elevation models (DEMs) are increasingly being used to explain different soil and site-related characteristics using a variety of modelled topographic properties and their frequencies. It is well known that the grid cell size of a raster DEM significantly affects derived primary and secondary topographic attirbutes, such as elevation, slope, topographic position index, and wind exposition index. This study investigates the impact of increasing DEM spatial resolution on spatial distortion (defined by mean absolute error (MAE)) in two contrasting sites and tests two different data-obtaining procedures (average plot vs. single point). Findings detected and quantified significant error(s) caused by coarsening DEM spatial resolution. In addition, the plot average measurement procedure was more representative for any bounded area than a value extracted from a single point measurement. These findings provide important guidance for creating and using topographic attributes across a range of applications. This study recommends using DEM resolution of <15 m to derive primary and secondary topographic attributes. However, the optimal resolution is scale dependent and related with landscape; henceforth, coarser resolution may be sufficient for representing large-scale topographic features.

Consumer‐grade UAV solid‐state LiDAR accurately quantifies topography in a vegetated fluvial environment

AbstractUnoccupied aerial vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion (SfM) photogrammetry. Advances in UAV payloads and the advent of solid‐state LiDAR have enabled consumer‐grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV‐acquired images. We evaluate the application of a DJI Zenmuse L1 solid‐state LiDAR sensor on a Matrice 300 RTK UAV to generate digital elevation models (DEMs). To assess flying height (60–80 m) and speed parameters (5–10 ms−1) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter and classify the point clouds to produce a raster DEM product. A dense control network showed that there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multiscale Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of −0.015 m on unvegetated gravel bars. Multiscale Model to Model Cloud Comparison (M3C2) residuals compared UAV LiDAR and Terrestrial Laser Scanner point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid‐state LiDAR was effective at penetrating sparse canopy‐type vegetation but was less penetrable through dense ground‐hugging vegetation (e.g. heather, thick grass). Whilst UAV solid‐state LiDAR needs to be supplemented with bathymetric mapping to produce wet–dry DEMs, by itself, it offers advantages to comparable geomatics technologies for kilometre‐scale surveys. Ten best practice recommendations will assist users of UAV solid‐state LiDAR to produce bare earth DEMs.

A Multi-Level Distributed Computing Approach to XDraw Viewshed Analysis Using Apache Spark

Viewshed analysis is a terrain visibility computation method based on the digital elevation model (DEM). With the rapid growth of remote sensing and data collection technologies, the volume of large-scale raster DEM data has reached a great size (ZB). However, the data storage and GIS analysis based on such large-scale digital data volume become extra difficult. The usually distributed approaches based on Apache Hadoop and Spark can efficiently handle the viewshed analysis computation of large-scale DEM data, but there are still bottleneck and precision problems. In this article, we present a multi-level distributed XDraw (ML-XDraw) algorithm with Apache Spark to handle the viewshed analysis of large DEM data. The ML-XDraw algorithm mainly consists of 3 parts: (1) designing the XDraw algorithm into a multi-level distributed computing process, (2) introducing a multi-level data decomposition strategy to solve the calculating bottleneck problem of the cluster’s executor, and (3) proposing a boundary approximate calculation strategy to solve the precision loss problem in calculation near the boundary. Experiments show that the ML-XDraw algorithm adequately addresses the above problems and achieves better speed-up and accuracy as the volume of raster DEM data increases drastically.

Automation algorithms for water bodies characteristics calculation on the example of the Volga-Akhtuba floodplain

Introduction. Organization and implementation of a special spring release, carried out annually in the interests of the agriculture and fisheries of the Lower Volga, is the most important and complex problem of the Volga-Kama cascade management. The way to increase the efficiency of solving this problem is to use of mathematical models for calculating the movement of water along the Volga-Akhtuba floodplain. However, the currently functioning mathematical hydrodynamic models of the Lower Volga do not provide enough accuracy and speed. It is planned to create an algorithm for modeling the movement of water masses, combining the use of nomograms of dynamic volumes and a tank model. The characteristics of water bodies and streams that form the water system of the Lower Volga should be obtained at the first stage.&#x0D; &#x0D; Materials and methods. The materials for the work were a raster digital elevation model with 30 m resolution, obtained from Earth remote sensing data. Algorithms for determining the position and boundaries of water bodies and calculating their characteristics are implemented in the Fortran programming language.&#x0D; &#x0D; Results. The description of algorithms of smoothing the digital elevation model, searching for local minima, and determining the boundaries of floodplain reservoirs developed to prepare information structures using in the created mathematical model of a water system with a developed network of floodplain streams and lakes is given on the example of the Volga-Akhtuba floodplain. The results of testing the developed algorithms are shown.&#x0D; &#x0D; Conclusions. The results of the algorithms for searching for local minima on the digital elevation model and determining the position and boundaries of floodplain reservoirs make it possible to calculate the characteristics of water bodies, building a model graph, and developing a data storage system at the next stages of work.

Influence of Block Geometry Configuration on Multi-Image Dense Matching

Digital photogrammetry is a widespread surveying technique in different fields of application due to its flexibility, versatility and cost-effectiveness. Despite its increasing automation and simplicity, a proper image block design is crucial to ensure high standards of performance and accuracy. Studies on camera network design have been largely dealt with in the scientific literature with reference to image orientation process, while they are still poor on dense matching. This paper investigates the influence of different block geometry configurations on multi-image dense matching. Starting from the same orientation solution, dense matching was performed considering different combinations of number of images and base length distance between the first and the last image within a strip. The raster Digital Elevation Models (DEM) resulting from each sequence of images were compared with a reference DEM to assess accuracy and completeness. The tests were conducted using different cameras and at various test sites to assess different survey conditions and generalize the findings. The presented results provide some operational guidance on block geometry optimization to maximize the accuracy and completeness.

Terrain representation using orientation

ABSTRACT A terrain data model using orientation rather than elevation permits more efficient analysis and stores its data in a multi-band raster. Representation techniques from the computer graphics industry are readily adopted with this data model. A common data model for terrain surfaces–the raster digital elevation model (DEM)–carries with it limitations by emphasizing height. Derived products such as relief shading require additional processing to determine orientation, even though they are used more frequently than those relying on elevation (e.g. hypsometric tinting). We show some of the benefits of encoding and analyzing terrain based on surface orientation, an approach that uses normal vectors stored as multi-band raster, the data storage convention in the computer graphics industry. A change in the data model and the conceptualization of the surface’s defining characteristics allows relief shading methods to run faster than conventional tools. Processing efficiencies are especially useful for more advanced shading models that may employ hundreds of relief shading calculations. In addition, an orientation-focused approach to terrain permits cartographic techniques to parallel common computer graphics methods. This project explores one such method, normal-mapping, an effect that adds texture to conventional relief shading by perturbing surface normal vectors.

A Parallel Computing Approach to Spatial Neighboring Analysis of Large Amounts of Terrain Data Using Spark.

Spatial neighboring analysis is an indispensable part of geo-raster spatial analysis. In the big data era, high-resolution raster data offer us abundant and valuable information, and also bring enormous computational challenges to the existing focal statistics algorithms. Simply employing the in-memory computing framework Spark to serve such applications might incur performance issues due to its lack of native support for spatial data. In this article, we present a Spark-based parallel computing approach for the focal algorithms of neighboring analysis. This approach implements efficient manipulation of large amounts of terrain data through three steps: (1) partitioning a raster digital elevation model (DEM) file into multiple square tile files by adopting a tile-based multifile storing strategy suitable for the Hadoop Distributed File System (HDFS), (2) performing the quintessential slope algorithm on these tile files using a dynamic calculation window (DCW) computing strategy, and (3) writing back and merging the calculation results into a whole raster file. Experiments with the digital elevation data of Australia show that the proposed computing approach can effectively improve the parallel performance of focal statistics algorithms. The results also show that the approach has almost the same calculation accuracy as that of ArcGIS. The proposed approach also exhibits good scalability when the number of Spark executors in clusters is increased.

Spark-Enabled XDraw Viewshed Analysis

Viewshed analysis is an indispensable part of digital terrain analysis and widely used in many application domains. High-resolution raster digital elevation model (DEM) data bring significant computational challenges to the existing viewshed analysis algorithms, which are computationally intensive and require a large memory space and massive computing power. The visibility calculation can be accelerated using Apache Spark. In this article, we present a Spark-based parallel computing approach for the XDraw algorithm, which is composed of a tile-based raster data storing strategy, an equivolume computing strategy, and a stream-merging write-back strategy. The parallel implementation of the XDraw algorithm mainly consists of three parts: partitioning a raster DEM file into square tile sets and reorganizing these tile sets to prevent tile overlap across data divisions of Hadoop Distributed File System, subdividing the DEM into multiple equivolume data sectors according to the viewpoint position, and performing the XDraw algorithm on the corresponding tile sets of each sector independently and writing back the viewshed results efficiently. Experiments on real-world datasets show that the proposed computing approach can achieve higher speedup and efficiency for XDraw viewshed analysis as the raster DEM data volume is dramatically increased. The results also show that the approach has also satisfactory scalability as the number of data nodes in clusters is increased.

Resource assessment for hydro-kinetic turbines in Ethiopian rivers and irrigation canals

We describe resource assessment of rivers and a canal in the Upper Blue Nile Basin, Ethiopia for hydro-kinetic turbines (HKTs). Various hydrodynamics methods are coupled with hydrological methods with the aim of transforming the measured flow rate or discharge into velocity data to determine the power density (PD), the available power per square meter of vertical flow area. We estimate the PD over the length of the rivers and canal and estimate the power density variation over the months of the year. The Gumara, Gilgel-Abay, and Bahirdar-Abay rivers in the Blue Nile Basin were studied along with the Koga irrigation main canal. Historical databases of yearly, monthly, and daily averaged discharge were used. The key elements of this analysis are (i) converting discharge into velocity for PD determination, and (ii) the period over which the velocity is averaged compared to the averaging time used to determine the HKT power curve. All software used in this work is publicly available. To predict PD, ARC-GIS was used to estimate the reach length and cross-section from raster Digital Elevation Models (DEMs). The hydraulic modeling of the velocity was done using HEC-RAS software. Field measurements were conducted to validate the hydrodynamic model. The result of the study was cross-sectional area average water velocity V¯A, which used to predict the PD. To relate the time average of velocity V¯T to the power curve of a HKT a 2-minute average velocity measurement was conducted with a total of 270 samples. It is found that 25% of the Gumara river is suitable for HKTs with a maximum of 1.4 kW/m2 power can be extracted. The Gilgel-Abay river has a maximum velocity above 1 m/s and 3.4 kW/m2 power can be extracted over 32% of its length. Similarly, for Bahirdar-Abay river, over 29% of the reach length 2.6 kW/m2 power can be extracted.

A semi-automated method for extracting channels and channel profiles from lidar-derived digital elevation models

Abstract With the advent of digital elevation models (DEMs) and geographic information systems (GIS), several methods have been proposed to extract channels from raster DEMs. Light detection and ranging (lidar) can produce high-resolution DEMs and poses new challenges to existing methods for channel extraction. This paper introduces a semi-automated method for extracting stream channels and channel profiles from high-resolution DEMs using image processing techniques. Based on user-specified approximate locations of start and end points and a few simple parameters, the method implements five automated steps: (1) channel detection using a local minimum value search; (2) channel delineation using Bresenham’s line algorithm and mathematical morphological operation; (3) vectorization; (4) profile generation; and (5) accuracy assessment. The method is implemented as an ArcGIS Python add-in toolbar named Channel Extraction. The application of the toolbar is demonstrated using a lidar-derived DEM in a study area along the San Andreas fault in California, USA. The software and test data are freely available for download (see Supplemental Files1). The demonstrated samples suggest that this new semi-automated method for extracting channels and channel profiles is flexible and user-friendly and can produce accurate results to support geomorphic studies.

A 3D discretization procedure for the material point method (MPM)

The land topography of certain mountainous regions can be highly variable. The geometry of the topography may be influenced by geological, seismic, weathering, and other processes. The creation of the full 3D numerical models of these topographies represents a challenge. In this work, we present a methodology that allows to create numerical material point method (MPM) models based on the contour lines of the land. The proposed methodology has three main characteristics: the construction of the MPM discrete model is based only on raster digital elevation model (DEM) data; finite element meshes are not required during the process; and the heterogeneities are defined by the DEM data of each material. The application of the proposed methodology to the Daguangbao landslide which occurred in China, in 2008, proved to be adequate for the creation of models with high topography variations and spatial heterogeneities.

LiDAR DEM Smoothing and the Preservation of Drainage Features

Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to subdue the roughness. These techniques can negatively impact the representation of topographic features, most notably drainage features, such as headwater streams. This paper presents the feature-preserving DEM smoothing (FPDEMS) method, which modifies surface normals to smooth the topographic surface in a similar manner to approaches that were originally designed for de-noising three-dimensional (3D) meshes. The FPDEMS method has been optimized for application with raster DEM data. The method was compared with several low-pass filters while using a 0.5-m resolution LiDAR DEM of an agricultural area in southwestern Ontario, Canada. The findings demonstrated that the technique was better at removing roughness, when compared with mean, median, and Gaussian filters, while also preserving sharp breaks-in-slope and retaining the topographic complexity at broader scales. Optimal smoothing occurred with kernel sizes of 11–21 grid cells, threshold angles of 10°–20°, and 3–15 elevation-update iterations. These parameter settings allowed for the effective reduction in roughness and DEM noise and the retention of terrace scarps, channel banks, gullies, and headwater streams.

A fast and simple algorithm for calculating flow accumulation matrices from raster digital elevation models

Abstract. Flow accumulation is an essential input for many hydrological and topographic analyses such as stream channel extraction, stream channel ordering and sub-watershed delineation. Flow accumulation matrices can be derived directly from DEMs and general have O(NlogN) time complexity (Arge, 2003; Bai et al., 2015). It is more common to derive the flow accumulation matrix from a flow direction matrix. This study focuses on calculating the flow accumulation matrix from the flow direction matrix that is derived using the single-flow D8 method (Barnes et al., 2014; Garbrecht &amp;amp; Martz, 1997; Nardi et al., 2008; O'Callaghan &amp;amp; Mark, 1984). In this study, we find give an overview of algorithms for flow accumulation calculation that have O(N) time complexity. These algorithms include algorithms are based on the concept of the number of input drainage paths (Wang et al.2011, Jiang et al. 2013), the algorithm based on the basin tree indices (Su et al. 2015), and the recursive algorithm (Choi, 2012; Freeman, 1991).We propose a fast and simple algorithm to calculate the flow accumulation matrix. Compared with the existing algorithms that have O(N) time complexity, our algorithm runs faster and generally requires less memory. Our algorithm is also simple to implement. In our algorithm, we define three types of cells within a flow direction matrix: source cells, interior cells and intersection cells. A source cell does not have neighboring cells that drain to it and its NIDP value is zero. An interior cell has only one neighboring cell that drains to it and its NIDP value is one. An intersection cell has more than one neighboring cell that drains to it and its NIDP value is greater than one. The proposed algorithm initializes the flow accumulation matrix with the value of one. Our algorithm first calculates the NIDP matrix from the flow direction matrix. The algorithm then traverses each cell within the flow direction matrix row by row and column by column, similar to the traversal algorithm. When a source cell c is encountered, the algorithm traces all downstream cells of c until it encounters an intersection cell i. During the tracing, the accumulation value of a cell is added to the accumulation value of its immediate downstream cell. An interior cell has only one neighboring cell that drains to it and its final accumulation value is obtained when the tracing is done. The accumulation value of the intersection cell i is updated from this drainage path. However, cell i has other unvisited neighboring cells that drain to it and its final accumulation value cannot be obtained after this round of tracing. The algorithm decreases the NIDP value of i by one. Cell i is visited again when other drainage paths that pass through it are traced. When all of the drainage paths that pass through it are traced, cell i is treated as an interior cell and the final accumulation value of i is obtained correctly and the last tracing process can continue the tracing after cell i is treated as an interior cell. A worked example of the proposed algorithm is shown in Figure 1.The five flow accumulation algorithms with O(N) time complexity, including Wang’s algorithm, Jiang’s algorithm, the BTI-based algorithm, the recursive algorithm and our proposed algorithm, are implemented in C++. The 3-m LiDAR-based DEMs of thirty counties in the state of Minnesota, USA, are downloaded from the FTP site operated by the Minnesota Geospatial Information Office. The first 30 counties in Minnesota in alphabetic order are chosen for the experiments to avoid selection bias. We use the algorithm proposed by Wang and Liu (2006) to fill the depressions and derive the flow direction matrices for all tested counties. The running times on the Windows system are listed in Figure 2.The average running times per 100 million cells are 14.42 seconds for Wang’s algorithm, 15.90 seconds for Jiang’salgorithm, 18.95 seconds for the BTI-based algorithm, 10.87 seconds for the recursive algorithm, and 5.26 seconds forour proposed algorithm. Our algorithm runs the fastest for all tested DEM. The speed-up ratios of our proposedalgorithm over the second fastest algorithm is about 51%.

A Variant of the Planchon and Darboux Algorithm for Filling Depressions in Raster Digital Elevation Models

Depression (pit or sink) filling is a key preprocessing step for the automatic hydrologic analysis of surface topography. The Planchon and Darboux (P&amp;D) algorithm is a widely used depression filling algorithm. In this study, we propose an improved variant over the fastest sequential variant of the P&amp;D algorithm for depression filling. Our variant introduces two important improvements compared with the fastest variant of the P&amp;D algorithm, and greatly reduces redundant computation, as well as requires less memory space. Our algorithm can be easily integrated into many of the existing hydrologic analysis software packages. Moreover, our algorithm shares the same versatility as the P&amp;D algorithm. Depressions can be replaced with surfaces either strictly horizontal, or slightly sloping. In the latter case, it is easier to calculate the flow direction matrix.

Evaluation of TanDEMx and SRTM DEM on watershed simulated runoff estimation

In hydrological models, digital elevation models (DEMs) are being used to extract stream network and delineation of the watershed. DEMs represent elevation surfaces of earth landscape. Spatial resolution refers to the dimension of the cell size representing the area covered on the ground. Spatial resolution is the main parameter of a DEM. The grid cell size of raster DEM has significant effects on derived terrain variables such as slope, aspect, curvature, the wetness index, etc. Selection of appropriate spatial resolution DEM depends on other input data being used in the model, type of application and analysis that needs to be performed, the size of the database and response time. Each DEM contains inherent errors due to the method of acquisition and processing. The accuracy of each DEM varies with spatial resolution. The present paper deals with Shuttle Radar Topography Mission (SRTM), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM DEMs) and compares their watershed delineation, slope, stream network and height with ground control points. It was found that the coarse resolution DEM-derived attributes and terrain morphological characteristics were strongly influenced by DEM accuracy. The objective of the present study is to investigate the impact of DEM resolution on topographic parameters and runoff estimation using TanDEM-12, TanDEM-30 and SRTM-90 m with the Soil and Water Assessment Tool. The analysis of the results using different DEM resolutions gave a varied number of sub-basins, Hydrological Response Units (HRUs) and watershed areas. The results were optimum at a specific threshold value as extraction of drainage network has a significant influence on simulated results. The accuracy of DEM is important, as the source of construction of DEM is the main factor causing uncertainty in the output. The results showed variable amounts of runoff at the watershed level, which may be attributed to varied stream lengths, minimum and maximum elevations and sub-basin areas.

Computing spatially distributed sediment delivery ratios: inferring functional sediment connectivity from repeat high‐resolution digital elevation models

AbstractHigh‐resolution digital elevation models (DEMs) from repeat LiDAR (light detection and ranging) or SfM (structure from motion) surveys have become an important tool in process geomorphology. The spatial pattern of negative and positive changes of surface elevation on raster DEMs of difference (DoD) can be interpreted in terms of geomorphic processes, and has been used for morphological budgeting. We show how the application of flow routing algorithms and flow accumulation opens new opportunities for the analysis of DoD. By accumulating the values of the DoD along downslope flowpaths delineated on a DEM, these algorithms lend themselves to computing the net balance, i.e. sediment yield (SY), for the contributing area of each cell. Doing the same for the negative subset of the DoD yields a minimum estimate of erosion (E) within the contributing area. The division of SY by E yields (a maximum estimate of) the sediment delivery ratio (SDR), that is the proportion of material eroded within the contributing area of each cell that has been exported from that area. The resulting SDR is a spatially distributed measure of functional sediment connectivity. In this letter, we develop the computationally simple approach by means of an example DoD from a lateral moraine section in the Upper Kaunertal Valley, Austrian Central Alps. We also discuss advantages, assumptions and limitations, and outline potential applications to connectivity research using field‐, laboratory‐, and model‐based DoD. Copyright © 2018 John Wiley &amp; Sons, Ltd.

Model of Numerical Spatial Classification for Sustainable Agriculture in Badung Regency and Denpasar City, Indonesia

Increasing number and activity of the population to meet the needs of their lives greatly affect the utilization of land resources. Land needs for activities of the population continue to grow, while the availability of land is limited. Therefore, there will be changes in land use. As a result, the problems faced by land degradation and conversion of agricultural land become non-agricultural. The objectives of this research are: (1) to determine parameter of spatial numerical classification of sustainable food agriculture in Badung Regency and Denpasar City (2) to know the projection of food balance in Badung Regency and Denpasar City in 2020, 2030, 2040, and 2050 (3) to specify of function of spatial numerical classification in the making of zonation model of sustainable agricultural land area in Badung regency and Denpasar city (4) to determine the appropriate model of the area to protect sustainable agricultural land in spatial and time scale in Badung and Denpasar regencies. The method used in this research was quantitative method include: survey, soil analysis, spatial data development, geoprocessing analysis (spatial analysis of overlay and proximity analysis), interpolation of raster digital elevation model data, and visualization (cartography). Qualitative methods consisted of literature studies, and interviews. The parameters observed for a total of 11 parameters Badung regency and Denpasar as much as 9 parameters. Numerical classification parameter analysis results used the standard deviation and the mean of the population data and projections relationship rice field in the food balance sheet by modelling. The result of the research showed that, the number of different numerical classification parameters in rural areas (Badung) and urban areas (Denpasar), in urban areas the number of parameters is less than the rural areas. The based on numerical classification weighting and scores generate population distribution parameter analysis results of a standard deviation and average value. Numerical classification produced 5 models, which was divided into three zones are sustainable neighbourhood, buffer and converted in Denpasar and Badung. The results of Population curve parameter analysis in Denpasar showed normal curve, in contrast to the Badung regency showed abnormal curve, therefore Denpasar modeling carried out throughout the region, while in the Badung regency modeling done in each district. Relationship modelling and projections lands role in food balance in Badung views of sustainable land area whereas in Denpasar seen from any connection to the green open spaces in the spatial plan Denpasar 2011-2031. Modelling in Badung (rural) is different in Denpasar (urban), as well as population curve parameter analysis results in Badung showed abnormal curve while in Denpasar showed normal curve. Relationship modelling and projections lands role in food balance in the Badung regency sustainable in terms of land area, while in Denpasar in terms of linkages with urban green space in Denpasar City’s regional landuse plan of 2011-2031.

Efficient Priority-Flood depression filling in raster digital elevation models

ABSTRACTDepressions in raster digital elevation models (DEM) present a challenge for extracting hydrological networks. They are commonly filled before subsequent algorithms are further applied. Among existing algorithms for filling depressions, the Priority-Flood algorithm runs the fastest. In this study, we propose an improved variant over the fastest existing sequential variant of the Priority-Flood algorithm for filling depressions in floating-point DEMs. The proposed variant introduces a series of improvements and greatly reduces the number of cells that need to be processed by the priority queue (PQ), the key data structure used in the algorithm. The proposed variant is evaluated based on statistics from 30 experiments. On average, our proposed variant reduces the number of cells processed by the PQ by around 70%. The speed-up ratios of our proposed variant over the existing fastest variant of the Priority-Flood algorithm range from 31% to 52%, with an average of 45%. The proposed variant can be used to fill depressions in large DEMs in much less time and in the parallel implementation of the Priority-Flood algorithm to further reduce the running time for processing huge DEMs that cannot be dealt with easily on single computers.

An integrated algorithm to evaluate flow direction and flow accumulation in flat regions of hydrologically corrected DEMs

In order to conduct an accurate hydrological analysis of a watershed, certain conditions need to be understood. Flow direction and flow accumulation are important watershed characteristics that need to be determined before an analysis can be made. Other important characteristics which can be gleaned from analysing the digital elevation model (DEM) of a watershed include channel networks, stream lengths and watershed boundaries. Determining flow direction and flow accumulation is usually carried out in separate steps. Flat regions are types of terrain in raster DEMs without local elevation gradients. Evaluating flow direction and flow accumulation in flat regions using DEMs is a well-known problem in watershed analysis because of the occurrence of problematic parallel flow lines. Calculations also tend to be time-consuming. We have developed an efficient and comprehensive integrated approach to assign flow directions and flow accumulation in flat regions. This approach uses values for non-flat flow accumulation and a maze algorithm with a weight value (MW method) to determine several things: a main flow line through the flat area to the local outlet, an octree tree, and first-in first-out queue structures to calculate flow accumulation. The MW method can be applied to hydrologically corrected DEMs and a single flow path can be provided to resolve all flat areas. To investigate the influence on the topological properties of the channel networks, we used this integrated algorithm to extract three sets of flow accumulation areas from existing DEMs. Using this new integrated method was faster than using the two existing methods and produced continuous channel networks without the occurrence of problematic parallel flow lines.

Parallel identification and filling of depressions in raster digital elevation models

ABSTRACTWith the increasing sizes of digital elevation models (DEMs), there is a growing need to design parallel schemes for existing sequential algorithms that identify and fill depressions in raster DEMs. The Priority-Flood algorithm is the fastest sequential algorithm in the literature for depression identification and filling of raster DEMs, but it has had no parallel implementation since it was proposed approximately a decade ago. A parallel Priority-Flood algorithm based on the fastest sequential variant is proposed in this study. The algorithm partitions a DEM into stripes, processes each stripe using the sequential variant in many rounds, and progressively identifies more slope cells that are misidentified as depression cells in previous rounds. Both Open Multi-Processing (OpenMP)- and Message Passing Interface (MPI)-based implementations are presented. The speed-up ratios of the OpenMP-based implementation over the sequential algorithm are greater than four for all tested DEMs with eight computing threads. The mean speed-up ratio of our MPI-based implementation is greater than eight over TauDEM, which is a widely used MPI-based library for hydrologic information extraction. The speed-up ratios of our MPI-based implementation generally become larger with more computing nodes. This study shows that the Priority-Flood algorithm can be implemented in parallel, which makes it an ideal algorithm for depression identification and filling on both single computers and computer clusters.