Modified Priority-Flood algorithm for hydrologic modelling-based digital terrain analysis using a hash heap structure

The extraction of the drainage hydrographical network is very important for various types of study such as hydrological analysis, geomorphology, environmental science, terrain analysis and still a research topic in the field of GIS. Drainage network is extracted through satellite image (e.g., digital elevation model) processing, contour map processing, and raster map processing. A raster map of an area contains many layers such as road network, building, forest area, waterbody, river pattern, text, and drainage pattern extracted from raster map is part of document image analysis. A toposheet or contour map contains the linear feature, namely elevation contour, waterbody, river network, text, and the extraction process of drainage line is time-consuming and traditional process. Due to the advances in satellite imagery, high-resolution digital elevation model (DEM) is captured by many satellites recently. The DEMs are advantageous over toposheet because it provides seamless provision of data with global coverage. Accurate drainage extraction from DEMs is used for morphometric analysis, hydrological analysis, terrain analysis, and many other areas in recent year across the world as DEM provides the fastest way to extract feature in various ways. This paper provides the evolution of satellite imagery and the accurate extraction of drainage network for various applications, namely geomorphometric analysis, hydrologic analysis, terrain analysis, and also describes the steps involved to extract drainage pattern from DEM, an up-to-date process.

A comparative appraisal of hydrological behavior of SRTM DEM at catchment level

The digital elevation model (DEM), an important source of information, is usually used to express a topographic surface in three dimensions and to imitate essential natural geography. DEM has been applied to physical geography, hydrology, ecology, and biology. This study analyzed digital elevation data sources and their structure, the arithmetic of terrain attribute extraction from DEM and its applications, and DEM’s error and uncertainty algorithm. The Hayachinesan mountain area (in northeastern Japan) was chosen as research site, and the focus was on terrain analysis and the impacts of DEM resolution on topographic attributes, analyzed using TNTmips GIS software (MicroImage, Inc., USA) and “Digital Map 25,000” (published by the Geographical Survey Institute of Japan in 1998). The results show that: (1) DEM is a very effective tool for terrain analysis: many terrain attributes (such as slope, aspect, slope type, watershed, and standard flow path) can be derived, and these attributes can be displayed with both image and attribute databases, with the help of GIS; (2) DEM resolution has a great influence on terrain attributes. The following details are shown: (a) DEM resolution has a significant effect on slope estimation: the average slope becomes smaller and the standard deviation becomes larger when DEM resolution changes from fine to coarse, and the different impacts of DEM resolution on different slope ranges can be classified into three gradient classes: 0–10° (underestimated slope), 10–35° (overestimated slope), and >35° (little impact on slope estimation); (b) DEM resolution has little effect on aspect estimation, but flat areas become larger when DEM resolution changes from fine to coarse; and (c) the quantity of hydrologic topography information declines as DEM resolution decreases.

As rapid urbanization occurs in cities worldwide, the importance of maintaining updated digital elevation models (DEM) will continue to increase. However, due to the cost of generating high-resolution DEM over large spatial extents, the temporal resolution of DEMs is coarse in many regions. Low-cost unmanned aerial vehicles (UAS) and DEM data fusion provide a partial solution to improving the temporal resolution of DEM but do not identify which areas of a DEM require updates. We present Rapid-DEM, a framework that identifies and prioritizes locations with a high likelihood of an urban topographic change to target UAS data acquisition and fusion to provide up-to-date DEM. The framework uses PlanetScope 3 m satellite imagery, Google Earth Engine, and OpenStreetMap for land cover classification. GRASS GIS generates a contextualized priority queue from the land cover data and outputs polygons for UAS flight planning. Low-cost UAS fly the identified areas, and WebODM generates a DEM from the UAS survey data. The UAS data is fused with an existing DEM and uploaded to a public data repository. To demonstrate Rapid-DEM a case study in the Walnut Creek Watershed in Wake County, North Carolina is presented. Two land cover classification models were generated using random forests with an overall accuracy of 89% (kappa 0.86) and 91% (kappa 0.88). The priority queue identified 109 priority locations representing 1.5% area of the watershed. Large forest clearings were the highest priority locations, followed by newly constructed buildings. The highest priority site was a 0.5 km2 forest clearing that was mapped with UAS, generating a 15 cm DEM. The UAS DEM was resampled to 3 m resolution and fused with USGS NED 1/9 arc-second DEM data. Surface water flow was simulated over the original and updated DEM to illustrate the impact of the topographic change on flow patterns and highlight the importance of timely DEM updates.

Calculating drainage accumulation in a digital elevation model (DEM) is a common requirement for hydrology and terrain analysis. This letter presents a basin tree index (BTI) algorithm to improve the efficiency of this calculation, achieving the time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="TeX">$O(N)$</tex-math></inline-formula> and the input–output efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="TeX">$O(\hbox{Scan}(N))$</tex-math></inline-formula> . We have developed a BTI to guide the calculation sequence, allowing us to avoid invalid and repeat manipulation and to reduce random scattered data access. The BTI provides a one-to-one correspondence between a basin and an outlet, and it maintains cells orderly in terms of both the elevation and the spatial distribution, as it is built by tracing the drainage path from the outlet to the source directly. This is achieved according to the drainage direction for each basin extracted from the DEM, where basins are divided based on watersheds. Therefore, the drainage accumulation can be calculated by traversing the BTIs from their leaves to roots linearly and simultaneously. These BTIs divide the entire study area into several basins that can be processed in isolation, reducing the search scope for basins and allowing the algorithm to efficiently utilize the main memory and decrease the data swapping between the main memory and the disk. A DEM for the Zhejiang Province in China was used to validate the results and compare the processing speeds. The results show that the algorithm provides the same calculation result as alternative algorithms but becomes more efficient as the volume of the DEM data increases. Furthermore, the BTI algorithm in this letter is easy to implement.

Topography is an important land surface characteristic that affects most aspects of the water balance in a catchment, including the generation of surface and subsurface runoff, the flow paths followed by water as it moves down and through hillslopes, and the rate of water movement. Topographic attributes derived from digital elevation models (DEMs) and automated terrain analyses are increasingly used in terrain analysis and geomorphological research. DEM is convenient for representing the continuously varying topographic surface of the Earth, and it is a common data source for terrain analysis and other spatial applications. The utility of the DEM is evidenced by widespread availability of satellite-based DEMs at different resolutions and by the ever-increasing list of uses from DEM. Common terrain attributes, which could be computed from a DEM include slope gradient, slope aspect, slope curvature, upslope length, specific catchment area, compound topographic index (CTI) etc. One of the most limiting factors of the use of the DEM is its accuracy and spatial resolution. DEM of different resolutions could be used to derive DEM-based attributes, which could be used to investigate and evaluate resources like soil, water, vegetation, etc., in given landscape. In digital terrain modeling, predictive relationships developed at one scale might not be much useful for prediction of variables at different scales. That may limit the use of terrain variables developed for large scale in small-scale studies.

Improving merge methods for grid-based digital elevation models

Pre-processing algorithms and landslide modelling on remotely sensed DEMs

How does modifying a DEM to reflect known hydrology affect subsequent terrain analysis?

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

ABSTRACTThere are three major mathematical problems in digital terrain analysis: (1) interpolation of digital elevation models (DEMs); (2) DEM generalization and denoising; and (3) computation of morphometric variables through calculating partial derivatives of elevation. Traditionally, these three problems are solved separately by means of procedures implemented in different methods and algorithms. In this article, we present a universal spectral analytical method based on high-order orthogonal expansions using the Chebyshev polynomials of the first kind with the subsequent Fejér summation. The method is intended for the processing of regularly spaced DEMs within a single framework including DEM global approximation, denoising, generalization, as well as calculating the partial derivatives of elevation and local morphometric variables.The method is exemplified by a portion of the Great Rift Valley and central Kenyan highlands. A DEM of this territory (the matrix 480 × 481 with a grid spacing of 30″) was extracted from the global DEM SRTM30_PLUS. We evaluated various sets of expansion coefficients (up to 7000) to approximate and reconstruct DEMs with and without the Fejér summation. Digital models of horizontal and vertical curvatures were computed using the first and second partial derivatives of elevation derived from the reconstructed DEMs. To evaluate the approximation accuracy, digital models of residuals (differences between the reconstructed DEMs and the initial one) were calculated. The test results demonstrated that the method is characterized by a good performance (i.e., a distinct monotonic convergence of the approximation) and a high speed of data processing. The method can become an effective alternative to common techniques of DEM processing.

Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.

Aerial characterization of surface depressions in urban watersheds

Automatic delineation of drainage basins from digital elevation models (DEMs) is a well established technique used in terrain analysis. The conventional methodological framework was first developed in the 1980s, after which time complexities and memory requirements of the algorithms for N-cell DEMs have been improved to the point where they are optimal O(N) or nearly optimal O(N log(N)). In addition to algorithmic developments, uncertainty in the drainage basin delineation results has been handled with spatial probability models, which replace a single DEM D with a distribution of possible correct DEMs p(D). If a probability model is used, results are in the form of distributions and can be visualized as a probability map of uncertain drainage basin delineations. In this paper, we improve existing algorithms by deriving distributed algorithms for computing probability maps of the delineations. Our work is based on the use of Monte Carlo integration, process convolution, and Wang & Liu's fast method for removal of DEM depressions. The new algorithms are designed to large, high resolution DEMs. When distributed algorithms process DEM data in a computer cluster with K nodes, the memory requirements for a single node grow according to O(N/K). The performance and behaviour of algorithms are measured in different settings.

Evaluation of Lidar-derived DEMs through Terrain Analysis and Field Comparison