scholarly journals Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method

2018 ◽  
Author(s):  
Qiusheng Wu ◽  
Charles R. Lane ◽  
Lei Wang ◽  
Melanie K. Vanderhoof ◽  
Jay R. Christensen ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Watershed Modeling ◽  
Hydrological Process ◽  
Terrain Analysis ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fine Resolution

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill-merge-spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level-set method based on graph theory. The proposed level-set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level-set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine-resolution LiDAR-derived DEMs show that the raster-based level-set algorithm is much more efficient (~150 times faster) than the vector-based contour tree method. The proposed level-set algorithm has great potential for being applied to large-scale ecohydrological analysis and watershed modeling.

scholarly journals Landslide site delineation from geometric signatures derived with the Hilbert–Huang transform for cases in Southern Taiwan

2020 ◽  
Vol 12 (1) ◽  
pp. 928-945
Author(s):  
Shun-Hsing Yang ◽  
Jyh-Jong Liao ◽  
Yi-Wen Pan ◽  
Peter Tian-Yuan Shih
Keyword(s):  
Large Scale ◽  
Global Climate ◽  
Terrain Analysis ◽  
Hilbert Huang Transform ◽  
Analysis Process ◽  
Digital Elevation ◽  
Main Scarp ◽  
Southern Taiwan ◽  
The Government ◽  
Elevation Model

AbstractLandslides are a frequently occurring threat to human settlements. Along with global climate change, the occurrence of landslides is the forecast to be even more frequent than before. Among numerous factors, topography has been identified as a correlated subject and from which hillslope landslide-prone areas could be analyzed. Geometric signatures, including statistical descriptors, topographic grains, etc., provide an analytical way to quantify terrain. Various published literature, fast Fourier transform, fractals, wavelets, and other mathematical tools were applied for this parameterization. This study adopts the Hilbert–Huang transform (HHT) method to identify the geomorphological features of a landslide from topographic profiles. The sites of the study are four “large-scale potential landslide areas” registered in the government database located in Meinong, Shanlin, and Jiasian in southern Taiwan. The topographic mapping was conducted with an airborne light detection and ranging instrument. The resolution of the digital elevation model is 1 m. Each topographic profile was decomposed into a number of intrinsic mode function (IMF) components. Terrain characterization was then performed with the spectrum resulting from IMF decomposition. This research found that the features of landslides, including main scarp-head, minor scarp, gully, and flank, have strong correspondence to the features in the IMF spectrum, mainly from the first and the second IMF components. The geometric signatures derived with HHT could contribute to the delineation of the landslide area in addition to other signatures in the terrain analysis process.

scholarly journals Morphometric analysis and prioritization of upper Benue River watershed, Northern Nigeria

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Caleb Akoji Odiji ◽  
Olaide Monsor Aderoju ◽  
Joseph Bisong Eta ◽  
Idris Shehu ◽  
Adama Mai-Bukar ◽  
...  
Keyword(s):  
Management Strategies ◽  
Decision Makers ◽  
Order System ◽  
Hydrological Process ◽  
Drainage Pattern ◽  
River Watershed ◽  
Digital Elevation ◽  
Hypsometric Integral ◽  
Cumulative Length ◽  
Elevation Model

AbstractThe upper Benue River watershed is undergoing remarkable modifications due to man-made and natural phenomena. Hence, an evaluation is required to understand the hydrological process of the watershed for planning and management strategies. This study aimed to assess the morphometric characteristics and prioritize the upper Benue River watershed. The boundary of the watershed and sub-watersheds, as well as stream networks, was extracted from the digital elevation model (DEM) coupled with hydrological and topographic maps. Twenty-eight morphometric parameters under three categories, i.e. linear, areal, and relief aspects were computed and mapped. Findings from the study revealed that the watershed is a seventh stream order system characterized by a dendritic drainage pattern. The result also showed that 4821 streams were extracted with a cumulative length of 30,232.84 km. The hypsometric integral of the watershed was estimated to be 0.22, indicating that it is in the old stage. In the prioritization of the watershed, the morphometric variables were utilized to calculate and classify the compound factor. The result showed that sub-watersheds 12, 16, 18, 24, 26, and 27 were ranked as very high priority for which conservation measures are required to mitigate the risk of flood and erosion. The outcome of this study can be used by decision-makers for sustainable watershed management and planning.

scholarly journals Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations

2020 ◽  
Vol 8 (2) ◽  
pp. 431-445
Author(s):  
Richard Barnes ◽  
Kerry L. Callaghan ◽  
Andrew D. Wickert
Keyword(s):  
Water Flow ◽  
Hydrological Modeling ◽  
Dynamic Models ◽  
Terrain Analysis ◽  
Flow Paths ◽  
Topographic Complexity ◽  
Morphological Segmentation ◽  
Surface Water Flow ◽  
Digital Elevation ◽  
Flow Through

Abstract. Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.

scholarly journals A Method of Optimizing Terrain Rendering Using Digital Terrain Analysis

2021 ◽  
Vol 10 (10) ◽  
pp. 666
Author(s):  
Lei Zhang ◽  
Ping Wang ◽  
Chengyi Huang ◽  
Bo Ai ◽  
Wenjun Feng
Keyword(s):  
Large Scale ◽  
Frame Rate ◽  
Terrain Analysis ◽  
Image Pyramid ◽  
Digital Terrain Analysis ◽  
Terrain Rendering ◽  
Digital Terrain ◽  
Elevation Data ◽  
View Frustum Culling ◽  
Elevation Model

Terrain rendering is an important issue in Geographic Information Systems and other fields. During large-scale, real-time terrain rendering, complex terrain structure and an increasing amount of data decrease the smoothness of terrain rendering. Existing rendering methods rarely use the features of terrain to optimize terrain rendering. This paper presents a method to increase rendering performance through precomputing roughness and self-occlusion information making use of GIS-based Digital Terrain Analysis. Our method is based on GPU tessellation. We use quadtrees to manage patches and take surface roughness in Digital Terrain Analysis as a factor of Levels of Detail (LOD) selection. Before rendering, we first regularly partition the terrain scene into view cells. Then, for each cell, we calculate its potential visible patch set (PVPS) using a visibility analysis algorithm. After that, A PVPS Image Pyramid is built, and each LOD level has its corresponding PVPS. The PVPS Image Pyramid is stored on a disk and is read into RAM before rendering. Based on the PVPS Image Pyramid and the viewpoint’s position, invisible terrain areas that are not culled through view frustum culling can be dynamically culled. We use Digital Elevation Model (DEM) elevation data of a square area in Henan Province to verify the effectiveness of this method. The experiments show that this method can increase the frame rate compared with other methods, especially for lower camera flight heights.

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

A new method for supporting interpretation of paleochannels in a large scale — Detrended Digital Elevation Model Interpretation

Geomorphology ◽  
2020 ◽  
Vol 369 ◽  
pp. 107374
Author(s):  
Shuyan Zhang ◽  
Yong Ma ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Fulong Chen ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Large Scale ◽  
New Method ◽  
Model Interpretation ◽  
Digital Elevation ◽  
Elevation Model

scholarly journals Morpho–Sedimentary Monitoring in a Coastal Area, from 1D to 2.5D, Using Airborne Drone Imagery

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 62 ◽  
Author(s):  
Antoine Mury ◽  
Antoine Collin ◽  
Dorothée James
Keyword(s):  
Coastal Areas ◽  
Global Changes ◽  
Sea Levels ◽  
Digital Elevation ◽  
Reduced Costs ◽  
Rising Sea Levels ◽  
Elevation Model ◽  
Fine Resolution ◽  
State Of The Science ◽  
Very High

Coastal areas are among the most endangered places in the world, due to their exposure to both marine and terrestrial hazards. Coastal areas host more than two-thirds of the world’s population, and will become increasingly affected by global changes, in particular, rising sea levels. Monitoring and protecting the coastlines have impelled scientists to develop adequate tools and methods to spatially monitor morpho-sedimentary coastal areas. This paper presents the capabilities of the aerial drone, as an “all-in-one” technology, to drive accurate morpho-sedimentary investigations in 1D, 2D and 2.5D at very high resolution. Our results show that drone-related fine-resolution, high accuracies and point density outperform the state-of-the-science manned airborne passive and active methods for shoreline position tracking, digital elevation model as well as point cloud creation. We further discuss the reduced costs per acquisition campaign, the increased spatial and temporal resolution, and demonstrate the potentialities to carry out diachronic and volumetric analyses, bringing new perspectives for coastal scientists and managers.

scholarly journals A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

2020 ◽  
Vol 12 (3) ◽  
pp. 561 ◽  
Author(s):  
Bruno Adriano ◽  
Naoto Yokoya ◽  
Hiroyuki Miura ◽  
Masashi Matsuoka ◽  
Shunichi Koshimura
Keyword(s):  
Disaster Response ◽  
Large Scale ◽  
Reference Data ◽  
Mass Movement ◽  
Recovery Planning ◽  
Additional Information ◽  
Digital Elevation ◽  
Before And After ◽  
Intensity Images ◽  
Elevation Model

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.

Sign in / Sign up

Export Citation Format

Share Document