scholarly journals Drainage Network Analysis and Structuring of Topologically Noisy Vector Stream Data

2019 ◽  
Vol 8 (9) ◽  
pp. 422
Author(s):  
John B. Lindsay ◽  
Wanhong Yang ◽  
Duncan D. Hornby
Keyword(s):  
Network Analysis ◽  
Channel Length ◽  
Drainage Network ◽  
Stream Networks ◽  
Stream Data ◽  
Suitable Alternative ◽  
Analysis Technique ◽  
Digital Elevation ◽  
Topological Errors ◽  
Fine Resolution

Drainage network analysis includes several operations that quantify the topological organization of stream networks. Network analysis operations are frequently performed on streams that are derived from digital elevation models (DEMs). While these methods are suited to application with fine-resolution DEM data, this is not the case for coarse DEMs or low-relief landscapes. In these cases, network analysis that is based on mapped vector streams is an alternative. This study presents a novel vector drainage network analysis technique for performing stream ordering, basin tagging, the identification of main stems and tributaries, and the calculation of total upstream channel length and distance to outlet. The algorithm uses a method for automatically identifying outlet nodes and for determining the upstream-downstream connections among links within vector stream networks while using the priority-flood method. The new algorithm was applied to test stream datasets in two Canadian study areas. The tests demonstrated that the new algorithm could efficiently process large hydrographic layers containing a variety of topological errors. The approach handled topological errors in the hydrography data that have challenged previous methods, including disjoint links, conjoined channels, and heterogeneity in the digitized direction of links. The method can provide a suitable alternative to DEM-based approaches to drainage network analysis, particularly in applications where stream burning would otherwise be necessary.

A vector-based method for drainage network analysis based on LiDAR data

2021 ◽  
pp. 104892
Author(s):  
Fangzheng Lyu ◽  
Zewei Xu ◽  
Xinlin Ma ◽  
Shaohua Wang ◽  
Zhiyu Li ◽  
...  
Keyword(s):  
Network Analysis ◽  
Lidar Data ◽  
Drainage Network

Lowlands Mapping in Forest Guinea

2013 ◽  
Vol 4 (1) ◽  
pp. 20-34 ◽  
Author(s):  
S. Saïdi ◽  
A. Camara ◽  
L. Gazull ◽  
M. Passouant ◽  
M. Soumaré
Keyword(s):  
Surface Topography ◽  
Digital Elevation Model ◽  
Low Cost ◽  
Mapping Method ◽  
Drainage Network ◽  
Digital Elevation ◽  
Agricultural Potential ◽  
Forested Areas ◽  
Main Index ◽  
Elevation Model

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

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 Selection of Sites for the Treatment and the Final Disposal of Construction and Demolition Waste, Using Two Approaches: An Analysis for Mexico City

2019 ◽  
Vol 11 (15) ◽  
pp. 4077 ◽  
Author(s):  
Juan Antonio Araiza-Aguilar ◽  
Constantino Gutiérrez-Palacios ◽  
María Neftalí Rojas-Valencia ◽  
Hugo Alejandro Nájera-Aguilar ◽  
Rubén Fernando Gutiérrez-Hernández ◽  
...  
Keyword(s):  
Network Analysis ◽  
Mexico City ◽  
Waste Treatment ◽  
Decision Makers ◽  
Construction And Demolition Waste ◽  
Evaluation Technique ◽  
Demolition Waste ◽  
Final Disposal ◽  
Multi Criteria Evaluation ◽  
Analysis Technique

This paper proposes a solution to the current problems of Mexico City (Ciudad de México) with respect to construction and demolition waste, through a spatial analysis to locate a waste treatment and disposal infrastructure. Two analysis methodologies, specifically the multi-criteria evaluation technique and network analysis, are used with the support of geographic information systems. The results of the multi-criteria evaluation technique indicate that the most suitable places for this infrastructure location are in the south and southeast of the study area, in the Tlalpan, Milpa Alta, Xochimilco and Cuajimalpa boroughs. The results of the network analysis technique indicate that four facilities strategically located in Miguel Hidalgo, Gustavo A. Madero, Tlahuac and Tlalpan boroughs would permit the provision of service to almost all waste generation points in the study area. Decision makers in Mexico City can use either of the two approaches. If the objective is to find the best location of a single place for the treatment or disposal of huge amounts of waste, the results obtained with the multi-criteria evaluation technique should be used. On the other hand, if waste treatment is favored over final disposal, decision makers should use the results of the network analysis technique.

scholarly journals Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

2020 ◽  
Vol 9 (5) ◽  
pp. 334
Author(s):  
Timofey E. Samsonov
Keyword(s):  
Spatial Analysis ◽  
Digital Elevation Model ◽  
Spatial Data ◽  
Drainage Network ◽  
Map Generalization ◽  
Data Types ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Different Sources

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

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