scholarly journals Terrain Analysis for Locating Erosion Channels: Assessing LiDAR Data and Flow Direction Algorithm

10.5772/51526 ◽  
2012 ◽  
Author(s):  
Adam Pike ◽  
Tom Mueller ◽  
Eduardo Rienzi ◽  
Surendran Neelakantan ◽  
Blazan Mijatovic ◽  
...  
Keyword(s):  
Flow Direction ◽  
Lidar Data ◽  
Terrain Analysis ◽  
Flow Direction Algorithm

scholarly journals Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

2021 ◽  
Vol 13 (13) ◽  
pp. 2485
Author(s):  
Yi-Chun Lin ◽  
Raja Manish ◽  
Darcy Bullock ◽  
Ayman Habib
Keyword(s):  
Flow Direction ◽  
Sediment Accumulation ◽  
Digital Terrain Model ◽  
Vertical Direction ◽  
Point Clouds ◽  
Lidar Data ◽  
Cross Sectional ◽  
Premature Failure ◽  
3D Point Clouds ◽  
Mapping Systems

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

Flow Direction Algorithm (FDA): a Novel Optimizer Approach for Solving Optimization Problems

2021 ◽  
pp. 107224
Author(s):  
Hojat Karami ◽  
Mahdi Valikhan Anaraki ◽  
Saeed Farzin ◽  
Seyedali mirjalili
Keyword(s):  
Optimization Problems ◽  
Flow Direction ◽  
Flow Direction Algorithm

scholarly journals Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

2019 ◽  
Vol 8 (9) ◽  
pp. 386 ◽  
Author(s):  
Natalija Stojanovic ◽  
Dragan Stojanovic
Keyword(s):  
Energy Consumption ◽  
Graphics Processing Units ◽  
High Performance ◽  
Programming Model ◽  
Flow Direction ◽  
Computing Methods ◽  
Terrain Analysis ◽  
Central Processing ◽  
Watershed Analysis ◽  
Programming Effort

Watershed analysis, as a fundamental component of digital terrain analysis, is based on the Digital Elevation Model (DEM), which is a grid (raster) model of the Earth surface and topography. Watershed analysis consists of computationally and data intensive computing algorithms that need to be implemented by leveraging parallel and high-performance computing methods and techniques. In this paper, the Multiple Flow Direction (MFD) algorithm for watershed analysis is implemented and evaluated on multi-core Central Processing Units (CPU) and many-core Graphics Processing Units (GPU), which provides significant improvements in performance and energy usage. The implementation is based on NVIDIA CUDA (Compute Unified Device Architecture) implementation for GPU, as well as on OpenACC (Open ACCelerators), a parallel programming model, and a standard for parallel computing. Both phases of the MFD algorithm (i) iterative DEM preprocessing and (ii) iterative MFD algorithm, are parallelized and run over multi-core CPU and GPU. The evaluation of the proposed solutions is performed with respect to the execution time, energy consumption, and programming effort for algorithm parallelization for different sizes of input data. An experimental evaluation has shown not only the advantage of using OpenACC programming over CUDA programming in implementing the watershed analysis on a GPU in terms of performance, energy consumption, and programming effort, but also significant benefits in implementing it on the multi-core CPU.

scholarly journals Road Ditch Line Mapping with Mobile LiDAR

2021 ◽  
Author(s):  
Ayman Habib ◽  
◽  
Darcy M. Bullock ◽  
Yi-Chun Lin ◽  
Raja Manish
Keyword(s):  
Flow Direction ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Lidar Data ◽  
Design Standards ◽  
Cross Sectional ◽  
Terrain Model ◽  
State Highway ◽  
3D Point Clouds ◽  
Longitudinal Profiles

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance of alternative MLMS units was performed. These MLMS included an unmanned ground vehicle, an unmanned aerial vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system. Point cloud from all the MLMS units were in agreement in the vertical direction within the ±3 cm range for solid surfaces, such as paved roads, and ±7 cm range for surfaces with vegetation. The portable backpack system that could be carried by a surveyor or mounted on a vehicle and was the most flexible MLMS. The report concludes that due to flexibility and cost effectiveness of the portable backpack system, it is the preferred platform for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulders, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data, and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulder, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively.

scholarly journals Generating and analyzing Terrain characteristics from Shuttle Radar Topographic Mission (SRTM), DEM

2021 ◽  
Vol 10 (3) ◽  
pp. 198-206
Author(s):  
Ugbelase Vincent Nwacholundu ◽  
Igbokwe Joel Izuchukwu ◽  
Emengini Josephine Ebele ◽  
Ejikeme Joseph Onyedika ◽  
Igbokwe Esomchukwu Chinagorom
Keyword(s):  
Flow Direction ◽  
Digital Elevation Models ◽  
Random Noise ◽  
Analysis Tool ◽  
Terrain Analysis ◽  
Srtm Dem ◽  
Hydrologic Modelling ◽  
Shuttle Radar Topographic Mission ◽  
Digital Terrain ◽  
Digital Elevation

Terrain analysis is the quantitative analysis of topographic surfaces. The purpose of a digital terrain system is to provide the digital representation of terrain so that environmental problem like soil erosion may be approached accurately and efficiently through automated means. Traditionally this was (and still is!) being done manually by using topographic/contour maps. With the availability of Digital Elevation Models (DEM) and GIS tools, watershed properties can be extracted by using automated procedures. Remote Sensing and Digital elevation models (DEMs) are known to be very useful data sources for the automated delineation of flow paths, sub watersheds and flow networks for hydrologic modelling and watershed characterization. The digital terrain model was extracted from a 90m resolution Shuttle Radar Topographic Mission (SRTM) of the study area. The SRTM data was corrected by removing voids, striping, tree offsets and random noise. The SRTM DEM data was projected from geographic coordinate WGS 84 to UTM zone 32 of the study area. The 3-D analysis tool of the ArcGIS 10.1 was used for this process. The DEM was processed to obtain the Slope, Contour, Flow direction, Flow accumulation, Flow length, Stream power Index of the study area. The study proved that SRTM elevation dataset has the ability to obviate the lack of terrain data for hydrologic modelling using ArcGIS where appropriate data for terrain modelling and simulation of hydrological processes is unavailable.

GPU-based lightweight parallel processing toolset for LiDAR data for terrain analysis

2019 ◽  
Vol 117 ◽  
pp. 55-68 ◽  
Author(s):  
Jing Li ◽  
You Xu ◽  
Hailey Macrander ◽  
Laura Atkinson ◽  
Tabris Thomas ◽  
...  
Keyword(s):  
Parallel Processing ◽  
Lidar Data ◽  
Terrain Analysis

scholarly journals Optimization of the Cross-Correlation Algorithm for Two-Component Wind Field Estimation from Single Aerosol Lidar Data and Comparison with Doppler Lidar

2016 ◽  
Vol 33 (1) ◽  
pp. 81-101 ◽  
Author(s):  
Masaki Hamada ◽  
Pierre Dérian ◽  
Christopher F. Mauzey ◽  
Shane D. Mayor
Keyword(s):  
Cross Correlation ◽  
Field Experiments ◽  
Flow Direction ◽  
Interrogation Window ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Mean Flow ◽  
Correlation Algorithm ◽  
Two Component ◽  
The Mean

AbstractNumerical and field experiments were conducted to test an optimized cross-correlation algorithm (CCA) for the remote sensing of two-component wind vectors from horizontally scanning elastic backscatter lidar data. Each vector is the result of applying the algorithm to a square and contiguous subset of pixels (an interrogation window) in the lidar scan area. Synthetic aerosol distributions and flow fields were used to investigate the accuracy and precision of the technique. Results indicate that in neutral static stability, when the mean flow direction over the interrogation window is relatively uniform, the random error of the estimates increases as the mean wind speed and turbulence intensity increases. In convective conditions, larger errors may occur as a result of the cellular nature of convection and the dramatic changes in wind direction that may span the interrogation window. Synthetic fields were also used to determine the significance of various image processing and numerical steps used in the CCA. Results show that an iterative approach that dynamically reduces the block size provides the largest performance gains. Finally, data from a field experiment conducted in 2013 in Chico, California, are presented. Comparisons with Doppler lidar data indicate excellent agreement for the 10-min mean wind velocity computed over a set of 150 h: the root-mean-square deviations (and slopes) for the u and υ components are 0.36 m s−1 (0.974) and 0.37 m s−1 (0.991), respectively, with correlation coefficients > 0.99.

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