Bathymetric mapping by means of remote sensing: methods, accuracy and limitations

2009 ◽  
Vol 33 (1) ◽  
pp. 103-116 ◽  
Author(s):  
Jay Gao
Keyword(s):  
Remote Sensing ◽  
Light Transmission ◽  
Effective Means ◽  
Operating Cost ◽  
Cost Effective ◽  
Sampling Interval ◽  
Influential Factors ◽  
Imaging Method ◽  
Cost Inefficiency ◽  
Remote Sensing Methods

Bathymetry has been traditionally charted via shipboard echo sounding. Alhough able to generate accurate depth measurements at points or along transects, this method is constrained by its high operating cost, inefficiency, and inapplicability to shallow waters. By comparison, remote sensing methods offer more flexible, efficient and cost-effective means of mapping bathymetry over broad areas. Remote sensing of bathymetry falls into two broad categories: non-imaging and imaging methods. The non-imaging method (as typified by LiDAR) is able to produce accurate bathymetric information over clear waters at a depth up to 70 m. However, this method is limited by the coarse bathymetric sampling interval and high cost. The imaging method can be implemented either analytically or empirically, or by a combination of both. Analytical or semi-analytical implementation is based on the manner of light transmission in water. It requires inputs of a number of parameters related to the properties of the atmosphere, water column, and bottom material. Thus, it is rather complex and difficult to use. By comparison, empirical implementation is much simpler and requires the input of fewer parameters. Both implementations can produce fine-detailed bathymetric maps over extensive turbid coastal and inland lake waters quickly, even though concurrent depth samples are essential. The detectable depth is usually limited to 20 m. The accuracy of the retrieved bathymetry varies with water depth, with the accuracy substantially lower at a depth beyond 12 m. Other influential factors include water turbidity and bottom materials, as well as image properties.

scholarly journals SPECTRAL IMAGING AND CLAY DETECTION IN LATGALE LAKES

2019 ◽  
Vol 1 ◽  
pp. 307
Author(s):  
Rasma Tretjakova ◽  
Sergejs Kodors ◽  
Juris Soms
Keyword(s):  
Remote Sensing ◽  
Incomplete Data ◽  
Spectral Imaging ◽  
Cost Effective ◽  
Indirect Methods ◽  
Sediment Samples ◽  
Pilot Experiment ◽  
Pearson Coefficient ◽  
Remote Sensing Methods ◽  
Sentinel 2

The survey of lake sediments is complex, time consuming and costly process with risks to human health. Additionally, manually obtained sediment samples provide incomplete data about a survey region. In turn, remote sensing methods are cost-effective and can provide continuous data about a survey region. Therefore, authors decided to perform a pilot experiment with a remote sensing method in order to detect clay sediments deposited in lakebeds. The evaluated method is the analysis of spectral images of Sentinel-2. Pearson coefficient and C4.5 datamining methods were applied for data analysis. Survey objects are Latgale lakes with and without clay sediments. The pilot experiment showed, that spectral imaging of lake water is not applicable method to detect definitely clay sediments in lakes, however, research results provide ideas about indirect methods, which must be studied in the future.

Nonlinear inversion of subsidence signatures induced by tunnels detected with surface and remote sensing measurements

2019 ◽  
Vol 38 (7) ◽  
pp. 550-553
Author(s):  
Jorge Parra ◽  
Jonathan Parra ◽  
Marius Necsoiu
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Cost Effective ◽  
Forward Modeling ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Gap Parameter ◽  
Remote Sensing Methods ◽  
Good Agreement

The state of the art in predicting tunnel-induced subsidence settlements is based on empirical and analytical methods. Empirical methods are useful when the equations are implemented with host medium properties where tunnels have been excavated. Analytical solutions can predict tunneling-induced ground movements, with the predictions accounting for tunnel radius and depth as well as ground-loss parameters in soft soils. The drawback is that these methods require human intervention, as each model must be adjusted manually by the interpreter until the model signature fits the observed data. It would take tremendous effort to evaluate displacement anomalies detected by remote sensing methods using such forward-modeling methods. Therefore, we present a method based on an inversion algorithm that automatically inverts subsidence signatures for tunnel radius, depth, Poisson's ratio, and the gap parameter. It is an advancement over conventional methods because it does not require a first guess, and it can invert several subsidence signatures in a matter of minutes. The algorithm, coupled with remote sensing-based displacement maps, is a cost-effective solution in operational characterization of displacement anomalies. We demonstrate that observed and predicted subsidence signatures are in good agreement with existing tunnel data in uniform clay and that the inversion parameters correspond to those predicted with forward modeling alone.

Parallel Computing for Tire Simulations

2011 ◽  
Vol 39 (3) ◽  
pp. 193-209 ◽  
Author(s):  
H. Surendranath ◽  
M. Dunbar
Keyword(s):  
High Performance ◽  
Effective Means ◽  
Cost Effective ◽  
Turnaround Time ◽  
Careful Consideration ◽  
Rolling Contact ◽  
Element Analysis ◽  
Parallel Solvers ◽  
Design Changes ◽  
Highly Nonlinear

Abstract Over the last few decades, finite element analysis has become an integral part of the overall tire design process. Engineers need to perform a number of different simulations to evaluate new designs and study the effect of proposed design changes. However, tires pose formidable simulation challenges due to the presence of highly nonlinear rubber compounds, embedded reinforcements, complex tread geometries, rolling contact, and large deformations. Accurate simulation requires careful consideration of these factors, resulting in the extensive turnaround time, often times prolonging the design cycle. Therefore, it is extremely critical to explore means to reduce the turnaround time while producing reliable results. Compute clusters have recently become a cost effective means to perform high performance computing (HPC). Distributed memory parallel solvers designed to take advantage of compute clusters have become increasingly popular. In this paper, we examine the use of HPC for various tire simulations and demonstrate how it can significantly reduce simulation turnaround time. Abaqus/Standard is used for routine tire simulations like footprint and steady state rolling. Abaqus/Explicit is used for transient rolling and hydroplaning simulations. The run times and scaling data corresponding to models of various sizes and complexity are presented.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Exploring the Benefits of Early Contractor Involvement (ECI) and the Mechanism for Automated 5D BIM Quantification Process in Offsite Modular Construction

2016 ◽  
Author(s):  
Tochukwu Moses ◽  
David Heesom ◽  
David Oloke ◽  
Martin Crouch
Keyword(s):  
Effective Means ◽  
Cost Effective ◽  
Modular Construction ◽  
Automated Quantification ◽  
Building Information ◽  
Project Risks ◽  
Value Add ◽  
The Uk ◽  
The Impact ◽  
Level 2

The UK Construction Industry through its Government Construction Strategy has recently been mandated to implement Level 2 Building Information Modelling (BIM) on public sector projects. This move, along with other initiatives is key to driving a requirement for 25% cost reduction (establishing the most cost-effective means) on. Other key deliverables within the strategy include reduction in overall project time, early contractor involvement, improved sustainability and enhanced product quality. Collaboration and integrated project delivery is central to the level 2 implementation strategy yet the key protocols or standards relative to cost within BIM processes is not well defined. As offsite construction becomes more prolific within the UK construction sector, this construction approach coupled with BIM, particularly 5D automated quantification process, and early contractor involvement provides significant opportunities for the sector to meet government targets. Early contractor involvement is supported by both the industry and the successive Governments as a credible means to avoid and manage project risks, encourage innovation and value add, making cost and project time predictable, and improving outcomes. The contractor is seen as an expert in construction and could be counter intuitive to exclude such valuable expertise from the pre-construction phase especially with the BIM intent of äóÖbuild it twiceäó», once virtually and once physically. In particular when offsite construction is used, the contractoräó»s construction expertise should be leveraged for the virtual build in BIM-designed projects to ensure a fully streamlined process. Building in a layer of automated costing through 5D BIM will bring about a more robust method of quantification and can help to deliver the 25% reduction in overall cost of a project. Using a literature review and a case study, this paper will look into the benefits of Early Contractor Involvement (ECI) and the impact of 5D BIM on the offsite construction process.

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

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