Possibility of acoustic noise interferometry applications for passive remote sensing in shallow water

2015 ◽  
Vol 137 (4) ◽  
pp. 2242-2242
Author(s):  
Sergei Sergeev ◽  
Andrey Shurup ◽  
Alisa Scherbina ◽  
Pavel Mukhanov
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Acoustic Noise ◽  
Passive Remote Sensing ◽  
Noise Interferometry

scholarly journals PENGGUNAAN LIDAR (LIGHT DETECTION AND RANGING) UNTUK MENGUKUR KEDALAMAN PERAIRAN DANGKAL

OSEANA ◽  
2019 ◽  
Vol 44 (1) ◽  
pp. 54-69
Author(s):  
Hollanda A Kusuma ◽  
Nadya Oktaviani
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Acoustic Noise ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Bathymetric Data ◽  
Depth Data ◽  
Remote Sensing Technology ◽  
Research Activities ◽  
Sensing Technology

UTILIZATION OF LIDAR (LIGHT DETECTION AND RANGING) TO MEASURE SHALLOW WATER DEPTH. Understanding on seabed characteristics such as the topography, composition and habitat conditions was very important to provide information not only for shipping activities, conservation, management and planning activities, but also for research activities with accurate bathymetry data. Accurate bathymetric data can be obtained from hydrographic surveys and remote sensing technology analysis. The hydrographic survey is used to obtain bathymetry data by applying singlebeam echosounder (SBES) and multibeam echosounder (MBES). At a depth of <15m (shallow water) was difficult to carry out an acoustic survey. At present there is one remote sensing technology that can be used to support hydrographic surveys namely Bathymetric LIDAR (Light Detection and Ranging). LIDAR was able to detect objects on land and waters due to being flown by a vehicle. Wide LIDAR sweep makes data acquisition faster and more effective than acoustic noise. Therefore, LIDAR was an alternative to obtain depth data, especially in coastal areas with a depth of less than 50 m.

Application of time reversal to acoustic noise interferometry in shallow water

2014 ◽  
Vol 136 (4) ◽  
pp. 2156-2156
Author(s):  
Boris Katsnelson ◽  
Oleg Godin ◽  
Jixing Qin ◽  
Nikolai Zabotin ◽  
Liudmila Zabotina ◽  
...  
Keyword(s):  
Shallow Water ◽  
Time Reversal ◽  
Acoustic Noise ◽  
Noise Interferometry

scholarly journals Effects of ice particle size vertical inhomogeneity on the passive remote sensing of ice clouds

2010 ◽  
Vol 115 (D17) ◽  
Author(s):  
Zhibo Zhang ◽  
Steven Platnick ◽  
Ping Yang ◽  
Andrew K. Heidinger ◽  
Jennifer M. Comstock
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Ice Clouds ◽  
Passive Remote Sensing ◽  
Vertical Inhomogeneity

scholarly journals Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

2010 ◽  
Vol 14 (12) ◽  
pp. 2661-2669 ◽  
Author(s):  
J. Colin ◽  
R. Faivre
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Land Cover ◽  
River Basin ◽  
Land Surface ◽  
Surface Density ◽  
Roughness Length ◽  
Aerodynamic Roughness Length ◽  
Passive Remote Sensing ◽  
Aerodynamic Roughness

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) respectively. These two models were then incorporated in two approaches: (i) a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii) a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD). The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

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