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Abstract

Light Detection and Ranging (LIDAR) provides high horizontal and vertical resolution of spatial data located in point cloud images, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. Bathymetric LIDAR systems and data are mainly focused to map depths in shallow and clear waters with a high degree of accuracy. Additionally, the backscattering produced by the different materials distributed over the bottom surface causes that the returned intensity signal contains important information about the reflection properties of these materials. Processing conveniently these values using a Simplified Radiative Transfer Model, allows the identification of different sea bottom types. This paper presents an original method for the classification of sea bottom by means of information processing extracted from the images generated through LIDAR data. The results are validated using a vector database containing benthic information derived by marine survey.

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... ALB data were used to discriminate cluster zones of massive stony coral colonies on patch reefs (Brock et al., 2006) and to map benthic habitats using the amplitude of the lidar bottom return (Wang and Philpot, 2007). Tulldahl and Wikström (2012) used data from the Hawkeye II to classify the seabed substratum and vegetation for a study site within the Baltic Sea, and Velasco et al. (2014) used Hawkeye II data to classify the seabed off the coast of Spain. The Tenix LADS (laser airborne depth sounder) Mk II lidar was used for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae off the coast of Australia (Zavalas et al., 2014). ...
... Several authors have described using ALB depth derivative such as slope, deviation of depth, and rugosity (Collin et al., 2011b;Wedding et al., 2008;Zavalas et al., 2014), and others have used depth derivatives in combination with amplitude or bottom reflectance (Chust et al., 2010;Collin et al., 2012;Velasco et al., 2014;Wang and Philpot, 2007). Others have extracted different metrics from the bottom waveform and used them in classification (Collin et al., 2011a;Tulldahl and Wikström, 2012). ...
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This article is in Free Access Publication and may be downloaded using the “Download Full Text PDF” link at right. © 1968, by the Association for the Sciences of Limnology and Oceanography, Inc.
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Coastal mapping is essential for a variety of applications such as coastal resource management, coastal environmental protection, and coastal development and planning. Various mapping techniques, like ground and aerial surveying, have been utilized in mapping coastal areas. Recently, multispectral and hyperspectral satellite images and elevation data from active sensors have also been used in coastal mapping. Integrating these datasets can provide more reliable coastal information. This paper presents a novel technique for coastal mapping from an airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral image and a light detection and ranging (LIDAR)-based digital elevation model (DEM). The DEM was used to detect and create a vector layer for building polygons. Subsequently, building pixels were removed from the AVIRIS image and the image was classified with a supervised classifier to discriminate road and water pixels. Two vector layers for the road network and the shoreline segments were vectorized from road pixels and water-body border pixels using several image-processing algorithms. The geometric accuracy and completeness of the results were evaluated. The average positional accuracies for the building, road network, and shoreline layers were 2.3, 5.7, and 7.2 m, respectively. The detection rates of the three layers were 93.2%, 91.3%, and 95.2%, respectively. Results confirmed that utilizing laser ranging data to detect and remove buildings from optical images before the classification process enhances the outcomes of this process. Consequently, integrating laser and optical data provides high-quality and more reliable coastal geospatial information.
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Eight years of SHOALS (Scanning Hydrographic Operational Airborne Lidar Survey) operations have proven that airborne lidar is an ideal tool for rapidly measuring shallow water depths and nearshore land elevations. SHOALS has produced high-density measurements of the coastal zone, enabling coastal engineers and scientists to quantify volumes of navigation channel shoaling, track movement of sand placed for beach nourishment, aid in coral reef mapping, and provide depths and navigation hazard locations for nautical charting. SHOALS success in producing valuable data for an ever-widening range of coastal applications has culminated in the development of the next-generation of airborne lidar sensors. SHOALS-1000 will be an integrated system including a bathymetric lidar component, a topographic lidar component, and a digital imagery capability. The bathymetric component will operate at a rate of 1,000 Hz, while the topographic component will operate at 10,000 Hz. SHOALS-1000 will collect data exceeding IHO Order 1 requirements and will easily mobilize in most photogrammetric aircraft of opportunity. Current SHOALS data processing schemes are automated for the system based on the expertise gained through eight years of evaluating lidar returns from the SHOALS system.
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An overview of basic relations and formulas concerning airborne laser scanning is given. They are divided into two main parts, the first treating lasers and laser ranging, and the second one referring to airborne laser scanning. A separate discussion is devoted to the accuracy of 3D positioning and the factors influencing it. Examples are given for most relations, using typical values for ALS and assuming an airplane platform. The relations refer mostly to pulse lasers, but CW lasers are also treated. Different scan patterns, especially parallel lines, are treated. Due to the complexity of the relations, some formulas represent approximations or are based on assumptions like constant flying speed, vertical scan, etc. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Airborne laser scanning; Terminology; Basic relations; Formulas; 3D accuracy analysis 1. Introduction In this article, some basic relations and formulas Z. Z. concerning a laser ranging, and b airborne laser sc...
Capacidades del LIDAR Batimétrico HAWK EYE MK II
  • G Ghust
  • M Grande
  • R Moncho
  • I Galparsoro
G. Ghust, M. Grande, R. Moncho and I. Galparsoro, "Capacidades del LIDAR Batimétrico HAWK EYE MK II", Teledetección: Agua y desarrollo sostenible, pp. 493-496, 2009.