Airborne Laser Bathymetry for Documentation of Submerged Archaeological Sites in Shallow Water

Doneus, Michael; Miholjek, Igor; Mandlburger, Gottfried; Doneus, Nives; Verhoeven, Geert; Briese, Christian; Pregesbauer, Michael

doi:10.5194/isprsarchives-xl-5-w5-99-2015

Cited by 47 publications

(30 citation statements)

References 16 publications

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“…Several studies show the advantages of multi-wavelength LiDAR data in different applications, such as atmospheric processing [8,9], geological analysis [10], fire detection [11], topographic mapping [12][13][14], map updating [15], and plant monitoring [16,17]. Bakula, (2015) [18] analyzed the accuracies of digital terrain models (DTMs) from Optech Titan and showed that the mean differences among DTM generation from three channels were less than 0.03 m, and the composition of the three intensities could be considered an alternative orthoimage product.…”

Section: Previous Studiesmentioning

confidence: 99%

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Teo

2017

Applied Sciences

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Featured Application: The featured application of this study is to combine the geometrical and spectral features from advanced multi-wavelength LiDAR in land cover classification. Abstract:The airborne multi-wavelength light detection and ranging (LiDAR) system measures different wavelengths simultaneously and usually includes two or more active channels in infrared and green to acquire both topographic and hydrographic information. The reflected multi-wavelength energy can also be used to identify different land covers based on physical properties of materials. This study explored the benefits of multi-wavelength LiDAR in object-based land cover classification, focusing on three major issues: (1) the evaluation of single-and multi-wavelength LiDARs for land cover classification; (2) the performance of spectral and geometrical features extracted from multi-wavelength LiDAR; and (3) the comparison of the vegetation index derived from active multi-wavelength LiDAR and passive multispectral images. The three-wavelength test data were acquired by Optech Titan in green, near-infrared, and mid-infrared channels, and the reference data were acquired from Worldview-3 image. The experimental results show that the multi-wavelength LiDAR provided higher accuracy than single-wavelength LiDAR in land cover classification, with an overall accuracy improvement rate about 4-14 percentage points. The spectral features performed better compared to geometrical features for grass, road, and bare soil classes, and the overall accuracy improvement is about 29 percentage points. The results also demonstrated the vegetation indices from Worldview-3 and Optech Titan have similar characteristics, with correlations reaching 0.68 to 0.89. Overall, the multi-wavelength LiDAR system improves the accuracy of land cover classification because this system provides more spectral information than traditional single-wavelength LiDAR.

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Section: Previous Studiesmentioning

confidence: 99%

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Teo

2017

Applied Sciences

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“…Advanced discrete return sensors ALTM-Orion could produce data of enhanced quality, which could represent complex structures of vegetation targets at the level of details. Doneus et al (2015) presented a compilation of recent airborne laser bathymetry scanning systems for documentation of submerged archaeological sites in shallow water. The SHOALS system (Irish and Lillycrop, 1999) employs Lidar technology to remotely measure bathymetry and topography in the coastal zone.…”

Section: Introductionmentioning

confidence: 99%

3d Land Cover Classification Based on Multispectral Lidar Point Clouds

Zou¹,

Zhao²,

Li³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

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“…However, the planar mapping resolution of ALB is limited by the size of the laser footprint which, in turn, is constrained by eye safety considerations (Guenther et al, 2000;Fernandez et al, 2014;Doneus et al, 2015). Precise capturing of blocks or boulders with a diameter of less than 50 cm is therefore impossible with typical ALB setups.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas coastal mapping is the main application of ALB, state-of-the art sensors also enable surveying of smaller inland water bodies and especially clear gravel-bed rivers (Hilldale and Raff, 2008;Kinzel et al, 2013;Fernandez et al, 2014;Mandlburger et al, 2015a). This was mainly made possible by two facts: (i) the increased measurement rate lead to a much higher sampling density (dozens of points per m 2 ), and (ii) by narrowing the beam divergence the laser footprint diameter on the surface was brought down from 2-5 m to approximately 0.5 m for a standard flying height of 500-600 m above ground/water level (Fernandez et al, 2014;Doneus et al, 2015). With those mission parameters capturing of small to medium rivers (width: 5-25 m, depth: 0-4 m) became feasible.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Novel Uav-Borne Topo-Bathymetric Laser Profiler

Mandlburger¹,

Pfennigbauer²,

Wieser³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:We present a novel topo-bathymetric laser profiler. The sensor system (RIEGL BathyCopter) comprises a laser range finder, an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, a control unit, and digital cameras mounted on an octocopter UAV (RiCOPTER). The range finder operates on the time-of-flight measurement principle and utilizes very short laser pulses (<1 ns) in the green domain of the spectrum (λ=532 nm) for measuring distances to both the water surface and the river bottom. For assessing the precision and accuracy of the system an experiment was carried out in October 2015 at a pre-alpine river (Pielach in Lower Austria). A 200 m longitudinal section and 12 river cross sections were measured with the BathyCopter sensor system at a flight altitude of 15-20 m above ground level and a measurement rate of 4 kHz. The 3D laser profiler points were compared with independent, quasi-simultaneous data acquisitions using (i) the RIEGL VUX1-UAV lightweight topographic laser scanning system (bare earth, water surface) and (ii) terrestrial survey (river bed). Over bare earth the laser profiler heights have a std. dev. of 3 cm, the water surface height appears to be underestimated by 5 cm, and river bottom heights differ from the reference measurements by 10 cm with a std. dev. of 13 cm. When restricting the comparison to laser profiler bottom points and reference measurements with a lateral offset below 1 m, the values improve to 4 cm bias with a std. dev. of 6 cm. We report additionally on challenges in comparing UAV-borne to terrestrial profiles. Based on the accuracy and the small footprint (3.5 cm at the water surface) we concluded that the acquired 3D points can potentially serve as input data (river bed geometry, grain roughness) and validation data (water surface, water depth) for hydrodynamic-numerical models.

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Airborne Laser Bathymetry for Documentation of Submerged Archaeological Sites in Shallow Water

Cited by 47 publications

References 16 publications

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

3d Land Cover Classification Based on Multispectral Lidar Point Clouds

Evaluation of a Novel Uav-Borne Topo-Bathymetric Laser Profiler

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