Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography

Flener, Claude; Vaaja, Matti; Jaakkola, Anttoni; Krooks, Anssi; Kaartinen, Harri; Kukko, Antero; Kasvi, Elina; Hyyppä, Hannu; Hyyppä, Juha; Alho, Petteri

doi:10.3390/rs5126382

Cited by 189 publications

(160 citation statements)

References 54 publications

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“…Bathymetry studies using UAVs are so far restricted to (i) spectral signature-depth correlation based on passive optical imagery (Flener et al, 2013;Lejot et al, 2007) or (ii) DEM (digital elevation model) generation through stereoscopic techniques from through-water pictures, correcting for the refractive index of water (Bagheri et al, 2015;Dietrich, 2016;Tamminga et al, 2014;Woodget et al, 2015).…”

Section: F Bandini Et Al: Bathymetry Observations Of Inland Water Bmentioning

confidence: 99%

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Bandini

Olesen

Jakobsen

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. High-quality bathymetric maps of inland water bodies are a common requirement for hydraulic engineering and hydrological science applications. Remote sensing methods, such as space-borne and airborne multispectral imaging or lidar, have been developed to estimate water depth, but are ineffective for most inland water bodies, because of the attenuation of electromagnetic radiation in water, especially under turbid conditions. Surveys conducted with boats equipped with sonars can retrieve accurate water depths, but are expensive, time-consuming, and unsuitable for unnavigable water bodies.We develop and assess a novel approach to retrieve accurate and high-resolution bathymetry maps. We measured accurate water depths using a tethered floating sonar controlled by an unmanned aerial vehicle (UAV) in a lake and in two different rivers located in Denmark. The developed technique combines the advantages of remote sensing with the potential of bathymetric sonars. UAV surveys can be conducted also in unnavigable, inaccessible, or remote water bodies. The tethered sonar can measure bathymetry with an accuracy of ∼ 2.1 % of the actual depth for observations up to 35 m, without being significantly affected by water turbidity, bed form, or bed material.

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Section: F Bandini Et Al: Bathymetry Observations Of Inland Water Bmentioning

confidence: 99%

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Bandini

Olesen

Jakobsen

et al. 2018

Hydrol. Earth Syst. Sci.

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“…If the water is clear enough, bed images under water can be captured, and the bed morphology can be measured with additional corrections of refraction (Tamminga et al, 2015;Woodget et al, 2015), but the floodwater is often unclear because of the abundant suspended sediment and disruptive flow current. Another option is the fusion of different data sets using a sonar-based measurement for the water-covered area, which is registered with the terrestrial data sets (Flener et al, 2013;Javernick et al, 2014). Image-based topographic data of bottom water taken by an unmanned underwater vehicle (UUV, also known as an autonomous underwater vehicle, AUV) can also be another option (e.g.…”

Section: Flood Monitoringmentioning

confidence: 99%

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Giordan

Hayakawa

Nex

et al. 2018

Nat. Hazards Earth Syst. Sci.

140

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Abstract. The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards. IntroductionIn the last three decades, the number of natural disasters showed a positive trend with an increase in the number of affected populations. Disasters not only affected the poor and characteristically more vulnerable countries but also those thought to be better protected. The Annual Disaster Statistical Review describes recent impacts of natural disasters on the population and reports 342 naturally triggered disasters in 2016 (Guha-Sapir et al., 2017). This is less than the annual average disaster frequency observed from 2006 to 2015 (376.4 events). However, natural disasters are still responsible for a high number of casualties (8733 death). In the period 2006-2015, the average number of causalities caused annually by natural disasters is 69 827. In 2016, hydrological disasters (177) had the largest share in natural disaster occurrence (51.8 %), followed by meteorological disasters (96; 28.1 %), climatological disasters (38; 11.1 %) and geophysical disasters (31; 9.1 %) (Guha-Sapir et al., 2017). To face these disasters, one of the most important solutions is the use of systems able to provide an adequate level of information for correctly understanding these events and their evolution. In this context, surveying and monitoring natural hazards gained importance. In particular, during the emergency phase it is very important to evaluate and control the phenomenon of evolution, preferably operating in near real time or real time, and consequently, use this information for a better risk assessment scenario. The available acquired data must be processed rapidly to support the emergency services and decision makers.Recently, the use of remote sensing (satellite and airborne platform) in the field of natural hazards and disasters has become common, also supported by the increase in geospatial technologies and the ability to provide and process up-to-date imagery (Joyce et al., 2009;Tarolli, 2014). Remotely sensed data play an integral role in predicting hazard events such as floods and landslides, subsidence events and other ground instabilities. Because of their acquisition mode and capabilPublished by Copern...

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“…UAV orientations rely also on GNSS and IMU devices, and the RMSE of ground points is typically reported to be 0.08-0.09 meters (Jaakkola et al, 2010;Flener et al 2013;Tulldahl et al, 2015). However, the advantage of UAV data collection is that typically there are no obstacles blocking the satellite visibility.…”

Section: Discussionmentioning

confidence: 99%

Quality Analysis and Correction of Mobile Backpack Laser Scanning Data

Rönnholm¹,

Liang²,

Kukko³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, WG I/3KEY WORDS: backpack laser scanning, UAV laser scanning, registration, quality, data correction ABSTRACT:Backpack laser scanning systems have emerged recently enabling fast data collection and flexibility to make measurements also in areas that cannot be reached with, for example, vehicle-based laser scanners. Backpack laser scanning systems have been developed both for indoor and outdoor use. We have developed a quality analysis process in which the quality of backpack laser scanning data is evaluated in the forest environment. The reference data was collected with an unmanned aerial vehicle (UAV) laser scanning system. The workflow included noise filtering, division of data into smaller patches, ground point extraction, ground data decimation, and ICP registration. As a result, we managed to observe the misalignments of backpack laser scanning data for 97 patches each including data from circa 10 seconds period of time. This evaluation revealed initial average misalignments of 0.227 m, 0.073 and -0.083 in the easting, northing and elevation directions, respectively. Furthermore, backpack data was corrected according to the ICP registration results. Our correction algorithm utilized the time-based linear transformation of backpack laser scanning point clouds. After the correction of data, the ICP registration was run again. This revealed remaining misalignments between the corrected backpack laser scanning data and the original UAV data. We found average misalignments of 0.084, 0.020 and -0.005 meters in the easting, northing and elevation directions, respectively.

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Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography

Cited by 189 publications

References 54 publications

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

Quality Analysis and Correction of Mobile Backpack Laser Scanning Data

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