{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:46:09Z","timestamp":1766137569925,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T00:00:00Z","timestamp":1608768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["No. G16AP00086"],"award-info":[{"award-number":["No. G16AP00086"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data\u2014both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1\/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management.<\/jats:p>","DOI":"10.3390\/rs13010035","type":"journal-article","created":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T09:02:44Z","timestamp":1608800564000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs"],"prefix":"10.3390","volume":"13","author":[{"given":"Izaak","family":"Cooper","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1391-6101","authenticated-orcid":false,"given":"Rollin H.","family":"Hotchkiss","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-0738","authenticated-orcid":false,"given":"Gustavious Paul","family":"Williams","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,24]]},"reference":[{"key":"ref_1","first-page":"15","article-title":"An analysis of bathymetric changes in Altinapa reservoir","volume":"6","author":"Ceylan","year":"2011","journal-title":"Carpathian J. Earth Environ. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S0924-2716(99)00005-2","article-title":"Laser scanning\u2014Surveying and mapping agencies are using a new technique for the derivation of digital terrain models","volume":"54","author":"Petzold","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","first-page":"154","article-title":"A comparison between analytical aerial photogrammetry, laser scanning, total station and global positioning system surveys for generation of digital terrain model","volume":"30","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_4","unstructured":"Zhang, L. (2005). Automatic Digital Surface Model (DSM) Generation from Linear Array Images, ETH."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ferrer-Gonz\u00e1lez, E., Ag\u00fcera-Vega, F., Carvajal-Ram\u00edrez, F., and Mart\u00ednez-Carricondo, P. (2020). UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points. Remote Sens., 12.","DOI":"10.3390\/rs12152447"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hammond, J.E., Vernon, C.A., Okeson, T.J., Barrett, B.J., Arce, S., Newell, V., Janson, J., Franke, K.W., and Hedengren, J.D. (2020). Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry. Remote Sens., 12.","DOI":"10.3390\/rs12142285"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jaud, M., Passot, S., Le Bivic, R., Delacourt, C., Grandjean, P., and Le Dantec, N. (2016). Assessing the accuracy of high resolution digital surface models computed by PhotoScan\u00ae and MicMac\u00ae in sub-optimal survey conditions. Remote Sens., 8.","DOI":"10.3390\/rs8060465"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.3390\/rs12122006","article-title":"Assessment of Tuff Sea Cliff Stability Integrating Geological Surveys and Remote Sensing. Case History from Ventotene Island (Southern Italy)","volume":"12","author":"Ruberti","year":"2020","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/rs4051392","article-title":"An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds","volume":"4","author":"Turner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.3390\/rs5126880","article-title":"Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography: The structure from motion approach on coastal environments","volume":"5","author":"Mancini","year":"2013","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7050","DOI":"10.3390\/rs6087050","article-title":"Small-scale surface reconstruction and volume calculation of soil erosion in complex Moroccan gully morphology using structure from motion","volume":"6","author":"Kaiser","year":"2014","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.3390\/rs70201736","article-title":"Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV)","volume":"7","author":"Turner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jensen, J.L., and Mathews, A.J. (2016). Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sens., 8.","DOI":"10.3390\/rs8010050"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Madson, A., Fielding, E., Sheng, Y., and Cavanaugh, K. (2019). High-resolution spaceborne, airborne and in situ landslide kinematic measurements of the slumgullion landslide in Southwest Colorado. Remote Sens., 11.","DOI":"10.3390\/rs11030265"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arce, S., Vernon, C.A., Hammond, J., Newell, V., Janson, J., Franke, K.W., and Hedengren, J.D. (2020). Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models. Remote Sens., 12.","DOI":"10.3390\/rs12132169"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jaud, M., Bertin, S., Beauverger, M., Augereau, E., and Delacourt, C. (2020). RTK GNSS-Assisted Terrestrial SfM Photogrammetry without GCP: Application to Coastal Morphodynamics Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12111889"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alvarez, L.V., Moreno, H.A., Segales, A.R., Pham, T.G., Pillar-Little, E.A., and Chilson, P.B. (2018). Merging unmanned aerial systems (UAS) imagery and echo soundings with an adaptive sampling technique for bathymetric surveys. Remote Sens., 10.","DOI":"10.3390\/rs10091362"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ecss.2014.08.012","article-title":"Study of wave runup using numerical models and low-altitude aerial photogrammetry: A tool for coastal management","volume":"149","author":"Casella","year":"2014","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Erena, M., Atenza, J.F., Garc\u00eda-Galiano, S., Dom\u00ednguez, J.A., and Bernab\u00e9, J.M. (2019). Use of drones for the topo-bathymetric monitoring of the reservoirs of the Segura river basin. Water, 11.","DOI":"10.3390\/w11030445"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huizinga, R.J., and Heimann, D.C. (2018). Hydrographic Surveys of Rivers and Lakes Using a Multibeam Echosounder Mapping System.","DOI":"10.3133\/fs20183021"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.geomorph.2014.01.006","article-title":"Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry","volume":"213","author":"Javernick","year":"2014","journal-title":"Geomorphology"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Langhammer, J., Lendzioch, T., Mi\u0159ijovsk\u00fd, J., and Hartvich, F. (2017). UAV-based optical granulometry as tool for detecting changes in structure of flood depositions. Remote Sens., 9.","DOI":"10.3390\/rs9030240"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.1007\/s00024-017-1707-7","article-title":"Application of low-cost fixed-wing UAV for inland lakes shoreline investigation","volume":"175","author":"Templin","year":"2018","journal-title":"Pure Appl. Geophys."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zanutta, A., Lambertini, A., and Vittuari, L. (2020). UAV photogrammetry and ground surveys as a mapping tool for quickly monitoring shoreline and beach changes. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8010052"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Thiel, C., Mueller, M.M., Epple, L., Thau, C., Hese, S., Voltersen, M., and Henkel, A. (2020). UAS Imagery-Based Mapping of Coarse Wood Debris in a Natural Deciduous Forest in Central Germany (Hainich National Park). Remote Sens., 12.","DOI":"10.3390\/rs12203293"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cucchiaro, S., Fallu, D.J., Zhang, H., Walsh, K., Van Oost, K., Brown, A.G., and Tarolli, P. (2020). Multiplatform-SfM and TLS data fusion for monitoring agricultural terraces in complex topographic and landcover conditions. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-3459"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gillan, J.K., Karl, J.W., Elaksher, A., and Duniway, M.C. (2017). Fine-resolution repeat topographic surveying of dryland landscapes using UAS-based structure-from-motion photogrammetry: Assessing accuracy and precision against traditional ground-based erosion measurements. Remote Sens., 9.","DOI":"10.3390\/rs9050437"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vandrol, J., Rivas Casado, M., Blackburn, K., Waine, T.W., Leinster, P., and Wright, R. (2018). In-Channel 3D Models of Riverine Environments for Hydromorphological Characterization. Remote Sens., 10.","DOI":"10.3390\/rs10071005"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luppichini, M., Favalli, M., Isola, I., Nannipieri, L., Giannecchini, R., and Bini, M. (2019). Influence of topographic resolution and accuracy on hydraulic channel flow simulations: Case study of the Versilia River (Italy). Remote Sens., 11.","DOI":"10.3390\/rs11131630"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Schumann, G.J.-P., Muhlhausen, J., and Andreadis, K.M. (2019). Rapid mapping of small-scale river-floodplain environments using UAV SfM supports classical theory. Remote Sens., 11.","DOI":"10.3390\/rs11080982"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mandlburger, G., Pfennigbauer, M., Schwarz, R., Fl\u00f6ry, S., and Nussbaumer, L. (2020). Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor. Remote Sens., 12.","DOI":"10.3390\/rs12060986"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zimmerman, T., Jansen, K., and Miller, J. (2020). Analysis of UAS Flight Altitude and Ground Control Point Parameters on DEM Accuracy along a Complex, Developed Coastline. Remote Sens., 12.","DOI":"10.3390\/rs12142305"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"034004","DOI":"10.1117\/1.JRS.10.034004","article-title":"Effects of image orientation and ground control points distribution on unmanned aerial vehicle photogrammetry projects on a road cut slope","volume":"10","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_34","first-page":"1","article-title":"Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points","volume":"72","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Skarlatos, D., Procopiou, E., Stavrou, G., and Gregoriou, M. (2013, January 8). Accuracy assessment of minimum control points for UAV photography and georeferencing. Proceedings of the First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013), Paphos, Cyprus.","DOI":"10.1117\/12.2028988"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tonkin, T.N., and Midgley, N.G. (2016). Ground-control networks for image based surface reconstruction: An investigation of optimum survey designs using UAV derived imagery and structure-from-motion photogrammetry. Remote Sens., 8.","DOI":"10.3390\/rs8090786"},{"key":"ref_37","first-page":"C22","article-title":"UAV photogrammetry for mapping and 3d modeling\u2013current status and future perspectives","volume":"38","author":"Remondino","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_38","first-page":"213","article-title":"Mapping with small UAS: A point cloud accuracy assessment","volume":"9","author":"Toth","year":"2015","journal-title":"J. Appl. Geod."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"05020004","DOI":"10.1061\/(ASCE)CP.1943-5487.0000936","article-title":"UAS Point Cloud Accuracy Assessment Using Structure from Motion\u2013Based Photogrammetry and PPK Georeferencing Technique for Building Surveying Applications","volume":"35","author":"Martinez","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kim, M., Park, S., Danielson, J., Irwin, J., Stensaas, G., Stoker, J., and Nimetz, J. (2019). General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud. Remote Sens., 11.","DOI":"10.3390\/rs11232737"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kim, M., Park, S., Irwin, J., McCormick, C., Danielson, J., Stensaas, G., Sampath, A., Bauer, M., and Burgess, M. (2020). Positional Accuracy Assessment of Lidar Point Cloud from NAIP\/3DEP Pilot Project. Remote Sens., 12.","DOI":"10.3390\/rs12121974"},{"key":"ref_42","unstructured":"Smith, D., Abdullah, Q., Maune, D., and Heidemann, K. (2020, December 19). New ASPRS Positional Accuracy Standards for Digital Geospatial Data. Available online: http:\/\/www.asprs.org\/a\/society\/divisions\/pad\/Accuracy\/Draft_ASPRS_Accuracy_Standards_for_Digital_Geospatial_Data_PE&RS.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Stott, E., Williams, R.D., and Hoey, T.B. (2020). Ground Control Point Distribution for Accurate Kilometre-Scale Topographic Mapping Using an RTK-GNSS Unmanned Aerial Vehicle and SfM Photogrammetry. Drones, 4.","DOI":"10.3390\/drones4030055"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"21","DOI":"10.5623\/cig2016-102","article-title":"Spatial accuracy of UAV-derived orthoimagery and topography: Comparing photogrammetric models processed with direct geo-referencing and ground control points","volume":"70","author":"Hugenholtz","year":"2016","journal-title":"Geomatica"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"807","DOI":"10.5194\/esurf-7-807-2019","article-title":"Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure-from-motion (SfM) photogrammetry and surface change detection","volume":"7","author":"Zhang","year":"2019","journal-title":"Earth Surf. Dyn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Toma\u0161t\u00edk, J., Mokro\u0161, M., Surov\u00fd, P., Grzn\u00e1rov\u00e1, A., and Mergani\u010d, J. (2019). UAV RTK\/PPK Method\u2014An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote Sens., 11.","DOI":"10.3390\/rs11060721"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"955","DOI":"10.5194\/tc-13-955-2019","article-title":"High-accuracy UAV photogrammetry of ice sheet dynamics with no ground control","volume":"13","author":"Chudley","year":"2019","journal-title":"Cryosphere"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.envsoft.2007.05.011","article-title":"Hydrographic survey methods for determining reservoir volume","volume":"23","author":"Furnans","year":"2008","journal-title":"Environ. Model. Softw."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:45:24Z","timestamp":1760179524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,24]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010035"],"URL":"https:\/\/doi.org\/10.3390\/rs13010035","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,12,24]]}}}