{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:17:21Z","timestamp":1776118641604,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T00:00:00Z","timestamp":1615075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC1511304"],"award-info":[{"award-number":["2019YFC1511304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods.<\/jats:p>","DOI":"10.3390\/rs13051007","type":"journal-article","created":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T21:52:15Z","timestamp":1615153935000},"page":"1007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Shipborne Mobile Photogrammetry for 3D Mapping and Landslide Detection of the Water-Level Fluctuation Zone in the Three Gorges Reservoir Area, China"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-209X","authenticated-orcid":false,"given":"Dingjian","family":"Jin","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China Aero Geophysical Survey &amp; Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Jianhua","family":"Gong","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey &amp; Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Yongzhi","family":"Li","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey &amp; Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey &amp; Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Kun","family":"Yu","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey &amp; Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey &amp; Remote Sensing Center for Natural Resources, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1177\/0309133310370286","article-title":"Three Gorges Project: Efforts and challenges for the environment","volume":"34","author":"Fu","year":"2010","journal-title":"Prog. Phys. Geogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.jrmge.2016.08.001","article-title":"Reservoir-induced landslides and risk control in Three Gorges Project on Yangtze River, China","volume":"8","author":"Yin","year":"2016","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, F., and Li, T. (2009). Landslide Disaster Mitigation in Three Gorges Reservoir, China, Springer.","DOI":"10.1007\/978-3-642-00132-1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105267","DOI":"10.1016\/j.enggeo.2019.105267","article-title":"Geohazards in the three Gorges Reservoir Area, China\u2014Lessons learned from decades of research","volume":"261","author":"Tang","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/J.ENG.2016.04.002","article-title":"Reflections on the Three Gorges Project since Its Operation","volume":"2","author":"Zheng","year":"2016","journal-title":"Engineering"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.earscirev.2015.07.005","article-title":"The water-level fluctuation zone of Three Gorges Reservoir\u2014A unique geomorphological unit","volume":"150","author":"Bao","year":"2015","journal-title":"Earth-Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1007\/s10346-015-0652-8","article-title":"The 2 September 2014 Shanshucao landslide, Three Gorges Reservoir, China","volume":"12","author":"Xu","year":"2015","journal-title":"Landslides"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s10346-016-0702-x","article-title":"Risk management study on impulse waves generated by Hongyanzi landslide in Three Gorges Reservoir of China on June 24, 2015","volume":"13","author":"Huang","year":"2016","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1007\/s10346-020-01394-7","article-title":"Research on recently occurred reservoir-induced Kamenziwan rockslide in Three Gorges Reservoir, China","volume":"17","author":"Yin","year":"2020","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1080\/01431161.2019.1672904","article-title":"Landslide mapping with remote sensing: Challenges and opportunities","volume":"41","author":"Zhong","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2016.10.008","article-title":"Landslide mapping from aerial photographs using change detection-based Markov random field","volume":"187","author":"Li","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1080\/01431161.2019.1701725","article-title":"Transferability of object-based image analysis approaches for landslide detection in the Himalaya Mountains of northern Pakistan","volume":"41","author":"Bacha","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qi, W., Wei, M., Yang, W., Xu, C., and Ma, C. (2020). Automatic Mapping of Landslides by the ResU-Net. Remote Sens., 12.","DOI":"10.3390\/rs12152487"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111235","DOI":"10.1016\/j.rse.2019.111235","article-title":"Landslide mapping from multi-sensor data through improved change detection-based Markov random field","volume":"231","author":"Lu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.5194\/nhess-18-1079-2018","article-title":"Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management","volume":"18","author":"Giordan","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s10712-020-09611-7","article-title":"Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards","volume":"41","author":"Antoine","year":"2020","journal-title":"Surv. Geophys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.enggeo.2011.03.012","article-title":"UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results","volume":"128","author":"Niethammer","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105264","DOI":"10.1016\/j.enggeo.2019.105264","article-title":"Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data","volume":"260","author":"Comert","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karantanellis, E., Marinos, V., Vassilakis, E., and Christaras, B. (2020). Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12111711"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Godone, D., Allasia, P., Borrelli, L., and Gull\u00e0, G. (2020). UAV and Structure from Motion Approach to Monitor the Maierato Landslide Evolution. Remote Sens., 12.","DOI":"10.3390\/rs12061039"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Desrues, M., Malet, J.P., Brenguier, O., Point, J., Stumpf, A., and Lorier, L. (2019). TSM\u2014Tracing Surface Motion: A Generic Toolbox for Analyzing Ground-Based Image Time Series of Slope Deformation. Remote Sens., 11.","DOI":"10.3390\/rs11192189"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"875","DOI":"10.5194\/isprs-archives-XLI-B5-875-2016","article-title":"Close range digital photogrammetry applied to topography and landslide measurements","volume":"41","author":"Liu","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2019.03.013","article-title":"Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017","volume":"225","author":"Chen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Miura, T., and Nagai, S. (2020). Landslide Detection with Himawari-8 Geostationary Satellite Data: A Case Study of a Torrential Rain Event in Kyushu, Japan. Remote Sens., 12.","DOI":"10.3390\/rs12111734"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mazzanti, P., Caporossi, P., and Muzi, R. (2020). Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA). Remote Sens., 12.","DOI":"10.3390\/rs12040592"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tavakkoli Piralilou, S., Shahabi, H., Jarihani, B., Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., and Aryal, J. (2019). Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas. Remote Sens., 11.","DOI":"10.3390\/rs11212575"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Frodella, W., Gigli, G., Morelli, S., Lombardi, L., and Casagli, N. (2017). Landslide Mapping and Characterization through Infrared Thermography (IRT): Suggestions for a Methodological Approach from Some Case Studies. Remote Sens., 9.","DOI":"10.3390\/rs9121281"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s10346-012-0367-z","article-title":"Application of infrared thermography for mapping open fractures in deep-seated rockslides and unstable cliffs","volume":"11","year":"2014","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1109\/JSTARS.2019.2951725","article-title":"Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning with Constrains","volume":"12","author":"Ye","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111816","DOI":"10.1016\/j.rse.2020.111816","article-title":"Persistent homology on LiDAR data to detect landslides","volume":"246","author":"Syzdykbayev","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bunn, M., Leshchinsky, B., Olsen, M., and Booth, A. (2019). A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives. Remote Sens., 11.","DOI":"10.3390\/rs11030303"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, W., Yamazaki, F., and Maruyama, Y. (2019). Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data. Remote Sens., 11.","DOI":"10.3390\/rs11192292"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.3390\/rs70809705","article-title":"Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rse.2014.07.004","article-title":"Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China","volume":"152","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.3390\/rs122785","article-title":"Application of a Terrestrial Laser Scanner (TLS) to the Study of the S\u00e9chilienne Landslide (Is\u00e8re, France)","volume":"2","author":"Kasperski","year":"2010","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5194\/nhess-9-267-2009","article-title":"Quantifying discontinuity orientation and persistence on high mountain rock slopes and large landslides using terrestrial remote sensing techniques","volume":"9","author":"Sturzenegger","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s10346-014-0542-5","article-title":"Landslide detection and monitoring capability of boat-based mobile laser scanning along Dieppe coastal cliffs, Normandy","volume":"12","author":"Michoud","year":"2015","journal-title":"Landslides"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lowry, B.W., Baker, S., and Zhou, W. (2020). A Case Study of Novel Landslide Activity Recognition Using ALOS-1 InSAR within the Ragged Mountain Western Hillslope in Gunnison County, Colorado, USA. Remote Sens., 12.","DOI":"10.3390\/rs12121969"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., and Cakir, Z. (2020). Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps. Remote Sens., 12.","DOI":"10.3390\/rs12081305"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Meng, Q., Confuorto, P., Peng, Y., Raspini, F., Bianchini, S., Han, S., Liu, H., and Casagli, N. (2020). Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data. Remote Sens., 12.","DOI":"10.3390\/rs12101541"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhao, C., Zhang, Q., Peng, J., Zhu, W., and Lu, Z. (2018). Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets\u2014A Case Study of Heifangtai Loess Landslides, China. Remote Sens., 10.","DOI":"10.3390\/rs10111756"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s11069-018-3492-8","article-title":"Landslide detection based on height and amplitude differences using pre- and post-event airborne X-band SAR data","volume":"95","author":"Uemoto","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bardi, F., Raspini, F., Frodella, W., Lombardi, L., Nocentini, M., Gigli, G., Morelli, S., Corsini, A., and Casagli, N. (2017). Monitoring the Rapid-Moving Reactivation of Earth Flows by Means of GB-InSAR: The April 2013 Capriglio Landslide (Northern Appennines, Italy). Remote Sens., 9.","DOI":"10.3390\/rs9020165"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"405","DOI":"10.5194\/nhess-18-405-2018","article-title":"Criteria for the optimal selection of remote sensing optical images to map event landslides","volume":"18","author":"Fiorucci","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s11769-018-1012-0","article-title":"Distribution and Susceptibility Assessment of Collapses and Landslides in the Riparian Zone of the Xiaowan Reservoir","volume":"29","author":"Zhong","year":"2019","journal-title":"Chin. Geogr. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.enggeo.2019.01.013","article-title":"Detection and analysis of mass wasting events in chalk sea cliffs using UAV photogrammetry","volume":"250","author":"Gilham","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/B978-0-444-64177-9.00001-1","article-title":"Structure from motion photogrammetric technique","volume":"23","author":"Eltner","year":"2020","journal-title":"Dev. Earth Surf. Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Carrivick, J.L., Smith, M.W., and Quincey, D.J. (2016). Structure from Motion in the Geosciences, Wiley-Blackwell.","DOI":"10.1002\/9781118895818"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1177\/0309133315615805","article-title":"Structure from motion photogrammetry in physical geography","volume":"40","author":"Smith","year":"2016","journal-title":"Prog. Phys. Geogr."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.geomorph.2015.05.008","article-title":"Riverscape mapping with helicopter-based Structure-from-Motion photogrammetry","volume":"252","author":"Dietrich","year":"2016","journal-title":"Geomorphology"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1002\/esp.4086","article-title":"Application of Structure-from-Motion photogrammetry to river restoration","volume":"42","author":"Marteau","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.2112\/JCOASTRES-D-17-00160.1","article-title":"A Quantitative Comparison of Low-Cost Structure from Motion (SfM) Data Collection Platforms on Beaches and Dunes","volume":"34","author":"Conlin","year":"2018","journal-title":"J. Coast. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jsg.2018.05.014","article-title":"An orientation based correction method for SfM-MVS point clouds\u2014Implications for field geology","volume":"113","author":"Fleming","year":"2018","journal-title":"J. Struct. Geol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9321","DOI":"10.1080\/01431161.2019.1630782","article-title":"An evaluation of a low-cost pole aerial photography (PAP) and structure from motion (SfM) approach for topographic surveying of small rivers","volume":"40","author":"Visser","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Duffy, J., Shutler, J., Witt, M., DeBell, L., and Anderson, K. (2018). Tracking Fine-Scale Structural Changes in Coastal Dune Morphology Using Kite Aerial Photography and Uncertainty-Assessed Structure-from-Motion Photogrammetry. Remote Sens., 10.","DOI":"10.3390\/rs10091494"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bunker, J., Nagisetty, R.M., and Crowley, J. (2021). sUAS Remote Sensing to Evaluate Geothermal Seep Interactions with the Yellowstone River, Montana, USA. Remote Sens., 13.","DOI":"10.3390\/rs13020163"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Grottoli, E., Biausque, M., Rogers, D., Jackson, D.W.T., and Cooper, J.A.G. (2021). Structure-from-Motion-Derived Digital Surface Models from Historical Aerial Photographs: A New 3D Application for Coastal Dune Monitoring. Remote Sens., 13.","DOI":"10.3390\/rs13010095"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.5194\/nhess-17-1961-2017","article-title":"A method for using unmanned aerial vehicles for emergency investigation of single geo-hazards and sample applications of this method","volume":"17","author":"Huang","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"105279","DOI":"10.1016\/j.enggeo.2019.105279","article-title":"Susceptibility of reservoir-induced landslides and strategies for increasing the slope stability in the Three Gorges Reservoir Area: Zigui Basin as an example","volume":"261","author":"Li","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10055-016-0297-5","article-title":"3D model reconstruction with common hand-held cameras","volume":"20","author":"Zheng","year":"2016","journal-title":"Virtual Real."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","article-title":"Accurate, Dense, and Robust Multiview Stereopsis","volume":"32","author":"Furukawa","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.cageo.2016.04.006","article-title":"Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient","volume":"92","author":"Zheng","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Benassi, F., Dall Asta, E., Diotri, F., Forlani, G., Morra Di Cella, U., Roncella, R., and Santise, M. (2017). Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation. Remote Sens., 9.","DOI":"10.3390\/rs9020172"},{"key":"ref_69","first-page":"1238","article-title":"An Overview on \u201cCloud Control\u201d Photogrammetry in Big Data Era","volume":"46","author":"Zhang","year":"2017","journal-title":"Acta Geod. Et Cartogr. Sin."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Huang, R., Zheng, S., and Hu, K. (2018). Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations. Sensors, 18.","DOI":"10.3390\/s18061770"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"731","DOI":"10.14358\/PERS.79.8.731","article-title":"Registration of Optical Images with Lidar Data and Its Accuracy Assessment","volume":"79","author":"Zheng","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_72","unstructured":"Song, M. (2018). LiDAR Point Cloud Assisted Aerotriangulation of Urban Airborne Image. [Ph.D. Thesis, Wuhan University]."},{"key":"ref_73","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_74","first-page":"130","article-title":"Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms","volume":"75","author":"Planas","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Forlani, G., Dall Asta, E., Diotri, F., Cella, U.M.D., Roncella, R., and Santise, M. (2018). Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning. Remote Sens., 10.","DOI":"10.3390\/rs10020311"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"F03017","DOI":"10.1029\/2011JF002289","article-title":"Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application","volume":"117","author":"James","year":"2012","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111666","DOI":"10.1016\/j.rse.2020.111666","article-title":"Mapping erosion and deposition in an agricultural landscape: Optimization of UAV image acquisition schemes for SfM-MVS","volume":"239","author":"Meinen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_78","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_79","first-page":"e866","article-title":"Vegetation of the water-level fluctuation zone in the Three Gorges Reservoir at the initial impoundment stage","volume":"21","author":"Zhu","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"212","DOI":"10.2166\/nh.2013.291","article-title":"Soil erosion in the riparian zone of the Three Gorges Reservoir, China","volume":"46","author":"Bao","year":"2015","journal-title":"Hydrol. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1002\/esp.4787","article-title":"Remotely sensed rivers in the Anthropocene: State of the art and prospects","volume":"45","author":"Arnaud","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1002\/esp.4378","article-title":"Recent remote sensing applications for hydro and morphodynamic monitoring and modelling","volume":"43","author":"Entwistle","year":"2018","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"106883","DOI":"10.1016\/j.geomorph.2019.106883","article-title":"Terrestrial structure-from-motion: Spatial error analysis of roughness and morphology","volume":"350","author":"Schwendel","year":"2020","journal-title":"Geomorphology"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1002\/esp.4747","article-title":"Three-dimensional reconstruction of fluvial surface sedimentology and topography using personal mobile laser scanning","volume":"45","author":"Williams","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.measurement.2017.10.023","article-title":"Template for high-resolution river landscape mapping using UAV technology","volume":"115","year":"2018","journal-title":"Measurement"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"8143","DOI":"10.1080\/01431161.2020.1752950","article-title":"Surveying coastal cliffs using two UAV platforms (multirotor and fixed-wing) and three different approaches for the estimation of volumetric changes","volume":"41","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.measurement.2019.02.024","article-title":"UAV survey of a coastal cliff face\u2014Selection of the best imaging angle","volume":"139","author":"Jaud","year":"2019","journal-title":"Measurement"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/15481603.2017.1408931","article-title":"Examining high-resolution survey methods for monitoring cliff erosion at an operational scale","volume":"55","author":"Letortu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/rse2.58","article-title":"Location, location, location: Considerations when using lightweight drones in challenging environments","volume":"4","author":"Duffy","year":"2018","journal-title":"Remote Sens. Ecol. 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