{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:51:26Z","timestamp":1776185486392,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T00:00:00Z","timestamp":1571356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010022","name":"University of Worcester","doi-asserted-by":"publisher","award":["No Grant Number"],"award-info":[{"award-number":["No Grant Number"]}],"id":[{"id":"10.13039\/100010022","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Much of the geomorphic work of rivers occurs underwater. As a result, high resolutionquantification of geomorphic change in these submerged areas is important. Currently, to quantify thischange, multiple methods are required to get high resolution data for both the exposed and submergedareas. Remote sensing methods are often limited to the exposed areas due to the challenges imposedby the water, and those remote sensing methods for below the water surface require the collection ofextensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimesprohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifyingabove- and below-water geomorphic change using Structure-from-Motion photogrammetry andinvestigate the implications of water surface elevations, refraction correction measures, and thespatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme,Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed usingStructure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification ofsubmerged geomorphic change to levels of accuracy commensurate with exposed areas is possiblewithout the need for calibration data or a dierent method from exposed areas; (2) there is minimaldierence in results produced by dierent refraction correction procedures using predominantlynadir imagery (small angle vs. multi-view), allowing users a choice of software packages\/processingcomplexity; (3) improvements to our estimations of water surface elevations are critical for accuratetopographic estimation in submerged areas and can reduce mean elevation error by up to 73%;and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian Na\u00efveBayes classifier, based on the relationship between error and 11 independent variables, to generate ahigh resolution, spatially continuous model of geomorphic change in submerged areas, constrained byspatially variable error estimates. Our multiple regression model is capable of explaining up to 54%of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-goingtesting and improvements, this machine learning approach has potential for routine application inspatially variable error estimation within the RPAS\u2013SfM workflow.<\/jats:p>","DOI":"10.3390\/rs11202415","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T11:24:15Z","timestamp":1571397855000},"page":"2415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8404-2758","authenticated-orcid":false,"given":"Amy S.","family":"Woodget","sequence":"first","affiliation":[{"name":"Department of Geography and Environment, School of Social Sciences and Humanities, Loughborough University, Loughborough LE11 3TU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2432-5243","authenticated-orcid":false,"given":"James T.","family":"Dietrich","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7352-8912","authenticated-orcid":false,"given":"Robin T.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Independent Scholar, Southampton, SO16 6DB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1002\/esp.1886","article-title":"Accounting for uncertainty in DEMs from repeat topographic surveys: Improved sediment budgets","volume":"35","author":"Wheaton","year":"2010","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1127\/zfg_suppl\/2017\/0330","article-title":"Analysis of geomorphic changes and quantification of sediment budgets of a small Arctic valley with the application of repeat TLS surveys","volume":"61","author":"Kociuba","year":"2017","journal-title":"Z. Fur Geomorphol. Suppl. Issues"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1002\/(SICI)1096-9837(199804)23:4<345::AID-ESP850>3.0.CO;2-B","article-title":"Grain size along two gravel-bed rivers: Statistical variation, spatial pattern and sedimentary links","volume":"23","author":"Rice","year":"1998","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1002\/esp.1780","article-title":"In situ characterization of grain-scale fluvial morphology using Terrestrial Laser Scanning","volume":"34","author":"Hodge","year":"2009","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1002\/esp.4139","article-title":"Subaerial gravel size measurement using topographic data derived from a UAV-SfM approach","volume":"42","author":"Woodget","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0169-555X(02)00374-4","article-title":"Quantifying channel development and sediment transfer following chute cutoff in a wandering gravel-bed river","volume":"54","author":"Fuller","year":"2003","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1002\/esp.1592","article-title":"Application of a 3D laser scanner in the assessment of erosion and deposition volumes and channel change in a proglacial river","volume":"32","author":"Milan","year":"2007","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.geomorph.2008.03.010","article-title":"A modified morphodynamic model for investigating the response of rivers to short-term climate change","volume":"101","author":"Verhaar","year":"2008","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/2014GL062482","article-title":"Hydrologic versus geomorphic drivers of trends in flood hazard","volume":"42","author":"Slater","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1080\/01431161.2017.1292074","article-title":"Lightweight UAV digital elevation models and orthoimagery for environmental applications: Data accuracy evaluation and potential for river flood risk modelling","volume":"38","author":"Coveney","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1177\/030913330002400203","article-title":"Geomorphology, ecology and river channel habitat: mesoscale approaches to basin-scale challenges","volume":"24","author":"Newson","year":"2000","journal-title":"Prog. Phys. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e1222","DOI":"10.1002\/wat2.1222","article-title":"Drones and digital photogrammetry: From classifications to continuums for monitoring river habitat and hydromorphology","volume":"4","author":"Woodget","year":"2017","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1002\/esp.3290190406","article-title":"Developments in monitoring and modelling small-scale river bed topography","volume":"19","author":"Lane","year":"1994","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1002\/(SICI)1096-9837(199901)24:1<51::AID-ESP948>3.0.CO;2-H","article-title":"Effective application of automated digital photogrammetry for geomorphological research","volume":"24","author":"Chandler","year":"1999","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1002\/esp.483","article-title":"Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing","volume":"28","author":"Lane","year":"2003","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1002\/esp.482","article-title":"Application of airborne LiDAR in river environments: the River Coquet, Northumberland, UK","volume":"28","author":"Charlton","year":"2003","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S0169-555X(02)00320-3","article-title":"Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport","volume":"53","author":"Brasington","year":"2003","journal-title":"Geomorphology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1641\/0006-3568(2002)052[0483:LTRBTG]2.0.CO;2","article-title":"Landscapes to Riverscapes: Bridging the Gap between Research and Conservation of Stream Fishes","volume":"52","author":"Fausch","year":"2002","journal-title":"BioScience"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.3390\/rs4061573","article-title":"Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery","volume":"4","author":"Harwin","year":"2012","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2013.04.009","article-title":"Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)","volume":"82","author":"Lague","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1177\/0309133313515293","article-title":"Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography","volume":"38","author":"Lucieer","year":"2014","journal-title":"Prog. Phys. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.geomorph.2015.05.011","article-title":"Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms","volume":"260","author":"Clapuyt","year":"2016","journal-title":"Geomorphology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1007\/s12665-018-7817-4","article-title":"Monitoring topographic changes through 4D-structure-from-motion photogrammetry: application to a debris-flow channel","volume":"77","author":"Cucchiaro","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1002\/esp.3648","article-title":"Investigating the geomorphological potential of freely available and accessible structure-from-motion photogrammetry using a smartphone","volume":"40","author":"Micheletti","year":"2015","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"W11519","DOI":"10.1029\/2012WR012223","article-title":"Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning","volume":"48","author":"Brasington","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1002\/esp.3613","article-title":"Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry","volume":"40","author":"Woodget","year":"2015","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1002\/esp.4060","article-title":"Bathymetric Structure-from-Motion: extracting shallow stream bathymetry from multi-view stereo photogrammetry","volume":"42","author":"Dietrich","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1029\/2018WR023586","article-title":"Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA","volume":"55","author":"Legleiter","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3390\/rs3030587","article-title":"Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner","volume":"3","author":"Vaaja","year":"2011","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.geomorph.2015.09.020","article-title":"Landscape-scale geomorphic change detection: Quantifying spatially variable uncertainty and circumventing legacy data issues","volume":"250","author":"Schaffrath","year":"2015","journal-title":"Geomorphology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8586","DOI":"10.3390\/rs70708586","article-title":"Multitemporal Monitoring of the Morphodynamics of a Mid-Mountain Stream Using UAS Photogrammetry","volume":"7","author":"Langhammer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.geomorph.2016.11.009","article-title":"An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection","volume":"278","author":"Cook","year":"2017","journal-title":"Geomorphology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1002\/rra.3183","article-title":"Quantifying streambank movement and topography using unmanned aircraft system photogrammetry with comparison to terrestrial laser scanning","volume":"33","author":"Hamshaw","year":"2017","journal-title":"River Res. Appl."},{"key":"ref_35","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":"Javemick","year":"2014","journal-title":"Geomorphology"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Starek, M.J., and Giessel, J. (2017, January 23\u201328). Fusion of uas-based structure-from-motion and optical inversion for seamless topo-bathymetric mapping. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127629"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6382","DOI":"10.3390\/rs5126382","article-title":"Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography","volume":"5","author":"Flener","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1002\/esp.3728","article-title":"UAS-based remote sensing of fluvial change following an extreme flood event","volume":"40","author":"Tamminga","year":"2015","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"e1328","DOI":"10.1002\/wat2.1328","article-title":"Fluvial and aquatic applications of Structure from Motion photogrammetry and unmanned aerial vehicle\/drone technology","volume":"6","author":"Carrivick","year":"2019","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_41","first-page":"77","article-title":"Reach scale application of UAV+SfM methods in shallow rivers hyperspatial bathymetry","volume":"Volume XL-1-W5","author":"Bagheri","year":"2015","journal-title":"Proceedings of the ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1080\/01431161.2017.1280636","article-title":"Comparing remote-sensing techniques collecting bathymetric data from a gravel-bed river","volume":"38","author":"Shintani","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","unstructured":"Dietrich, J.T. (2019). pyBathySfM v4.0, GitHub."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1191\/0309133306pp492ra","article-title":"Causes and consequences of error in digital elevation models","volume":"30","author":"Fisher","year":"2006","journal-title":"Prog. Phys. Geogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S1464-1909(00)00033-2","article-title":"Surface modelling of upland river channel topography and sedimentology using GIS","volume":"25","author":"Sear","year":"2000","journal-title":"Phys. Chem. Earthpart B Hydrol. Ocean. Atmos."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1002\/1096-9837(200008)25:9<973::AID-ESP111>3.0.CO;2-Y","article-title":"Monitoring and modelling morphological change in a braided gravel-bed river using high resolution GPS-based survey","volume":"25","author":"Brasington","year":"2000","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jaud, M., Grasso, F., Le Dantec, N., Verney, R., Delacourt, C., Ammann, J., Deloffre, J., and Grandjean, P. (2016). Potential of UAVs for Monitoring Mudflat Morphodynamics (Application to the Seine Estuary, France). ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5040050"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.geomorph.2010.09.012","article-title":"Filtering spatial error from DEMs: Implications for morphological change estimation","volume":"125","author":"Milan","year":"2011","journal-title":"Geomorphology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1002\/esp.4125","article-title":"3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: precision maps for ground control and directly georeferenced surveys","volume":"42","author":"James","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2903","DOI":"10.1080\/01431161.2016.1277045","article-title":"UAV and TLS for monitoring a creek in an alpine environment, Styria, Austria","volume":"38","author":"Seier","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.geomorph.2009.06.024","article-title":"Influence of survey strategy and interpolation model on DEM quality","volume":"112","author":"Heritage","year":"2009","journal-title":"Geomorphology"},{"key":"ref_52","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning Publications. [1st ed.]."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Rivas-Casado, M., Gonz\u00e1lez, R.B., Ortega, J.F., Leinster, P., and Wright, R. (2017). Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization. Sensors, 17.","DOI":"10.3390\/s17102210"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Buscombe, D., and Ritchie, A.C. (2018). Landscape Classification with Deep Neural Networks. Geosciences, 8.","DOI":"10.31223\/OSF.IO\/5MX3C"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.rse.2018.08.035","article-title":"Robust quantification of riverine land cover dynamics by high-resolution remote sensing","volume":"217","author":"Milani","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Boonpook, W., Tan, Y., Ye, Y., Torteeka, P., Torsri, K., and Dong, S. (2018). A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring. Sensors, 18.","DOI":"10.3390\/s18113921"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.1080\/01431161.2017.1420940","article-title":"Combining image processing and machine learning to identify invasive plants in high-resolution images","volume":"39","author":"Baron","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","unstructured":"Heritage, G.L., Hemsworth, M., and Hicks, L. (2013). Restoring the River Teme SSSI: A River Restoration Plan\u2014Technical Report Draft (v4.2), JBA for Natural England."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/esp.3609","article-title":"Mitigating systematic error in topographic models derived from UAV and ground-based image networks","volume":"39","author":"James","year":"2014","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1111\/j.1477-9730.2011.00623.x","article-title":"Minimising systematic error surfaces in digital elevation models using oblique convergent imagery","volume":"26","author":"Wackrow","year":"2011","journal-title":"Photogramm. Rec."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1111\/j.1477-9730.2005.00302.x","article-title":"Metric capabilities of low-cost digital cameras for close range surface measurement","volume":"20","author":"Chandler","year":"2005","journal-title":"Photogramm. Rec."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s11004-006-9056-6","article-title":"Forward and Inverse Transformations between Cartesian and Channel-fitted Coordinate Systems for Meandering Rivers","volume":"38","author":"Legleiter","year":"2006","journal-title":"Math. Geol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., and Moore, J.H. (2016). Automating Biomedical Data Science through Tree-Based Pipeline Optimization. Applications of Evolutionary Computation, Proceedings of EvoApplications 2016, Springer.","DOI":"10.1007\/978-3-319-31204-0_9"},{"key":"ref_65","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_67","first-page":"559","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","unstructured":"Mapbox (2018, December 12). Rasterio v1.0. Available online: https:\/\/github.com\/mapbox\/rasterio."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The NumPy Array: A Structure for Efficient Numerical Computation","volume":"13","author":"Colbert","year":"2011","journal-title":"Comput. Sci. Eng."},{"key":"ref_70","unstructured":"McKinney, W. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_72","unstructured":"Kluyver, T., Ragan-Kelley, B., P\u00e9rez, F., Granger, B.E., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J.B., Grout, J., and Corlay, S. (2016, January 9). Jupyter Notebooks-a publishing format for reproducible computational workflows. Proceedings of the 20th International Conference on Electronic Publishing, G\u00f6ttingen, Germany."},{"key":"ref_73","unstructured":"Wilson, R.T., and Woodget, A.S. (2019). Code for Woodget, Dietrich and Wilson, GitHub."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1002\/esp.4012","article-title":"Cost-effective non-metric photogrammetry from consumer-grade sUAS: Implications for direct georeferencing of structure from motion photogrammetry","volume":"42","author":"Carbonneau","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Buscombe, D. (2019). SediNet: A configurable deep learning model for mixed qualitative and quantitative optical granulometry. EarthArXiv.","DOI":"10.31223\/OSF.IO\/FWSNP"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:32Z","timestamp":1760189252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,18]]},"references-count":75,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11202415"],"URL":"https:\/\/doi.org\/10.3390\/rs11202415","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,18]]}}}