{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:55:14Z","timestamp":1775112914427,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.<\/jats:p>","DOI":"10.3390\/rs16122169","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T04:48:12Z","timestamp":1718599692000},"page":"2169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8902-5575","authenticated-orcid":false,"given":"Phillipe Alan","family":"Wernette","sequence":"first","affiliation":[{"name":"Great Lakes Research Center, Michigan Technological University, Houghton, MI 49931, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1029\/2017JF004401","article-title":"A Model Ensemble for Projecting Multidecadal Coastal Cliff Retreat During the 21st Century","volume":"123","author":"Limber","year":"2018","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1080\/02723646.2021.1923389","article-title":"Short Communication: Evidence for Geologic Control of Rip Channels along Prince Edward Island, Canada","volume":"43","author":"Wernette","year":"2022","journal-title":"Phys. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107721","DOI":"10.1016\/j.geomorph.2021.107721","article-title":"Short Communication: Storm Impact and Recovery of a Beach-Dune System in Prince Edward Island","volume":"384","author":"George","year":"2021","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1080\/01431161.2017.1303218","article-title":"Accounting for Positional Uncertainty in Historical Shoreline Change Analysis without Ground Reference Information","volume":"38","author":"Wernette","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Grottoli, E., Biausque, M., Rogers, D., Jackson, D.W.T., and Cooper, J.A.G. (2020). 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_6","doi-asserted-by":"crossref","first-page":"107119","DOI":"10.1016\/j.geomorph.2020.107119","article-title":"Investigating the Impact of Hurricane Harvey and Driving on Beach-Dune Morphology","volume":"358","author":"Wernette","year":"2020","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.geomorph.2017.09.026","article-title":"Short Communication: Multi-Scale Topographic Anisotropy Patterns on a Barrier Island","volume":"297","author":"Houser","year":"2017","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e2022JF006934","DOI":"10.1029\/2022JF006934","article-title":"Sound-Side Inundation and Seaward Erosion of a Barrier Island During Hurricane Landfall","volume":"128","author":"Sherwood","year":"2023","journal-title":"JGR Earth Surf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.geomorph.2018.12.013","article-title":"3D Mapping Efficacy of a Drone and Terrestrial Laser Scanner over a Temperate Beach-Dune Zone","volume":"328","author":"Jackson","year":"2019","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sturdivant, E., Lentz, E., Thieler, E.R., Farris, A., Weber, K., Remsen, D., Miner, S., and Henderson, R. (2017). UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9101020"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Di Paola, G., Minervino Amodio, A., Dilauro, G., Rodriguez, G., and Rosskopf, C.M. (2022). Shoreline Evolution and Erosion Vulnerability Assessment along the Central Adriatic Coast with the Contribution of UAV Beach Monitoring. Geosciences, 12.","DOI":"10.3390\/geosciences12100353"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1002\/esp.4551","article-title":"Uncertainty in Quantitative Analyses of Topographic Change: Error Propagation and the Role of Thresholding","volume":"44","author":"Anderson","year":"2019","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.geomorph.2017.10.010","article-title":"Decadal-Scale Coastal Cliff Retreat in Southern and Central California","volume":"300","author":"Young","year":"2018","journal-title":"Geomorphology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106994","DOI":"10.1016\/j.geomorph.2019.106994","article-title":"Short-Term Patterns and Processes of Coastal Cliff Erosion in Santa Barbara, California","volume":"353","author":"Alessio","year":"2020","journal-title":"Geomorphology"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hayakawa, Y.S., and Obanawa, H. (2020). Volumetric Change Detection in Bedrock Coastal Cliffs Using Terrestrial Laser Scanning and UAS-Based SfM. Sensors, 20.","DOI":"10.3390\/s20123403"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.geomorph.2014.01.005","article-title":"Coastal Cliff Monitoring and Analysis of Mass Wasting Processes with the Application of Terrestrial Laser Scanning: A Case Study of Rugen, Germany","volume":"213","author":"Kuhn","year":"2014","journal-title":"Geomorphology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107545","DOI":"10.1016\/j.geomorph.2020.107545","article-title":"Three Years of Weekly Observations of Coastal Cliff Erosion by Waves and Rainfall","volume":"375","author":"Young","year":"2021","journal-title":"Geomorphology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5194\/nhess-11-205-2011","article-title":"Short-Term Retreat Statistics of a Slowly Eroding Coastal Cliff","volume":"11","author":"Young","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104799","DOI":"10.1016\/j.csr.2022.104799","article-title":"Crowd-Sourced SfM: Best Practices for High Resolution Monitoring of Coastal Cliffs and Bluffs","volume":"245","author":"Wernette","year":"2022","journal-title":"Cont. Shelf Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1007\/s10346-019-01160-4","article-title":"Characterizing the Catastrophic 2017 Mud Creek Landslide, California, Using Repeat Structure-from-Motion (SfM) Photogrammetry","volume":"16","author":"Warrick","year":"2019","journal-title":"Landslides"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3547","DOI":"10.1007\/s10346-021-01723-4","article-title":"An Algorithm for Measuring Landslide Deformation in Terrestrial Lidar Point Clouds Using Trees","volume":"18","author":"Weidner","year":"2021","journal-title":"Landslides"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106457","DOI":"10.1016\/j.catena.2022.106457","article-title":"Rocky Coastal Cliffs Reinforced by Vegetation Roots and Potential Collapse Risk Caused by Sea-Level Rise","volume":"217","author":"Kogure","year":"2022","journal-title":"Catena"},{"key":"ref_23","unstructured":"(2020, May 01). Agisoft LLC Agisoft Metashape 1.8.5\u2014Professional Edition 2020. Available online: https:\/\/www.agisoft.com\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D Terrestrial Lidar Data Classification of Complex Natural Scenes Using a Multi-Scale Dimensionality Criterion: Applications in Geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105326","DOI":"10.1016\/j.enggeo.2019.105326","article-title":"Classification Methods for Point Clouds in Rock Slope Monitoring: A Novel Machine Learning Approach and Comparative Analysis","volume":"263","author":"Weidner","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_26","unstructured":"(2020, May 01). CloudCompare 2019. Available online: https:\/\/www.danielgm.net\/cc\/."},{"key":"ref_27","unstructured":"Buscombe, D. (2022). Doodler\u2014A Web Application Built with Plotly\/Dash for Image Segmentation with Minimal Supervision, U.S. Geological Survey Software Release."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., and Ritchie, A.C. (Earth Space Sci., 2022). Human-in-the-Loop Segmentation of Earth Surface Imagery, Earth Space Sci.","DOI":"10.31223\/X59K83"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Anders, N., Valente, J., Masselink, R., and Keesstra, S. (2019). Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds. Drones, 3.","DOI":"10.3390\/drones3030061"},{"key":"ref_30","first-page":"102580","article-title":"Urban Vegetation Segmentation Using Terrestrial LiDAR Point Clouds Based on Point Non-Local Means Network","volume":"105","author":"Chen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mesas-Carrascosa, F.-J., de Castro, A.I., Torres-S\u00e1nchez, J., Trivi\u00f1o-Tarradas, P., Jim\u00e9nez-Brenes, F.M., Garc\u00eda-Ferrer, A., and L\u00f3pez-Granados, F. (2020). Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications. Remote Sens., 12.","DOI":"10.3390\/rs12020317"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4673","DOI":"10.5194\/bg-13-4673-2016","article-title":"A Pilot Project Combining Multispectral Proximal Sensors and Digital Camerasfor Monitoring Tropical Pastures","volume":"13","author":"Handcock","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1006\/anbo.1997.0544","article-title":"An Algorithm for Estimating Chlorophyll Content in Leaves Using a Video Camera","volume":"81","author":"Kawashima","year":"1998","journal-title":"Ann. Bot."},{"key":"ref_34","first-page":"102592","article-title":"Improving Unmanned Aerial Vehicle (UAV) Remote Sensing of Rice Plant Potassium Accumulation by Fusing Spectral and Textural Information","volume":"104","author":"Lu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1117\/12.336896","article-title":"Machine Vision Detection Parameters for Plant Species Identification","volume":"Volume 3543","author":"Meyer","year":"1998","journal-title":"Proceedings of the SPIE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of Color Vegetation Indices for Automated Crop Imaging Applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","unstructured":"Neto, J.C. (2004). A Combined Statistical-Soft Computing Approach for Classification and Mapping Weed Species in Minimum\u2014Tillage Systems, University of Nebraska."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Zhu, H., Sun, D., Zhou, W., and He, Y. (2018). Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape. Remote Sens., 10.","DOI":"10.3390\/rs10091484"},{"key":"ref_39","unstructured":"Mao, W., Wang, Y., and Wang, Y. (2003, January 27\u201330). Real-Time Detection of Between-Row Weeds Using Machine Vision. Proceedings of the 2003 American Society of Agricultural and Biological Engineers, Las Vegas, NV, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_41","unstructured":"DeShazer, J.A., and Meyer, G.E. (1993). Plant Species Identification, Size, and Enumeration Using Machine Vision Techniques on near-Binary Images. Proceedings of the SPIE: The International Society for Optical Engineering, SPIE."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, M.-D., Tseng, H.-H., Hsu, Y.-C., and Tsai, H.P. (2020). Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-Date UAV Visible Images. Remote Sens., 12.","DOI":"10.3390\/rs12040633"},{"key":"ref_43","first-page":"149","article-title":"Greenness Identification Based on HSV Decision Tree","volume":"2","author":"Yang","year":"2015","journal-title":"Inf. Process. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"115723","DOI":"10.1016\/j.jenvman.2022.115723","article-title":"Integrating Low-Altitude Drone Based-Imagery and OBIA for Mapping and Manage Semi Natural Grassland Habitats","volume":"321","author":"Ventura","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11119-005-2324-5","article-title":"Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status","volume":"6","author":"Hunt","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_46","first-page":"79","article-title":"Combining UAV-Based Plant Height from Crop Surface Models, Visible, and near Infrared Vegetation Indices for Biomass Monitoring in Barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106040108542184","article-title":"Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat","volume":"16","author":"Louhaichi","year":"2001","journal-title":"Geocarto Int."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2014.02.009","article-title":"Multi-Temporal Mapping of the Vegetation Fraction in Early-Season Wheat Fields Using Images from UAV","volume":"103","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"157","DOI":"10.5194\/isprs-annals-IV-1-W1-157-2017","article-title":"Geometric Features and Their Relevance for 3D Point Cloud Classification","volume":"IV-1\/W1","author":"Weinmann","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic Point Cloud Interpretation Based on Optimal Neighborhoods, Relevant Features and Efficient Classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107365","DOI":"10.1016\/j.geomorph.2020.107365","article-title":"Machine-Learning Classification of Debris-Covered Glaciers Using a Combination of Sentinel-1\/-2 (SAR\/Optical), Landsat 8 (Thermal) and Digital Elevation Data","volume":"369","author":"Alifu","year":"2020","journal-title":"Geomorphology"},{"key":"ref_54","first-page":"289","article-title":"Coast Type Based Accuracy Assessment for Coastline Extraction from Satellite Image with Machine Learning Classifiers","volume":"25","year":"2022","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yang, Z., Xu, C., and Li, L. (2022). Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments. Remote Sens., 14.","DOI":"10.3390\/rs14122885"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"13","DOI":"10.5120\/ijca2018917757","article-title":"Vegetation Mapping of a Tomato Crop Using Multilayer Perceptron (MLP) Neural Network in Images Acquired by Remote Sensing from a UAV","volume":"182","author":"Kestur","year":"2018","journal-title":"IJCA"},{"key":"ref_57","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2020, May 01). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems 2015. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_58","unstructured":"Chollet, F. (2020, May 01). Others Keras 2015. Available online: https:\/\/keras.io\/."},{"key":"ref_59","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_60","unstructured":"Ruder, S. (2016). An Overview of Gradient Descent Optimization Algorithms. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Geisz, J.K., Wernette, P.A., and Esselman, P.C. (2024). Classification of Lakebed Geologic Substrate in Autonomously Collected Benthic Imagery Using Machine Learning. Remote Sens., 16.","DOI":"10.3390\/rs16071264"},{"key":"ref_62","unstructured":"Geisz, J.K., Wernette, P.A., Esselman, P.C., and Morris, J.M. (2024). Autonomously Collected Benthic Imagery for Substrate Prediction, Lake Michigan 2020\u20132021. U.S. Geol. Surv. Data Release."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.eswa.2018.10.010","article-title":"Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation","volume":"118","author":"Tabik","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Raphael, A., Dubinsky, Z., Iluz, D., and Netanyahu, N.S. (2020). Neural Network Recognition of Marine Benthos and Corals. Diversity, 12.","DOI":"10.3390\/d12010029"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.3390\/rs5041809","article-title":"Image-Based Coral Reef Classification and Thematic Mapping","volume":"5","author":"Shihavuddin","year":"2013","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"157","DOI":"10.4319\/lom.2009.7.157","article-title":"Automated Processing of Coral Reef Benthic Images: Coral Reef Benthic Imaging","volume":"7","author":"Stokes","year":"2009","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chen, R., Wu, J., Luo, Y., and Xu, G. (2024). PointMM: Point Cloud Semantic Segmentation CNN under Multi-Spatial Feature Encoding and Multi-Head Attention Pooling. Remote Sens., 16.","DOI":"10.3390\/rs16071246"},{"key":"ref_68","unstructured":"Wernette, P. (2020). Coastal Bluff Point Clouds Derived from SfM near Elwha River Mouth, Washington from 18 April 2016 to 8 May 2020. Dryad."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"181","DOI":"10.5194\/isprsannals-II-3-181-2014","article-title":"Semantic 3D Scene Interpretation: A Framework Combining Optimal Neighborhood Size Selection with Relevant Features","volume":"II\u20133","author":"Weinmann","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel Algorithms for Remote Estimation of Vegetation Fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ashapure, A., Jung, J., Chang, A., Oh, S., Maeda, M., and Landivar, J. (2019). A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data. Remote Sens., 11.","DOI":"10.3390\/rs11232757"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2169\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:59:19Z","timestamp":1760108359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,15]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16122169"],"URL":"https:\/\/doi.org\/10.3390\/rs16122169","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,15]]}}}