{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:43:37Z","timestamp":1762508617565,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"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>River systems face negative impacts from development and removal of riparian vegetation that provide critical shading in the face of climate change. This study used supervised deep learning to accurately classify the land cover, including shading, of the Chauga River watershed, located in Oconee County, South Carolina, for 2011 and 2019. The study examined the land cover differences along the Chauga River and its tributaries, inside and outside the Sumter National Forest. LiDAR data were incorporated in solar radiation calculations for the Chauga River inside and outside the National Forest. The deep learning classifications produced land cover maps with high overall accuracy (97.09% for 2011; 97.58% for 2019). The most significant difference in land cover was in tree cover in the 50 m buffer of the tributaries inside the National Forest compared to the tributaries outside the National Forest (2011: 95.39% vs. 81.84%, 2019: 92.86% vs. 82.06%). The solar radiation calculations also confirmed a difference between the area inside and outside the National Forest, with the mean temperature being greater outside the protected area (outside: 455.845 WH\/m2; inside: 416,770 WH\/m2). This study suggests that anthropogenic influence in the Chauga River watershed is greater in the areas outside the Sumter National Forest, which could cause damage to the river ecosystem if left unchecked in the future as development pressures increase. This study demonstrates the accurate application of deep learning for high-resolution classification of river shading combined with the use of LiDAR data to estimate solar radiation reaching the Chauga River. Techniques to monitor riparian zones and shading at high spatial resolutions are critical for the mitigation of the negative impacts of warming climates on aquatic ecosystems.<\/jats:p>","DOI":"10.3390\/rs13204172","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"4172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Evaluation of Riparian Tree Cover and Shading in the Chauga River Watershed Using LiDAR and Deep Learning Land Cover Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-369X","authenticated-orcid":false,"given":"Madeleine M.","family":"Bolick","sequence":"first","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]},{"given":"Christopher J.","family":"Post","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1711-7910","authenticated-orcid":false,"given":"Elena A.","family":"Mikhailova","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9250-4534","authenticated-orcid":false,"given":"Hamdi A.","family":"Zurqani","sequence":"additional","affiliation":[{"name":"University of Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, University of Arkansas at Monticello, Monticello, AR 71655, USA"}]},{"given":"Andrew P.","family":"Grunwald","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0654-7456","authenticated-orcid":false,"given":"Elizabeth A.","family":"Saldo","sequence":"additional","affiliation":[{"name":"Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jenvman.2018.09.037","article-title":"A novel application of remote sensing for modelling impacts of tree shading on water quality","volume":"230","author":"Hutchins","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3754","DOI":"10.1002\/2014WR016802","article-title":"Seeing the landscape for the trees: Metrics to guide riparian shade management in river catchments","volume":"51","author":"Johnson","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Martin-Ortega, J., Ferrier, R.C., Gordon, I.J., and Khan, S. (2015). Water Ecosystem Services: A Global Perspective, UNESCO Publishing.","DOI":"10.1017\/CBO9781316178904"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.gloenvcha.2012.11.002","article-title":"Global river discharge and water temperature under climate change","volume":"23","author":"Franssen","year":"2013","journal-title":"Glob. Environ. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00267-009-9329-1","article-title":"Climate change and river ecosystems: Protection and adaptation options","volume":"44","author":"Palmer","year":"2009","journal-title":"Environ. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ecoleng.2008.09.014","article-title":"Model-based assessment of shading effect by riparian vegetation on river water quality","volume":"35","author":"Ghermandi","year":"2009","journal-title":"Ecol. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1086\/687837","article-title":"Adapting boreal streams to climate change: Effects of riparian vegetation on water temperature and biological assemblages","volume":"35","author":"Johnson","year":"2016","journal-title":"Freshw. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1002\/rra.3598","article-title":"An evaluation of different forest cover geospatial data for riparian shading and river temperature modelling","volume":"36","author":"Dugdale","year":"2020","journal-title":"River Res. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kwan, C., Ayhan, B., Budavari, B., Lu, Y., Perez, D., Li, J., Bernabe, S., and Plaza, A. (2020). Deep learning for land cover classification using only a few bands. Remote Sens., 12.","DOI":"10.3390\/rs12122000"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s12524-020-01231-3","article-title":"Deep learning based supervised image classification using UAV images for forest areas classification","volume":"49","author":"Haq","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using Landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.scitotenv.2017.12.129","article-title":"Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data","volume":"624","author":"Loicq","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1111\/1752-1688.12655","article-title":"Historical and future stream temperature change predicted by a lidar-based assessment of riparian condition and channel width","volume":"54","author":"Seixas","year":"2018","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Olpenda, A.S., Stere\u0144czak, K., and B\u0119dkowski, K. (2018). Modeling solar radiation in the forest using remote sensing data: A review of approaches and opportunities. Remote Sens., 10.","DOI":"10.3390\/rs10050694"},{"key":"ref_20","unstructured":"U.S. Census Bureau (2021, August 09). County Population Totals: 2010\u20132019, Available online: https:\/\/www.census.gov\/data\/datasets\/time-series\/demo\/popest\/2010s-counties-total.html."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/21553769.2014.933716","article-title":"Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas","volume":"8","author":"Khatri","year":"2015","journal-title":"Front. Life Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41561-018-0262-x","article-title":"Anthropogenic stresses on the world\u2019s big rivers","volume":"12","author":"Best","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_23","unstructured":"Robert, J. (2004). Revised Land and Resource Management Plan, Sumter National Forest."},{"key":"ref_24","unstructured":"(2021, August 09). What Are Wild and Scenic Rivers? (U.S. National Park Service), Available online: https:\/\/www.nps.gov\/orgs\/1912\/what-are-wild-and-scenic-rivers.htm."},{"key":"ref_25","first-page":"137","article-title":"River bed shade and its importance in the process of studying of the fundamental physico-chemical characteristics of small river waters","volume":"Volume 7","author":"Bartnik","year":"2011","journal-title":"Contemporary Problems of Management and Environmental Protection. Issues of landscape Conservation and Water Management in Rural Areas"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.2134\/jeq2005.0433","article-title":"Relationships among nutrients, chlorophyll-a, and dissolved oxygen in agricultural streams in Illinois","volume":"35","author":"Morgan","year":"2006","journal-title":"J. Environ. Qual."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"930","DOI":"10.2112\/JCOASTRES-D-11-00176.1","article-title":"Using remote sensing of land cover change in coastal watersheds to predict downstream water quality","volume":"28","author":"Huang","year":"2012","journal-title":"J. Coast. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1175\/2009BAMS2769.1","article-title":"Impacts of land use\/land cover change on climate and future research priorities","volume":"91","author":"Mahmood","year":"2010","journal-title":"Bull. Am. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106891","DOI":"10.1016\/j.agee.2020.106891","article-title":"Managing riparian buffer strips to optimise ecosystem services: A review","volume":"296","author":"Cole","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1893\/0005-3155-91.3.167","article-title":"An unprotected tributary has no detectable impact on macroinvertebrates in a wild and scenic river in the Southeast (Chattooga)","volume":"91","author":"Edwards","year":"2021","journal-title":"Bios"},{"key":"ref_31","unstructured":"Dolloff, C.A. (2020). Monitoring for Changes in Chattooga River Mussel Populations. 2012\u20132019; Francis Marion-Sumter National Forest, South Carolina, USDA."},{"key":"ref_32","unstructured":"Poling, B.T., and Dolloff, A.C. (2016). Soil Erosion from Eastern Hemlock (Tsuga Canadensis) Windthrow Mounds Following Hemlock Wooly Adelgid (Adelges Tsugae) Infestations in Riparian Areas If the Chattooga Wild and Scenic River and Tributaries, USDA."},{"key":"ref_33","unstructured":"Creek, J. Chauga River 03060102-03. South Carolina Department of Health and Environmental Control, Savana River Basin."},{"key":"ref_34","first-page":"77","article-title":"Vascular flora of the Chauga River Gorge Oconee County, South Carolina","volume":"57","author":"Tobe","year":"1992","journal-title":"Castanea"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.jog.2004.02.005","article-title":"Tectonic assembly of the Brevard-Chauga Belt, South Carolina: Fluid inclusion evidence from Appalachian deep core site investigation hole 2 (ADCOH-2)","volume":"37","author":"Becker","year":"2004","journal-title":"J. Geodyn."},{"key":"ref_36","unstructured":"Acker, L.L., and Hatcher, R.D. (1970). Relationships between Structure and Topography in Northwest South Carolina, Geologic Notes, Division of Geology, State Development Board."},{"key":"ref_37","unstructured":"(2021, September 21). NAIP Imagery, Available online: https:\/\/fsa.usda.gov\/programs-and-services\/aerial-photography\/imagery-programs\/naip-imagery\/index."},{"key":"ref_38","unstructured":"ESRI (2021, September 17). ArcGIS Pro: Release 7. Redlands, CA. Available online: https:\/\/www.esri.com\/en-us\/arcgis\/products\/arcgis-pro\/overview."},{"key":"ref_39","unstructured":"(2021, September 21). Classify Pixels Using Deep Learning (Image Analyst)\u2014Arcgis Pro Documentation. Available online: https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/image-analyst\/classify-pixels-using-deep-learning.htm."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0304-3800(92)90003-W","article-title":"Comparing global vegetation maps with the Kappa statistic","volume":"62","author":"Monserud","year":"1992","journal-title":"Ecol. Model."},{"key":"ref_43","unstructured":"Horning, N. (2004). Overview of Accuracy Assessment of Land Cover Products, American Museum of Natural History."},{"key":"ref_44","first-page":"44","article-title":"Lidar remote sensing for forestry","volume":"98","author":"Dubayah","year":"2000","journal-title":"J. For."},{"key":"ref_45","unstructured":"Rich, P., Dubayah, R., Hetrick, W., and Saving, S. Using viewshed models to calculate intercepted solar radiation: Applications in ecology. American Society for Photogrammetry and Remote Sensing Technical Papers. Proceedings of the American Society of Photogrammetry and Remote Sensing, Available online: http:\/\/www.professorpaul.com\/publications\/rich_et_al_1994_asprs.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0168-1699(02)00115-1","article-title":"A geometric solar radiation model with applications in agriculture and forestry","volume":"37","author":"Fu","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Anderson, J.R. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data, US Government Printing Office.","DOI":"10.3133\/pp964"},{"key":"ref_48","unstructured":"May, C.W., and Horner, R.R. (2000, January 28\u201331). The cumulative impacts of watershed urbanization on stream-riparian ecosystems. Proceedings of the American Water Resources Association International Conference on Riparian Ecology and Management in Multi-Land Use Watersheds, Portland, OR, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Johnson, L.R., Trammell, T.L.E., Bishop, T.J., Barth, J., Drzyzga, S., and Jantz, C. (2020). Squeezed from all sides: Urbanization, invasive species, and climate change threaten riparian forest buffers. Sustainability, 12.","DOI":"10.3390\/su12041448"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"467","DOI":"10.2134\/jeq1993.00472425002200030010x","article-title":"Nutrient interception by a riparian forest receiving inputs from adjacent cropland","volume":"22","author":"Jordan","year":"1993","journal-title":"J. Environ. Qual."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"581","DOI":"10.2307\/2404238","article-title":"Spatial and temporal patterns of denitrification in a riparian forest","volume":"30","author":"Pinay","year":"1993","journal-title":"J. Appl. Ecol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.2134\/jeq2000.00472425002900040025x","article-title":"Multispecies riparian buffers trap sediment and nutrients during rainfall simulations","volume":"29","author":"Lee","year":"2000","journal-title":"J. Environ. Qual."},{"key":"ref_53","first-page":"231","article-title":"The effect of site quality on the costs of reducing soil erosion with riparian buffers","volume":"55","author":"Nakao","year":"2000","journal-title":"J. Soil Water Conserv."},{"key":"ref_54","unstructured":"Wynn, T.M., Mostaghimi, S., and Alphin, E.F. (2004, January 1\u20134). The effects of vegetation on stream bank erosion. Proceedings of the 2004 ASAE Annual Meeting, Ottawa, ON, Canada."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.2134\/jeq2013.10.0413","article-title":"Spatial characterization of riparian buffer effects on sediment loads from watershed systems","volume":"43","author":"Momm","year":"2014","journal-title":"J. Environ. Qual."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1002\/hyp.11073","article-title":"Assessing impacts of riparian buffer zones on sediment and nutrient loadings into streams at watershed scale using an integrated REMM-SWAT model","volume":"31","author":"Zhang","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1577\/T09-050.1","article-title":"Assemblage and population-level responses of stream fish to riparian buffers at multiple spatial scales","volume":"139","author":"Fischer","year":"2010","journal-title":"Trans. Am. Fish. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/02705060.2017.1422558","article-title":"Impacts of stream riparian buffer land use on water temperature and food availability for fish","volume":"33","author":"Albertson","year":"2018","journal-title":"J. Freshw. Ecol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Knouft, J.H., Botero-Acosta, A., Wu, C.-L., Charry, B., Chu, M.L., Dell, A.I., Hall, D.M., and Herrington, S.J. (2021). Forested riparian buffers as climate adaptation tools for management of riverine flow and thermal regimes: A case study in the Meramec River Basin. Sustainability, 13.","DOI":"10.3390\/su13041877"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.rse.2014.01.028","article-title":"Subcanopy solar radiation model: Predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models","volume":"154","author":"Bode","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kalu\u017ca, T., Sojka, M., Wr\u00f3\u017cy\u0144ski, R., Jaskula, J., Zaborowski, S., and H\u00e4mmerling, M. (2020). Modeling of river channel shading as a factor for changes in hydromorphological conditions of small lowland rivers. Water, 12.","DOI":"10.3390\/w12020527"},{"key":"ref_62","first-page":"39","article-title":"Seasonality of imagery: The impact on object-based classification accuracy of shelterbelts","volume":"13","author":"Pankiw","year":"2010","journal-title":"Prairie Perspect. Geogr. Essays"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolind.2017.06.022","article-title":"Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl","volume":"96","author":"Kienast","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kalny, G., Laaha, G., Melcher, A., Trimmel, H., Weihs, P., and Rauch, H.P. (2017). The influence of riparian vegetation shading on water temperature during low flow conditions in a medium sized river. Knowl. Manag. Aquat. Ecosyst., 5.","DOI":"10.1051\/kmae\/2016037"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Horne, J.P., and Hubbart, J.A. (2020). A spatially distributed investigation of stream water temperature in a contemporary mixed-land-use watershed. Water, 12.","DOI":"10.3390\/w12061756"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.15244\/pjoes\/81559","article-title":"Shading of river channels as an important factor reducing macrophyte biodiversity","volume":"28","author":"Jusik","year":"2019","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Rice, S., Roy, A., and Rhoads, B. (2008). River Confluences, Tributaries and The Fluvial Network, John Wiley & Sons.","DOI":"10.1002\/9780470760383"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1002\/rse2.61","article-title":"Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality","volume":"4","author":"Fisher","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:17:22Z","timestamp":1760167042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":68,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204172"],"URL":"https:\/\/doi.org\/10.3390\/rs13204172","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,10,18]]}}}