[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122197-en":3,"doc-seo-122197-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122197,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting Barrier Island Shrub Presence Using Remote Sensing Products and Machine Learning Techniques","Barrier islands are dynamic coastal landforms with substantial economic, ecological, and societal value. Woody vegetation within their interiors can modify overwash patterns and influence periods of barrier island retreat, making it important to identify what controls woody (shrub) presence. Using LiDAR, LANDSAT, and aerial imagery for an undeveloped mixed-energy system in Virginia, the study quantifies shrub presence, dune metrics, and island characteristics. Decision tree and random forest models reveal strong thresholds (dune elevations ~1.9 m and interior widths ~160 m) and show predictive errors linked to geomorphic hysteresis, where vegetation response lags faster coastal changes.","RESEARCH ARTICLE  \n10.1029/2023JF007465  \nSpecial Section:  \nPrediction in coastal geomorphology  \nKey Points:  \n• Decision tree analysis and random forest modeling can predict shrub presence on barrier islands in Virginia with ∼90% accuracy  \n• Shrub presence on barrier islands correlates with dune elevations > 1.9 mand maintenance of island interior widths > 160 m over a ∼6‐year period  \n• Shrub establishment and removal lags changes in geomorphic conditions, indicating hysteresis  \nCorrespondence to:  \nB. Franklin,  \n[wbenton@email.unc.edu](wbenton@email.unc.edu)  \n[Citation:](Citation:)  \nFranklin, B., Moore, L. J., & Zinnert, J. C.(2024) . Predicting barrier island shrub presence using remote sensing products and machine learning techniques. Journal of Geophysical Research: Earth Surface, 129, e2023JF007465. [https://doi.org/10](https://doi.org/10) .  \n1029/2023JF007465  \nReceived 26 SEP 2023  \nAccepted 3 APR 2024  \nAuthor Contributions:  \nConceptualization: Benton Franklin, Laura J. Moore, Julie C. Zinnert Data curation: Benton Franklin, Julie C. Zinnert  \nFormal analysis: Benton Franklin  \nFunding acquisition: Laura J. Moore, Julie C. Zinnert  \nInvestigation: Benton Franklin, Laura J. Moore  \nMethodology: Benton Franklin, Laura J. Moore, Julie C. Zinnert  \nSoftware: Benton Franklin  \nSupervision: Laura J. Moore Writing – original draft:  \nBenton Franklin, Laura J. Moore  \nWriting – review & editing:  \nBenton Franklin, Laura J. Moore, Julie  \nC. Zinnert  \n© 2024. American Geophysical Union. All Rights Reserved.  \nPredicting Barrier Island Shrub Presence Using Remote Sensing Products and Machine Learning Techniques  \nBenton Franklin1 , Laura J. Moore1 , and Julie C. Zinnert2  \n1Department of Earth, Marine and Environmental Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 2Department of Biology, Virginia Commonwealth University, Richmond, VA, USA  \nAbstract Barrier islands are highly dynamic coastal landforms that are economically, ecologically, and societally important. Woody vegetation located within barrier island interiors can alter patterns of overwash, leading to periods of periodic‐barrier island retreat. Due to the interplay between island interior vegetation and patterns of barrier island migration, it is critical to better understand the factors controlling the presence of woody vegetation on barrier islands. To provide new insight into this topic, we use remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence, coastal dune metrics, and island characteristics (e.g., beach width, island width) for an undeveloped mixed‐energy barrier island system in Virginia along the US mid‐Atlantic coast. We apply decision tree and random forest machine learning methods to identify new empirical relationships between island geomorphology and shrub presence. We find that shrubs are highly likely (90% likelihood) to be present in areas where dune elevations are above ∼1.9 m and island interior widths are greater than ∼160 m and that shrubs are unlikely (10% likelihood) to be present in areas where island interior widths are less than ∼160 m regardless of dune elevation. Our machine learning predictions are 90% accurate for the Virginia Barrier Islands, with almost half of our incorrect predictions (5% of total transects) being attributable to system hysteresis; shrubs require time to adapt to changing conditions and therefore their growth and removal lags changes in island geomorphology, which can occur more rapidly.  \nPlain Language Summary In this study, we present two machine learning models for predicting the presence of shrubs on barrier islands. We use data derived from satellites, LiDAR, and aerial imagery to create machine learning models. Using these models, we find that whether or not shrubs are present on barrier islands depends on dune elevation and the width of island interior sustained over time; sufficiently high dune elevationsand sufficiently wi","cbCaiinEqqWBJsEH","https://ap.wps.com/l/cbCaiinEqqWBJsEH","pdf",7492200,1,21,"English","en",105,"# Introduction\n## Study Motivation and Context\n# Data and Methods\n## Remote Sensing Inputs (LiDAR, LANDSAT, Aerial Imagery)\n## Machine Learning Models (Decision Trees, Random Forest)\n# Results\n## Threshold Relationships for Shrub Presence\n## Model Accuracy and Hysteresis Effects\n# Discussion\n## Lag Between Geomorphic Change and Vegetation Response","[{\"question\":\"Which remote sensing data sources are used to predict shrub presence?\",\"answer\":\"The study uses remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence and related coastal metrics.\"},{\"question\":\"What thresholds most strongly relate to shrub presence on Virginia barrier islands?\",\"answer\":\"Shrubs are highly likely where dune elevations are above about 1.9 m and where island interior widths are greater than about 160 m.\"},{\"question\":\"Why do some predictions differ from observations?\",\"answer\":\"About half of the incorrect predictions are attributed to system hysteresis, because shrubs require time to adapt as island geomorphic conditions change.\"}]","Predicting Barrier Island Shrub Presence Using Remote Sensing Products and Machine Learning Techniques | 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remote sensing data sources are used to predict shrub presence?","Question",{"text":75,"@type":76},"The study uses remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence and related coastal metrics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What thresholds most strongly relate to shrub presence on Virginia barrier islands?",{"text":80,"@type":76},"Shrubs are highly likely where dune elevations are above about 1.9 m and where island interior widths are greater than about 160 m.",{"name":82,"@type":73,"acceptedAnswer":83},"Why do some predictions differ from observations?",{"text":84,"@type":76},"About half of the incorrect predictions are attributed to system hysteresis, because shrubs require time to adapt as island geomorphic conditions 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