[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119532-en":3,"doc-seo-119532-105":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},119532,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Guiding riparian vegetation planting with machine learning","Mechanistic riparian models for river projects consider diverse environmental requirements but often lack spatial explicitness. This study evaluates whether machine learning using airborne LiDAR terrain metrics can predict preferential riparian planting locations and interpret site attributes. A random forest model was trained on 29,263 vegetation presence/absence observations sampled from 2017 LiDAR-derived polygons along a 34 km reach of the regulated lower Yuba River, California, using spatial-independence resampling and 16 topographic predictors at 0.91 m resolution.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nGuiding riparian vegetation planting with machine learning  \nPermalink  \n[https://escholarship.org/uc/item/82v4g1q2](https://escholarship.org/uc/item/82v4g1q2)  \nJournal  \nJournal of Ecohydraulics, ahead-of-print(ahead-of-print)  \nISSN  \n2470-5357  \nAuthors  \nDiaz-Gomez, Romina  \nPasternack, Gregory BGuillon, Hervé  \nPublication Date  \n2025  \nDOI  \n10.1080/24705357.2025.2481033  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n1 Guiding riparian vegetation planting with machine learning  \n2 Romina Diaz-Gomez 1*, Gregory B. Pasternack2, and Hervé Guillon3  \n3 Department of Land, Air, and Water Resources, University of California at Davis, Davis, CA  \n4 1[https://orcid.org/0000-0002-8919-554X](https://orcid.org/0000-0002-8919-554X)  \n5 [https://www.linkedin.com/in/romina-diaz-gomez-0939a9200/](https://www.linkedin.com/in/romina-diaz-gomez-0939a9200/)  \n6 2 [https://orcid.org/0000-0002-1977-4175](https://orcid.org/0000-0002-1977-4175)  \n7 [https://www.linkedin.com/in/gregory-pasternack-455b6485/](https://www.linkedin.com/in/gregory-pasternack-455b6485/)  \n8 3 [https://orcid.org/0000-0002-6297-8253](https://orcid.org/0000-0002-6297-8253)  \n9 [https://www.linkedin.com/in/hguillon/](https://www.linkedin.com/in/hguillon/)  \n10 *Corresponding author: [rdiazgomez@ucdavis.edu](rdiazgomez@ucdavis.edu)[ ](rdiazgomez@ucdavis.edu)11  \n12  \n13 Cite as: Romina Diaz-Gomez, Gregory B. Pasternack, and Hervé Guillon. 2025. Guiding riparian  \n14 vegetation planting with machine learning, Journal of Ecohydraulics, DOI:  \n15 10. 1080/24705357 .2025.2481033  \n16  \n17 This is the final, corrected version  \n18  \n19 Abstract  \n20 Mechanistic riparian models for river projects consider various environmental requirements, but  \n21 few are spatially explicit. This study explores whether machine learning using airborne LiDAR  \n22 terrain metrics can accurately predict preferential planting locations and help understand  \n23 beneficial site attributes. A random forest machine learning model was trained on 29,263  \n24 vegetation presence/absence observations randomly selected from 2017 LiDAR-derived  \n25 polygons of natural vegetation from 34 km of the regulated lower Yuba River, California. A  \n26 spatial-independence resampling strategy split the river into 67 sections, with 32 training, 28  \n27 validation and 7 test polygons. Sixteen LiDAR-derived topographic predictors were computed at  \n28 0.91-m (3-ft) resolution. The best model had an Area Under the Curve performance of 0.77.  \n29 Vegetation presence was mainly associated with microtopographic ‘vector ruggedness’, followed  \n30 by terrain ruggedness and roughness. Despite controlling water availability, detrended elevation  \n31 and distance from wetted areas were not dominant variables. To ecologically interpret vegetation  \n32 presence probability and identify the best planting locations, probability thresholds  \n33 differentiating avoidance, preference, and high preference were quantified using a forage ratio  \n34 electivity index analysis with statistical bootstrapping and cross-validation re-sampling.  \n35 Locations with presence probability >80% had vegetation present 3.55 times as often as expected  \n36 by chance (p\u003C0.05), so bare locations with such probabilities should be prioritized for planting.  \n37 Globally, it is best to apply the overall study framework to determine local correlates to guide  \n38 planting and identify priority planting locations. Nevertheless, microtopographic roughness is  \n39 intertwined with naturally occurring riparian vegetation and should be designed before planting  \n40 to increase plant establishment and growth.  \n41 K","cbCaiv9EOjZ2ytEa","https://ap.wps.com/l/cbCaiv9EOjZ2ytEa","pdf",5423210,1,60,"English","en",105,"# Abstract\n# Introduction\n## Scientific background","[{\"question\":\"What is the document’s main research question?\",\"answer\":\"Whether machine learning with airborne LiDAR terrain metrics can accurately predict preferential riparian vegetation planting locations and help interpret beneficial site attributes.\"},{\"question\":\"How was the machine learning model trained and evaluated?\",\"answer\":\"A random forest model was trained on 29,263 vegetation presence/absence observations from LiDAR-derived polygons and evaluated using a spatial-independence resampling strategy splitting the river into training, validation, and test sections.\"},{\"question\":\"Which terrain predictors were most associated with vegetation presence?\",\"answer\":\"Vegetation presence was mainly associated with microtopographic “vector ruggedness,” followed by terrain ruggedness and roughness, while detrended elevation and distance from wetted areas were not dominant.\"}]","Guiding riparian vegetation planting with machine learning | 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