[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127951-en":3,"doc-seo-127951-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127951,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Spatiotemporal pattern of water hyacinth (Pontederia crassipes) distribution in Lake Tana, Ethiopia - random forest machine learning model study","Water hyacinth (Pontederia crassipes) is an invasive aquatic weed that significantly covers Lake Tana and threatens ecological integrity and socioeconomic functions. Early, reliable detection of its spread is essential for effective management. This study analyzes spatiotemporal distribution from 2016–2022 using a random forest machine learning model with 16 variables from Sentinel-2A, Sentinel-1 SAR, and SRTM DEM. Results show high classification accuracy and kappa across seasons, identifying key spectral and topographic drivers.","TYPE Original Research PUBLISHED 14 November 2024 DOI 10.3389/fenvs.2024.1476014  \nOPEN ACCESS  \nEDITED BY  \nYoonKyung Cha,  \nUniversity of Seoul, Republic of Korea  \nREVIEWED BY  \nDoru Stelian Banaduc,  \nLucian Blaga University of Sibiu, Romania Solomon Newete,  \nAgricultural Research Council of South Africa (ARC-SA), South Africa  \n*CORRESPONDENCE  \nMatiwos Belayhun,  \n [matibelayhun@gmail.com](matibelayhun@gmail.com)  \nRECEIVED 05 August 2024  \nACCEPTED 24 October 2024  \nPUBLISHED 14 November 2024  \nCITATION  \nBelayhun M, Chere Z, Abay NG, Nicola Y and Asmamaw A (2024) Spatiotemporal pattern of  \nwater hyacinth (Pontederia crassipes) distribution in Lake Tana, Ethiopia, using a random forest machine learning model. Front. Environ. Sci. 12:1476014 .  \ndoi: 10.3389/fenvs.2024.1476014  \nCOPYRIGHT  \n© 2024 Belayhun, Chere, Abay, Nicola and Asmamaw. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nSpatiotemporal pattern of water hyacinth (Pontederia crassipes) distribution in Lake Tana, Ethiopia, using a random forest machine learning model  \nMatiwos Belayhun 1*, Zerihun Chere 1, Nigus Gebremedhn Abay 1, Yonas Nicola 2 and Abay Asmamaw 3  \n1Dire Dawa University, Dire Dawa, Ethiopia, 2Ethiopian Space Science and Technology Institute (ESSTI), Addis Ababa, Addis Ababa, Ethiopia, 3Addis Ababa University, Addis Ababa, Addis Ababa, Ethiopia  \nWater hyacinth (Pontederia crassipes) is an invasive weed that covers a significant portion of Lake Tana. The infestation has an impact on the lake’s ecological and socioeconomic systems. Early detection of the spread of water hyacinth using geospatial techniques is crucial for its effective management and control. The main objective of this study was to examine the spatiotemporal distribution of water hyacinth from 2016 to 2022 using a random forest machine learning model. The study used 16 variables obtained from Sentinel-2A, Sentinel-1 SAR, and SRTM DEM, and a random forest supervised classification model was applied. Seven spectral indices, five spectral bands, two Sentinel-1 SAR bands, and two topographic variables were used in combination to model the spatial distribution of water hyacinth. The model was evaluated using the overall accuracy and kappa coefficient. The findings demonstrated that the overall accuracy ranged from 0.91 to 0.94 and kappa coefficient from 0.88 to 0.92 in the wet season and 0.93 to 0. 95 and 0 . 90 to 0 . 93 in the dry season, respectively. B11 and B5 (2022), VH, soil adjusted vegetation index (SAVI), and normalized difference water index (NDWI)(2020), B5 and B12 (2018), and VH and slope (2016) are the highly important variables in the classification. The study found that the spatial coverage of water hyacinth was 686.5 and 650.4 ha (2016), 1,851 and 1,259 ha (2018), 1,396.7 and 1,305.7 ha (2020), and 1,436.5 and 1,216.5 ha (2022) in the wet and dry seasons, respectively. The research findings indicate that variables derived from optical (Sentinel-2A and SRTM) and non-optical (Sentinel-1 SAR) satellite imagery effectively identify water hyacinth and display its spatiotemporal spread using the random forest machine learning algorithm.  \nKEYWORDS  \naquatic invasive plant, Lake Tana, machine learning model, remote sensing indices, Sentinel image, water hyacinth  \n1 Introduction  \nWater hyacinth, scientifically known as Pontederia crassipes, is an aquatic plant that originates from South America. However, it has been introduced to numerous other regions globally, where it frequently becomes invasive and leads to ecological issues (Coetzee et al., 2009; Jones, 2009; Degaga, 2018; Aya","cbCaijm7484mgK7T","https://ap.wps.com/l/cbCaijm7484mgK7T","pdf",2036072,2,1,13,"English","en",105,"# Introduction\n## Water hyacinth invasiveness and impacts\n## Study area context (Lake Tana, Ethiopia)\n# Methods\n## Data sources and variables (Sentinel-2A, Sentinel-1 SAR, SRTM DEM)\n## Random forest classification approach\n## Model evaluation (accuracy and kappa)\n# Results\n## Classification performance by season\n## Key variables for water hyacinth mapping\n## Spatiotemporal coverage estimates (2016–2022)","[{\"question\":\"What is the main goal of the study on Lake Tana water hyacinth?\",\"answer\":\"To examine the spatiotemporal distribution of water hyacinth from 2016 to 2022 using a random forest machine learning model.\"},{\"question\":\"Which data sources and variables were used to model water hyacinth distribution?\",\"answer\":\"Sixteen variables were derived from Sentinel-2A, Sentinel-1 SAR, and SRTM DEM, including spectral indices, spectral bands, SAR bands, and topographic variables.\"},{\"question\":\"How accurate was the random forest model in identifying water hyacinth?\",\"answer\":\"The overall accuracy ranged about 0.91–0.94 in the wet season and about 0.93–0.95 in the dry season, with kappa values roughly 0.88–0.92 and 0.90–0.93, respectively.\"}]","Spatiotemporal pattern of water hyacinth (Pontederia crassipes) distribution in Lake Tana, Ethiopia - random forest machine learning model study | PDF",1785943215,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"spatiotemporal-pattern-of-water-hyacinth-pontederia-crassipes-distribution-in-lake-tana-ethiopia-random-forest-machine-learning-model-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/spatiotemporal-pattern-of-water-hyacinth-pontederia-crassipes-distribution-in-lake-tana-ethiopia-random-forest-machine-learning-model-study/127951/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the study on Lake Tana water hyacinth?","Question",{"text":76,"@type":77},"To examine the spatiotemporal distribution of water hyacinth from 2016 to 2022 using a random forest machine learning model.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data sources and variables were used to model water hyacinth distribution?",{"text":81,"@type":77},"Sixteen variables were derived from Sentinel-2A, Sentinel-1 SAR, and SRTM DEM, including spectral indices, spectral bands, SAR bands, and topographic variables.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate was the random forest model in identifying water hyacinth?",{"text":85,"@type":77},"The overall accuracy ranged about 0.91–0.94 in the wet season and about 0.93–0.95 in the dry season, with kappa values roughly 0.88–0.92 and 0.90–0.93, respectively.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]