[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123431-en":3,"doc-seo-123431-105":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},123431,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Comparative Analysis of UAVSAR Polarimetric Decompositions for Wetland Aboveground Biomass Mapping - Using Machine Learning Models","Wetlands are crucial for carbon sequestration, biodiversity conservation, and water regulation, making reliable monitoring essential for environmental management. This study uses UAVSAR quad-polarization data to estimate aboveground biomass (AGB) in southern Louisiana wetlands. A total of 103 UAVSAR features are derived from multiple polarimetric decomposition methods (e.g., Zhang, Huynen, Van Zyl). Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HGB) models evaluate decomposition effectiveness. Zhang with HGB achieves the highest accuracy (R² = 0.74, RMSE = 183.95 g m−2), while RF performs strongly and SVM underperforms, indicating that feature decomposition selection plus advanced ML improves biomass mapping.","Comparative Analysis of UAVSAR Polarimetric Decompositions for Wetland Aboveground Biomass Mapping Using Machine Learning Models  \nMohammadali Hemati 1, Masoud Mahdianpari 1,2* Hodjat Shiri3, and Fariba Mohammadimanesh4  \n1 Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Sciences, Memorial University of  \nNewfoundland, [Canada. mhemati@mun.ca](Canada. mhemati@mun.ca)  \n2 C-CORE, 1 Morrissey Road, St. John's, Newfoundland and Labrador, [Canada. masoud.mahdianpari@c-core.ca](Canada. masoud.mahdianpari@c-core.ca)[ ](Canada. masoud.mahdianpari@c-core.ca)3 Civil Engineering Department, Faculty of Engineering and Applied Sciences, Memorial University of Newfoundland, Canada.  \n[hshiri@mun.ca](hshiri@mun.ca)  \n4 Canada Centre for Remote Sensing, Natural Resources Canada, 580 Booth Street, Ottawa, ON K1A 1M1, Canada.  \nfariba.mohammadimanesh@nrcan-rncan.gc.ca  \nKeywords: Wetland, Biomass, UAVSAR, Machine learning, Polarimetric decomposition.  \nAbstract  \nWetlands play a vital role in carbon sequestration, biodiversity conservation, and water regulation, making their accurate monitoring essential for environmental management. Synthetic Aperture Radar (SAR) is particularly effective for assessing wetland ecosystems due to its ability to penetrate vegetation and capture biomass dynamics under various weather conditions. This study leverages UAVSAR quad-polarization data to estimate aboveground biomass (AGB) in the wetlands of southern Louisiana, USA, a region with diverse wetland types and significant ecological importance. A total of 103 features were extracted from UAVSAR data using various polarimetric decomposition methods, including Zhang, Huynen, Van Zyl, and others. Three machine learning models, including Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HGB) were employed to evaluate the effectiveness of these decompositions. Results indicated that the Zhang decomposition, combined with HGB, achieved the highest accuracy with an R² of 0.74 and an RMSE of 183.95 g m−2, outperforming other decomposition methods and classifiers. Additionally, RF showed strong performance, while SVM consistently underperformed. These findings highlight the potential of UAVSAR-derived polarimetric features for wetland biomass estimation, demonstrating that targeted decomposition selection and advanced machine learning models can enhance accuracy. This study provides valuable insights for improving wetland monitoring and conservation efforts, supporting ecosystem management, and climate change mitigation strategies.  \n1. Introduction  \nWetlands are among the most productive and ecologically significant ecosystems on Earth, playing a crucial role in maintaining biodiversity, regulating water quality, and providing habitat for a wide range of species (Banks et al., 2019; Hemati et al., 2023) . Beyond their biodiversity value, wetlands also serve a critical function in global carbon cycles. Although wetlands are one of the largest natural emitters of methane, their ability to sequester carbon outweighs their emissions, making them essential for climate change mitigation (Kleinen et al., 2023; Hemati et al., 2024a) . These ecosystems are substantial carbon sinks, sequestering large amounts of carbon dioxide through both plant growth and the accumulation of organic matter in waterlogged conditions (Cao and Tzortziou, 2021) . This process, referred to as \"blue carbon,\" is vital for mitigating climate change, as wetlands store more carbon per unit area than forests (Kuwae et al., 2022) . As such, wetland degradation, particularly through drainage or land-use changes, results in the release of significant amounts of stored carbon, contributing to global warming (Mitsch et al., 2013; Hemati et al., 2022) . Understanding the carbon storage potential of wetlands, particularly through accurate estimation of aboveground biomass (AGB), is therefore essential for the conservation and manage","cbCaimhrs1B23jzr","https://ap.wps.com/l/cbCaimhrs1B23jzr","pdf",957749,1,7,"English","en",105,"# Introduction\n## Wetland importance and need for AGB monitoring\n## Remote sensing for AGB estimation and the role of SAR\n## UAVSAR polarimetric data and motivation","[{\"question\":\"Why is accurate aboveground biomass (AGB) mapping important for wetlands?\",\"answer\":\"AGB is a key indicator of wetland carbon storage and ecosystem health. Accurate mapping supports conservation, management, and climate change mitigation by improving estimates of stored carbon.\"},{\"question\":\"What UAVSAR data and polarimetric decomposition methods are used in the study?\",\"answer\":\"The study leverages UAVSAR quad-polarization data and extracts 103 features using several decomposition methods, including Zhang, Huynen, and Van Zyl. These decompositions produce different polarimetric feature sets for biomass modeling.\"},{\"question\":\"Which machine learning model and decomposition combination performed best?\",\"answer\":\"Zhang decomposition combined with Histogram-based Gradient Boosting (HGB) achieved the highest accuracy, with R² = 0.74 and RMSE = 183.95 g m−2. Random Forest performed well, while Support Vector Machine consistently underperformed.\"}]","Comparative Analysis of UAVSAR Polarimetric Decompositions for Wetland Aboveground Biomass Mapping - Using Machine Learning Models | PDF",1785816433,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparative-analysis-of-uavsar-polarimetric-decompositions-for-wetland-aboveground-biomass-mapping-using-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-analysis-of-uavsar-polarimetric-decompositions-for-wetland-aboveground-biomass-mapping-using-machine-learning-models/123431/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is accurate aboveground biomass (AGB) mapping important for wetlands?","Question",{"text":75,"@type":76},"AGB is a key indicator of wetland carbon storage and ecosystem health. Accurate mapping supports conservation, management, and climate change mitigation by improving estimates of stored carbon.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What UAVSAR data and polarimetric decomposition methods are used in the study?",{"text":80,"@type":76},"The study leverages UAVSAR quad-polarization data and extracts 103 features using several decomposition methods, including Zhang, Huynen, and Van Zyl. These decompositions produce different polarimetric feature sets for biomass modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model and decomposition combination performed best?",{"text":84,"@type":76},"Zhang decomposition combined with Histogram-based Gradient Boosting (HGB) achieved the highest accuracy, with R² = 0.74 and RMSE = 183.95 g m−2. Random Forest performed well, while Support Vector Machine consistently underperformed.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]