[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122743-en":3,"doc-seo-122743-105":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},122743,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models","Rapid and accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management. Because field surveys are costly and difficult, multi-source remote sensing is used to improve modeled AGB predictions. Four machine learning approaches—Random Forest, Gradient Boosting Decision Tree, Classification and Regression Trees, and Minimum Distance—were trained with single and multi-source remote sensing variables on the Google Earth Engine platform for the Taiyue Mountain forest in Shanxi, China. Results indicate GBDT with spectral indices yields the highest predictive performance across coniferous, broadleaved, and mixed forests.","PR IFYS GOL BANGOR / BANGOR  \nEvaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models  \nYan, Xingguang; Li, Jing; Smith, Andy; Yang, Di; Ma, Tianyue; Su, Yiting; Shao, Jiahao  \nInternational Journal of Digital Earth  \nDOI:  \n10.1080/17538947.2023.2270459  \nPublished: 01/11/2023  \nPeer reviewed version  \nCyswllt i'r cyhoeddiad / Link to publication  \nDyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA):  \nYan, X. , Li, J. , Smith, A. , Yang, D. , Ma, T. , Su, Y. , & Shao, J. (2023) . Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest aboveground biomass models. International Journal of Digital Earth, 16(2), Article 4471-4491. [https://doi.org/10.1080/17538947.2023.2270459](https://doi.org/10.1080/17538947.2023.2270459)  \nHawliau Cyffredinol / General rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \n1 Evaluation of machine learning methods and multi-source remote  \n2 sensing data combinations to construct forest above-ground biomass  \n3 models  \n4  \n5 Xingguang Yana,b,c, Jing Lia*, Andrew R. Smithb,c, Di Yangd, Tianyue  \n6 Maa, YiTing Sua and Jiahao Shaoa  \n7 a College of Geoscience and Surveying Engineering, China University of Mining and  \n8 Technology-Beijing, Beijing 100083, China;  \n9 bSchool of Natural Sciences, Bangor University, Bangor, Gwynedd, LL57 2UW, UK  \n10 cEnvironment Centre Wales, Bangor University, Bangor, Gwynedd, LL57 2UW, UK  \n11 d Wyoming Geographic Information Science Center, University of Wyoming, WY  \n12 82070, USA  \n13  \n14 *Author for Correspondence: Jing Li, Email: [lijing](lijing@cumtb.edu.cn)[@cumtb.edu.cn](lijing@cumtb.edu.cn)  \n15 ORCID  \n16 Xingguang Yan [https://orcid.org/0009-0001-8280-4568](https://orcid.org/0009-0001-8280-4568)  \n17 Andrew R. Smith [http://orchid.org/0000-0001-8580-278X](http://orchid.org/0000-0001-8580-278X)  \n18 Di Yang [http://orchid.org/0000-0002-4010-6163](http://orchid.org/0000-0002-4010-6163)  \n19  \n20  \n21 Abstract: Rapid and accurate estimation of forest biomass is essential to drive  \n22 sustainable management of forests. Field-based measurements of forest above- 23 ground biomass (AGB) can be costly and difficult to conduct. Multi-source  \n24 remote sensing data offers potential to improve the accuracy of modelled AGB  \n25 predictions. Here, four machine learning methods: Random Forest (RF),  \n26 Gradient Boosting Decision Tree (GBDT), Classification and Regression Trees  \n27 (CART) and Minimum Distance (MD) were used to construct forest AGB  \n28 models ofTaiyue Mountain forest, Shanxi Province, China using single and  \n29 multi-sourced remote sensing data and the Google Earth Engine platform.  \n30 Results showed that the machine learning method that most accurately  \n31 predicted AGB was GBDT and spectral index for coniferous (R2=0 .99;  \n32 RMSE=65 .52 Mg/ha), broadleaved (R2=0 .97; RMSE=29 . 14 Mg/ha), and  \n33 mixed species (R2=0 .97; RMSE=81 . 12 Mg/ha) forest types. Models  \n34 constructed using bivariate variable combinations that included the spectral  \n35 index improved the AGB estimation accuracy of mixed species (R2=0.99;  \n36 RMSE=59.52 Mg/ha) forest types","cbCailMN5yq1MKJo","https://ap.wps.com/l/cbCailMN5yq1MKJo","pdf",2113172,1,46,"English","en",105,"# Abstract\n# Introduction\n## Forest AGB estimation and remote sensing rationale\n## Machine learning for improved large-area prediction\n# Materials and Methods\n## Study area and data sources\n## Google Earth Engine workflow\n## Machine learning models (RF, GBDT, CART, MD)\n# Results\n## Model performance across forest types\n## Effects of single vs multi-source inputs\n## Effects of bivariate variable combinations\n# Discussion\n## Implications for mixed-species forest modeling\n# Conclusions","[{\"question\":\"Which machine learning method performed best for predicting forest AGB?\",\"answer\":\"Gradient Boosting Decision Tree (GBDT) achieved the most accurate AGB predictions across coniferous, broadleaved, and mixed forest types.\"},{\"question\":\"How did multi-source remote sensing data affect AGB model accuracy?\",\"answer\":\"Parameterizing machine learning algorithms with multi-source remote sensing variables improved prediction accuracy, particularly for mixed species forests.\"},{\"question\":\"What role did spectral index variables play in the models?\",\"answer\":\"Models including bivariate variable combinations with spectral indices improved AGB estimation accuracy for mixed species forests and had smaller impacts for coniferous and broadleaved forests.\"}]","Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models | 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machine learning method performed best for predicting forest AGB?","Question",{"text":75,"@type":76},"Gradient Boosting Decision Tree (GBDT) achieved the most accurate AGB predictions across coniferous, broadleaved, and mixed forest types.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did multi-source remote sensing data affect AGB model accuracy?",{"text":80,"@type":76},"Parameterizing machine learning algorithms with multi-source remote sensing variables improved prediction accuracy, particularly for mixed species forests.",{"name":82,"@type":73,"acceptedAnswer":83},"What role did spectral index variables play in the models?",{"text":84,"@type":76},"Models including bivariate variable combinations with spectral indices improved AGB estimation accuracy for mixed species forests and had smaller impacts for coniferous and broadleaved 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