[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127895-en":3,"doc-seo-127895-105":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},127895,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Landsat-based Spatiotemporal Estimation of Subtropical Forest Aboveground Carbon Storage Using Machine Learning Algorithms with Hyperparameter Tuning","Landsat-driven modeling is used to estimate the aboveground carbon storage (AGC) of subtropical forests over Lishui City, addressing uncertainty in regional AGC assessment. The study compares backpropagation neural network, random forest, and CatBoost models and selects the best performer for spatiotemporal analysis across 1989–2019. Texture variables derived from 9×9 and 11×11 windows prove influential. CatBoost achieves the highest accuracy, with R2 values up to 0.95 (training) and 0.83 (testing), and improves RMSE to 2.98 and 4.93 Mg C ha-1. Results show increasing AGC and spatial contrasts between western/central higher stocks and eastern/northeastern lower levels.","TYPE Original Research PUBLISHED 29 August 2024 DOI 10.3389/fpls.2024.1421567  \nOPEN ACCESS  \nEDITED BY  \nLuca Brillante,  \nCalifornia State University, Fresno, United States  \nREVIEWED BY Ting Hua, NTNU, Norway Alessia Cogato,  \nUniversity of Padua, Italy  \n*CORRESPONDENCE Huaqiang Du  \n [dhqrs@126.com](dhqrs@126.com)  \nRECEIVED 22 April 2024  \nACCEPTED 08 August 2024  \nPUBLISHED 29 August 2024  \nCITATION  \nHuang L, Huang Z, Zhou W, Wu S, Li X, Mao F, Song M, Zhao Y, Lv L, Yu J and Du H (2024) Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning. Front. Plant Sci. 15:1421567 .  \ndoi: 10.3389/fpls.2024.1421567  \nCOPYRIGHT  \n© 2024 Huang, Huang, Zhou, Wu, Li, Mao, Song, Zhao, Lv, Yu and Du. This is an openaccess 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.  \nLandsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning  \nLei Huang 1,2, Zihao Huang 1,2, Weilong Zhou 3, Sumei Wu 3, Xuejian Li 1,2, Fangjie Mao 1,2, Meixuan Song 1,2, Yinyin Zhao 1,2, Lujin Lv 1,2, Jiacong Yu 1,2 and Huaqiang Du 1,2*  \n1State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, China, 2 Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou, China, 3Qianjiangyuan-Baishanzu National Park, Lishui, Zhejiang, China  \nIntroduction: The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province.  \nMethods: Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989–2019) .  \nResults: The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0 .95 and 0 . 83, and RMSE of 2 . 98 Mg C ha-1 and 4 . 93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest  \nFrontiers in Plant Science 01 [frontiersin.org](frontiersin.org)  \nAGC in Lishui City over the past three decades, escalating from 1 .36×107 Mg C in 1989 to 6 . 16×107 Mg C in 2019 ","cbCaiaxd8Zenbg4I","https://ap.wps.com/l/cbCaiaxd8Zenbg4I","pdf",16540004,3,1,19,"English","en",105,"# Introduction\n## Forest aboveground carbon storage and remote sensing needs\n# Methods\n## Landsat-based modeling and machine learning with hyperparameter tuning\n# Results\n## Key variables and model performance comparison\n## Spatiotemporal patterns (1989–2019)\n# Discussion\n## Implications for carbon sequestration capacity and modeling reference","[{\"question\":\"Which machine learning algorithms are used to estimate forest AGC from Landsat data?\",\"answer\":\"Backpropagation neural network (BPNN), random forest (RF), and CatBoost are used to model forest aboveground carbon storage.\"},{\"question\":\"What role do texture variables play in the estimation model?\",\"answer\":\"Texture information calculated from 9×9 and 11×11 windows is identified as an important variable for constructing the AGC estimation model.\"},{\"question\":\"How does CatBoost perform compared with the other models?\",\"answer\":\"CatBoost outperforms the others, reaching training R2 of 0.95 and testing R2 of 0.83, with RMSE of 2.98 and 4.93 Mg C ha-1, respectively.\"}]","Landsat-based Spatiotemporal Estimation of Subtropical Forest Aboveground Carbon Storage Using Machine Learning Algorithms with Hyperparameter Tuning | 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machine learning algorithms are used to estimate forest AGC from Landsat data?","Question",{"text":76,"@type":77},"Backpropagation neural network (BPNN), random forest (RF), and CatBoost are used to model forest aboveground carbon storage.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role do texture variables play in the estimation model?",{"text":81,"@type":77},"Texture information calculated from 9×9 and 11×11 windows is identified as an important variable for constructing the AGC estimation model.",{"name":83,"@type":74,"acceptedAnswer":84},"How does CatBoost perform compared with the other models?",{"text":85,"@type":77},"CatBoost outperforms the others, reaching training R2 of 0.95 and testing R2 of 0.83, with RMSE of 2.98 and 4.93 Mg C ha-1, 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