[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120601-en":3,"doc-seo-120601-105":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},120601,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","A machine learning approach to fill gaps in dendrometer data","Dendrometer time series often suffer long missing intervals due to technical failures such as battery or logger issues, moisture intrusion, animal damage, extreme weather, or vandalism, which undermines sample size and complicates analysis across the growing season. Existing gap-filling strategies usually require short gaps, additional tree data, or climatic inputs. This study tests eight supervised machine learning models for imputing missing dendrometer data for individual trees in urban and non-urban settings. Extreme gradient boosting (XGB) provides the highest skill, accurately reconstructing gaps up to 30 consecutive days, performing particularly well near season transitions, and remaining independent of climate variables and neighboring-tree information.","Trees (2024) 38:1557–1567  \n[https://doi.org/10.1007/s00468-024-02573-y](https://doi.org/10.1007/s00468-024-02573-y)  \nA machine learning approach to fill gaps in dendrometer data  \nEileen Kuhl1 · Emanuele Ziaco1 · Jan Esper1,2 · Oliver Konter1 · Edurne Martinez del Castillo1  \nReceived: 26 June 2024 / Accepted: 22 September 2024 / Published online: 15 October 2024 © The Author(s) 2024  \nAbstract  \nKey message The machine learning algorithm extreme gradient boosting can be employed to address the issue of long data gaps in individual trees, without the need for additional tree-growth data or climatic variables.  \nAbstract The susceptibility of dendrometer devices to technical failures often makes time-series analyses challenging. Resulting data gaps decrease sample size and complicate time-series comparison and integration. Existing methods either focus on bridging smaller gaps, are dependent on data from other trees or rely on climate parameters. In this study, we test eight machine learning (ML) algorithms to fill gaps in dendrometer data of individual trees in urban and non-urban environments. Among these algorithms, extreme gradient boosting (XGB) demonstrates the best skill to bridge artificially created gaps throughout the growing seasons of individual trees. The individual tree models are suited to fill gaps up to 30 consecutive days and perform particularly well at the start and end of the growing season. The method is independent of climate input variables or dendrometer data from neighbouring trees. The varying limitations among existing approaches call for crosscomparison of multiple methods and visual control. Our findings indicate that ML is a valid approach to fill gaps in individual trees, which can be of particular importance in situations of limited inter-tree co-variance, such as in urban environments.  \nKeywords Dendroecology · Imputation · Acer platanoides · Platanus x hispanica · Tree growth · Urban trees  \nIntroduction  \nThe growth of trees on intra-annual level has been the subject of numerous studies ranging from urban tree growth (Lindén et al. 2016 ; Moser-Reischl et al. 2019) over experimental orchard settings (Corell et al. 2014) to forest tree analyses (King et al. 2013 ; Ziaco and Biondi 2018; Salomónet al. 2022 ; Zhang et al. 2024) . Whilst undisturbed time series of dendrometer data over multiple years are desirable, many datasets contain longer periods of missing data. The primary cause of data loss is irregular physical monitoring due to the accessibility of the sites (e.g. remoteness of sites, time/cost minimization or travel restrictions during pandemics), which can result in battery power or logger failure, full  \nCommunicated by Eryuan Liang.  \n* Eileen Kuhl[eikuhl@uni-mainz.de](eikuhl@uni-mainz.de)  \n1 Department of Geography, Johannes Gutenberg University, Johann-Joachim-Becher Weg 32, 55128 Mainz, Germany  \n2 Global Change Research Centre (CzechGlobe), Brno, Czech Republic  \ndata storage capacity or dendrometers at maximum. Furthermore, other technical damages like moisture intrusion, animal bites, extreme weather events (e.g. storm damage) or vandalism can result in missing values over multiple days to months. The presence of prolonged phases of missing data can impede the ability to conduct a comprehensive analysis on a given dataset, particularly when these periods coincide with the growing season, and can reduce sample size (e.g. in King et al. 2013 ; Corell et al. 2014 ; Dulamsuren et al. 2023) .  \nTo date, the most common approaches for addressing gaps in dendrometer data have been incorporated into R packages like treenetproc (Haeni et al. 2020 ; Knüsel et al. 2021), or dendRoAnalyst (Aryal et al. 2020) . The imputation approaches are primarily based on linear or spline interpolation and are constrained to a short period of consecutive missing values (e.g. 24 measuring points) in order to achieve acceptable results (Aryal et al. 2020 ; Knüsel et al. 2021) . In addition to these m","cbCaitgiebdIEM7P","https://ap.wps.com/l/cbCaitgiebdIEM7P","pdf",2419337,1,11,"English","en",105,"# Introduction\n## Motivation: dendrometer data gaps\n## Related work and limitations\n# Methods\n## Study location and data collection\n# Results and Discussion\n## Model performance and gap-filling skill\n## Practical implications for urban trees","[{\"question\":\"Why are dendrometer data gaps a problem for time-series analyses?\",\"answer\":\"Technical failures create prolonged missing intervals, reducing sample size and making comparisons and integrations across time more difficult, especially when gaps coincide with the growing season.\"},{\"question\":\"Which machine learning algorithm performs best for filling gaps in individual-tree dendrometer data?\",\"answer\":\"Extreme gradient boosting (XGB) demonstrates the best skill for bridging artificially created gaps throughout the growing seasons.\"},{\"question\":\"Does the proposed gap-filling method require climatic variables or data from neighboring trees?\",\"answer\":\"No. The individual tree models work without climate input variables and without dendrometer data from neighboring trees.\"}]","A machine learning approach to fill gaps in dendrometer data | 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are dendrometer data gaps a problem for time-series analyses?","Question",{"text":75,"@type":76},"Technical failures create prolonged missing intervals, reducing sample size and making comparisons and integrations across time more difficult, especially when gaps coincide with the growing season.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithm performs best for filling gaps in individual-tree dendrometer data?",{"text":80,"@type":76},"Extreme gradient boosting (XGB) demonstrates the best skill for bridging artificially created gaps throughout the growing seasons.",{"name":82,"@type":73,"acceptedAnswer":83},"Does the proposed gap-filling method require climatic variables or data from neighboring trees?",{"text":84,"@type":76},"No. The individual tree models work without climate input variables and without dendrometer data from neighboring 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