Modelling in-ground wood decay using time-series retrievals from the 5 th European climate reanalysis (ERA5-Land)

IF 3.7 4区 地球科学 Q2 REMOTE SENSING
Brendan N. Marais, Marian Schönauer, Philip Bester van Niekerk, Jonas Niklewski, Christian Brischke
{"title":"Modelling in-ground wood decay using time-series retrievals from the 5 <sup>th</sup> European climate reanalysis (ERA5-Land)","authors":"Brendan N. Marais, Marian Schönauer, Philip Bester van Niekerk, Jonas Niklewski, Christian Brischke","doi":"10.1080/22797254.2023.2264473","DOIUrl":null,"url":null,"abstract":"This article presents models to predict the time until mechanical failure of in-ground wooden test specimens resulting from fungal decay. Historical records of decay ratings were modelled by remotely sensed data from ERA5-Land. In total, 2,570 test specimens of 16 different wood species were exposed at 21 different test sites, representing three continents and climatic conditions from sub-polar to tropical, spanning a period from 1980 until 2022. To obtain specimen decay ratings over their exposure time, inspections were conducted in mostly annual and sometimes bi-annual intervals. For each specimen’s exposure period, a laboratory developed dose–response model was populated using remotely sensed soil moisture and temperature data retrieved from ERA5-Land. Wood specimens were grouped according to natural durability rankings to reduce the variability of in-ground wood decay rate between wood species. Non-linear, sigmoid-shaped models were then constructed to describe wood decay progression as a function of daily accumulated exposure to soil moisture and temperature conditions (dose). Dose, a mechanistic weighting of daily exposure conditions over time, generally performed better than exposure time alone as a predictor of in-ground wood decay progression. The open-access availability of remotely sensed soil-state data in combination with wood specimen data proved promising for in-ground wood decay predictions.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"279 1","pages":"0"},"PeriodicalIF":3.7000,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/22797254.2023.2264473","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0

Abstract

This article presents models to predict the time until mechanical failure of in-ground wooden test specimens resulting from fungal decay. Historical records of decay ratings were modelled by remotely sensed data from ERA5-Land. In total, 2,570 test specimens of 16 different wood species were exposed at 21 different test sites, representing three continents and climatic conditions from sub-polar to tropical, spanning a period from 1980 until 2022. To obtain specimen decay ratings over their exposure time, inspections were conducted in mostly annual and sometimes bi-annual intervals. For each specimen’s exposure period, a laboratory developed dose–response model was populated using remotely sensed soil moisture and temperature data retrieved from ERA5-Land. Wood specimens were grouped according to natural durability rankings to reduce the variability of in-ground wood decay rate between wood species. Non-linear, sigmoid-shaped models were then constructed to describe wood decay progression as a function of daily accumulated exposure to soil moisture and temperature conditions (dose). Dose, a mechanistic weighting of daily exposure conditions over time, generally performed better than exposure time alone as a predictor of in-ground wood decay progression. The open-access availability of remotely sensed soil-state data in combination with wood specimen data proved promising for in-ground wood decay predictions.
利用第5次欧洲气候再分析(ERA5-Land)的时间序列反演模拟地下木材腐烂
本文提出了一种预测地下木质试件因真菌腐烂而发生机械失效时间的模型。利用ERA5-Land的遥感数据模拟了衰减等级的历史记录。总共有2570个测试样本,来自16种不同的木材物种,在21个不同的测试地点进行了测试,这些地点代表了三大洲和从亚极地到热带的气候条件,时间跨度从1980年到2022年。为了获得样品在暴露时间内的衰变等级,检查通常每年进行一次,有时每两年进行一次。对于每个样品的暴露期,使用ERA5-Land遥感土壤湿度和温度数据填充实验室开发的剂量反应模型。根据木材的自然耐久性等级对木材进行分组,以减少不同树种间木材在地腐烂率的变异。然后构建非线性的s形模型来描述木材腐烂进程,作为每日累积暴露于土壤湿度和温度条件(剂量)的函数。剂量是每日暴露条件随时间的机械加权,通常比单独暴露时间更能预测地下木材腐烂的进展。开放获取的遥感土壤状态数据与木材样本数据相结合,证明了对地下木材腐烂预测的希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.00
自引率
2.50%
发文量
51
审稿时长
>12 weeks
期刊介绍: European Journal of Remote Sensing publishes research papers and review articles related to the use of remote sensing technologies. The Journal welcomes submissions on all applications related to the use of active or passive remote sensing to terrestrial, oceanic, and atmospheric environments. The most common thematic areas covered by the Journal include: -land use/land cover -geology, earth and geoscience -agriculture and forestry -geography and landscape -ecology and environmental science -support to land management -hydrology and water resources -atmosphere and meteorology -oceanography -new sensor systems, missions and software/algorithms -pre processing/calibration -classifications -time series/change analysis -data integration/merging/fusion -image processing and analysis -modelling European Journal of Remote Sensing is a fully open access journal. This means all submitted articles will, if accepted, be available for anyone to read anywhere, at any time, immediately on publication. There are no charges for submission to this journal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信