Modelling in-ground wood decay using time-series retrievals from the 5th European climate reanalysis (ERA5-Land). Marais, B. N., Schönauer, M., van Niekerk, P. B., Niklewski, J., & Brischke, C. European Journal of Remote Sensing, 56(1):2264473, December, 2023. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/22797254.2023.2264473![Modelling in-ground wood decay using time-series retrievals from the 5th European climate reanalysis (ERA5-Land) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{marais_modelling_2023,
title = {Modelling in-ground wood decay using time-series retrievals from the 5th {European} climate reanalysis ({ERA5}-{Land})},
volume = {56},
issn = {null},
url = {https://doi.org/10.1080/22797254.2023.2264473},
doi = {10.1080/22797254.2023.2264473},
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.},
number = {1},
urldate = {2024-03-13},
journal = {European Journal of Remote Sensing},
author = {Marais, Brendan N. and Schönauer, Marian and van Niekerk, Philip Bester and Niklewski, Jonas and Brischke, Christian},
month = dec,
year = {2023},
note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/22797254.2023.2264473},
keywords = {dose–response model, Fungal wood decay, geospatial modelling, IRG-WP durability database, soil moisture, soil temperature},
pages = {2264473},
file = {Full Text PDF:C\:\\Users\\Eva\\Zotero\\storage\\XWDES8I2\\Marais et al. - 2023 - Modelling in-ground wood decay using time-series r.pdf:application/pdf},
}
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