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Modelling in-ground wood decay using time-series retrievals from the 5th European climate reanalysis (ERA5-Land)
- Source :
- European Journal of Remote Sensing, Vol 56, Iss 1 (2023)
- Publication Year :
- 2023
- Publisher :
- Taylor & Francis Group, 2023.
-
Abstract
- ABSTRACTThis 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.
Details
- Language :
- English
- ISSN :
- 22797254
- Volume :
- 56
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- European Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b4b5d4b576084370baa8205b4604b38a
- Document Type :
- article
- Full Text :
- https://doi.org/10.1080/22797254.2023.2264473