Comparison of methods to estimate fuel moisture content in different forest stand types in central British Columbia
Bibliographic record
Abstract
Wildfires are a growing threat due to climate change, and they often leave unburned forest patches called fire refugia. While young forests in some regions burn more severely, preliminary observations in central British Columbia suggest that managed juvenile forests exhibit lower fire severity, potentially influenced by fuel moisture conditions, and stand characteristics. To identify the role of fuel moisture in the formation of juvenile stand fire refugia, this research collects and examines groundbased, empirically modelled, and remote sensing indices of greenness, moisture, and fire severity. This thesis investigates the fuel moisture contents (FMC) of duff, fine woody debris, and foliage at six locations near Prince George and Smithers, British Columbia (BC), over two summers (2021 and 2022). A total of 6116 individual samples of foliage, fine woody debris, and duff were collected from open, juvenile, and mature conifer forest stands and analysed for moisture content (MC). On average, the MC of duff and fine woody debris samples was higher in juvenile and mature forests than open sites. In contrast, open forests had higher foliage MC than the other forests. Observations of FMC were used to evaluate the accuracy of FMC estimates extracted from the Canadian Forest Fire Weather Index (FWI) system. Observations of FMC were also compared with remote sensing indices to assess the utility of using spaceborne (Landsat 8&9, Sentinel 2) remote sensing to predict local FMC. Three versions of the FWI model were used to estimate FMC: the original FWI model which uses the closest fire weather station, and versions that used updated parameters based on local fuel conditions and in-stand weather data. When estimating fine woody debris MC, the best statistical results are obtained with locally calibrated models at open stands. However, the original FWI model provides better estimates of duff MC in juvenile stands. For remote sensing of foliar MC in juvenile stands, the Normalized Difference Moisture Index (NDMI) had a higher R2 value (0.334) and a lower RMSE than other indices, while the NDMI gave the best result for foliar MC in mature forests (R2 = 0.160). For fine woody debris and duff MC in open stands, none of the remote sensing indices tested have R2 > 0.1 when estimating duff and fine woody debris MC. However, the RMSE of using empirical models from FWI to estimate duff (lowest at 59.95% RMSE) and fine woody debris (21.13%) MC was higher than remote sensing (41.64% for duff, 17.50% for fine woody debris). Remote sensing indices such as NDMI and GNDVI (Green Normalized Difference Vegetation Index) were used to estimate pre-burn FMC, and the estimated FMC results were found to be generally higher at juvenile stands than mature forest from a case study area from Plateau Complex Wildfire of 2017. Lower remote sensing estimates of FMC in mature stands corresponded to higher burn severities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".