Comparison of methods to estimate fuel moisture content in different forest stand types in central British Columbia
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».