Development of a Satellite-Based Forest Fire Danger Forecasting System and its Implementation Over the Forest Dominant Regions in Alberta, Canada
Notice bibliographique
Résumé
Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is forecasting of fire danger conditions. My aim was to develop a daily-scale forest fire danger forecasting system (FFDFS) using remote sensing inputs over the northern part of Canadian province of Alberta during 2009-2011 fire seasons. In this research, I critically analyzed the current operational fire danger forecasting systems and other remote sensing-based methods in order to determine the knowledge gaps. In general, the operational systems use point-based measurements of meteorological variables and generate danger maps upon employing interpolation techniques. It is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. It was observed that most of the fire danger monitoring systems focused on determining the danger during and/or after the period of image acquisition, thus unable to forecast the fire danger accurately. A limited number of studies were conducted to forecast fire danger conditions, which could be adaptable. In this thesis, I developed FFDFS’s useful for mid-term (i.e., 8-day) and daily-scale forecasting. The newly developed 8-day scale FFDFS uses Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 8-day composite of surface temperature (TS), normalized multiband drought index (NMDI), and normalized difference vegetation index (NDVI). In order to eliminate the data gaps in the input variables, I propose a gap-filling technique that considered both of the spatial and temporal dimensions. The input variables were calculated during the i period and then integrated to forecast the danger conditions into four categories during the i + 1 period. I observed that 90.94% of the fire fell under ‘very high’ to ‘moderate’ danger classes when compared with Alberta Environment and Sustainable Resource Development (ESRD) fire spots. As regards to operational perspective, I opted to develop daily-scale FFDFS comprised of MODIS-derived 8-day composite of TS, NDVI, and NMDI; and daily precipitable water (PW). The TS, NMDI, and NDVI variables were calculated during i period and PW during j day; and then integrated to forecast fire danger conditions into five categories during j+1 day. Results were significant with 95.51% of fires in the ‘extremely high’ to ‘moderate’ danger classes. Therefore, I infer that the refined FFDFS approach developed using remote sensing variables has operational value and can be routinely incorporated into meteorological based fire forecasting systems. Therefore, I apprehend that FFDFS could be used as an operational one; and has the potential to supplement information to the operational meteorological-based forecasting systems.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».