Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations

TitleConsistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
Publication TypeJournal Article
Year of Publication2023
AuthorsMcClure, CD, Pavlovic, NR, Huang, SM, Chaveste, M, Wang, N
JournalInternational Journal of Wildland Fire
Date Published04/2023
Keywordsfire behaviour, fire detection, fire growth, fire history, MODIS, remote sensing, technical reports and journal articles, VIIRS, wildfire perimeters

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.

Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports.

Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset.

Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R2 = 0.95) and VIIRS (R2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available.

Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring.