Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
Crystal D. McClure![https://orcid.org/0000-0001-7477-5528](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0003-2127-3940](/media/client/orcid_16x16.png)
A Sonoma Technology, Inc., 1450 N. McDowell Boulevard, Suite 200, Petaluma, CA 94954, USA.
International Journal of Wildland Fire 32(5) 694-708 https://doi.org/10.1071/WF22048
Submitted: 9 April 2022 Accepted: 23 February 2023 Published: 3 April 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
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.
Keywords: fire behaviour, fire detection, fire growth, fire history, MODIS, remote sensing, VIIRS, wildfire perimeters.
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