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Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
by
Wang, Ningxin
, Huang, ShihMing
, McClure, Crystal D.
, Pavlovic, Nathan R.
, Chaveste, Melissa
in
Archives & records
/ California
/ data collection
/ Datasets
/ fire behavior
/ fire weather
/ Mapping
/ Modelling
/ MODIS
/ Prescribed fire
/ Real time
/ Satellite observation
/ satellites
/ smoke
/ Wildfires
2023
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Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
by
Wang, Ningxin
, Huang, ShihMing
, McClure, Crystal D.
, Pavlovic, Nathan R.
, Chaveste, Melissa
in
Archives & records
/ California
/ data collection
/ Datasets
/ fire behavior
/ fire weather
/ Mapping
/ Modelling
/ MODIS
/ Prescribed fire
/ Real time
/ Satellite observation
/ satellites
/ smoke
/ Wildfires
2023
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Do you wish to request the book?
Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
by
Wang, Ningxin
, Huang, ShihMing
, McClure, Crystal D.
, Pavlovic, Nathan R.
, Chaveste, Melissa
in
Archives & records
/ California
/ data collection
/ Datasets
/ fire behavior
/ fire weather
/ Mapping
/ Modelling
/ MODIS
/ Prescribed fire
/ Real time
/ Satellite observation
/ satellites
/ smoke
/ Wildfires
2023
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Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
Journal Article
Consistent, high-accuracy mapping of daily and sub-daily wildfire growth with satellite observations
2023
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Overview
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.
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