Building a Small Fire Database for Sub-Saharan Africa from Sentinel-2 High-Resolution Images
Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work using Sentinel-2 A data to develop a small fire dataset (SFD) for the Sub-Saharan African, this paper presents a new version of the dataset (called FireCCISFD20) for 2019 using now the two Sentinel-2 satellites and VIIRS active fires. Total estimated BA was 4.8 Mkm 2 . This value was much higher than estimations from two global, coarser-spatial resolution BA products based on MODIS data for the same area and period: 80% greater than estimations from FireCCI51 (based on MODIS 250 m bands) and 120% larger than MCD64A1 (based on MODIS 500 m bands). The main differences were observed in those months with higher fire occurrence (November to January for the Northern Hemisphere regions and June to September for the Southern hemisphere ones).Accuracy assessment of the BA product was based on a novel sampling strategy designed to obtain independent fire reference perimeters. Validation results showed remarkable high accuracy values comparing to existing global BA products. Overall omission errors (OE) were estimated as 8.5%, commission errors (CE) as 15.0%, with a Dice Coefficient of 87.7%. All of these estimations implied significant improvements over the global, coarser spatial resolution BA products (OE > 50% and CE > 20% for the same area and period), as well as over the previous Sentinel-2 BA product for 2016 of the same area, generated from a single satellite and MODIS active fires (OE = 26.5 % and CE=19.3%). Temporal accuracies greatly increased as well with the new product, with 92.5% of fires detected within the first 10 days of occurrence
Year of publication: |
[2022]
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Authors: | Chuvieco, Emilio ; Roteta, Ekhi ; Sali, Matteo ; Stroppiana, Daniela ; Boettcher, Martin ; Kirches, Grit ; Storm, Thomas ; Khairoun, Amin ; Pettinari, M. Lucrecia ; Albergel, Clément |
Publisher: |
[S.l.] : SSRN |
Subject: | Zentralafrika | Central Africa | Datenbank | Database | KMU | SME |
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