Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa
Roteta Otaegui, Ekhi
Bastarrika Iriarte, Ainhoa
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Remote Sensing of Environment 222 : 1-17 (2019)
A locally-adapted multitemporal two-phase burned area (BA) algorithm has been developed using as inputs Sentinel-2 MSI reflectance measurements in the short and near infrared wavebands plus the active fires detected by Terra and Aqua MODIS sensors. An initial burned area map is created in the first step, from which tile dependent statistics are extracted for the second step. The whole Sub-Saharan Africa (around 25 M km(2)) was processed with this algorithm at a spatial resolution of 20 m, from January to December 2016. This period covers two half fire seasons on the Northern Hemisphere and an entire fire season in the South. The area was selected as existing BA products account it to include around 70% of global BA. Validation of this product was based on a two-stage stratified random sampling of Landsat multitemporal images. Higher accuracy values than existing global BA products were observed, with Dice coefficient of 77% and omission and commission errors of 26.5% and 19.3% respectively. The standard NASA BA product (MCD64A1 c6) showed a similar commission error (20.4%), but much higher omission errors (59.6%), with a lower Dice coefficient (53.6%). The BA algorithm was processed over > 11,000 Sentinel-2 images to create a database that would also include small fires (< 100 ha). This is the first time a continental BA product is generated from medium resolution sensors (spatial resolution = 20 m), showing their operational potential for improving our current understanding of global fire impacts. Total BA estimated from our product was 4.9 M km(2), around 80% larger area than what the NASA BA product (MCD64A1 c6) detected in the same period (2.7 M km(2)). The main differences between the two products were found in regions where small fires (< 100 ha) account for a significant proportion of total BA, as global products based on coarse pixel sizes (500 m for MCD64A1) unlikely detect them. On the negative side, Sentinel-2 based products have lower temporal resolution and consequently are more affected by cloud/cloud shadows and have less temporal reporting accuracy than global BA products. The product derived from S2 imagery would greatly contribute to better understanding the impacts of small fires in global fire regimes, particularly in tropical regions, where such fires are frequent. This product is named FireCCISFD11 and it is publicly available at: https://www.esa-fire-cci.org/node/262, last accessed on November 2018.