Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies
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Abstract. This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer
(MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly
data, thus providing the highest spatial resolution (approx. 250 m) among
the existing global BA datasets. The product includes the full times series
(2001–2016) of the Terra-MODIS archive. The BA detection algorithm was based
on monthly composites of daily images, using temporal and spatial distance
to active fires. The algorithm has two steps, the first one aiming to reduce
commission errors by selecting the most clearly burned pixels (seeds), and
the second one targeting to reduce omission errors by applying contextual
analysis around the seed pixels. This product was developed within the
European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the
Fire Disturbance project (Fire_cci). The final output
includes two types of BA files: monthly full-resolution continental tiles
and biweekly global grid files at a degraded resolution of 0.25∘.
Each set of products includes several auxiliary variables that were defined
by the climate users to facilitate the ingestion of the product into global
dynamic vegetation and atmospheric emission models. Average annual burned
area from this product was 3.81 Mkm2, with maximum burning in 2011 (4.1 Mkm2)
and minimum in 2013 (3.24 Mkm2). The validation was based on
a stratified random sample of 1200 pairs of Landsat images, covering the
whole globe from 2003 to 2014. The validation indicates an overall accuracy
of 0.9972, with much higher errors for the burned than the unburned category
(global omission error of BA was estimated as 0.7090 and global commission
as 0.5123). These error values are similar to other global BA products, but
slightly higher than the NASA BA product (named MCD64A1, which is produced
at 500 m resolution). However, commission and omission errors are better
compensated in our product, with a tendency towards BA underestimation
(relative bias −0.4033), as most existing global BA products. To understand
the value of this product in detecting small fire patches (<100 ha),
an additional validation sample of 52 Sentinel-2 scenes was generated
specifically over Africa. Analysis of these results indicates a better
detection accuracy of this product for small fire patches (<100 ha)
than the equivalent 500 m MCD64A1 product, although both have high errors for
these small fires. Examples of potential applications of this dataset to
fire modelling based on burned patches analysis are included in this paper.
The datasets are freely downloadable from the Fire_cci
website (https://www.esa-fire-cci.org/, last access: 10 November 2018) and their repositories (pixel at
full resolution: https://doi.org/cpk7, and grid: https://doi.org/gcx9gf).
