Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

Emilio Chuvieco, Joshua Lizundia-Loiola, Maria Lucrecia Pettinari, Ruben Ramo, Marc Padilla, Kevin Tansey, Florent Mouillot, Pierre Laurent, Thomas Storm, Angelika Heil, Stephen Plummer
2018 Earth System Science Data  
<p><strong>Abstract.</strong> <span id="page2016"/>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<span class="thinspace"></span>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
more » ... tion 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<span class="inline-formula"><sup>∘</sup></span>. 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<span class="thinspace"></span>Mkm<span class="inline-formula"><sup>2</sup></span>, with maximum burning in 2011 (4.1<span class="thinspace"></span>Mkm<span class="inline-formula"><sup>2</sup></span>) and minimum in 2013 (3.24<span class="thinspace"></span>Mkm<span class="inline-formula"><sup>2</sup></span>). 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<span class="thinspace"></span>m resolution). However, commission and omission errors are better compensated in our product, with a tendency towards BA underestimation (relative bias <span class="inline-formula">−0.4033</span>), as most existing global BA products. To understand the value of this product in detecting small fire patches (<span class="inline-formula">&amp;lt;100</span><span class="thinspace"></span>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 (<span class="inline-formula">&amp;lt;100</span><span class="thinspace"></span>ha) than the equivalent 500<span class="thinspace"></span>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 (<span class="uri"></span>, last access: 10 November 2018) and their repositories (pixel at full resolution: <a href=""></a>, and grid: <a href=""></a>).</p>
doi:10.5194/essd-10-2015-2018 fatcat:6up3efz5djcrvchroe5jcsqkiu