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

Earth System Science Data - Tập 10 Số 4 - Trang 2015-2031
Emilio Chuvieco1, Joshua Lizundia-Loiola1, M. Lucrecia Pettinari1, Rubén Ramo1, Marc Padilla2, Kevin Tansey2, Florent Mouillot3, Pierre Laurent4, Thomas Storm5, Angelika Heil6, Stephen Plummer7
1Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, Universidad de Alcalá, Calle Colegios 2, Alcalá de Henares, 28801, Spain
2Centre for Landscape & Climate Research, Leicester Institute for Space and Earth Observation, School of Geography, University of Leicester, Leicester, LE1 7RH, UK
3UMR CEFE 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, IRD, 1919 route de Mende, 34293 Montpellier CEDEX 5, France
4Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, 91440, France
5Brockmann Consult GmBH, Max-Planck-Straße 2, 21502 Geesthacht, Germany
6Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, B.3.53, 55128 Mainz, Germany
7ESA Earth Observation Climate Office, ECSAT, Fermi Avenue Harwell Campus, Didcot, Oxfordshire, OX11 0FD, UK

Tóm tắt

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).

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Earth System Science Data

Tập 10 Số 4

2015-2031

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