Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?

25. December 2016 - 0:00
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A new article by the Fire_cci team has been published in Remote Sensing (doi:10.3390/rs9010007).

Global burned area (BA) datasets from satellite Earth observations provide information for carbon emission and for Dynamic Global Vegetation Model (DGVM) benchmarking. Fire patch identification from pixel-level information recently emerged as an additional way of providing informative features about fire regimes through the analysis of patch size distribution. We evaluated the ability of global BA products to accurately represent morphological features of fire patches, in the fire-prone Brazilian savannas. We used the pixel-level burned area from LANDSAT images, as well as two global products: MODIS MCD45A1 and the European Space Agency (ESA) fire Climate Change Initiative (FIRE_CCI) product for the 2002–2009 time period. Individual fire patches were compared by linear regressions to test the consistency of global products as a source of burned patch shape information. Despite commission and omission errors respectively reaching 0.74 and 0.81 for ESA FIRE_CCI and 0.64 and 0.62 for MCD45A1 when compared to LANDSAT due to missing small fires, correlations between patch areas showed R2 > 0.6 for all comparisons, with a slope of 0.99 between ESA FIRE_CCI and MCD45A1 but a lower slope (0.6–0.8) when compared to the LANDSAT data. Shape complexity between global products was less correlated (R2 = 0.5) with lower values (R2 = 0.2) between global products and LANDSAT data, due to their coarser resolution. For the morphological features of the ellipse fitted over fire patches, R2 reached 0.6 for the ellipse’s eccentricity and varied from 0.4 to 0.8 for its azimuthal directional angle. We conclude that global BA products underestimate total BA as they miss small fires, but they also underestimate burned patch areas. Patch complexity is the least correlated variable, but ellipse features appear to provide information to be further used for quality product assessment, global pyrogeography or DGVM benchmarking.