Abstract
A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those candidates are then used to estimate the burn probability for each scene. The burning dates are identified by analyzing the temporal evolution of burn probabilities. The algorithm was processed, and its quality assessed globally using reference data from 2019 derived from Sentinel-2 data at 10 m, which involved 369 pairs of consecutive images in total located in 50 20 × 20 km2 areas selected by stratified random sampling. Commissions were around 10% with both satellites, although omissions ranged between 27 (Sentinel-2) and 35% (Landsat), depending on the selected resolution and dataset, with highest omissions being in croplands and forests; for their part, BA from Sentinel-2 data at 20 m were the most accurate and fastest to process. In addition, three 5 × 5 degree regions were randomly selected from the biomes where most fires occur, and BA were detected from Sentinel-2 images at 20 m. Comparison with global products at coarse resolution FireCCI51 and MCD64A1 would seem to show to a reliable extent that the algorithm is procuring spatially and temporally coherent results, improving detection of smaller fires as a consequence of higher-spatial-resolution data. The proposed automatic algorithm has shown the potential to map BA globally using medium-spatial-resolution data (Sentinel-2 and Landsat) from 2000 onwards, when MODIS satellites were launched.
Highlights
Burned areas (BA) and active fires have been detected by satellite Earth observation, the main purpose of which is to obtain a better understanding of fire regimes to analyze their effect on climate change, since both fires and climate have a mutual effect on the fact that fire can be affected by droughts and high temperatures [5], and climate change is impacted by biomass burning and greenhouse gas emissions into the atmosphere [3], among many other factors
A new automatic global BA detection algorithm based on Landsat or Sentinel-2 reflectance and MODIS active fires is presented in this paper, which may be processed at three different spatial resolutions—10, 20, or 30 m—depending on whether Sentinel-2 mission (S2) or Landsat data are chosen; this is still a preliminary algorithm, and a rigorous validation with independent data should still be done in order to statistically estimate the algorithm’s accuracy, identify error sources and propose necessary modifications for the algorithm before an operational global BA detection
The new methodology involves detecting some burned candidate pixels around hotspots based on their spectral changes, using these candidate pixels to classify single images from a dense time series with burn probability values, and analyzing the temporal evolution of these probabilities to detect BA and their dates
Summary
Fire disturbance is one of the Essential Climate Variables (ECV) defined by the Global Climate Observing System (GCOS) program [1], since it affects land-cover changes, soil erosion, emissions of gases and aerosols into the atmosphere, and people’s lives [2,3,4]. The first global BA products were released almost two decades ago based on data at coarse spatial resolution (>100 m): GBA2000 and GLOBSCAR, derived from SPOTVegetation and ATSR-2 sensors respectively, both at 1 km resolution [6,7]. NASA released two BA products, Remote Sens. MCD64A1 is NASA’s standard BA product, and its collection 6 is the latest version, released in
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