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Article

A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine

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Department of Geography, Prehistory and Archaeology, University of the Basque Country UPV/EHU, Tomás y Valiente s/n, 01006 Vitoria-Gasteiz, Spain
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Department of Mining and Metallurgical Engineering and Materials Science, School of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
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Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, University of Alcalá UAH, C/Colegios 2, 28801 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Fangjun Li and Xiaoyang Zhang
Remote Sens. 2021, 13(21), 4298; https://doi.org/10.3390/rs13214298
Received: 11 August 2021 / Revised: 20 October 2021 / Accepted: 22 October 2021 / Published: 26 October 2021
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. View Full-Text
Keywords: burned-area mapping; Landsat; Sentinel-2; active fires; global; Google Earth Engine burned-area mapping; Landsat; Sentinel-2; active fires; global; Google Earth Engine
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MDPI and ACS Style

Roteta, E.; Bastarrika, A.; Ibisate, A.; Chuvieco, E. A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine. Remote Sens. 2021, 13, 4298. https://doi.org/10.3390/rs13214298

AMA Style

Roteta E, Bastarrika A, Ibisate A, Chuvieco E. A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine. Remote Sensing. 2021; 13(21):4298. https://doi.org/10.3390/rs13214298

Chicago/Turabian Style

Roteta, Ekhi, Aitor Bastarrika, Askoa Ibisate, and Emilio Chuvieco. 2021. "A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine" Remote Sensing 13, no. 21: 4298. https://doi.org/10.3390/rs13214298

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