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

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 cha...

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Autores principales:	Ekhi Roteta, Aitor Bastarrika, Askoa Ibisate, Emilio Chuvieco
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	burned-area mapping Landsat Sentinel-2 active fires global Google Earth Engine Science Q
Acceso en línea:	https://doaj.org/article/18b99a85d08f4ad9993ef1f49252aec7
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spelling	oai:doaj.org-article:18b99a85d08f4ad9993ef1f49252aec72021-11-11T18:53:21ZA Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine10.3390/rs132142982072-4292https://doaj.org/article/18b99a85d08f4ad9993ef1f49252aec72021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4298https://doaj.org/toc/2072-4292A 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 km<sup>2</sup> 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.Ekhi RotetaAitor BastarrikaAskoa IbisateEmilio ChuviecoMDPI AGarticleburned-area mappingLandsatSentinel-2active firesglobalGoogle Earth EngineScienceQENRemote Sensing, Vol 13, Iss 4298, p 4298 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	burned-area mapping Landsat Sentinel-2 active fires global Google Earth Engine Science Q
spellingShingle	burned-area mapping Landsat Sentinel-2 active fires global Google Earth Engine Science Q Ekhi Roteta Aitor Bastarrika Askoa Ibisate Emilio Chuvieco A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
description	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 km<sup>2</sup> 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.
format	article
author	Ekhi Roteta Aitor Bastarrika Askoa Ibisate Emilio Chuvieco
author_facet	Ekhi Roteta Aitor Bastarrika Askoa Ibisate Emilio Chuvieco
author_sort	Ekhi Roteta
title	A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
title_short	A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
title_full	A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
title_fullStr	A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
title_full_unstemmed	A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
title_sort	preliminary global automatic burned-area algorithm at medium resolution in google earth engine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/18b99a85d08f4ad9993ef1f49252aec7
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A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine

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