Burned area detection based on time-series analysis in a cloud computing environment
There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to impr...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2018-06-01
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| Series: | Revista de Teledetección |
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| Online Access: | https://polipapers.upv.es/index.php/raet/article/view/8618 |
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| author | J.A. Anaya W.F. Sione A.M. Rodriguez-Montellano |
| author_facet | J.A. Anaya W.F. Sione A.M. Rodriguez-Montellano |
| author_sort | J.A. Anaya |
| collection | DOAJ |
| description | There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina. |
| format | Article |
| id | doaj-art-0775ff48618843fc851fc7832b8bb201 |
| institution | OA Journals |
| issn | 1133-0953 1988-8740 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Revista de Teledetección |
| spelling | doaj-art-0775ff48618843fc851fc7832b8bb2012025-08-20T02:36:06ZengUniversitat Politècnica de ValènciaRevista de Teledetección1133-09531988-87402018-06-01051617310.4995/raet.2018.86187011Burned area detection based on time-series analysis in a cloud computing environmentJ.A. Anaya0W.F. Sione1A.M. Rodriguez-Montellano2Universidad de MedellínUniversidad Autónoma de Entre RíosFundación Amigos de la Naturaleza; Universidad Autónoma Gabriel René MorenoThere are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.https://polipapers.upv.es/index.php/raet/article/view/8618Área quemadaincendiosNBRGEEComputación en la nube |
| spellingShingle | J.A. Anaya W.F. Sione A.M. Rodriguez-Montellano Burned area detection based on time-series analysis in a cloud computing environment Revista de Teledetección Área quemada incendios NBR GEE Computación en la nube |
| title | Burned area detection based on time-series analysis in a cloud computing environment |
| title_full | Burned area detection based on time-series analysis in a cloud computing environment |
| title_fullStr | Burned area detection based on time-series analysis in a cloud computing environment |
| title_full_unstemmed | Burned area detection based on time-series analysis in a cloud computing environment |
| title_short | Burned area detection based on time-series analysis in a cloud computing environment |
| title_sort | burned area detection based on time series analysis in a cloud computing environment |
| topic | Área quemada incendios NBR GEE Computación en la nube |
| url | https://polipapers.upv.es/index.php/raet/article/view/8618 |
| work_keys_str_mv | AT jaanaya burnedareadetectionbasedontimeseriesanalysisinacloudcomputingenvironment AT wfsione burnedareadetectionbasedontimeseriesanalysisinacloudcomputingenvironment AT amrodriguezmontellano burnedareadetectionbasedontimeseriesanalysisinacloudcomputingenvironment |