Burned area detection based on time-series analysis in a cloud computing environment

[EN] 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.[ES] Los productos globales de área quemada tienden a omitir una importante extensión de área afectada por el fuego, este error luego se traslada a otros modelos, por ejemplo, en las estimaciones nacionales de gases efecto invernadero utilizando el método conocido como “bottom up”. En este trabajo se evalúan métodos temporales para mejorar la identificación de áreas quemadas con datos de resolución espacial media (Landsat 5-TM y 8-OLI). En este proceso se utiliza el índice de proporción de quema normalizada (NBR) para resaltar las áreas quemadas y el método de selección de umbrales para separar las áreas quemadas de las no quemadas. Con el fin de maximizar la detección de área quemada se utilizaron dos métodos alternativos al método temporal dNBR: la forma relativa del método temporal, RdNBR, y el uso de métricas de series de tiempo. El procesamiento, el desarrollo del algoritmo y el acceso a los datos Landsat fue realizado en la plataforma de Google Earth Engine, GEE. Se evaluaron tres regiones con alta ocurrencia del fuego en Latino América: los bosques de la Amazonía colombiana, la transición de Bosque Chiquitano a Bosque Amazónico en Bolivia y El Chaco en Argentina. La evaluación de la calidad de los productos generados se basa en los protocolos de área quemada. Los resultados muestran que el mejor modelo identifica el 85% de las áreas quemadas en el Bosque Chiquitano de Bolivia, el 63% en los Bosques Amazónicos de Colombia y el 69% en El Chaco de Argentina.Los autores quieren agradecer el apoyo institucional de la Universidad de Medellín (UDEM) y de la Universidad Autónoma de Entre Ríos (UADER). El proyecto fue realizado en el contexto de REDLATIF y liderado por la línea de investigación de “Geomática aplicada a los recursos naturales” del grupo GEMA, con el apoyo de la Vicerrectoría de investigación de la UDEM. Convenio 294 código 789Anaya, J.; Sione, W.; Rodríguez-Montellano, A. (2018). Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube. Revista de Teledetección. (51):61-73. https://doi.org/10.4995/raet.2018.8618617351Alonso-Canas, I., Chuvieco, E. 2015. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. 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