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


  • J.A. Anaya Universidad de Medellín https://orcid.org/0000-0001-8242-5712
  • W.F. Sione Universidad Autónoma de Entre Ríos
  • A.M. Rodriguez-Montellano Fundación Amigos de la Naturaleza; Universidad Autónoma Gabriel René Moreno




burned area, fires, NBR, GEE, cloud computing


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.


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Author Biographies

J.A. Anaya, Universidad de Medellín

Facultad de IngenieríaProfesor Titular

W.F. Sione, Universidad Autónoma de Entre Ríos

Centro Regional de GeomáticaProfesor


Alonso-Canas, I., Chuvieco, E. 2015. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sensing of Environment, 163, 140-152. https://doi.org/10.1016/j.rse.2015.03.011

Anaya, J. A., Chuvieco, E., 2010. Caracterización de la eficiencia del quemado a partir del análisis de series de tiempo del índice de vegetación EVI. Paper presented at the XVI Simposio internacional SELPER, Guanajuato, México.

Anaya, J. A., Chuvieco, E. 2012. Accuracy assessment of burned area products in the Orinoco basin. Photogrammetric Engineering and Remote Sensing, 78(1), 53-60. https://doi.org/10.14358/PERS.78.1.53

Armenteras, D., Gibbes, C., Anaya, J. A., Dávalos, L. M. 2017. Integrating remotely sensed fires for predicting deforestation for REDD+. Ecological Applications, 27(4), 1294-1304. https://doi.org/10.1002/eap.1522

Bastarrika, A., Chuvieco, E., Martín, M. P. 2011. Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sensing of Environment, 115(4), 1003-1012. https://doi.org/10.1016/j.rse.2010.12.005

Congalton, R. G., Green, K., 2009. Assessing the accuracy of remotely sensed data (2nd ed.). Boca Raton, FL, USA: CRC Press. https://doi.org/10.1201/9781420055139

Chander, G., Markham, B. L., Helder, D. L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893-903. https://doi.org/10.1016/j.rse.2009.01.007

Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z., Sun, R., Lan, G., Xie, G., Clinton, N., Giri, C. 2017. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131(Supplement C), 104-120. https://doi.org/10.1016/j.isprsjprs.2017.07.011

Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W. T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A., Whetton, P., 2007. Regional Climate Projections. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (Ed.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Chuvieco, E., Martin, M. P., Palacios, A. 2002. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23, 5103- 5110. https://doi.org/10.1080/01431160210153129

Chuvieco, E., Yue, C., Heil, A., Mouillot, F., AlonsoCanas, I., Padilla, M., Pereira, J. M., Oom, D., Tansey, K. 2016. A new global burned area product for climate assessment of fire impacts. Global Ecology and Biogeography, 25(5), 619-629. https://doi.org/10.1111/geb.12440

Devisscher, T., Malhi, Y., Rojas Landívar, D., Oliveras, I., 2015. Understanding ecological transitions under recurrent wildfire: A case study in the seasonally dry tropical forests of the Chiquitania, Bolivia. Forest Ecology and Management, 360, 273-286. https://doi.org/10.1016/j.foreco.2015.10.033

Giglio, L., Loboda, T., Roy, D. P., Quayle, B., Justice, C. O. 2009. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment, 113(2), 408-420. https://doi.org/10.1016/j.rse.2008.10.006

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031

Key, C., Benson, N., 2006. Landscape Assessment (LA) Sampling and Analysis Method. In U. F. S. G. T. Rep. (Ed.), RMRS-GTR-164-CD (pp. 51).

Key, C. H., 2005. Remote sensing sensitivity to fire severity and fire recovery. Paper presented at the International workshop on Remote Sensing and GIS applications to forest fire management: fire effects assessment, Universidad de Zaragoza, Spain.

Libonati, R., DaCamara, C. C., Pereira, J. M. C., Peres, L. F. 2010. Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS. Remote Sensing of Environment, 114(4), 831-843. https://doi.org/10.1016/j.rse.2009.11.018

Liss, B., Howland, M. D., Levy, T. E. 2017. Testing Google Earth Engine for the automatic identification and vectorization of archaeological features: A case study from Faynan, Jordan. Journal of Archaeological Science: Reports, 15, 299-304. https://doi.org/10.1016/j.jasrep.2017.08.013

Melchiori, A. E., Candido, P. d. A., Libonati, R., Morelli, F., Setzer, A., de Jesús, S. C., GarciaFonseca, L. M., Korting, T. S., 2015. Spectral indices and multi-temporal change image detection algorithms for burned area extraction in the Brazilian Cerrado. Paper presented at the Anais XVII Simpsio Brasileiro de Sensoramiento Remoto, Joao Pessoa-PB, Brasil.

Miller, J. D., Knapp, E. E., Key, C. H., Skinner, C. N., Isbell, C. J., Creasy, R. M., Sherlock, J. W. 2009. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sensing of Environment, 113(3), 645-656. https://doi.org/10.1016/j.rse.2008.11.009

Miller, J. D., Thode, A. E. 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109(1), 66-80. https://doi.org/10.1016/j.rse.2006.12.006

Padilla, M., Stehman, S. V., Chuvieco, E. 2014. Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144, 187-196. https://doi.org/10.1016/j.rse.2014.01.008

Padilla, M., Stehman, S. V., Ramo, R., Corti, D., Hantson, S., Oliva, P., Alonso-Canas, I., Bradley, A. V., Tansey, K., Mota, B., Pereira, J. M., Chuvieco, E. 2015. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment, 160, 114-121. https://doi.org/10.1016/j.rse.2015.01.005

Pereira, J. M. C. 2003. Remote sensing of burned areas in tropical savannas. International Journal of Wildland Fire, 12(4), 259-270. https://doi.org/10.1071/WF03028

Potter, C., Tan, P.-N., Steinbach, M., Klooster, S., Kumar, V., Myneni, R., Genovese, V. 2003. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology, 9(7), 1005-1021. https://doi.org/10.1046/j.1365-2486.2003.00648.x

Rodríguez-Montellano, A., Libonatti, R., Melchiori, A. E. 2015, Sensibilidad en la detección de áreas quemadas en tres ecosistemas vegetales de Bolivia, utilizando tres productos regionales. Simpósio Brasileiro de Sensoramiento Remoto, João Pessoa, Brasil, 25 a 29 de abril de 2015.

Roy, D., Boschetti, L., O’Neal, K. 2006. MODIS Collection 5 Burned Area Product MCD45 User’s Guide. USGS, University of Maryland, 12.

Roy, D. P., Boschetti, L. 2009. Southern Africa Validation of the MODIS, L3JRC and GlobCarbon Burned-Area Products. IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1-13. https://doi.org/10.1109/TGRS.2008.2009000

Roy, D. P., Jin, Y., Lewis, P. E., Justice, C. O. 2005. Prototyping a global algorithm for systematic fireaffected area mapping using MODIS time series data. Remote Sensing of Environment, 97(2), 137- 162. https://doi.org/10.1016/j.rse.2005.04.007

Schroeder, W., Oliva, P., Giglio, L., Csiszar, I. A. 2014. The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment, 143(0), 85-96. https://doi.org/10.1016/j.rse.2013.12.008

Sofía, M., Grau, H. R., Néstor Ignacio, G., Tobias, K., Matthias, B. 2017. Differences in production, carbon stocks and biodiversity outcomes of land tenure regimes in the Argentine Dry Chaco. Environmental Research Letters, 12(4), 045003. https://doi.org/10.1088/1748-9326/aa625c

Tansey, K., Grégoire, J.-M., Pereira, J. M. C., Defourny, P., Leigh, R., Pekel, J.-F., Barros, A., Silva, J. N. M., van Bogaert, E., Bartholomé, E., Bontemps, S., 2007, 11-14 September 2007. L3JRC - A global, multi-year (2000-2007) burnt area product (1 km resolution and daily time steps). Paper presented at the Remote Sensing and Photogrammetry Society Annual Conference 2007, Newcastle upon Tyne, UK. https://doi.org/10.1029/2007GL031567

Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J. 2016. Implementation and evaluation of the landsat ecosystem disturbance adaptive processing systems (LEDAPS) model: A case study in the Colombian andes. Revista de Teledeteccion, 46, 83-101. https://doi.org/10.4995/raet.2016.3582

Vermote, E. F., El Saleous, N. Z., Justice, C. O. 2002. Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sensing of Environment, 83(1-2), 97-111. https://doi.org/10.1016/S0034-4257(02)00089-5

Zhu, Z., Woodcock, C. E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83-94. https://doi.org/10.1016/j.rse.2011.10.028

Zhu, Z., Woodcock, C. E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152, 217-234. https://doi.org/10.1016/j.rse.2014.06.012





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