Methodology for the detection of land cover changes in time series of daily satellite images. Application to burned area detection


  • J.A. Moreno-Ruiz Universidad de Almería
  • M. Arbelo Universidad de la Laguna
  • J.R. García-Lázaro Universidad de Almería
  • D. Riaño-Arribas University of California



boreal forest, burned area algorithm, bayesian network, time series


We have developed a methodology for detection of observable phenomena at pixel level over time series of daily satellite images, based on using a Bayesian classifier. This methodology has been applied successfully to detect burned areas in the North American boreal forests using the LTDR dataset. The LTDR dataset represents the longest time series of global daily satellite images with 0.05° (~5 km) of spatial resolution. The proposed methodology has several stages: 1) pre-processing daily images to obtain composite images of n days; 2) building of space of statistical variables or attributes to consider; 3) designing an algorithm, by selecting and filtering the training cases; 4) obtaining probability maps related to the considered thematic classes; 5) post-processing to improve the results obtained by applying multiple techniques (filters, ranges, spatial coherence, etc.). The generated results are analyzed using accuracy metrics derived from the error matrix (commission and omission errors, percentage of estimation) and using scattering plots against reference data (correlation coefficient and slope of the regression line). The quality of the results obtained improves, in terms of spatial and timing accuracy, to other burned area products that use images of higher spatial resolution (500 m and 1 km), but they are only available after year 2000 as MCD45A1 and BA GEOLAND-2: the total burned area estimation for the study region for the years 2001-2011 was 28.56 millions of ha according to reference data and 12.41, 138.43 and 19.41 millions of ha for the MCD45A1, BA GEOLAND-2 and BA-LTDR burned area products, respectively.


Download data is not yet available.

Author Biographies

J.A. Moreno-Ruiz, Universidad de Almería

Grupo de Tratamiento de Imágenes, Departamento de Informática, Universidad de Almería.

M. Arbelo, Universidad de la Laguna

Grupo de Observación de la Tierra y la Atmósfera (GOTA), Departamento de Física FEES, Universidad de la Laguna

J.R. García-Lázaro, Universidad de Almería

Grupo de Tratamiento de Imágenes, Departamento de Informática, Universidad de Almería.

D. Riaño-Arribas, University of California

Center for Spatial Technologies and Remote Sensing (CSTARS), University of California


Al-Rawi, K.R., Casanova, J.L., Calle, A. 2001. Burned area mapping system and fire detection system, based on neural networks and NOAA-AVHRR imagery. International Journal of Remote Sensing, 22(10), 2015-2032.

Barbosa, P.M., Pereira, J.M.C., Grégoire, J.M. 1998. Compositing Criteria for Burned Area Assessment Using Multitemporal Low Resolution Satellite Data. Remote Sensing of Environment, 65(1), 38-49.

Bayes, T. 1763. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370-418.

Burks, A.W. 1970. Essays on cellular automata.University of Illinois Press, Urbana, Illinois.

Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data.Remote Sensing of Environment, 37(1), 35-46.

Crosetto, M., Moreno Ruiz, J.A., Crippa, B. 2001. Uncertainty propagation in models driven by remotely sensed data. Remote Sensing of Environment, 76(3), 373-385.

Chuvieco, E., Englefield, P., Trishchenko, A.P., Luo, Y.2008. Generation of long time series of burn area maps of the boreal forest from NOAA–AVHRR composite data. Remote Sensing of Environment, 112(5), 2381-2396.

Chuvieco, E., Ventura, G., Martín, M.P., Gómez, I. 2005. Assessment of multitemporal compositing techniques of MODIS and AVHRR images

for burned land mapping. Remote Sensing of Environment, 94(4), 450-462.

Foody, G.M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.

García Lázaro, J.R., Moreno Ruiz, J.A., Arbeló, M. 2013. Effect of spatial resolution on the accuracy of satellite based fire scar detection in the north-west of the Iberian Peninsula. International Journal of

Remote Sensing, 34(13), 4736-4753.

Hall, M., Frank, E., Holmes, G., Pfahringer, B.,Reutemann, P., Witten, I.H. 2009. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1), 10-18.

Moreno-Ruiz, J.A., Riaño, D., García-Lazaro, J.R., Ustin, S.L. 2009. Intercomparison of AVHRR PAL and LTDR version 2 long-term data sets for Africa from 1982 to 2000 and its impact on mapping burned area. IEEE Geoscience and Remote Sensing Letters, 6(4), 738-742.

Moreno Ruiz, J.A., Riaño, D., Arbelo, M., French, N.H.F., Ustin, S.L., Whiting, M.L. 2012. Burned area mapping time series in Canada (1984-1999) from NOAA-AVHRR LTDR: A comparison with other remote sensing products and fire perimeters. Remote Sensing of Environment, 117, 407-414.

Moreno Ruiz, J.A., García Lázaro, J.R., del Águila Cano,I., Hernández Leal, P. 2014. Burned area mapping in the North American boreal forest using terra-MODIS LTDR (2001-2011): A comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 products. Remote Sensing, 6(1), 815-840.

Nuñez-Casillas, L., García Lázaro, J.R., Moreno-Ruiz, J.A., Arbelo, M. 2013. A Comparative Analysis of Burned Area Datasets in Canadian Boreal Forest in 2000. The Scientific World Journal, 2013: 13.

Pedelty, J., Devadiga, S., Masuoka, E., Brown, M.,Pinzon, J., Tucker, C., Pinheiro, A., 2007. Generating a long-term land data record from the AVHRR and MODIS instruments. En: Geoscience and Remote

Sensing Symposium (IGARSS). Barcelona (España), 23-28, julio. pp. 1021-1025.

Pinty, B., Verstraete, M.M. 1992. GEMI: a non-linear index to monitor global vegetation from satellites.Vegetatio, 101(1), 15-20.

Riaño, D., Moreno Ruiz, J.A., Isidoro, D., Ustin, S.L. 2007. Global spatial patterns and temporal trends of burned area between 1981 and 2000 using NOAA-NASA Pathfinder. Global Change Biology, 13(1), 40-50.

Roy, D.P., Boschetti, L., Justice, C.O., Ju, J. 2008. The collection 5 MODIS burned area product - Global evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment, 112(9), 3690-3707.

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.

Stehman, S.V. 1997. Estimating standard errors of accuracy assessment statistics under cluster sampling. Remote Sensing of Environment,

(3), 258-269.

Tansey, K., Bradley, A., Smets, B., van Best, C., Lacaze,R. 2012. The Geoland2 BioPar burned area product. En: EGU General Assembly Conference Abstracts. Viena (Austria), 22-27, abril, pp. 4727.

Zhan, X., Sohlberg, R.A., Townshend, J.R.G., DiMiceli, C., Carroll, M.L., Eastman, J.C., Hansen, M.C., DeFries, R.S. 2002. Detection of land cover

changes using MODIS 250 m data. Remote Sensing of Environment, 83(1-2), 336-350.





Research articles