An intercomparison of Satellite Burned Area Maps derived from MODIS, MERIS, SPOT-VEGETATION, and ATSR images. An application to the August 2006 Galicia (Spain) forest fires

  • M. Huesca ETSI Montes, Universidad Politécnica de Madrid, Madrid.
  • S. Merino de Miguel EUIT Forestal, Universidad Politécnica de Madrid, Madrid.
  • F. González-Alonso Laboratorio de Teledetección, CIFOR-INIA, Madrid.

Abstract

Aim of study: The following paper presents an inter-comparison of three global products: MCD45A1 (MODIS - MODerate resolution Imaging Spectrometer - Burned Area Product), L3JRC (Terrestrial Ecosystem Monitoring Global Burnt Area Product), and GLOBCARBON Burnt Area Estimate (BAE) Product; and three local products, two of them based on MODIS data and the other one based on MERIS (MEdium Resolution Imaging Spectrometer) data.

Area of study: The study was applied to the Galician forest fires occurred in 2006.

Materials and Methods: Materials used involved the three already mentioned global products together with two MODIS and one MERIS reflectance images, and MODIS thermal anomalies. The algorithm we used, which is based on the determination of thresholds values on infrared bands, allowed the identification of burned pixels. The determination of such threshold values was based on the maximum spatial correlation between MODIS thermal anomalies, and infrared reflectance values. This methodology was applied to MODIS and MERIS reflectance bands, and to the NBR (Normalized Burn Ratio). Burned area validation was evaluated using burned area polygons as derived from an AWiFS (Advanced Wide Field Sensor) image of 60m pixel size.

Main results: Best results were reached when using the MERIS infrared bands, followed by the MODIS infrared bands. Worst results were reached when using the MCD45A1 product, which clearly overestimated; and when using the L3JRC product, which clearly underestimated.

Research highlights: Since the efficiency of the performance of the available burned area products is highly variable, much work is needed in terms of comparison among the available sensors, the burned area mapping algorithms and the resulting products.

Keywords: forest fires; MODIS; MERIS; MCD45A1; L3JRC; GLOBCARBON-BAE; SPOT-VEGETATION; ATSR.

Abbreviations used: ATSR: Along Scanning Radiometer; AVHRR: Advanced Very High Resolution Radiometer; AWiFS: Advanced Wide Field Sensor; EOS: Earth Observation System; ESA: European Space Agency; GBA2000: Global Burnt Area 2000; GLOBCARBON-BAE: GLOBCARBON Burnt Area Estimate Product; L3JRC: Terrestrial Ecosystem Monitoring Global Burnt Area Product; MCD45A1: MODIS Burned Area Product; MERIS: MEdium Resolution Imaging Spectrometer; MOD09GA: Terra MODIS Surface Reflectance Daily L2G Global 500 m; MOD09GQ: Terra MODIS Surface Reflectance Daily L2G Global 250 m; MODIS: MODerate resolution Imaging Spectrometer; NBR: Normalized Burn Ratio; NDVI: Normalized Difference Vegetation Index; NIR: near-infrared; SPOT: Satellite Pour l’Observation de la Terre; SWIR: short-wave infrared; UTM: Universal Transverse Mercator.

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Author Biographies

M. Huesca, ETSI Montes, Universidad Politécnica de Madrid, Madrid.
ETSI Montes
S. Merino de Miguel, EUIT Forestal, Universidad Politécnica de Madrid, Madrid.
EUIT Forestal
F. González-Alonso, Laboratorio de Teledetección, CIFOR-INIA, Madrid.

Laboratorio de Teledetección

References

Alcamo J, Moreno J, Nováky B, Bindi M, Corobov R, Devoy R et al. 2007. Climate change 2007, impacts, adaptation and vulnerability. Europe.Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, 541-580.

Al-Rawi K, Casanova J, Romo A. 2001. IFEMS: a new approach for monitoring wildfire evolution with NOAA-AVHRR imagery. Int J Remote Sens 22: 2033-2042.

Anaya-Acevedo J, Chuvieco-Salinero E. 2010. Validación para Colombia de la estimación de área quemada del producto L3JRC en el periodo 2001-2007. Actual Biol 32: 29-40.

Anaya JA, Chuvieco E. 2012. Accuracy assessment of burned area products in the Orinoco basin. Photogramm Eng Rem S 78: 53-60.

Barbosa PM, Grégoire JM, Pereira JMC. 1999. An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale. Remote Sens Environ 69: 253-263. http://dx.doi.org/10.1016/S0034-4257(99)00026-7

Bastarrika A, Chuvieco E, Martín MP. 2011. Automatic Burned Land Mapping From MODISTime Series Images: Assessment in Mediterranean Ecosystems. IEEE T Geosci Remote 49: 3401-3413. http://dx.doi.org/10.1109/TGRS.2011.2128327

Boschetti L, Eva H, Brivio PA, Gregoire JM. 2004a. Lessons to be learned from the comparison of three satellite-derived biomass burning products. Geophys Res Lett 31: L21501. http://dx.doi.org/10.1029/2004GL021229

Boschetti L, Flasse SP, Brivio PA. 2004b. Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary. Remote Sens Environ 91: 280-292. http://dx.doi.org/10.1016/j.rse.2004.02.015

Calle A, González-Alonso F, Merino-de-Miguel S. 2008. Validation of active forest fires detected by MSG-SEVIRI by means of MODIS hot spots and AWiFS images. Int J Remote Sens 29: 3407-3415. http://dx.doi.org/10.1080/01431160701596164

Chang D, Song Y. 2009. Comparison of L3JRC and MODIS global burned area products from 2000 to 2007. J Geophys Res 114, D16106. http://dx.doi.org/10.1029/2008JD011361

Chuvieco E, Opazo S, Sione W, Valle H, Anaya J, Di Bella C, Cruz I, Manzo L, López G, Mari N, González-Alonso F, Morelli F, Setzer A, Csiszar I, Kanpandegi JA, Bastarrika A, Libonati R. 2008. Global burned-land estimation in Latin America using MODIS composite data. Ecol Appl 18: 64-79. http://dx.doi.org/10.1890/06-2148.1 PMid:18372556

Crutzen PJ, Andreae MO. 1990. Biomass burning in the tropics: Impact on atmospheric chemistry and biogeochemical cycles. Science 250: 1669-1678. http://dx.doi.org/10.1126/science.250.4988.1669 PMid:17734705

De Santis A, Chuvieco E. 2007. Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models. Remote Sens Environ 108: 422-435. http://dx.doi.org/10.1016/j.rse.2006.11.022

Fraser R, Li Z, Cihlar J. 2000. Hotspot and NDVI differencing synergy (HANDS): A new technique for burned area mapping over boreal forest. Remote Sens Environ 74: 362-376. http://dx.doi.org/10.1016/S0034-4257(00)00078-X

Giglio L, Descloitres J, Justice CO, Kaufman YJ. 2003. An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87: 273-282. http://dx.doi.org/10.1016/S0034-4257(03)00184-6

Giglio L, Randerson JT, van der Werf GR, Kasibhatla PS, Collatz GJ, Morton DC, DeFries RS. 2010. Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences 7: 1171–1186. http://dx.doi.org/10.5194/bg-7-1171-2010

González-Alonso F, Merino-de-Miguel S. 2009. Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA). Int J Wildland Fire 18: 404-414. http://dx.doi.org/10.1071/WF07081

González-Alonso F, Merino-De-Miguel S, Roldán-Zamarrón A, García-Gigorro S, Cuevas J. 2007. MERIS Full Resolution data for mapping level-of-damage caused by forest fires: the Valencia de Alcántara event in August 2003. Int J Remote Sens 28: 797-809. http://dx.doi.org/10.1080/01431160600979115

Grégoire JM, Tansey K, Silva J. 2003. The GBA2000 initiative: developing a global burnt area database from SPOT-VEGETATION imagery. Int J Remote Sens 24: 1369-1376. http://dx.doi.org/10.1080/0143116021000044850

Huesca M, González-Alonso F, Cuevas J, Merino-de-Miguel S. 2008. Estimación de la superficie quemada en los incendios forestales de Canarias en 2007 utilizando sinérgicamente imágenes MODIS y anomalías térmicas. Investigación agraria.Sistemas y recursos forestales 17: 308-316.

Ichoku C, Giglio L, Wooster MJ, Remer LA. 2008. Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sens Environ 112: 2950-2962. http://dx.doi.org/10.1016/j.rse.2008.02.009

Justice C, Giglio L, Korontzi S, Owens J, Morisette J, Roy D et al. 2002. The MODIS fire products. Remote Sens Environ 83: 244-262. http://dx.doi.org/10.1016/S0034-4257(02)00076-7

Key CH, Benson NC. 1999. Measuring and remote sensing of burn severity: the CBI and NBR, in: Neuenschwander, L.F., Ryan, K.C. (Eds.), Proceedings Joint Fire Science Conference and Workshop, vol. II, Boise, ID, 15-17 June 1999. University of Idaho and International Association of Wildland Fire, 284 pp.

Laris PS.2005. Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments. Remote Sens Environ 99: 412-424. http://dx.doi.org/10.1016/j.rse.2005.09.012

Lentile LB, Holden ZA, Smith AMS, Falkowski MJ, Hudak AT, Morgan P et al. 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. Int J Wildland Fire 15: 319-345. http://dx.doi.org/10.1071/WF05097

Levine JS. 1991. Global biomass burning: atmospheric, climatic, and biospheric implications. The MIT Press. Cambridge, USA.

Li RR, Kaufman YJ, Hao WM, Salmon JM, Gao BC. 2004. A technique for detecting burn scars using MODIS data. IEEE Transactions on Geoscience and Remote Sensing 42: 1300-1308. http://dx.doi.org/10.1109/TGRS.2004.826801

Loboda T, O'Neal K, Csiszar I. 2007. Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data. Remote Sens Environ 109: 429-442. http://dx.doi.org/10.1016/j.rse.2007.01.017

Merino-de-Miguel S, González-Alonso F, Huesca M, Armenteras D, Franco C. 2011. MODIS Reflectance and Active Fire Data for Burn Mapping in Colombia. Earth Interact 15: 1-17. http://dx.doi.org/10.1175/2010EI344.1

Merino-de-Miguel S, Huesca M, González-Alonso F. 2010. MODIS reflectance and active fire data for burn mapping and assessment at regional level. Ecol Model 221: 67-74. http://dx.doi.org/10.1016/j.ecolmodel.2009.09.015

NASA/University of Maryland 2002 MODIS Hotspot/Active Fire Detections. Data set. MODIS Rapid Response Project, NASA/GSFC [producer], University of Maryland, Fire Information for Resource Management System [distributors]. Available on-line [http://maps.geog.umd.edu].

Padilla M, Chuvieco E. 2009. Validación de productos globales de área quemada en la Península Ibérica. Revista de Teledetección 31: 69-79.

Palacios-Orueta A, Chuvieco E, Parra A, Carmona-Moreno C. 2005. Biomass burning emissions: a review of models using remote-sensing data. Environ Monit Assess 104: 189-209. http://dx.doi.org/10.1007/s10661-005-1611-y PMid:15931987

Pausas JG. 2004. Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Clim Change 63: 337-350. http://dx.doi.org/10.1023/B:CLIM.0000018508.94901.9c

Pereira JMC. 2003. Remote sensing of burned areas in tropical savannas. Int J Wildland Fire 12: 259-270. http://dx.doi.org/10.1071/WF03028

Pereira MG, Trigo RM, da Camara CC, Pereira J, Leite SM. 2005. Synoptic patterns associated with large summer forest fires in Portugal. Agric For Meteorol 129: 11-25. http://dx.doi.org/10.1016/j.agrformet.2004.12.007

Plummer S, Arino O, Simon M, Steffen W. 2006. Establishing a earth observation product service for the terrestrial carbon community: The GLOBCARBON initiative. Mitigation Adapt Strat Global Change 11: 97-111. http://dx.doi.org/10.1007/s11027-006-1012-8

Pu R, Gong P, Li Z, Scarborough J. 2004. A dynamic algorithm for wildfire mapping with NOAA/AVHRR data. Int J Wildland Fire 13: 275-285. http://dx.doi.org/10.1071/WF03054

Pyne S, Andrews P, Laven R. 1996. Introduction to Wildland Fire, Final Report MMF Practices–3015, Canada.

Riaño D, Moreno Ruiz J, Isidoro D, Ustin S. 2007. Global spatial patterns and temporal trends of burned area between 1981 and 2000 using NOAA-NASA Pathfinder. Global Change Biol 13: 40-50. http://dx.doi.org/10.1111/j.1365-2486.2006.01268.x

Riaño D, Chuvieco E, Ustin S, Zomer R, Dennison P, Roberts D, Salas J. 2002. Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sens Environ 79: 60-71. http://dx.doi.org/10.1016/S0034-4257(01)00239-5

Roldán-Zamarrón A, Merino-de-Miguel S, González-Alonso F, García-Gigorro S, Cuevas J. 2006. Minas de Riotinto (south Spain) forest fire: Burned area assessment and fire severity mapping using Landsat 5-TM, Envisat-MERIS, and Terra-MODIS postfire images. Journal of geophysical research 111, G04S11. http://dx.doi.org/10.1029/2005JG000136

Roy D. 1999. Multi-temporal active-fire based burn scar detection algorithm. Int J Remote Sens 20: 1031-1038. http://dx.doi.org/10.1080/014311699213073

Roy D, Jin Y, Lewis P, Justice C. 2005. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sens Environ 97: 137-162. http://dx.doi.org/10.1016/j.rse.2005.04.007

Roy D, Boschetti L, Justice C, Ju J. 2008. The collection 5 MODIS burned area product—Global evaluation by comparison with the MODIS active fire product. Remote Sens Environ 112: 3690-3707. http://dx.doi.org/10.1016/j.rse.2008.05.013

Roy D, Boschetti L. 2009. Southern Africa Validation of the MODIS, L3JRC and GlobCarbon Burned-Area Products. IEEE T Geosci Remote 47: 1032-1044. http://dx.doi.org/10.1109/TGRS.2008.2009000

Salmon J, Hao W, Miller M, Nordgren B, Kaufman Y, Li R. 2003. Validation of two MODIS single-scene fire products for mapping burned area: hot spots and NIR spectral test burn scars. In: 4th International Workshop on Remote Sensing and GIS–Applications to Forest Fire Management: Innovative concepts and methods in fire danger estimation, 197-202.

Silva Cardozo F, Pereira G, Edemir Shimabukuro Y, Caria Moraes E. 2012. Validation of MODIS MCD45A1 Product to identify burned areas in Acre State - Amazon Forest. IGARSS 2012: 6741-6744.

Simon M, Plummer S, Fierens F, Hoelzemann J, Arino O. 2004. Burnt area detection at global scale using ATSR-2: The GLOBSCAR products and their qualification. Journal of Geophysical Research 109, D14S02. http://dx.doi.org/10.1029/2003JD003622

Stehman SV. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62: 77-89. http://dx.doi.org/10.1016/S0034-4257(97)00083-7

Tansey K, Grégoire JM, Defourny P, Leigh R, Pekel JF, Van Bogaert E, Bartholomé E. 2008. A new, global, multi-annual (2000-2007) burnt area product at 1 km resolution. Geophys Res Let 35: L01401.

Van der Werf GR, Randerson JT, Giglio L, Collatz G, Mu M, Kasibhatla PS et al. 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos.Chem.Phys 10: 11707-11735. http://dx.doi.org/10.5194/acp-10-11707-2010

Van Leeuwen WJD, Casady GM, Neary DG, Bautista S, Alloza JA, Carmel Y et al. 2010. Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. Int J Wildland Fire 19: 75-93. http://dx.doi.org/10.1071/WF08078

Wittenberg L, Malkinson D, Beeri O, Halutzy A, Tesler N. 2007. Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a Mediterranean landscape, Mt. Carmel Israel. Catena 71: 76-83. http://dx.doi.org/10.1016/j.catena.2006.10.007

Published
2013-07-29
How to Cite
Huesca, M., Merino de Miguel, S., & González-Alonso, F. (2013). An intercomparison of Satellite Burned Area Maps derived from MODIS, MERIS, SPOT-VEGETATION, and ATSR images. An application to the August 2006 Galicia (Spain) forest fires. Forest Systems, 22(2), 222-231. https://doi.org/10.5424/fs/2013222-03477
Section
Research Articles