Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective

Forest Ecology and Management

Volumn 262, Issue 8, 2011, Pages 1597-1607

  Quintano, Carmen ^a   Fernández Manso, Alfonso ^b   Stein, Alfred ^c   Bijker, Wietske ^c  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b UNIVERSITY OF LEÓN   (Spain)

^c UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Forest burned areas; Mediterranean countries; Satellite data

Indexed keywords

ACCURATE ESTIMATION; ADAPTIVE THRESHOLDING; BURN PATTERNS; BURNED AREAS; CARBON LOSS; FOREST FIRES; FOREST HEALTH MONITORING; GAUSSIAN FUNCTIONS; IMAGE MINING; MCNEMAR; MEDITERRANEAN COUNTRIES; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER SATELLITES; OPTIMAL ALGORITHM; POST-FIRE; REFERENCE METHOD; REMOTE SENSING DATA; SATELLITE DATA; SPATIAL RESOLUTION; SPECTRAL INDICES; TREE MORTALITY;

ALGORITHMS; DEFORESTATION; ESTIMATION; GLOBAL POSITIONING SYSTEM; OPTICAL WAVEGUIDES; OPTIMIZATION; RADIOMETERS; REMOTE SENSING; SATELLITE IMAGERY;

FIRES;

ACCURACY ASSESSMENT; ALGORITHM; BIOMASS BURNING; DATA MINING; DATA SET; ESTIMATION METHOD; FOREST FIRE; FOREST HEALTH; FOREST MANAGEMENT; GPS; IMAGE ANALYSIS; MEDITERRANEAN ENVIRONMENT; MODIS; MORTALITY; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; SMOOTHING; SPATIAL RESOLUTION; SPECTRAL ANALYSIS;

ALGORITHMS; DEFORESTATION; ESTIMATION; FOREST FIRES; IMAGE ANALYSIS; MEDITERRANEAN; OPTIMIZATION; RADIOMETRY; REMOTE SENSING; SATELLITES;

MEDITERRANEAN REGION;

EID: 80051822505     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2011.07.010     Document Type: Article

References (56)

1. Agresti A. An Introduction to Categorical Data Analysis 2007, Wiley, New York, pp. 312.
2. Arroyo L.A., Pascual C., Manzanera J.A. Fire models and methods to map fuel types: the role of remote sensing. Forest Ecol. Manag. 2008, 256:1239-1252.
3. Baskent E.Z., Jordan G.A. Characterizing spatial structure of forest landscapes. Can. J. Forest. Res. 1995, 25:1830-1849.
4. Boschetti L., Roy D., Barbosa P., Boca R., Justice C. A MODIS assessment of the summer 2007 extent burned in Greece. Int. J. Remote Sens. 2008, 29:2433-2436.
5. Bowman A.W., Azzalini A. Applied smoothing techniques for data analysis. The kernel approach with S-plus illustrations 1997, Clarendon press, Oxford, pp. 193.
6. Brian B.D., Chen J.M. Forest fire scar aging using SPOT-vegetation for Canadian ecoregions. Can. J. For. Res. 2003, 33:1116-1125.
7. Camia, A., San-Miguel-Ayanz, J., Oehler, F., Santos de Oliveira, S., Durrant, T., Kucera, J., Boca, R., Whitmore, C., Giovando, C., Amatulli, G., Libertà, G., Schmuck, G., Schulte, E., Bucki, M. 2009. Forest Fires in Europe 2008. Scientific and Technical Research series EUR 23971 EN - Joint Research Centre - Institute for Environment and Sustainability Luxembourg: Office for Official Publications of the European Communities. pp.
8. Chacon, J.E., Duon, T., and Wand, M.P. 2009. Asymptotics for General Multivariate Kernel Density Derivative Estimators. Centre for Statistical and Survey Methodology, University of Wollongong, Working Paper 0809, pp. 30.
9. Chuvieco E., Huete A. Fundamentals of Satellite Remote Sensing 2009, CRC press.
10. Chuvieco E., Martín M.P., Palacios A. Assessment of different spectral indexes in the red-near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens. 2002, 23:5103-5110.
11. Cocke A.E., Fule P.Z., Crouse J.E. Comparison of burn severity assessments using differenced normalized burn ratio and ground data. Int. J. Wildland Fire. 2005, 14:189-198.
12. Congalton R.G., Green K. Assessing the accuracy of remotely sensed data, Principles and practices 2009, CRC press, Taylor & Francis, Boca Ratón.
13. Díaz-Delgado R., Pons X. Spatial patterns of forest fires in Catalonia (NE of Spain) along the period 1975-1995: analysis of vegetation recovery after fire. For. Ecol. Manage. 2001, 147:67-74.
14. Dietterich T.G. Approximate statistical test for comparing supervised classification learning algorithms. Neural Comput. 1998, 10:1895-1923.
15. Duncan B.W., Shao G., Adrian F.W. Delineating a managed fire regime and exploring its relationship to the natural fire regime in East Central Florida, USA: a remote sensing and GIS approach. For. Ecol. Manage. 2009, 258:132-145.
16. Fernández A., Illera P., Casanova J.L. Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data. Remote Sens. Environ. 1997, 60:153-162.
17. Flannigan M.D., VonderHaar T.H. Forest fire monitoring using NOAA satellite AVHRR. Can. J. For. Res. 1986, 16:975-982.
18. Foody G.M. Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogramm. Eng. Rem. S. 2004, 70:627-633.
19. Fraser R.H., Fernandes R., Latifovic R. Multi-temporal mapping of burned forest over canada using satellite-based change metrics. Geocarto Int. 2003, 18:37-47.
20. González-Alonso F., Merino-de-Miguel S. 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. 2009, 18:404-414.
21. Hastie T., Tibshirani R. Generalized additive models. Monographs on statistics and applied probability 1990, Chapman and Hall/CRC, pp. 335.
22. Hastie T., Tibshirani R., Friedman J. The elements of the statistical learning, data mining, inference and prediction 2001, Springer-Verlag, pp. 533.
23. Holden Z.A., Morgan P., Evans J.S. A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area. For. Ecol. Manage. 2009, 258:2399-2406.
24. Jia G.J., Burke I.C., Kaufmann M.R., Goetz A.F.H., Kindel B.C., Pu Y. Estimates of forest canopy fuel attributes using hyperspectral data. For. Ecol. Manage. 2006, 229:27-38.
25. Kamath C. Scientific Data Mining: A Practical Perspective 2009, Siam, Philadelphia, PA, USA, pp. 286.
26. Keramitsoglou I., Kontoes C., Sykioti O., Sifakis N., Xofis P. Reliable, accurate and timely forest mapping for wildfire management using ASTER and Hyperion satellite imagery. For. Ecol. Manage. 2008, 255:3556-3562.
27. Key C.H., Benson N.C. Measuring and remote sensing of burn severity: the CBI and NBR. Proceedings Joint Fire Science Conference and Workshop, vol. 2 1999, 284. University of Idaho and International Association of Wildland Fire, (Boise, ID, 15-17 June 1999). L.F. Neuenschwander, K.C. Ryan (Eds.).
28. Key, C.H., Benson, N.C. 2005. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. In: FIREMON: Fire Effects Monitoring and Inventory System. (D.C. Lutes, Keane, R.E., Caratti, J.F., C.H. Key, N.C Benson, L.J Gangi Eds.). Ogden, Utah, USA, United States Department of Agriculture (USDA) Forest Service, Rocky Mountain Research Station, General Technical Report. RMRS-GTR-164: CD:LA1-LA51.
29. Key, C.H., Zhu, Z., Ohlen, D., Howard, S., McKinley, R., Benson, N. 2002. The normalized burn ratio and relationships to burn severity: ecology, remote sensing and implementation. In J.D. Greer, ed. Rapid Delivery of Remote Sensing Products. Proceedings of the Ninth Forest Service Remote Sensing Applications Conference, San Diego, CA 8-12 April, 2002. American Society for Photogrammetry and Remote Sensing, Bethesda, MD.
30. Kontoes C.C., Poilve H., Florsch G., Keramitsoglou I., Paralikidis S. A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping. Int. J. Appl. Earth Obs. 2009, 11:299-316.
31. Lasaponara R. Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data. Ecol. Model. 2006, 196:265-270.
32. López-García M.J., Caselles V. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto Int. 1991, 1:31-37.
33. Maingi J.K. Mapping fire scars in a mixed-oak forest in Eastern Kentucky, USA, using Landsat ETM+ data. Geocarto Int. 2005, 20:51-63.
34. Martín, M.P. 1998. Cartografía e inventario de incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR. In Departamento de Geografía. Alcalá de Henares: Universidad de Alcalá. Mapping and inventary of forest fires in Spain from NOAA-AVHRR data. PhD dissertation, Geography department. Alcalá University, Spain.
35. Martín, M.P., Gómez, I., Chuvieco, E. 2005. Performance of a burned-area index (BAIM) for mapping Mediterranean burned scars from MODIS data. Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment, Zaragoza, Universidad de Zaragoza.
36. Martín, M.P., Gómez, I., Chuvieco, E. 2006. Burnt area index (BAIM) for burned area discrimination at regional scale using MODIS data. In: X. Viegas (Ed.), Proceedings of the V International Conference on Forest Fire Research, Coimbra, Portugal.
37. Merino-de-Miguel S., Huesca M., González-Alonso F. MODIS reflectance and active fire data for burn mapping and assessment at regional level. Ecol. Model. 2010, 221:67-74.
38. Mitri G.H., Gitas I.Z. Fire type mapping using object-based classification of Ikonos imagery. Int. J. Wildland Fire. 2006, 15:457-462.
39. Öhman K., Lämås T. Reducing forest fragmentation in long-term forest planning by using the shape index. For. Ecol. Manage. 2005, 212:346-357.
40. Pontius Jr, R.G., Millones, M. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens., in press.
41. Quintano C., Cuesta E. Improving satellite image classification by using fractional type convolution filtering. Int. J. App. Earth Obs. 2010, 12:298-301.
42. Quintano C., Stein A., Bijker W., Fernández-Manso A. Pattern validation for data mining of burned area objects from MODIS satellite images. Int. J. Remote Sens. 2010, 31:3065-3087.
43. Rajasekar U., Bijker W., Stein A. Image mining for modelling of forest fires from Meteosat images. IEEE T. Geosci. Remote. 2007, 45:246-253.
44. Ripple W.J., Bradshaw G.A., Spies T.A. Measuring forest landscape patterns in the Cascade Range of Oregon, USA. Biol. Conserv. 1991, 57:73-88.
45. Rogan J., Franklin J. Mapping wildfire burn severity in Southern California Forests and shrublands using Enhanced Thematic Mapper imagery. Geocarto Int. 2001, 16:89-99.
46. Rogan J., Yool S.R. Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico. Int. J. Remote Sens. 2001, 22:3101-3121.
47. Roy D.P., Jin Y., Lewis P.E., Justice C.O. Prototyping a global algorithm for systematic fire affected area mapping using MODIS time series data. Remote Sens. Environ. 2005, 97:137-162.
48. Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis 2004, Cambridge University Press, Cambridge, United Kingdom, pp. 462.
49. Smith A.M.S., Drake N.A., Wooster M.J., Hudak A.T., Holden Z.A., Gibbons C.J. Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS. Int. J. Remote Sens. 2007, 28:2753-2775.
50. Smith A.M.S., Wooster M.J., Drake N.A., Dipotso F.M., Falkowski M.J., Hudak A.T. Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs. Remote Sens. Environ. 2005, 97:92-115.
51. Solans-Vila J.P., Barbosa P. Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data. Ecol. Model. 2010, 221:75-84.
52. Tottrup C. Improving tropical forest mapping using multi-date Landsat TM data and preclassification image smoothing. Int. J. Remote Sens. 2004, 25:717-730.
53. Trigg S., Flasse S. An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. Int. J. Remote Sens. 2001, 22:2641-2647.
54. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8:127-150.
55. Van Wagtendonk J.W., Root R.R., Key C.H. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sens. Environ. 2004, 92:397-408.
56. Venables W.N., Ripley B.D. Modern Applied Statistics with S 2002, Springer, pp. 495.