Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data

Ecological Modelling

Volumn 221, Issue 1, 2010, Pages 75-84

  Solans Vila, José Pablo ^a   Barbosa, Paulo ^b  

^a CRANFIELD UNIVERSITY   (United Kingdom)

^b JOINT RESEARCH CENTRE   (Italy)

Author keywords

Forest fires; MSAVI; NDVI; SMA; Vegetation regeneration

Indexed keywords

ACCURACY ASSESSMENT; ATMOSPHERIC CORRECTIONS; AUTOMATED SELECTION; COMPARISON OF METHODS; FIELD DATA; FOREST FIRES; GEOMETRIC CORRECTION; HUMAN INTERVENTION; INVARIANT FEATURES; LANDSAT TM; MULTI-TEMPORAL; NATURAL ENVIRONMENTS; NORMALIZED DIFFERENCE VEGETATION INDEX; POST-FIRE; PRE-PROCESSING; QUANTITATIVE ESTIMATES; QUANTITATIVE INDICES; QUANTITATIVE INFORMATION; SPECTRAL MIXTURE ANALYSIS; STUDY AREAS; TOPOGRAPHIC CORRECTION; VEGETATION COVER; VEGETATION INDEX; VEGETATION REGENERATION;

CLIMATE CHANGE; DECISION MAKING; DEFORESTATION; DISASTERS; ECOSYSTEMS; ERROR CORRECTION; FIRES; PRINCIPAL COMPONENT ANALYSIS; REFORESTATION;

VEGETATION;

ACCURACY ASSESSMENT; CLIMATE CHANGE; COMPARATIVE STUDY; DESERTIFICATION; ERROR CORRECTION; FIELDWORK; FOREST FIRE; LANDSAT THEMATIC MAPPER; NDVI; PRECISION; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE ANALYSIS; REGROWTH; VEGETATION COVER;

CLIMATES; DECISION MAKING; DEFORESTATION; DISASTERS; ECOSYSTEMS; FOREST FIRES; PLANTS; REFORESTATION;

EURASIA; EUROPE; ITALY; LIGURIA; SOUTHERN EUROPE;

EID: 70449727213     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2009.03.011     Document Type: Article

References (46)

1. Baret F., Clevers J.C.P.W., and Steven M.D. The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches. Remote Sensing of Environment 54 (1995) 141-151
2. Belda F., and Meliá J. Relationships between climatic parameters and forest vegetation: application to burned area in Alicante (Spain). Forest Ecology and Management 135 (2000) 195-204
3. Carlson T.N., and Ripley D.A. On the relation between NDVI, fractional vegetation cover, and Leaf Area Index. Remote Sensing of Environment 62 (1997) 241-252
4. Chavez Jr. P.S. Image-based atmospheric corrections-revisited and revised. Photogrammetric Engineering and Remote Sensing 62 9 (1996) 1025-1036
5. Chuvieco, E., 2002. Teledetección ambiental. La observación de la Tierra desde el Espacio. Ed. Ariel, S.A., Barcelona.
6. De Luis M., Raventós J., and González-Hidalgo J.C. Post-fire vegetation succession in Mediterranean gorse shrublands. Acta Oecologica 30 (2006) 54-61
7. Diaz-Delgado R., and Pons X. Spatial patterns of forest fires in Catatonia (NE of Spain) along the period 1975-1995. Analysis of vegetation recovery after fire. Forest Ecology and Management 147 (2001) 67-74
8. Diaz-Delgado R., Lloret F., Pons X., and Terradas J. Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology 83 8 (2002) 2293-2303
9. Diaz-Delgado R., Lloret F., and Pons X. Influence of fire severity on plant regeneration through remote sensing imagery. International Journal of Remote Sensing 24 8 (2003) 1751-1763
10. Dymond J.R., Stephens P.R., Newsome P.F., and Wilde R.H. Percentage vegetation cover of a degrading rangeland from SPOT. International Journal of Remote Sensing 13 11 (1992) 1999-2007
11. García-Haro F.J., Gilabert M.A., and Melià J. Monitoring fire-affected areas using Thematic Mapper data. International Journal of Remote Sensing 22 4 (2001) 533-549
12. Gitelson A.A., Kaufman Y.J., Stark R., and Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80 (2002) 76-87
13. Gruninger A.J., Ratkowski A.J., and Hoke M.L. The Sequential Maximum Angle Convex Cone (SMACC) endmember model. Proceedings SPIE, Algorithms for Multispectral and Hyperspectral and Ultraspectral Imagery, vol. 5425-1. Orlando, April 2004 (2004)
14. Gutman G., and Ignatov A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing 19 (1998) 1533-1543
15. Habrouk, A., 2001. Regeneración natural y restauración de la zona afectada por el gran incendio del Bagès y Bergueda de 1994. Tesis Doctoral. Unitat d'Ecologia-CREAF. Departament de Biologia animal, Vegetal i Ecologia. Facultat de Ciències, Universitat Autònoma de Barcelona, Spain.
16. Huete A.R., Jackson R.D., and Post D.F. Spectral response of plant canopy with different soil background. Remote Sensing of Environment 17 (1985) 37-53
17. Kozlowski T.T. Physiological ecology of natural regeneration of harvested and disturbed forest stands: implications for forest management. Forest Ecology and Management 158 (2002) 195-221
18. Leprieur C., Verstraete M.M., and Pinty B. Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data. Remote Sensing Reviews 10 (1994) 265-284
19. Lloret F., Calvo E., Pons X., and Díaz-Delgado R. Wildfires and landscape patterns in the Eastern Iberian Peninsula. Landscape Ecology 17 (2002) 745-759
20. Marchetti M., Ricotta C., and Volpe F. A qualitative approach to the mapping of post-fire regrowth in Mediterranean vegetation with Landsat TM data. International Journal of Remote Sensing 16 13 (1995) 2487-2494
21. Martínez B., Melià J., San-Miguel-Ayanz J., and Barbosa P. An optimized burned area detection method based on the GESAVI. In: Viegas (Ed). Forest Fire Research and Wildland Fire Safety (2002), Millpress, Rotterdam. 90-77017-72-0
22. Pausas J.G., and Vallejo V.R. The role of fire in European Mediterranean ecosystems. In: Chuvieco E. (Ed). Remote Sensing of Large Wildfires in the European Mediterranean Basin (1999), Springer-Verlag, Berlin 3-16
23. Peddle D.R., Brunke S.P., and Hall F.G. A comparison of spectral mixture analysis and ten vegetation indices for estimating boreal forest biophysical information from airborne data. Canadian Journal of Remote Sensing 27 6 (2001) 627-635
24. Peterson S.H., and Stow D.A. Using multiple image endmember spectral mixture analysis to study chaparral re-growth in southern California. International Journal of Remote Sensing 24 22 (2003) 4481-4505
25. Qi J., Chehbouni A., Huete A.R., and Kerr Y.H. Modified Soil Adjusted Vegetation Index (MSAVI). Remote Sensing of Environment 48 (1994) 119-126
26. Quintano C., Shimabukuro Y.E., Fernandez A., and Delgado J.A. A spectral unmixing approach for mapping burned areas in Mediterranean countries. International Journal of Remote Sensing 26 7 (2005) 1493-1498
27. Renzullo, L.J., 2004. Radiometric processing of multitemporal sequences of satellite imagery for surface reflectance retrievals in change detection studies. Ph.D. Thesis. Curtin University of Technology, Australia.
28. Riaño D., Chuvieco E., Ustin S., Zomer R., Dennison P., Roberts D., and Salas J. Assessment of vegetation regeneration after fire through multi-temporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sensing of Environment 79 (2002) 60-71
29. Roberts D.A., Gardner M., Church R., Ustin S., Scheer G., and Green R.O. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sensing of Environment 65 3 (1998) 267-279
30. Roberts D.A., Batista G.T., Pereira J.L.G., Waller E.K., and Nelson B.W. Change identification using multitemporal spectral mixture analysis: applications in Eastern Amazonia. In: Elvidge C.D., and Lunetta R. (Eds). Remote Sensing Change Detection: Environmental Monitoring Applications and Methods (2001), Ann Arbor Press, MI
31. Rodrigo A., Retana J., and Picó F.X. Direct regeneration is not the only response of Mediterranean forests to large fires. Ecology 85 3 (2004) 716-729
32. Rogan J., Franklin J., and Roberts D.A. A comparison of methods for monitoring multi-temporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment 80 (2002) 143-156
33. Román-Cuesta R.M., Retana J., Gracia M., and Rodríguez R. A quantitative comparison of methods for classifying burned areas with LISS-III imagery. International Journal of Remote Sensing 36 9 (2005) 1979-2003
34. Rouse J.W., Hass R.H., Schell J.A., and Deering D.W. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium. Goddard Space Flight Center, NASA SP- 351, NASA, Washington, DC, December 1973 (1973) 309-317
35. Sabol Jr. D.E., Gillespie A.R., Adams J.B., Smith M.O., and Tucker C.J. Structural stage in Pacific Northwest forests estimated using simple mixing models of multi-spectral images. Remote Sensing of Environment 80 (2002) 1-16
36. Scarascia-Mugnozza G., Oswald H., Piussi P., and Radoglou K. Forests of the Mediterranean region: gaps in knowledge and research needs. Forest Ecology and Management 132 (2000) 97-109
37. Schott J.R., Salvaggio C., and Volchok W.J. Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment 26 (1988) 1-16
38. Souza Jr. C., Firestone L., Silva L.M., and Roberts D. Mapping forest degradation in the Eastern Amazon from SPOT4 through spectral mixture models. Remote Sensing of Environment 87 (2003) 494-506
39. SPREAD Project, 2004. Forest Fire Spread Prevention and Mitigation. J.M. Moreno (Document Leader). Contract number EVG1-CT-2001-00043.
40. Theseira M.A., Thomas G., and Sannier C.A.D. An evaluation of spectral mixture modeling applied to a semi-arid environment. International Journal of Remote Sensing 23 4 (2002) 687-700
41. Theseira M.A., Thomas G., and Taylor J.C. Sensitivity of mixture modeling to end-member selection. International Journal of Remote Sensing 24 7 (2003) 1559-1575
42. Twele, A., 2003. Burned area analysis. An evaluation for mapping fire extent and monitoring regeneration after forest fires using remotely sensed data. Thesis Degree. Wesfälische Wilhelms-Universität Mϋnster./JRC, Italy.
43. van der Meer F., and de Jong S.M. Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of end-members. International Journal of Remote Sensing 21 15 (2000) 2781-2797
44. Verstraete M.M., and Pinty B. The potential contribution of satellite remote sensing to the understanding of arid lands processes. Vegetatio 91 (1991) 59-72
45. Viedma O., Meliá J., Segarra D., and García-Haro J. Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing of Environment 61 (1997) 383-398
46. Wittich K.P., and Hansing O. Area-averaged vegetative cover fraction estimated from satellite data. International Journal of Biometeorology 38 (1995) 209-215