An object-based approach for fire history reconstruction by using three generations of landsat sensors

Remote Sensing

Volumn 6, Issue 6, 2014, Pages 5480-5496

  Katagis, Thomas ^a   Gitas, Ioannis Z ^a   Mitri, George H ^b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b UNIVERSITY OF BALAMAND   (Lebanon)

Author keywords

Burned area mapping; Fire history reconstruction; Forest fires; Geographic object based image analysis; Landsat

Indexed keywords

DEFORESTATION; FIRES; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; MAPPING;

BURNED-AREA MAPPING; FIRE HISTORY; FOREST FIRES; GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS; LANDSAT;

IMAGE PROCESSING;

EID: 84986893002     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6065480     Document Type: Article

References (59)

1. Kutiel, P.; Inbar, M. Fire impacts on soil nutrients and soil erosion in a mediterranean pine forest plantation. Catena 1993, 20, 129-139.
2. Capitanio, R.; Carcaillet, C. Post-fire mediterranean vegetation dynamics and diversity: A discussion of succession models. Forest Ecol. Manag. 2008, 255, 431-439.
3. Pérez-Cabello, F.; de la Riva Fernández, J.; Montorio Llovería, R.; García-Martín, A. Mapping erosion-sensitive areas after wildfires using fieldwork, remote sensing, and geographic information systems techniques on a regional scale. J. Geophys. Res.: Biogeosci. 2006, doi:10.1029/2005JG000148.
4. Pausas, J.G.; Llovet, J.; Rodrigo, A.; Vallejo, R. Are wildfires a disaster in the mediterranean basin?-A review. Int. J. Wildland Fire 2008, 17, 713-723.
5. Díaz-Delgado, R.; Lloret, F.; Pons, X. Statistical analysis of fire frequency models for catalonia (NE Spain, 1975-1998) based on fire scar maps from Landsat MSS data. Int. J. Wildland Fire 2004, 13, 89-99.
6. Turner, M.G. Disturbance and landscape dynamics in a changing world. Ecology 2010, 91, 2833-2849.
7. Caldararo, N. Human ecological intervention and the role of forest fires in human ecology. Sci. Total Environ. 2002, 292, 141-165.
8. Pausas, J.; Fernández-Muñoz, S. Fire regime changes in the western mediterranean basin: From fuel-limited to drought-driven fire regime. Clim. Chang. 2012, 110, 215-226.
9. Moreno, M.V.; Conedera, M.; Chuvieco, E.; Pezzatti, G.B. Fire regime changes and major driving forces in spain from 1968 to 2010. Environ. Sci. Policy 2014, 37, 11-22.
10. Pyne, S.J. Introduction to Wildland Fire: Fire Management in the United States; John Wiley & Sons Inc.: New York, NY, USA, 1984.
11. Chuvieco, E.; Englefield, P.; Trishchenko, A.P.; Luo, Y. Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sens. Environ. 2008, 112, 2381-2396.
12. Röder, A.; Hill, J.; Duguy, B.; Alloza, J.A.; Vallejo, R. Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain). Remote Sens. Environ. 2008, 112, 259-273.
13. Loepfe, L.; Lloret, F.; Román-Cuesta, R.M. Comparison of burnt area estimates derived from satellite products and national statistics in Europe. Int. J. Remote Sens. 2011, 33, 3653-3671.
14. Bengtsson, J.; Nilsson, S.G.; Franc, A.; Menozzi, P. Biodiversity, disturbances, ecosystem function and management of European forests. Forest Ecol. Manag. 2000, 132, 39-50.
15. Lentile, L.B.; Holden, Z.A.; Smith, A.M.S.; Falkowski, M.J.; Hudak, A.T.; Morgan, P.; Lewis, S.A.; Gessler, P.E.; Benson, N.C. Remote sensing techniques to assess active fire characteristics and post-fire effects. Int. J.Wildland Fire 2006, 15, 319-345.
16. Csiszar, I.; Denis, L.; Giglio, L.; Justice, C.O.; Hewson, J. Global fire activity from two years of MODIS data. Int. J. Wildland Fire 2005, 14, 117-130.
17. Chuvieco, E.; Congalton, R.G. Mapping and inventory of forest fires from digital processing of TM data. Geocarto Int. 1988, 3, 41-53.
18. 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. Forest Ecol. Manag. 2009, 258, 132-145.
19. Barbosa, P.M.; Grégoire, J.-M.; Pereira, J.M.C. An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale. Remote Sens. Environ. 1999, 69, 253-263.
20. Roy, D.P.; Boschetti, L.; Justice, C.O.; Ju, J. The collection 5 modis burned area product-Global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 2008, 112, 3690-3707.
21. Justice, C.O.; Giglio, L.; Korontzi, S.; Owens, J.; Morisette, J.T.; Roy, D.; Descloitres, J.; Alleaume, S.; Petitcolin, F.; Kaufman, Y. The MODIS fire products. Remote Sens. Environ. 2002, 83, 244-262.
22. Tansey, K.; Grégoire, J.-M.; Defourny, P.; Leigh, R.; Pekel, J.-F.; van Bogaert, E.; Bartholomé, E. A new, global, multi-annual (2000-2007) burnt area product at 1 km resolution. Geophys. Res. Lett. 2008, doi:10.1029/2007GL031567.
23. Carmona-Moreno, C.; Belward, A.; Malingreau, J.-P.; Hartley, A.; Garcia-Alegre, M.; Antonovskiy, M.; Buchshtaber, V.; Pivovarov, V. Characterizing interannual variations in global fire calendar using data from earth observing satellites. Glob. Chang. Biol. 2005, 11, 1537-1555.
24. Xie, Y.; Sha, Z.; Yu, M. Remote sensing imagery in vegetation mapping: A review. J. Plant Ecol. 2008, 1, 9-23.
25. Salvador, R.; Valeriano, J.; Pons, X.; Diaz-Delgado, R. A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series. Int. J. Remote Sens. 2000, 21, 655-671.
26. Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat data continuity mission. Remote Sens. Environ. 2012, 122, 11-21.
27. Chuvieco, E.; Martín, M.P.; Palacios, A. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens. 2002, 23, 5103-5110.
28. Loboda, T.; O'Neal, K.J.; Csiszar, I. Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data. Remote Sens. Environ. 2007, 109, 429-442.
29. Maingi, J.K.; Henry, M.C. Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. Int. J. Wildland Fire 2007, 16, 23-33.
30. Quintano, C.; Fernández-Manso, A.; Fernández-Manso, O.; Shimabukuro, Y.E. Mapping burned areas in mediterranean countries using spectral mixture analysis from a uni-temporal perspective. Int. J. Remote Sens. 2006, 27, 645-662.
31. Koutsias, N.; Karteris, M. Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 thematic mapper image. Int. J. Remote Sens. 2000, 21, 673-687.
32. Chuvieco, E.; Ventura, G.; Martín, M.P.; Gómez, I. Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping. Remote Sens. Environ. 2005, 94, 450-462.
33. Al-Rawi, K.R.; Casanova, J.L.; Calle, A. Burned area mapping system and fire detection system, based on neural networks and NOAA-AVHRR imagery. Int. J. Remote Sens. 2001, 22, 2015-2032.
34. Cao, X.; Chen, J.; Matsushita, B.; Imura, H.; Wang, L. An automatic method for burn scar mapping using support vector machines. Int. J. Remote Sens. 2009, 30, 577-594.
35. Katagis, T.; Gitas, I.Z.; Toukiloglou, P.; Veraverbeke, S.; Goossens, R. Trend analysis of medium-and coarse-resolution time series image data for burned area mapping in a mediterranean ecosystem. Int. J. Wildland Fire 2014, doi:10.1071/WF12055.
36. Bastarrika, A.; Chuvieco, E.; Martin, M.P. Automatic burned land mapping from MODIS time series images: Assessment in mediterranean ecosystems. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3401-3413.
37. Mitri, G.H.; Gitas, I.Z. A semi-automated object-oriented model for burned area mapping in the mediterranean region using Landsat-TM imagery. Int. J. Wildland Fire 2004, 13, 367-376.
38. Bastarrika, A.; Chuvieco, E.; Martín, M.P. Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sens. Environ. 2011, 115, 1003-1012.
39. Oliveira, S.L.J.; Pereira, J.M.C.; Carreiras, J.M.B. Fire frequency analysis in Portugal (1975-2005), using Landsat-based burnt area maps. Int. J. Wildland Fire 2012, 21, 48-60.
40. Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58, 239-258.
41. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65, 2-16.
42. Hay, G.J.; Castilla, G.; Wulder, M.A.; Ruiz, J.R. An automated object-based approach for the multiscale image segmentation of forest scenes. Int. J. Appl. Earth Obs. Geoinf. 2005, 7, 339-359.
43. Gitas, I.Z.; Mitri, G.H.; Ventura, G. Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery. Remote Sens. Environ. 2004, 92, 409-413.
44. Mitri, G.H.;Gitas, I.Z. Fire type mapping using object-based classification of Ikonos imagery. Int. J. Wildland Fire 2006, 15, 457-462.
45. Polychronaki, A.; Gitas, I.Z. Burned area mapping in greece using SPOT-4 HRVIR images and object-based image analysis. Remote Sens. 2012, 4, 424-438.
46. Gitas, I. Geographical Information Systems and Remote Sensing in Mapping and Monitoring Fire-Altered Forest Landscapes. Ph.D. Dissertation, Department of Geography, University of Cambridge, Cambridge, UK, 1999.
47. Makedos, I. The Pinus Brutia Forest of Thasos. In Pinus Halepensis and Pinus Brutia Forests (Ecology, Management and Development); Papanastasis, V., Ed.; Hellenic Association of Foresters: Chalkida, Greece, 1987.
48. Chander, G.; Markham, B.L.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113, 893-903.
49. Chavez, P.S.J. Radiometric calibration of Landsat thematic mapper multispectral images. Photogramm. Eng. Remote Sens. 1989, 55, 1285-1294.
50. Song, C.; Woodcock, C.E.; Seto, K.C.; Lenney, M.P.; Macomber, S.A. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ. 2001, 75, 230-244.
51. Baatz, M.; Benz, U.; Dehghani, S.; Heynen, M.; Holtje, A.; Hofmann, P.; Lingenfelder, I.; Mimler, M.; Sohlbach, M.; Weber, M. eCognition Professional User Guide 4; Definiens Imaging: Munich, Germany, 2004.
52. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
53. Pontius, R., Jr.; Boersma, W.; Castella, J.-C.; Clarke, K.; Nijs, T.; Dietzel, C.; Duan, Z.; Fotsing, E.; Goldstein, N.; Kok, K.; et al. Comparing the input, output, and validation maps for several models of land change. Ann. Reg. Sci. 2008, 42, 11-37.
54. Pontius, R.G.; Millones, M. Death to kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 4407-4429.
55. Czaplewski, R.L. Statistical Design and Methodological Considerations for the Accuracy Assessment of Maps of Forest Condition. In Remote Sensing of Forest Environments: Concepts and Case Studies; Wulder, M.A., Franklin, S.E., Eds.; Kluwer Academic: Boston, MA, USA, 2003; pp. 115-141.
56. Radoux, J.; Bogaert, P.; Fasbender, D.; Defourny, P. Thematic accuracy assessment of geographic object-based image classification. Int. J. Geogr. Inf. Sci. 2010, 25, 895-911.
57. Key, C.H. Ecological and sampling constraints on defining landscape fire severity. Fire Ecol. 2006, 2, 178-203.
58. Key C.; Benson, N. Landscape Assessment: Ground Measure of Severity; the Composite Burn Index, and Remote Sensing of Severity, the Normalized Burn Index. In FIREMON: Fire Effects Monitoring and Inventory System; Lutes, D., Keane, R., Caratti, J., Key, C., Benson, N., Sutherland, S., Grangi, L., Eds.; General Technical Report RMRS-GTR-164-CD LA; USDA Forest Service, Rocky Mountains Research Station: Ogden, UT, USA, 2006; pp. 1-51.
59. Chafer, C.J.N.M.M.E. The post-fire measurement of fire severity and intensity in the christmas 2001 Sidney wildfires. Int. J. Wildland Fire 2004, 13, 227-240.