Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Remote Sensing of Environment

Volumn 184, Issue , 2016, Pages 112-123

  Fernandez Manso, Alfonso ^a   Quintano, Carmen ^b   Roberts, Dar A ^c  

^a UNIVERSITY OF LEÓN   (Spain)

^b UNIVERSITY OF VALLADOLID   (Spain)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Landsat; Mediterranean ecosystem; MESMA; Pinus pinaster Ait.; Post fire; Resilience; Seeders; SMA; Vegetation cover recovery; VRI

Indexed keywords

ANALYSIS OF VARIANCE (ANOVA); DEFORESTATION; ECOLOGY; ECOSYSTEMS; FIRE HAZARDS; FORESTRY; MEAN SQUARE ERROR; RECOVERY; REFORESTATION; SOIL CONSERVATION; TIME SERIES; VEGETATION;

LANDSAT; MEDITERRANEAN ECOSYSTEM; MESMA; PINUS PINASTER AIT; POST-FIRE; RESILIENCE; SEEDERS; VEGETATION COVER;

FIRES;

CONIFEROUS TREE; ECOSYSTEM RESILIENCE; ENVIRONMENTAL MONITORING; FIRE HISTORY; FOREST ECOSYSTEM; FOREST FIRE; LANDSAT THEMATIC MAPPER; MEDITERRANEAN ENVIRONMENT; REGROWTH; SATELLITE IMAGERY; TIME SERIES; VEGETATION COVER;

SPAIN;

PINUS PINASTER;

EID: 84975460457     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.06.015     Document Type: Article

References (103)

1. Abdul-Malak D., Pausas J.G., Pardo-Pascual J.E., Ruiz L.A. Recurrence and the dynamics of the enhanced vegetation index in a Mediterranean ecosystem. Int. J. Appl. Geosp. Res. 2015, 6:18-35.
2. Adams J.B., Sabol D.E., Kapos V., Filho R.A., Roberts D.A., Smith M.O., Gillespie A.R. Classification of multispectral images based on fractions of endmembers: Application to land cover change in the Brazilian Amazon. Remote Sens. Environ. 1995, 52:137-154.
3. Baeza M.J., Valdecantos A., Alloza J.A., Vallejo V.R. Human disturbance and environmental factors as drivers of long-term post-fire regeneration patterns in Mediterranean forest. J. Veg. Sci. 2007, 18:243-252.
4. Barbero M., Loisel R., Quezel P., Richardson D.M., Romane F. Pines of the Mediterranean basin. Ecology and biogeography of Pinus 1998, 153-170. Cambridge University Press, Cambridge. D.M. Richardson (Ed.).
5. Bastos A., Gouveia C.M., DaCamara C.C., Trigo R.M. Modelling post-fire vegetation recovery in Portugal. Biogeosciences 2011, 8:3593-3607.
6. Boardman J.W. Inversion of high spectral resolution data. Proceedings SPIE, 1298, imaging spectroscopy, terrestrial. Environment, 222-233, Apr. 16-19, 1990, Orlando, Florida, (USA) 1990.
7. Bolton D.K., Coops N.C., Wulder M.A. Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne lidar data. Remote Sens. Environ. 2015, 163:48-60.
8. Broncano M.J., Retana J., Rodrigo A. Predicting the recovery of Pinus halepensis and Quercus ilex forests after a large wildfire in northeastern Spain. Plant Ecol. 2005, 180:47-56.
9. Calvo L., Santalla S., Marcos E., Valbuena L., Tárrega R., Luis E. Regeneration after wildfire in communities dominated by Pinus pinaster, an obligate seeder, and others dominated by Quercus pyrenaica, a typical resprouter. For. Ecol. Manag. 2003, 184:209-223.
10. Calvo L., Santalla S., Valbuena L., Marcos E., Tárrega R., Luis-Calabuig L. Post-fire natural regeneration of a Pinus pinaster forest in NW Spain. Plant Ecol. 2008, 197:81-90.
11. Calvo L., Torres O., Valbuena L., Luis-Calabuig E. Recruiment and early growth of Pinus pinaster seedlings over five years after a wildfire in NW Spain. For. Syst. 2013, 22:582-586.
12. Canty M.J., Nielsen A.A. Automatic radiometric normalization of multitem-poral satellite imagery with the iteratively re-weighted MAD transformation. Remote Sens. Environ. 2008, 112:1025-1036.
13. 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.
14. Chavez P.S. Radiometric calibration of Landsat thematic mapper multispectral images. Photogramm. Eng. Remote. Sens. 1989, 55:1285-1294.
15. Chavez P.S. Image-based atmospheric corrections - Revisited and improved. Photogramm. Eng. Remote. Sens. 1996, 62:1025-1036.
16. Chen X., Vogelmann J.E., Rollins M., Ohlen D., Key C.H., Yang L., Huang C., Shi H. Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. Int. J. Remote Sens. 2011, 32:7905-7927.
17. Chen W., Moriya K., Sakai T., Koyama L., Cao C. Monitoring of post-fire forest recovery under different restoration modes based on time series Landsat data. Eur. J. Remote Sens. 2014, 47:153-168.
18. Chu T., Guo X. Remote sensing techniques in monitoring post-fire effects and patterns of forest recovery in boreal forest regions: A review. Remote Sens. 2014, 6:470-520.
19. Earth observation of wildland fires in Mediterranean ecosystems 2009, Springer, Heidelberg Dordrecht London New York. E. Chuvieco (Ed.).
20. Covington W.W., Sackett S.S. Soil mineral nitrogen changes following prescribed burning in ponderosa pine. For. Ecol. Manag. 1992, 54:175-191.
21. Dennison P.E., Roberts D.A. Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sens. Environ. 2003, 87:123-135.
22. Dennison P.E., Roberts D.A. The effects of vegetation phenology on endmember selection and species mapping in Southern California chaparral. Remote Sens. Environ. 2003, 87:295-309.
23. Dennison P.E., Halligan K.Q., Roberts D.A. A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper. Remote Sens. Environ. 2004, 93:359-367.
24. Di Mauro B., Fava F., Busetto L., Crosta G.F., Colombo R. Post-fire resilience in the Alpine region estimated from MODIS satellite multispectral data. Int. J. Appl. Earth Obs. Geoinf. 2014, 32:163-172.
25. 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. Manag. 2001, 147:67-74.
26. Díaz-Delgado R., Lloret F., Pons X., Terradas J. Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology 2002, 83:2293-2303.
27. Díaz-Delgado R., Lloret F., Pons X. Influence of fire severity on plant regeneration by means of remote sensing imagery. Int. J. Remote Sens. 2003, 24:1751-1763.
28. Dubovyk O., Landmann T., Erasmus B.F.N., Tewes A., Schellberg J. Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa. Int. J. Appl. Earth Obs. Geoinf. 2015, 38:175-183.
29. Dudley K.L., Dennison P.E., Roth K.L., Roberts D.A., Coates A.R. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sens. Environ. 2015, 167:121-134.
30. Elmore A., Mustard J., Manning S., Lobell D. Quantifying vegetation change in semiarid environments: Precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sens. Environ. 2000, 73:87-102.
31. Fernández-Manso O., Quintano C., Fernández-Manso A. Combining spectral mixture analysis and object-based classification for fire severity mapping. For. Syst. 2009, 18:296-313.
32. Gitas I., Mitri G., Veraverbeke S., Polychronaki A. Advances in remote sensing of post-fire vegetation recovery monitoring - A review. 'Remote sensing of biomass - principles and applications' 2012, InTech. L. Fatoyinbo (Ed.).
33. Goetz S.J., Fiske G.J., Bunn A.G. Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada. Remote Sens. Environ. 2006, 101:352-365.
34. He C., Wei A., Shi P., Zhang Q., Zhao Y. Detecting land-use/land-cover change in rural-urban fringe areas using extended change-vector analysis. Int. J. Appl. Earth Obs. Geoinf. 2011, 13:572-585.
35. Herold M., Roberts D.A., Gardner M.E., Dennison P.E. Spectrometry for urban area remote sensing - Development and analysis of a spectral library from 350 to 2400 nm. Remote Sens. Environ. 2004, 91:304-319.
36. Herranz J.M., Martínez-Sánchez J.J., Marín A., Ferrandis P. Post-fire regeneration of Pinus halepensis Miller in a semi-arid area in Albacete province (south-eastern Spain). Ecoscience 1997, 4:86-90.
37. Herrero C., San Martin R., Bravo F. Effect of heat and ash treatments on germination of Pinus pinaster and Cistus laurifolius. J. Arid Environ. 2007, 70:540-548. (2007).
38. Hostert P., Röder A., Hill J. Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands. Remote Sens. Environ. 2003, 87:183-197.
39. Idris M., Kuraji K., Suzuki M. Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images. J. For. Res. 2005, 10:101-111.
40. Ireland G., Petropoulos G.P. Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Appl. Geogr. 2015, 56:232-248.
41. Jakubauskas M.E., Lulla K.P., Mausel P.W. Assessment of vegetation change in a fire-altered forest landscape. Photogramm. Eng. Remote. Sens. 1990, 56:371-377.
42. Kaufman Y.J. The atmospheric effect on remote sensing and its corrections. Theory and Applications of Optical Remote Sensing 1989, 336-428. Wiley-Interscience, New York, NY. G. Asrar (Ed.).
43. Kazanis D., Arianoutsou M. Long-term post-fire vegetation dynamics in Pinus halepensis forests of Central Greece: A functional group approach. Plant Ecol. 2004, 171:101-121.
44. Keeley J.E. Resilience of Mediterranean shrub communities to fire. Resilience in Mediterranean-type ecosystems 1986, 95-112. Dr. W. Junk Publishers, Dordrecht, (168 pp). B. Dell, A.J.M. Hopkins, B.B. Lamont (Eds.).
45. Keeley J.E. Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int. J. Wildland Fire 2009, 18:116-126.
46. Keeley J.E., Bond W.J., Bradstock R.A., Pausas J.G., Rundel P.W. Fire in mediterranean ecosystems: Ecology, evolution and management 2012, Cambridge University Press.
47. Key C.H., Benson N.C. Landscape assessment: Ground measure of severity, the composite burn index; and remote sensing of severity, the normalized burn ratio. Gen. Tech. Rep. RMRS-GTR-164-CD: LA 2006, 1-51. D.C. Lutes, R.E. Keane, J.F. Caratti, C.H. Key, N.C. Benson, S. Sutherland, L.J. Gangi (Eds.).
48. Lanorte A., Lasaponara R., Lovallo M., Telesca L. Fisher-Shannon information plane analysis of SPOT/VEGETATION normalized difference vegetation index (NDVI) time series to characterize vegetation recovery after fire disturbance. Int. J. Appl. Earth Obs. Geoinf. 2014, 26:441-446.
49. Lee S.W., Lee M.B., Lee Y.G., Won M.S., Kim J.J., Hong S. Relationship between landscape structure and burn severity at the landscape and class levels in Samchuck, South Korea. For. Ecol. Manag. 2009, 258:1594-1604.
50. Lhermitte S., Verbesselt J., Verstraeten W.W., Coppin P. A pixel based regeneration index using time series similarity and spatial context. Photogramm. Eng. Remote. Sens. 2010, 76:673-682.
51. Lhermitte S., Verbesselt J., Verstraeten W.W., Veraverbeke S., Coppin P. Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index. ISPRS J. Photogramm. Remote Sens. 2011, 66:17-27.
52. Li L., Ustin S.L., Lay M. Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: A case study in China camp, CA, USA. Int. J. Remote Sens. 2005, 26:5193-5207.
53. Li M., Qu J., Hao X. Detecting vegetation change with satellite remote sensing over 2007 Georgia wildfire regions. J. Appl. Remote. Sens. 2008, 2:021505.
54. Maia P., Juli G., Pausas J., Vasques A., Keizer J.J. Fire severity as a key factor in post-fire regeneration of Pinus pinaster (Ait.) in Central Portugal. Ann. For. Sci. 2012, 69:489-498.
55. Maselli F. Multiclass spectral decomposition of remotely sensed scenes by selective pixel unmixing. IEEE Trans. Geosci. Remote Sens. 1998, 36:1809-1820.
56. Meng R., Dennison P.E., Huang C., Moritz M.A., D'Antonio C. Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California. Remote Sens. Environ. 2015, 171:311-325.
57. Moreira F., Viedma O., Arianoutsou M., Curt T., Koutsias N., Rigolot E., Barbati A., Corona P., Vaz P., Xanthopoulos G., Mouillot F., Bilgili E. Landscape - Wildfire interactions in Southern Europe: Implications for landscape management. J. Environ. Manag. 2011, 92:2389-2402.
58. Neary D.G., Klopatek C.C., DeBano L.F., Ffolliott P.F. Fire effects on belowground sustainability: A review and synthesis. For. Ecol. Manag. 1999, 122:51-71.
59. Okin G.S., Okin W.J., Murray B., Roberts D.A. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ. 2001, 77:212-225.
60. Patterson M.W., Yool S.R. Mapping fireinduced vegetation mortality using Landsat thematic mapper data: A comparison of linear transformation techniques. Remote Sens. Environ. 1998, 65:132-142.
61. 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.
62. Petropoulos G.P., Griffiths H.M., Kalivas D.P. Quantifying spatial and temporal vegetation recovery dynamics following a wildfire event in a Mediterranean landscape using EO data and GIS. Appl. Geogr. 2014, 50:120-131.
63. Polychronaki A., Gitas I.Z., Minchella A. Monitoring post-fire vegetation recovery in the Mediterranean using SPOT and ERS imagery. Int. J. Wildland Fire 2014, 23:631-642.
64. Powell R.L., Roberts D.A., Dennison P.E., Hess L.L. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sens. Environ. 2007, 106:253-267.
65. Quintano C., Fernández-Manso A., Shimabukuro Y.E., Pereira G. Spectral unmixing: A review. Int. J. Remote Sens. 2012, 33:5307-5340.
66. Quintano C., Fernández-Manso A., Roberts D.A. Multiple endmember spectral mixture analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sens. Environ. 2013, 136:76-88.
67. Riaño D., Chuvieco E., Ustin S., Zomer R., Dennison P., Roberts D., Salas J. Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sens. Environ. 2002, 79:60-71.
68. Roberts D.A., Gardner M., Church R., Ustin S.L., Scheer G., Green R.O. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens. Environ. 1998, 65:267-279.
69. Roberts D.A., Dennison P.E., Gardner M., Hetzel Y., Ustin S.L., Lee C. Evaluation of the potential of hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer. IEEE Trans. Geosci. Remote Sens. 2003, 41:1297-1310.
70. Roberts D.A., Halligan K., Dennison P. VIPER Tools User Manual. V1.5 2007.
71. Roberts D.A., Quattrochi D.A., Hulley G.C., Hook S.J., Green R.O. Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the hyperspectral infrared imager (HyspIRI) decadal survey mission. Remote Sens. Environ. 2012, 117:83-101.
72. 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.
73. Rodrigues M., Ibarra P., Echeverría M., Pérez-Cabello F., de la Riva J. A method for regional-scale assessment of vegetation recovery time after high-severity wildfires: Case study of Spain. Prog. Phys. Geogr. 2014, 38:556-575.
74. Rogan J., Franklin J. Mapping wildfire burn severity in Southern California forests and shrublands using enhanced thematic mapper imagery. Geocarto Int. 2001, 16:91-101.
75. Rogan J., Yool S.R. Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico. Int. J. Remote Sens. 2001, 22(16):3101-3121.
76. Román M.A., Azqueta D., Rodrigues M. Methodological approach to assess the socio-economic vulnerability to wildfires in Spain. For. Ecol. Manag. 2013, 294:158-165.
77. Ruíz-Gallardo J.R., Castaño S., Calera A. Application of remote sensing and GIS to locate priority intervention areas after wildland fires in Mediterranean systems: A case study from southeastern Spain. Int. J. Wildland Fire 2004, 13:241-252.
78. Sankey T., Moffet C., Weber K. Postfire recovery of sagebrush communities: Assessment using SPOT-5 and very large-scale aerial imagery. Rangel. Ecol. Manag. 2008, 61:598-604.
79. Sá-Torres J.P.C. Patterns and drivers of wildfire occurrence and post-fire vegetation resilience across scales in Portugal 2013, (Dissertation PhD), University of Porto (Portugal).
80. Sever L., Leach J., Bren L. Remote sensing of post-fire vegetation recovery; a study using Landsat 5 TM imagery and NDVI in North-East Victoria. J. Spat. Sci. 2012, 57:75-191.
81. Shimabukuro Y.E., Smith J.A. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans. Geosci. Remote Sens. 1991, 29:16-20.
82. Silva J.S.,.P., Vaz F., Moreira F., Catry, Rego F.C. Wildfires as a major driver of landscape dynamics in three fire-prone areas of Portugal. Landsc. Urban Plan. 2011, 101:349-358.
83. Silván-Cárdenas J.L., Wang L. On quantifying post-classification subpixel landcover changes. ISPRS J. Photogramm. Remote Sens. 2014, 98:94-105.
84. Smith A.M., Lentile L.B., Hudak A.T., Morgan P. Evaluation of linear spectral unmixing and DNBR for predicting post-fire recovery in a North American ponderosa pine forest. Int. J. Remote Sens. 2007, 28:5159-5166.
85. Smithwick E.A.H., Turner M.G., Mack M.C., Chapin F.S. Postfire soil N cycling in northern conifer forests affected by severe, stand-replacing wildfires. Ecosystems 2005, 8:163-181.
86. Song C., Woodcock C.E., Seto K.C., Lenney M.P., Macomber A.S. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects. Remote Sens. Environ. 2001, 75:230-244.
87. Souza C., Firestone L., Silva L.M., Roberts D. Mapping forest degradation in the eastern Amazon from SPOT 4 through spectral mixture models. Remote Sens. Environ. 2003, 87:494-506.
88. Tapias R., Climent J., Pardos J.A., Gil L. Life histories of Mediterranean pines. Plant Ecol. 2004, 171:53-68.
89. Technologies S. Manual statgraphics centurion XVI. Statpoint technologies (Ed.) 2012.
90. Teillet P.M., Guindon B., Goodenough D.G. On the slope-aspect correction of multispectral scanner data. Can. J. Remote Sens./J. Can. Télédét. 1982, 8:84-106.
91. Thanos C.A., Daskakakou E.N., Nikolaidou S. Early post-fire regeneration of a Pinus halepensis forest on Mount Parnis, Greece. J. Veg. Sci. 1996, 7:273-280.
92. Tompkins S., Mustard J.F., Pieters C.M., Forsyth D.W. Optimization of endmembers for spectral mixture analysis. Remote Sens. Environ. 1997, 59:472-489.
93. Trabaud L. Post-fire vegetation recovery and dynamics in the Mediterranean area. Fire, landscape and biodiversity: An appraisal of the effects and effectiveness. Lectures from the 3rd international summer school on the environment 2002, 39-56. Universitat de Girona, Institut de Medi Ambient, Girona. G. Pardini, J. Pintó (Eds.).
94. Twele A. Post-fire vegetation regeneration. The case study of the Masif de l'Étoile fire. European commission, directorate general joint research centre (EUR 21010) 2004.
95. van der Meer F.D., Jia X. Collinearity and orthogonality of endmembers in linear spectral unmixing. Int. J. Appl. Earth Obs. Geoinf. 2012, 18:491-503.
96. van Leeuwen W.J.D., Casady G.M., Neary D.G., Bautista S., Alloza J.A., Carmel Y., Wittenberg L., Malkinson D., Orr B.J. Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. Int. J. Wildland Fire 2010, 19:75-93.
97. Vega J.A., Fernández C., Pérez-Gorostiaga P., Fonturbe T. Response of maritime pine (Pinus pinaster Ait.) recruitment to fire severity and post-fire management in a coastal burned area in Galicia (NW Spain). Plant Ecol. 2010, 206:297-308.
98. Veraverbeke S., Lhermitte S., Verstraeten W.W., Goossens R. A time integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBRMT). Int. J. Appl. Earth Obs. Geoinf. 2011, 13:52-58.
99. Veraverbeke S., Somers B., Gitas I., Katagis T., Polychronaki A., Goossens R. Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat thematic mapper imagery: accounting for soil brightness variation. Int. J. Appl. Earth Obs. Geoinf. 2012, 14:1-11.
100. Vicente-Serrano S.M., Pérez-Cabello F., Lasanta T. Pinus halepensis regeneration after a wildfire in a semiarid environment: Assessment using multitemporal Landsat images. Int. J. Wildland Fire 2011, 20:195-208.
101. Vila G., 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.
102. White D., Minotti P., Barczak M., Sifneos J., Freemark K., Santelmann M., Steinitz C., Ross, Kiester A., Preston C. Assessing risks to biodiversity from future landscape change. Conserv. Biol. 1997, 11:349-360.
103. Yang X., Lo C.P. Relative radiometric normalization for change detection from multi-date satellite images. Photogramm. Eng. Remote. Sens. 2000, 66:967-980.