International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH FRONT

Monitoring post-fire vegetation recovery in the Mediterranean using SPOT and ERS imagery

A. Polychronaki A C , I. Z. Gitas A and A. Minchella B
+ Author Affiliations
- Author Affiliations

A Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, PO Box 248, University Campus, GR-54124 Thessaloniki, Greece. Email: igitas@for.auth.gr

B Remote Sensing Applications Consultants Ltd, c/o European Space Agency (ESA), European Space Research Institute (ESRIN), Via G. Galilei 64, I-00044 Frascati, Italy. Email: andrea.minchella@esa.int

C Corresponding author. Email: anpolych@for.auth.gr

International Journal of Wildland Fire 23(5) 631-642 https://doi.org/10.1071/WF12058
Submitted: 1 April 2012  Accepted: 5 February 2013   Published: 15 August 2013

Abstract

This study examined the effect of two different forest fires 19 and 23 years ago on the Mediterranean island of Thasos. An object-based classification scheme was developed to map the major land-cover types using multi-temporal Système Pour l’Observation de la Terre (SPOT) and European Remote-Sensing (ERS) (C-band VV) images covering the time period from 1993 to 2007. The developed scheme mapped the post-fire land-cover types accurately: 0.84 Kappa coefficient and 90.5% overall accuracy. The use of the ERS backscatter coefficient contributed to decreasing the commission errors related to the mapping of forested areas and to overcoming misclassifications that occurred between forested areas and shrublands located in shadowed areas. Results indicated that the forest regeneration rate is rather slow, especially in areas where the degree of burn severity was high while the largest part of the burned area is, to date, covered by low vegetation and shrubs. Nevertheless, a gradual shift from low vegetation to shrubland was observed. A preliminary investigation on the use of the ERS backscatter coefficient and the Normalised Difference Vegetation Index to monitor forest regeneration revealed that the backscatter coefficient could provide information related to changes in dense regenerating pine forests for the first 18 years after the fire event, whereas the Normalised Difference Vegetation Index was found to be sensitive to the regenerating forest understorey vegetation. However, further investigation is needed to confirm these findings.

Additional keywords: Mediterranean pine forests, object-based classification.


References

Amarsaikhan D, Blotevogel HH, van Genderen JL, Ganzorig M, Gantuya R, Nergui B (2010) Fusing high-resolution SAR and optical imagery for improved urban land-cover study and classification. International Journal of Image and Data Fusion 1, 83–97.
Fusing high-resolution SAR and optical imagery for improved urban land-cover study and classification.CrossRef |

Ban Y, Hu H, Rangel IM (2010) Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach. International Journal of Remote Sensing 31, 1391–1410.
Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach.CrossRef |

Benz UP, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58, 239–258.
Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information.CrossRef |

Blaes X, Vanhalleb L, Defourny P (2005) Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment 96, 352–365.
Efficiency of crop identification based on optical and SAR image time series.CrossRef |

Blaschke T (2010) Object-based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65, 2–16.
Object-based image analysis for remote sensing.CrossRef |

Bourgeau-Chavez LL, Kasischke ES, Riordan K, Brunzell S, Nolan M, Hyer E, Slawski J, Medvecz M, Walters T, Ames S (2007) Remote monitoring of spatial and temporal surface soil moisture in fire-disturbed boreal forest ecosystems with ERS SAR imagery. International Journal of Remote Sensing 28, 2133–2162.
Remote monitoring of spatial and temporal surface soil moisture in fire-disturbed boreal forest ecosystems with ERS SAR imagery.CrossRef |

Chavez P (1996) Image-based atmospheric corrections-Revisited and improved. Photogrammetric Engineering and Remote Sensing 6, 1025–1036.

Chust G, Ducrot D, Pretus JLL (2004) Land-cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediterranean cultural landscape. International Journal of Remote Sensing 25, 3513–3528.
Land-cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediterranean cultural landscape.CrossRef |

Cuevas-González M, Gerard F, Baltzer H, Riano D (2009) Analysing forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices. Global Change Biology 15, 561–577.
Analysing forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices.CrossRef |

Desclée B, Bogaert P, Defourny P (2006) Forest change detection by statistical object-based method. Remote Sensing of Environment 102, 1–11.
Forest change detection by statistical object-based method.CrossRef |

Díaz-Delgado R, Lloret F, Pons X (2003) Influence of fire severity on plant regeneration by means of remote sensing. International Journal of Remote Sensing 24, 1751–1763.
Influence of fire severity on plant regeneration by means of remote sensing.CrossRef |

Dobson MC, Ulaby FT, LeToan T, Beaudoin A, Kasischke ES, Christensen N (1992) Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing 30, 412–415.
Dependence of radar backscatter on coniferous forest biomass.CrossRef |

Esch T, Schenk A, Ullmann T, Thiel M, Roth A, Dech S (2011) Characterization of land-cover types in TerraSAR-X images by combined analysis of speckle statistics and intensity information. IEEE Transactions on Geoscience and Remote Sensing 49, 1911–1925.
Characterization of land-cover types in TerraSAR-X images by combined analysis of speckle statistics and intensity information.CrossRef |

Espelta JM, Verkaik I, Eugenio M, Lloret F (2008) Recurrent wildfires constrain long-term reproduction ability in Pinus halepensis Mill. International Journal of Wildland Fire 17, 579–585.
Recurrent wildfires constrain long-term reproduction ability in Pinus halepensis Mill.CrossRef |

Gitas I, Mitri G, Veraverbeke S, Polychronaki A (2012). Advances in remote sensing of post-fire vegetation recovery monitoring – a review. In ‘Remote Sensing of Biomass – Principles and Applications’. (Ed. L Fatoyinbo) (InTech) Available at http://www.intechopen.com/books/remote-sensing-of-biomass-principles-and-applications/advances-in-remote-sensing-of-post-fire-monitoring-a-review [Verified 20 June 2013]

Hall F, Botkin D, Strebel D, Woods K, Goetz S (1991) Large-scale patterns of forest succession as determined by remote sensing. Ecology 72, 628–640.
Large-scale patterns of forest succession as determined by remote sensing.CrossRef |

Harrell PA, Bourgeau-Chavez LL, Kasischke ES, French NHF, Christensen NLJ (1995) Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest. Remote Sensing of Environment 54, 247–260.
Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest.CrossRef |

Hernández Clemente R, Navarro Cerrillo R, Gitas I (2009) Monitoring post-fire regeneration in Mediterranean ecosystems by employing multitemporal satellite imagery. International Journal of Wildland Fire 18, 648–658.
Monitoring post-fire regeneration in Mediterranean ecosystems by employing multitemporal satellite imagery.CrossRef |

Jabukauskas M, Lulla K, Mausel P (1990) Assessment of vegetation change in a fire-altered forest landscape. Photogrammetric Engineering and Remote Sensing 56, 371–377.

Kamal M, Phinn S (2011) Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach. Remote Sensing 3, 2222–2242.
Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach.CrossRef |

Kasischke ES, Bourgeau-Chavez LL, Christensen NLJ, Haney E (1994) Observations on the sensitivity of ERS-l SAR image intensity to changes in aboveground biomass in young loblolly pine forests. International Journal of Remote Sensing 15, 3–16.
Observations on the sensitivity of ERS-l SAR image intensity to changes in aboveground biomass in young loblolly pine forests.CrossRef |

Kasischke ES, Christensen NLJ, Bourgeau-Chavez LL (1995) Correlating radar backscatter with components of biomass in loblolly pine forests. IEEE Transactions on Geoscience and Remote Sensing 33, 643–659.
Correlating radar backscatter with components of biomass in loblolly pine forests.CrossRef |

Kasischke ES, Melack JM, Dobson MC (1997) The use of imaging radars for ecological applications – a review. Remote Sensing of Environment 59, 141–156.
The use of imaging radars for ecological applications – a review.CrossRef |

Kasischke ES, Tanase MA, Bourgeau-Chavez LL, Borr M (2011) Soil moisture limitations on monitoring boreal forest recovery using spaceborne L-band SAR data. Remote Sensing of Environment 115, 227–232.
Soil moisture limitations on monitoring boreal forest recovery using spaceborne L-band SAR data.CrossRef |

Key C, Benson N (2005) 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’. (Eds RKD Lutes, JF Caratti, CH Key, NC Benson, S Sutherland, LJ Gangi) USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD LA, pp. 1–51. (Ogden, UT)

Kuplich TM (2006) Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network. Forest Ecology and Management 234, 1–9.
Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network.CrossRef |

Le Hegarat-Mascle S, Quesney A, Vidal-Madjar D, Taconet O, Normand M, Loumagne C (2000) Land cover discrimination from multitemporal ERS images and multispectral Landsat images: a study case in an agricultural area in France. International Journal of Remote Sensing 21, 435–456.
Land cover discrimination from multitemporal ERS images and multispectral Landsat images: a study case in an agricultural area in France.CrossRef |

Lhermitte S, Verbesselt J, Verstraeten WW, Veraverbeke S, Coppin P (2011) Assessing intra-annual vegetation recovery after fire using the pixel-based regeneration index. ISPRS Journal of Photogrammetry and Remote Sensing 66, 17–27.
Assessing intra-annual vegetation recovery after fire using the pixel-based regeneration index.CrossRef |

Liang S, Fang H, Chen M (2001) Atmospheric correction of Landsat ETM+ land surface imagery – Part I: methods. IEEE Transactions on Geoscience and Remote Sensing 39, 2490–2498.
Atmospheric correction of Landsat ETM+ land surface imagery – Part I: methods.CrossRef |

Longépé N, Rakwatin P, Isoguchi O, Shimada M, Uryu Y, Yulianto K (2011) Assessment of ALOS PALSAR 50-m orthorectified FBD data for regional land cover classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing 49, 2135–2150.
Assessment of ALOS PALSAR 50-m orthorectified FBD data for regional land cover classification by support vector machines.CrossRef |

Michelson DB, Liljeberg BM, Pilesjo P (2000) Comparison of algorithms for classifying Swedish landcover using Landsat TM and ERS-1 SAR data. Remote Sensing of Environment 71, 1–15.
Comparison of algorithms for classifying Swedish landcover using Landsat TM and ERS-1 SAR data.CrossRef |

Minchella A, Del Frate F, Capogna F, Anselmi S, Manes F (2009) Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas. Remote Sensing of Environment 113, 588–597.
Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas.CrossRef |

Mitri G, Gitas IZ (2010) Post-fire vegetation recovery using EO-1 Hyperion imagery. IEEE Transactions on Geoscience and Remote Sensing 48, 1613–1618.
Post-fire vegetation recovery using EO-1 Hyperion imagery.CrossRef |

Pausas JG, Ribeiro E, Vallejo R (2004) Post-fire regeneration variability of Pinus halepensis in the eastern Iberian Peninsula. Forest Ecology and Management 203, 251–259.
Post-fire regeneration variability of Pinus halepensis in the eastern Iberian Peninsula.CrossRef |

Pausas JG, Llovet L, Rodrigo A, Vallejo R (2008) Are wildfires a disaster in the Mediterranean basin? – A review. International Journal of Wildland Fire 17, 713–723.
Are wildfires a disaster in the Mediterranean basin? – A review.CrossRef |

Peters J, Van Coillie F, Westra T, De Wulf R (2011) Synergy of very high resolution optical and radar data for object-based olive-grove mapping. International Journal of Geographical Information Science 25, 971–989.
Synergy of very high resolution optical and radar data for object-based olive-grove mapping.CrossRef |

Peterson S, Stow D (2003) Using multiple image endmember spectral mixture analysis to study chaparral recovery in southern California. International Journal of Remote Sensing 24, 4481–4504.
Using multiple image endmember spectral mixture analysis to study chaparral recovery in southern California.CrossRef |

Pohl C, Van Genderen JL (1998) Review article Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing 19, 823–854.
Review article Multisensor image fusion in remote sensing: concepts, methods and applications.CrossRef |

Qi Z, Gar-On Yeh A, Li X, Lin Z (2012) A novel algorithm for land-use and land-cover classification using RADARSAT-2 polarimetric SAR data. Remote Sensing of Environment 118, 21–39.
A novel algorithm for land-use and land-cover classification using RADARSAT-2 polarimetric SAR data.CrossRef |

Ramsey E, Nelson G, Sapkota S, Laine S, Verdi J, Krasznay S (1999) Using multiple-polarization L-band radar to monitor marsh burn recovery. IEEE Transactions on Geoscience and Remote Sensing 37, 635–639.
Using multiple-polarization L-band radar to monitor marsh burn recovery.CrossRef |

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 Sensing of Environment 79, 60–71.
Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains.CrossRef |

Roder A, Hill J, Duguy B, Alloza J, Vallejo R (2008) 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 Sensing of Environment 112, 259–273.
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).CrossRef |

Sankey T, Moffet C, Weber K (2008) Post-fire recovery of sagebrush communities: assessment using SPOT-5 and very large-scale aerial imagery. Rangeland Ecology and Management 61, 598–604.
Post-fire recovery of sagebrush communities: assessment using SPOT-5 and very large-scale aerial imagery.CrossRef |

Shoshany M (2000) Satellite remote sensing of natural Mediterranean vegetation: a review within an ecological context. Progress in Physical Geography 24, 153–178.

Shupe SM, Marsh SE (2004) Cover- and density-based vegetation classifications of the Sonoran Desert using Landsat TM and ERS-1 SAR imagery. Remote Sensing of Environment 93, 131–149.
Cover- and density-based vegetation classifications of the Sonoran Desert using Landsat TM and ERS-1 SAR imagery.CrossRef |

Smara Y, Belhadj-Aissa A, Sansal B, Lichtenegger J, Bouzenoune A (1998) Multisource ERS-1 and optical data for vegetal cover assessment and monitoring in a semi-arid region of Algeria. International Journal of Remote Sensing 19, 3551–3568.
Multisource ERS-1 and optical data for vegetal cover assessment and monitoring in a semi-arid region of Algeria.CrossRef |

Solans Vila G, Barbosa P (2010) Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria – Italy) using Landsat TM and ETM+ data. Ecological Modelling 221, 75–84.
Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria – Italy) using Landsat TM and ETM+ data.CrossRef |

Stueve K, Cerney D, Rochefort R, Kurt L (2009) Post-fire tree establishment patterns at the Alpine treeline ecotone: Mount Rainier National Park, Washington, USA. Journal of Vegetation Science 20, 107–120.
Post-fire tree establishment patterns at the Alpine treeline ecotone: Mount Rainier National Park, Washington, USA.CrossRef |

Tanase M, de la Riva J, Santoro M, Pérez-Cabello F, Kasischke E (2011) Sensitivity of SAR data to post-fire forest recovery in Mediterranean and boreal forests. Remote Sensing of Environment 115, 2075–2085.
Sensitivity of SAR data to post-fire forest recovery in Mediterranean and boreal forests.CrossRef |

Thiel CJ, Thiel C, Schmullius CC (2009) Operational large-area forest monitoring in Siberia using ALOS PALSAR summer intensities and winter coherence. IEEE Transactions on Geoscience and Remote Sensing 47, 3993–4000.
Operational large-area forest monitoring in Siberia using ALOS PALSAR summer intensities and winter coherence.CrossRef |

Trabaud L, Christensen NL, Gill AM (1993) Historical biogeography of fire in temperate and Mediterranean ecosystems. In ‘Fire in the Environment: the Ecological, Atmospheric and Climatic Importance of Vegetation Fires’. (Eds PJ Crutzen and JG Goldammer) pp. 277–295. (Wiley: New York)

van Leeuwen W, Casady G, Neary D, Bautista S, Alloza J, Carmel J, Wittenberg L, Malkinson D, Orr B (2010) Monitoring post-wildfire vegetation response with remotely sensed time series data in Spain, USA and Israel. International Journal of Wildland Fire 19, 75–93.
Monitoring post-wildfire vegetation response with remotely sensed time series data in Spain, USA and Israel.CrossRef |

Veraverbeke S, Lhermitte S, Verstraeten WW, Goossens R (2010a) The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece. Remote Sensing of Environment 114, 2548–2563.
The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece.CrossRef |

Veraverbeke S, Verstraeten WW, Lhermitte S, Goossens R (2010b) Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece. International Journal of Wildland Fire 19, 558–569.
Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece.CrossRef |

Veraverbeke S, Gitas I, Katagis T, Polychronaki A, Somers B, Goossens R (2012a) Assessing post-fire vegetation recovery using red-near infrared vegetation indices: accounting for background and vegetation variability. ISPRS Journal of Photogrammetry and Remote Sensing 68, 28–39.
Assessing post-fire vegetation recovery using red-near infrared vegetation indices: accounting for background and vegetation variability.CrossRef |

Veraverbeke S, Somers B, Gitas I, Katagis T, Polychronaki A, Goossens R (2012b) Spectral mixture analysis to assess post-fire vegetation 45 regeneration using Landsat Thematic mapper imagery: accounting for soil brightness variation. International Journal of Applied Earth Observation and Geoinformation 14, 1–11.
Spectral mixture analysis to assess post-fire vegetation 45 regeneration using Landsat Thematic mapper imagery: accounting for soil brightness variation.CrossRef |

Viedma O, Melia J, Segarra D, Garcia-Haro J (1997) Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing of Environment 61, 383–398.
Modeling rates of ecosystem recovery after fires by using Landsat TM data.CrossRef |

Yan G, Mas J-F, Maathuis BHP, Xiangmin Z, Van Dijk PM (2006) Comparison of pixel-based and object-oriented image classification approaches – a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing 27, 4039–4055.
Comparison of pixel-based and object-oriented image classification approaches – a case study in a coal fire area, Wuda, Inner Mongolia, China.CrossRef |

Zhu Z, Woodcock CE, Rogan J, Kellndorfer J (2012) Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data. Remote Sensing of Environment 117, 72–82.
Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data.CrossRef |



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