Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Use of linguistic estimates and vegetation indices to assess post-fire vegetation regrowth in woodland areas

Carol R. Jacobson
+ Author Affiliations
- Author Affiliations

A Department of Environment and Geography, Macquarie University, Sydney, NSW 2109, Australia.

B Email: carol.jacobson@mq.edu.au

International Journal of Wildland Fire 19(1) 94-103 https://doi.org/10.1071/WF07129
Submitted: 6 September 2007  Accepted: 8 May 2009   Published: 5 February 2010

Abstract

This study examined an area of woodland that was recovering from severe fire in Royal National Park (NSW, Australia). A non-destructive method of field sampling is required for vulnerable recovering vegetation and therefore classification of digital photographs using linguistic terms was trialled. The linguistic data for three vegetation strata (canopy, shrub and ground) were converted to crisp scores and compared with vegetation index data derived from remotely sensed imagery. All possible subset regression was used to test the proposition that the combined vegetation scores (independent variables) would explain the values of NDVI (Normalized Difference Vegetation Index) and NDMI (Normalized Difference Moisture Index). Vegetation scores for the three strata were also combined using simplified weighting ratios to assess broad relationships between the indices and field data. The combined vegetation scores explained ~60% of the variation in the vegetation index data and inclusion of variables representing multiple strata explained more of the variation than any single variable. The precise value of the weights used to combine the layers did not affect the strength of the association. A simple ratio is proposed that may be useful to estimate woodland parameters under similar conditions, by inversion of the relationship with vegetation index data.

Additional keywords: forest fire, post-fire regeneration, remote sensing, vegetation strata.


Acknowledgements

The author thanks Andrew Horton of NSW National Parks and Wildlife Service for his exceptional support of the project and insights into the vegetation communities of the study area. Thanks are also due to NSW National Parks and Wildlife Service for providing access to fire-sensitive areas, for financial assistance and for support of the field teams. Marcus Bingemann, Lachlan Feggans, Peter Griffiths, Jenna Hore, Allison Shepherd and Robert Wells are thanked for their assistance with fieldwork. I am also grateful to three anonymous referees whose comments significantly improved the paper.


References


Belda F , Meliá J (2000) Relationships between climatic parameters and forest vegetation: application to burned area in Alicante (Spain). Forest Ecology and Management  135, 195–204.
Crossref | GoogleScholarGoogle Scholar | Bian L (1997) Multiscale nature if spatial data is scaling up environmental models. In ‘Scale in Remote Sensing and GIS’. (Eds MF Goodchild, DA Quattrochi) pp. 13–25. (Lewis Publishers: Boca Raton, FL)

Bureau of Meteorology (2009) Climate data online. Available at http://www.bom.gov.au/climate/averages/ [Verified 19 January 2009]

Chafer CJ, Noonan M , Macnaught E (2004) The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires. International Journal of Wildland Fire  13, 227–240.
Crossref | GoogleScholarGoogle Scholar | Chen SJ, Hwang CL (1992) ‘Fuzzy Multiple Attribute Decision Making.’ (Spring-Verlag: Berlin)

Cheney NP (1981) Fire behaviour. In ‘Fire and the Australia Biota’. (Eds AM Gill, RH Groves, IR Noble) pp. 151–175. (Australian Academy of Science: Canberra, ACT)

Chuvieco E (1999) Measuring changes in landscape pattern from satellite images: short-term effects of fire on spatial diversity. International Journal of Remote Sensing  20, 2331–2346.
Crossref | GoogleScholarGoogle Scholar | Gill AM (1981) Adaptive responses of Australian vascular plant species to fires. In ‘Fire and the Australian Biota’. (Eds AM Gill, RH Groves, IR Noble) pp. 243–271. (Australian Academy of Science: Canberra, ACT)

Hobbs R (2002) Fire regimes and their effects in Australian temperate woodlands. In ‘Flammable Australia: the Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, M Gill) pp. 305–326. (Cambridge University Press: Cambridge, UK)

Hope A, Tague C , Clark R (2007) Characterizing post-fire vegetation recovery of California chaparral using TM/ETM+ time-series data. International Journal of Remote Sensing  28, 1339–1354.
Crossref | GoogleScholarGoogle Scholar | Key CH, Benson NC (2002) Measuring and remote sensing of burn severity. In ‘US Geological Survey Wildland Fire Workshop Report’, 31 October–3 November 2000, Los Alamos, NM. (Eds JL Coffelt, RK Livingston) USGS Open-File Report 02–11. (Denver, CO)

King DA (1999) Juvenile foliage and the scaling of tree proportions, with emphasis on eucalyptus. Ecology  80, 1944–1954.
Neter J, Wasserman W, Kutner MH (1985) ‘Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs.’ (Irwin: Homewood, IL)

NSW National Parks and Wildlife Service (2000) Royal National Park, Heathcote National Park and Garawarra State Recreation Area plan of management. Available at http://www.nationalparks.nsw.gov.au/PDFs/pom_final_royal_garawarra_heathcote.pdf [Verified 29 October 2002]

Pereira JMC (1999) A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing  37, 217–226.
Crossref | GoogleScholarGoogle Scholar | Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In ‘Third Earth Resources Technology Satellite-1 Symposium’, 10–14 December, Washington, DC. pp. 309–317. (NASA: Washington, DC)

Smith AMS, Lentile LB, Hudak AT , Morgan P (2007) Evaluation of linear spectral unmixing and dNBR for predicting post-fire recovery in a North American ponderosa pine forest. International Journal of Remote Sensing  28, 5159–5166.
Crossref | GoogleScholarGoogle Scholar | Specht RL (1970) Vegetation. In ‘The Australian Environment’ (Ed. GW Leeper) pp. 44–67. (CSIRO in association with Melbourne University Press: Melbourne).

SPOT Image (2004) Solar equivalent irradiances for SPOT instruments. Available at http://www.spotimage.fr/automne_modules_files/standard/public/p555_fileLINKEDFILE_spectral-sens.xls [Verified 9 December 2008]

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

Tucker CJ (1979) The combinations used to merge the values form the canopy and shrub layers. Remote Sensing of Environment  8, 127–150.
Crossref | GoogleScholarGoogle Scholar | Whelan RJ, Rodgerson L, Dickman CR, Sutherland EF (2002) Critical life cycles of plants and animals: developing a process-based understanding of population changes in fire-prone landscapes. In ‘Flammable Australia: the Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, M Gill) pp. 94–124. (Cambridge University Press: Cambridge, UK)

Williams RJ, Griffiths AD, Allan GE (2002) Fire regimes and biodiversity in the savannas of northern Australia. In ‘Flammable Australia: the Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, M Gill) pp. 281–304. (Cambridge University Press: Cambridge)

Wilson EH , Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment  80, 385–396.
Crossref | GoogleScholarGoogle Scholar |

Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning, Part I. Information Sciences  8, 199–249.
Crossref | GoogleScholarGoogle Scholar |