journal article Open Access Oct 24, 2018

Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests

Remote Sensing Vol. 10 No. 11 pp. 1680 · MDPI AG
View at Publisher Save 10.3390/rs10111680
Abstract
Spectral indices derived from optical remote sensing data have been widely used for fire-severity classification in forests from local to global scales. However, comparative analyses of multiple indices across diverse forest types are few. This represents an information gap for fire management agencies in areas like temperate south-eastern Australia, which is characterised by a diversity of natural forests that vary in structure, and in the fire-regeneration strategies of the dominant trees. We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in south-eastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 7.9M ha region’s forest area. Forest types were aggregated into six forest groups based on their fire-regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index performance was evaluated for each forest type and forest group by examining its sensitivity to four fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, separability, and optimality). For the best-performing indices, we calculated index-specific thresholds (by forest types and groups) to separate between the four severity classes, and evaluated the accuracy of fire-severity classification on independent samples. Our results indicated that the best-performing indices of fire severity varied with forest type and group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values ranged from 0.33 (fair agreement) to 0.77 (substantial agreement), depending on the forest group and index. Fire severity in resprouter open forests and woodlands was most accurately mapped using the delta Normalised Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference vegetation index) performed best for open forests with mixed fire responses (resprouters and seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate seeder closed forests. Our analysis highlighted the low sensitivity of all indices to fire impacts in Rainforest. We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes.
Topics

No keywords indexed for this article. Browse by subject →

References
80
[1]
Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems

W BOND, J KEELEY

Trends in Ecology & Evolution 2005 10.1016/j.tree.2005.04.025
[2]
Fire in the Earth System

David M. J. S. Bowman, Jennifer K. Balch, Paulo Artaxo et al.

Science 2009 10.1126/science.1163886
[3]
Barbosa "An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions" Glob. Biogeochem. Cycles (1999) 10.1029/1999gb900042
[4]
Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability

S. Veraverbeke, I. Gitas, T. Katagis et al.

ISPRS Journal of Photogrammetry and Remote Sensing 2012 10.1016/j.isprsjprs.2011.12.007
[5]
Collins "Spatial patterns of large natural fires in Sierra Nevada wilderness areas" Landsc. Ecol. (2007) 10.1007/s10980-006-9047-5
[6]
Fairman "Too much, too soon? A review of the effects of increasing wildfire frequency on tree mortality and regeneration in temperate eucalypt forests" Int. J. Wildland Fire (2016) 10.1071/wf15010
[7]
Patterson "Mapping fire-induced vegetation mortality using Landsat thematic mapper data: A comparison of linear transformation techniques" Remote Sens. Environ. (1998) 10.1016/s0034-4257(98)00018-2
[8]
Jakubauskas "Assessment of vegetation change in a fire-altered forest landscape" PE RS Photogramm. Eng. Remote Sens. (1990)
[9]
Brewer "Classifying and mapping wildfire severity" Photogramm. Eng. Remote Sens. (2005) 10.14358/pers.71.11.1311
[10]
Tanase "Fire severity estimation from space: A comparison of active and passive sensors and their synergy for different forest types" Int. J. Wildland Fire (2015) 10.1071/wf15059
[11]
Veraverbeke "Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece" Int. J. Wildland Fire (2010) 10.1071/wf09069
[12]
White "Remote sensing of forest fire severity and vegetation recovery" Int. J. Wildland Fire (1996) 10.1071/wf9960125
[13]
Chuvieco "Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data" Remote Sens. Environ. (2008) 10.1016/j.rse.2007.11.007
[14]
Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)

Jay D. Miller, Andrea E. Thode

Remote Sensing of Environment 2007 10.1016/j.rse.2006.12.006
[15]
Trigg "An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah" Int. J. Remote Sens. (2001) 10.1080/01431160110053185
[16]
Pereira "Satellite monitoring of fire in the EXPRESSO study area during the 1996 dry season experiment: Active fires, burnt area, and atmospheric emissions" J. Geophys. Res. Atmos. (1999) 10.1029/1999jd900422
[17]
Escuin "Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images" Int. J. Remote Sens. (2008) 10.1080/01431160701281072
[18]
Key, C., and Benson, N. (2006). Landscape Assessment: Ground Measure of Severity, the Composite Burn Index; and Remote Sensing of Severity, the Normalized Burn Ratio, USDA Forest Service, Rocky Mountain Research Station.
[19]
Epting "Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+" Remote Sens. Environ. (2005) 10.1016/j.rse.2005.03.002
[20]
Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results

Nancy H. F. French, Eric S. Kasischke, Ronald J. Hall et al.

International Journal of Wildland Fire 2008 10.1071/wf08007
[21]
Harris "Evaluating spectral indices for assessing fire severity in chaparral ecosystems (Southern California) using MODIS/ASTER (MASTER) airborne simulator data" Remote Sens. (2011) 10.3390/rs3112403
[22]
Root "Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity" Remote Sens. Environ. (2004) 10.1016/j.rse.2003.12.015
[23]
Chuvieco "Using cluster analysis to improve the selection of training statistics in classifying remotely sensed data" Photogramm. Eng. Remote Sens. (1988)
[24]
Asner "Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery" Remote Sens. Environ. (2010) 10.1016/j.rse.2010.02.008
[25]
Murphy "Evaluating the ability of the differenced Normalized Burn Ratio (dNBR) to predict ecologically significant burn severity in Alaskan boreal forests" Int. J. Wildland Fire (2008) 10.1071/wf08050
[26]
Lloret "Influence of fire severity on plant regeneration by means of remote sensing imagery" Int. J. Remote Sens. (2003) 10.1080/01431160210144732
[27]
Veraverbeke "Evaluating spectral indices and spectral mixture analysis for assessing fire severity, combustion completeness and carbon emissions" Int. J. Wildland Fire (2013) 10.1071/wf12168
[28]
Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

Raymond F. Kokaly, Barnaby W. Rockwell, Sandra L. Haire et al.

Remote Sensing of Environment 2007 10.1016/j.rse.2006.08.006
[29]
Holden "Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data" Int. J. Remote Sens. (2005) 10.1080/01431160500239008
[30]
Smith "Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS" Int. J. Remote Sens. (2007) 10.1080/01431160600954704
[31]
Veraverbeke "Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data" Remote Sens. Environ. (2011) 10.1016/j.rse.2011.06.010
[33]
Timbal, B., Ekström, M., Fiddes, S., Grose, M., Kirono, D., Lim, E.-P., Lucas, C., and Wilson, L. (2016). Climate Change Science and Victoria, Bureau of Meteorology. Bureau Research Report No. 014. 10.22499/4.0014
[34]
Updated world map of the Köppen-Geiger climate classification

M. C. Peel, B. L. Finlayson, T. A. McMahon

Hydrology and Earth System Sciences 2007 10.5194/hess-11-1633-2007
[35]
Hennessey, K., Lucas, C., Nicholls, N., Bathols, J., Suppiah, R., and Ricketts, J. (2005). Climate Change Impacts on Fire-Weather in South-East Australia, CSIRO Marine and Atmospheric Research.
[36]
Cheal, D. (2010). Growth Stages and Tolerable Fire Intervals for Victoria’s Native Vegetation Data Sets, Victorian Government Department of Sustainability and Environment. Fire and Adaptive Management Report No. 84.
[37]
Department of Environment, Land, Water & Planning (DELWP) (2017). Fire History Records of Fires Primarily on Public Land, Department of Environment, Land, Water & Planning.
[38]
Specht, R.L. (1972). The Vegetation of South Australia, Govt. Pr.
[39]
Kasel "Environmental heterogeneity promotes floristic turnover in temperate forests of south-eastern Australia more than dispersal limitation and disturbance" Landsc. Ecol. (2017) 10.1007/s10980-017-0526-7
[40]
USGS (2017, January 15). Earth Explorer, Available online: https://earthexplorer.usgs.gov/.
[41]
Zhu "Object-based cloud and cloud shadow detection in Landsat imagery" Remote Sens. Environ. (2012) 10.1016/j.rse.2011.10.028
[42]
Erdas Inc (2017, September 20). Erdas Imagine. Available online: http://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine.
[43]
A synthesis of postfire recovery traits of woody plants in Australian ecosystems

Peter J. Clarke, Michael J. Lawes, Brett P. Murphy et al.

Science of The Total Environment 2015 10.1016/j.scitotenv.2015.04.002
[44]
Nicolle "A classification and census of regenerative strategies in the eucalypts (Angophora, Corymbia and Eucalyptus—Myrtaceae), with special reference to the obligate seeders" Aust. J. Bot. (2006) 10.1071/bt05061
[45]
Government of Victoria (2018, March 01). Bioregions and EVC Benchmarks, Available online: https://www.environment.vic.gov.au/biodiversity/bioregions-and-evc-benchmarks#hsf.
[46]
Specht, R.L., and Wood, J.G. (1972). British Science Guild, Handbooks Committee, South Australian Branch. The Vegetation of South Australia: Handbook of the Flora and Fauna of South Australia, Govt. Pr.. [2nd ed.].
[47]
Allen "Assessing the differenced Normalized Burn Ratio’s ability to map burn severity in the boreal forest and tundra ecosystems of Alaska’s national parks" Int. J. Wildland Fire (2008) 10.1071/wf08034
[48]
Chuvieco "Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models" Remote Sens. Environ. (2007) 10.1016/j.rse.2006.11.022
[49]
Tanase "Estimating burn severity at the regional level using optically based indices" Can. J. For. Res. (2011) 10.1139/x11-011
[50]
Duffy "Analysis of Alaskan burn severity patterns using remotely sensed data" Int. J. Wildland Fire (2007) 10.1071/wf06034

Showing 50 of 80 references