Classification of burn severity using Moderate Resolution Imaging Spectroradiometer (MODIS): A case study in the jarrah-marri forest of southwest Western Australia

The southwest of Western Australia is a fire-prone landscape. In this Mediterranean region, prescribed fuel reduction burning is applied as a management tool by the state government's Department of Conservation and Land Management (CALM). Remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with multiple observations per day are investigated for operational monitoring of prescribed burning activities.The Normalized Burn Ratio (NBR) is sensitive to the amount of biomass, soil exposure and equivalent water content. The differenced Normalized Burn Ratio (ΔNBR) shows the greatest response of landscape change due to fire. The ratios, originally applied to 30-m Landsat 7 ETM+ data, have been transferred to 250–500 m MODIS data. The high temporal resolution and direct broadcast capability of MODIS are considered favorable for monitoring prefire and postfire conditions, in particular in near-real time. This study applies the ΔNBR to classify burn severity using MODIS data with various levels of preprocessing. On the basis of field studies, four burn severity classes are distinguished with best discrimination for high burn severity where the top layer of the vegetation canopy is altered. As expected, the spatial detail of the classifications from MODIS is reduced when compared to results from Landsat 7 ETM+, but the large-scale spatial patterns are similar. NBR time series of daily data showed that classes of burn severity can be separated for each acquisition date. Large temporal variations of the NBR limit class separation with absolute thresholds, in particular for data uncorrected for effects due to varying viewing geometries. However, MODIS top of atmosphere data allow near-real-time assessment of burn severity, important to fire managers for monitoring postfire conditions.

[1]  J. W. Wagtendonk,et al.  Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity , 2004 .

[2]  P. Fulé,et al.  Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data , 2005 .

[3]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[4]  D. Roy,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[5]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[6]  Jay D. Miller,et al.  Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data , 2002 .

[7]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[8]  B. W. Wilgen,et al.  Fire and Plants , 1995, Population and Community Biology Series.

[9]  J. R. Jensen Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[10]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[11]  Chad J. Shuey,et al.  Validating MODIS land surface reflectance and albedo products: methods and preliminary results , 2002 .

[12]  J. C. Price Comparing MODIS and ETM+ data for regional and global land classification , 2003 .

[13]  D. Roy,et al.  Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach , 2002 .

[14]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[15]  G. Dedieu,et al.  SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum , 1994 .

[16]  T. Landmann,et al.  Characterizing sub-pixel Landsat ETM+ fire severity on experimental fires in the Kruger National Park, South Africa. , 2003 .

[17]  W. Hargrove,et al.  Effects of fire on landscape heterogeneity in Yellowstone National Park, Wyoming , 1994 .

[18]  F. Lloret,et al.  Influence of fire severity on plant regeneration by means of remote sensing imagery , 2003 .

[19]  E. Chuvieco,et al.  Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping , 2005 .

[20]  Grant Wardell-Johnson,et al.  Fire and Plant Interactions in Forested Ecosystems of South-West Western Australia , 2003 .

[21]  S. Howard,et al.  An Evaluation of Gap-Filled Landsat SLC-Off Imagery for Wildland Fire Burn Severity Mapping , 2004 .

[22]  Mark W. Patterson,et al.  Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper Data: A Comparison of Linear Transformation Techniques , 1998 .

[23]  John Rogan,et al.  Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico , 2001 .

[24]  D. Verbyla,et al.  Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM , 2005 .

[25]  Liam E. Gumley,et al.  International MODIS and AIRS Processing Package (IMAPP): A Direct Broadcast Software Package for the NASA Earth Observing System , 2004 .

[26]  R. D. Johnson,et al.  Using Landsat TM data to estimate carbon release from burned biomass in an Alaskan spruce forest complex , 2000 .

[27]  S. Running,et al.  Remote Sensing of Forest Fire Severity and Vegetation Recovery , 1996 .

[28]  Ana C. L. Sá,et al.  Assessing the feasibility of sub-pixel burned area mapping in miombo woodlands of northern Mozambique using MODIS imagery , 2003 .