Beyond Landsat: a comparison of four satellite sensors for detecting burn severity in ponderosa pine forests of the Gila Wilderness, NM, USA

Methods of remotely measuring burn severity are needed to evaluate the ecological and environmental impacts of large, remote wildland fires. The challenges that were associated with the Landsat program highlight the need to evaluate alternative sensors for characterising post-fire effects. We compared statistical correlations between 55 Composite Burn Index field plots and spectral indices from four satellite sensors varying in spatial and spectral resolution on the 2003 Dry Lakes Fire in the Gila Wilderness, NM. Where spectrally feasible, burn severity was evaluated using the differenced Enhanced Vegetation Index (dEVI), differenced Normalised Difference Vegetation Index (dNDVI) and the differenced Normalised Burn Ratio (dNBR). Both the dEVI derived from Quickbird and the dNBR derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) showed similar or slightly improved correlations over the dNBR derived from Landsat Thematic Mapper data (R 2 ¼0.82, 0.84, and 0.78 respectively). The relativelycoarseresolutionMODIS-derivedNDVIimagewasweaklycorrelatedwithgrounddata(R 2 ¼0.38).Ourresults

[1]  Xuexia Chen,et al.  Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest , 2004 .

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

[3]  P. Morgan,et al.  Evaluation of linear spectral unmixing and ΔNBR for predicting post‐fire recovery in a North American ponderosa pine forest , 2007 .

[4]  P. Robichaud,et al.  Effectiveness of needle cast at reducing erosion after forest fires , 2003 .

[5]  L. Vierling,et al.  Estimating equivalent water thickness in a conifer forest using Landsat TM and ASTER data: a comparison study , 2006 .

[6]  Alistair M. S. Smith,et al.  Remote sensing for prediction of 1-year post-fire ecosystem condition , 2009 .

[7]  Paul E. Gessler,et al.  Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling , 2005 .

[8]  Sorin C. Popescu,et al.  Mapping surface fuel models using lidar and multispectral data fusion for fire behavior , 2008 .

[9]  Sarah A. Lewis,et al.  Assessing burn severity and comparing soil water repellency, Hayman Fire, Colorado , 2006 .

[10]  Matthew G. Rollins,et al.  Mapping Fire Regimes Across Time and Space: Understanding Coarse and Fine-scale Fire Patterns , 2001 .

[11]  Alan R. Gillespie,et al.  Scientific results from ASTER , 2005 .

[12]  Malcolm K. Hughes,et al.  The climate of the US Southwest , 2002 .

[13]  L. F. Curtis.  Remote sensing techniques , 1978, Nature.

[14]  Rosa Lasaponara,et al.  On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape , 2007 .

[15]  Martin J. Wooster,et al.  Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African savannahs , 2005 .

[16]  R. Landry,et al.  Remote sensing of burn severity: experience from western Canada boreal fires* , 2008 .

[17]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[18]  Xuexia Chen,et al.  A simple and effective radiometric correction method to improve landscape change detection across sensors and across time , 2005 .

[19]  S. A. Lewis,et al.  Remote sensing techniques to assess active fire characteristics and post-fire effects , 2006 .

[20]  P. Gessler,et al.  Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data , 2005 .

[21]  Ron Kwok,et al.  Mass Balance of the Cryosphere: Remote-sensing techniques , 2004 .

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

[23]  R. Hall,et al.  Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results , 2008 .

[24]  A. Huete,et al.  An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction , 2001 .

[25]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[26]  Jay D. Miller,et al.  Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR) , 2007 .

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

[28]  L. Rowan,et al.  Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data , 2003 .

[29]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[30]  A. Smith,et al.  Fire season precipitation variability influences fire extent and severity in a large southwestern wilderness area, United States , 2007 .

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