Remote sensing for prediction of 1-year post-fire ecosystem condition

Appropriate use of satellite data in predicting >1 year post-fire effects requires remote measurement of sur- face properties that can be mechanistically related to ground measures of post-fire condition. The present study of burned ponderosa pine (Pinus ponderosa) forests in the Black Hills of South Dakota evaluates whether immediate fractional cover estimates of char, green vegetation and brown (non-photosynthetic) vegetation within a pixel are improved predictors of 1-year post-fire field measures, when compared with single-date and differenced Normalized Burn Ratio (NBR and dNBR) indices. The modeled estimate of immediate char fraction either equaled or outperformed all other immediate metrics in predicting 1-year post-fire effects. Brown cover fraction was a poor predictor of all effects (r 2 < 0.30), and each remote measure produced only poor predictions of crown scorch (r 2 < 0.20). Application of dNBR (1 year post) provided a consid- erable increase in regression performance for predicting tree survival. Immediate post-fire NBR or dNBR produced only marginal differences in predictions of all the 1-year post-fire effects, perhaps limiting the need for prefire imagery.Although further research is clearly warranted to evaluate fire effects data available 2-20 years after fire, char and green vegetation fractions may be viable alternatives to dNBR and similar indices to predict longer-term post-fire ecological effects.

[1]  D. Odion,et al.  Fire Severity in the Sierra Nevada Revisited: Conclusions Robust to Further Analysis , 2008, Ecosystems.

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

[3]  N. Benson,et al.  Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio , 2006 .

[4]  R. Crockford,et al.  Partitioning of rainfall into throughfall, stemflow and interception: effect of forest type, ground cover and climate. , 2000 .

[5]  Frederick W. Smith,et al.  Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA , 2006 .

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

[7]  H. Eva,et al.  Burnt area mapping in Central Africa using ATSR data , 1998 .

[8]  K. Ryan,et al.  Evaluating Prescribed Fires , 1985 .

[9]  J. Townshend,et al.  Beware of per-pixel characterization of land cover , 2000 .

[10]  Raymond F. Kokaly,et al.  Postfire soil burn severity mapping with hyperspectral image unmixing , 2007 .

[11]  P. Atkinson,et al.  Mapping sub-pixel proportional land cover with AVHRR imagery , 1997 .

[12]  S. A. Lewis,et al.  The Relationship of Multispectral Satellite Imagery to Immediate Fire Effects , 2007 .

[13]  Alistair M. S. Smith,et al.  Estimating combustion of large downed woody debris from residual white ash , 2005 .

[14]  A. Hudak,et al.  Characterizing Stand- Replacing Harvest and Fire Disturbance Patches in a Forested Landscape: A Case Study from Cooney Ridge, Montana , 2007 .

[15]  Bernard Pinty,et al.  Designing optimal spectral indexes for remote sensing applications , 1996, IEEE Trans. Geosci. Remote. Sens..

[16]  Pinty Bernard,et al.  Designing Optimal Spectral Indices for Remote Sensing Applications , 1996 .

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

[18]  James K. Brown,et al.  Wildland fire in ecosystems: effects of fire on flora , 2000 .

[19]  R. Means,et al.  Value and challenges of conducting rapid response research on wildland fires , 2007 .

[20]  Athanasios T. Vafeidis,et al.  A two‐step method for estimating the extent of burnt areas with the use of coarse‐resolution data , 2005 .

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

[22]  W. Shepperd,et al.  Modeling Postfire Mortality of Ponderosa Pine following a Mixed-Severity Wildfire in the Black Hills: The Role of Tree Morphology and Direct Fire Effects , 2006, Forest Science.

[23]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

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

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

[26]  R. Lawrence,et al.  Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape : Mount St. Helens, Washington , 1998 .

[27]  M. G. Ryan,et al.  Soil‐surface carbon dioxide efflux and microbial biomass in relation to tree density 13 years after a stand replacing fire in a lodgepole pine ecosystem , 2003 .

[28]  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 .

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

[30]  W. Shepperd,et al.  Trembling aspen response to a mixed-severity wildfire in the Black Hills, South Dakota, USA , 2005 .

[31]  Wayne D. Shepperd,et al.  Ecology, silviculture, and management of Black Hills ponderosa pine , 2002 .

[32]  J. Pereira,et al.  Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia , 2002 .

[33]  V. Caselles,et al.  Mapping burns and natural reforestation using thematic Mapper data , 1991 .

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

[35]  J. Settle,et al.  Mapping Vegetation, Soils, and Geology in Semiarid Shrublands Using Spectral Matching and Mixture Modeling of SWIR AVIRIS Imagery , 1999 .

[36]  S. Trumbore.  Carbon respired by terrestrial ecosystems – recent progress and challenges , 2006 .

[37]  W. Qin,et al.  3-D Scene Modeling of Semidesert Vegetation Cover and its Radiation Regime , 2000 .

[38]  E. Chuvieco,et al.  Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models , 2007 .

[39]  Carl H. Key,et al.  Ecological and Sampling Constraints on Defining Landscape Fire Severity , 2006 .

[40]  A. Smith,et al.  Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS , 2007 .

[41]  David P. Roy,et al.  Remote sensing of fire severity: assessing the performance of the normalized burn ratio , 2006, IEEE Geoscience and Remote Sensing Letters.

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

[43]  Alistair M. S. Smith,et al.  Post-Fire Burn Severity and Vegetation Response Following Eight Large Wildfires Across the Western United States , 2007 .

[44]  J. C. Taylor,et al.  Sensitivity of mixture modelling to end‐member selection , 2003 .

[45]  Jay D. Miller,et al.  BAER Soil Burn Severity Maps Do Not Measure Fire Effects to Vegetation: A Comment on Odion and Hanson (2006) , 2008, Ecosystems.

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

[47]  Alan R. Gillespie,et al.  Spectral-mixture analysis , 2006 .

[48]  R. Clark,et al.  Spectral properties of ice‐particulate mixtures and implications for remote sensing: 1. Intimate mixtures , 1984 .

[49]  M. Cochrane.  Linear mixture model classification of burned forests in the Eastern Amazon , 1998 .

[50]  D. Odion,et al.  Fire Severity in Conifer Forests of the Sierra Nevada, California , 2006, Ecosystems.

[51]  R. Lucas,et al.  Non-linear mixture modelling without end-members using an artificial neural network , 1997 .

[52]  Carol A. Wessman,et al.  DETECTING FIRE AND GRAZING PATTERNS IN TALLGRASS PRAIRIE USING SPECTRAL MIXTURE ANALYSIS , 1997 .

[53]  Richard A. Minnich,et al.  Spatial distribution and properties of ash and thermally altered soils after high-severity forest fire, southern California , 2005 .

[54]  H. Eva,et al.  Remote Sensing of Biomass Burning in Tropical Regions: Sampling Issues and Multisensor Approach , 1998 .

[55]  S. Doerr.  Fire effects on soil system functioning: new insights and future challenges , 2005 .

[56]  D. Opitz,et al.  Classifying and mapping wildfire severity : A comparison of methods , 2005 .

[57]  A. Cracknell.  Review article Synergy in remote sensing-what's in a pixel? , 1998 .

[58]  Sarah A. Lewis,et al.  Mapping Ground Cover Using Hyperspectral Remote Sensing after the 2003 Simi and Old Wildfires in Southern California , 2007 .

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

[60]  Frederick W. Smith,et al.  Patch structure, fire-scar formation, and tree regeneration in a large mixed-severity fire in the South Dakota Black Hills, USA , 2005 .

[61]  José M. C. Pereira,et al.  A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping , 1999, IEEE Trans. Geosci. Remote. Sens..

[62]  M. Wimberly,et al.  Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery , 2007 .

[63]  C. Daughtry,et al.  Plant Litter and Soil Reflectance , 2000 .

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

[65]  Giles M. Foody,et al.  Estimation of sub-pixel land cover composition in the presence of untrained classes , 2000 .

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

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

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

[69]  C. Elvidge.  Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[70]  A. Huete,et al.  Assessment of biophysical soil properties through spectral decomposition techniques , 1991 .

[71]  S. Gerstl,et al.  Nonlinear spectral mixing models for vegetative and soil surfaces , 1994 .

[72]  G. Thomas,et al.  An evaluation of spectral mixture modelling applied to a semi-arid environment , 2002 .