Determination of Burnt Scars Using Logistic Regression and Neural Network Techniques from a Single Post-Fire Landsat-7 ETM+ Image

Using logistic regression (LR) and artificial neural network (NN) algorithms, probabilities of presence/absence (p/a) of burned scars were calculated from post-fire Landsat 7 Enhanced Thematic Mapper plus (ETM+) images in mountainous areas of northern California. The discriminating power of six original TM bands (TM bands 1 through 5 and band 7) and five vegetation indices between burned and unburned areas were analyzed. The LR and NN techniques were applied to two study sites with varied topography. We evaluated the performance of both methods in predicting burned scars based on predictive accuracy, uncertainty index, and computation time. The experimental results indicate that (1) the LR is more efficient than the NN in predicting burned scars, but both techniques can produce similar and acceptable prediction accuracy (overall average accuracy greater than 97 percent for both methods at the two study sites) of p/a of burned areas; (2) among all six original TM bands and five vegetation indices, original TM4 and TM7 and NDVII (TM4, TM7) and NDVI2 (TM4, TM3) exhibit the highest discrimination between burned scars and unburned vegetation areas; and (3) the predictive accuracy produced with samples from the shaded and shadowed areas is lower than that from the sunlit areas.

[1]  N. Koutsias,et al.  Logistic regression modelling of multitemporal Thematic Mapper data for burned area mapping , 1998 .

[2]  Alan H. Strahler,et al.  Pixel- and site-based calibration and validation methods for evaluating supervised classification of remotely sensed data , 2002 .

[3]  M. Vasconcelos,et al.  Spatial Prediction of Fire Ignition Probabilities: Comparing Logistic Regression and Neural Networks , 2001 .

[4]  P. Gong,et al.  Conifer species recognition: Effects of data transformation , 2001 .

[5]  Raymond J. Dezzani,et al.  Spatial autocorrelation of wildfire distribution in the Idyllwild Quadrangle, San Jacinto Mountain, California. , 1990 .

[6]  Yoram J. Kaufman,et al.  Remote Sensing of Biomass Burning in the Tropics , 1990 .

[7]  S. Manel,et al.  Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird , 1999 .

[8]  Prasanna H. Gowda,et al.  Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices , 1997 .

[9]  David L. Martell,et al.  A logistic model for predicting daily people-caused forest fire occurrence in Ontario , 1987 .

[10]  D. Hall,et al.  Landsat digital analysis of the initial recovery of burned tundra at Kokolik River, Alaska , 1980 .

[11]  C. Justice,et al.  Satellite remote sensing of fires during the SAFARI campaign using NOAA Advanced Very High Resolution Radiometer data , 1996 .

[12]  Ruiliang Pu,et al.  Conifer species recognition: An exploratory analysis of in situ hyperspectral data , 1997 .

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

[14]  Yrjö Rauste,et al.  Satellite-based forest fire detection for fire control in boreal forests , 1997 .

[15]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[16]  Octávio S. Paulo,et al.  Modelling wildlife distributions: Logistic Multiple Regression vs Overlap Analysis , 1999 .

[17]  T. H. Haar,et al.  Forest fire monitoring using NOAA satellite AVHRR , 1986 .

[18]  D. Pozo.  Fire detection and growth monitoring using a multitemporal technique on AVHRR mid-infrared and thermal channels , 1997 .

[19]  N. Koutsias,et al.  Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image , 2000 .

[20]  David L. Martell,et al.  Modelling seasonal variation in daily people-caused forest fire occurrence , 1989 .

[21]  P. Gong,et al.  Integrated Analysis of Spatial Data from Multiple Sources: Using Evidential Reasoning and Artificial Neural Network Techniques for Geological Mapping , 1996 .

[22]  P. Gong,et al.  Mapping Ecological Land Systems and Classification Uncertainties from Digital Elevation and Forest-Cover Data Using Neural Networks , 1996 .

[23]  C.Y. Jim,et al.  Assessing and modeling hill fire impact in country parks with SPOT HRV images and GIS , 1998 .

[24]  Louis Moreau,et al.  Monitoring fire activities in the boreal ecosystem , 1997 .

[25]  E. Chuvieco,et al.  Mapping and inventory of forest fires from digital processing of tm data , 1988 .

[26]  Richard A. Minnich,et al.  Mapping probability of fire occurrence in San Jacinto Mountains, California, USA , 1993 .

[27]  Sunil Narumalani,et al.  Aquatic macrophyte modeling using GIS and logistic multiple regression , 1997 .

[28]  J. Grégoire,et al.  The Global Fire Product: Daily fire occurrence from April 1992 to December 1993 derived from NOAA AVHRR data , 2000 .