Accuracy of discontinuous binary surfaces: a case study using boreal forest fires

Confidence in the conclusions of GIS and remote sensing analyses depends on our ability to specify their accuracy. The square error matrix, which is commonly used for accuracy assessment when the database contains continuous digital choropleth data, allows computation of user's, producer's, overall, and Kappa coefficient accuracy values. However, when the underlying assumptions of the square error matrix method are violated, these accuracy values may become unreliable. We demonstrate how traditional forms of accuracy assessment can fail for discontinuous binary surfaces and provide an improved spatial assessment method. We developed 15 new accuracy measures that allow analysts to assess scene- and feature-level accuracy from many new perspectives.

[1]  Robert Frouin,et al.  Methodology for estimating burned area from AVHRR reflectance data , 1995 .

[2]  Gerard B. M. Heuvelink,et al.  Propagation of errors in spatial modelling with GIS , 1989, Int. J. Geogr. Inf. Sci..

[3]  S. V. Stehman,et al.  Comparison of systematic and random sampling for estimating the accuracy of maps generated from remotely sensed data , 1992 .

[4]  R. W. Fitzgerald,et al.  Assessing the classification accuracy of multisource remote sensing data , 1994 .

[5]  Erik Næsset,et al.  Use of the Weighted Kappa Coefficient in Classification Error Assessment of Thematic Maps , 1996, Int. J. Geogr. Inf. Sci..

[6]  A. Fernández,et al.  Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data , 1997 .

[7]  G. H. Rosenfield,et al.  A coefficient of agreement as a measure of thematic classification accuracy. , 1986 .

[8]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[9]  Susan L. Ustin,et al.  Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data , 1993 .

[10]  R. Lunetta,et al.  Remote sensing and Geographic Information System data integration: error sources and research issues , 1991 .

[11]  Tarmo K. Remmel,et al.  Fire mapping in a northern boreal forest: assessing AVHRR/NDVI methods of change detection , 2001 .

[12]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[13]  J. Faundeen,et al.  The 1 km AVHRR global land data set: first stages in implementation , 1994 .

[14]  Eric S. Kasischke,et al.  Constraints on using AVHRR composite index imagery to study patterns of vegetation cover in boreal forests , 1997 .

[15]  Peter V. August,et al.  Accuracy Assessment of Wetland Boundary Delineation Using Aerial Photography and Digital Orthophotography , 2000 .

[16]  E. Kasischke,et al.  Locating and estimating the areal extent of wildfires in alaskan boreal forests using multiple-season AVHRR NDVI composite data , 1995 .

[17]  R. Lunetta,et al.  Remote Sensing Change Detection: Environmental Monitoring Methods and Applications , 1999 .

[18]  R. G. Oderwald,et al.  Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. , 1983 .

[19]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[20]  Russell G. Congalton,et al.  A practical look at the sources of confusion in error matrix generation , 1993 .

[21]  G. H. Rosenfield Analysis of thematic map classification error matrices. , 1986 .

[22]  R. Congalton A Quantitative Method to Test for Consistency and Correctness in Photointerpretation , 1983 .

[23]  Michael Blakemore,et al.  Part 4: Mathematical, Algorithmic and Data Structure Issues: Generalisation and Error in Spatial Data Bases , 1984 .