A Fuzzy Accuracy Assessment of the MidAtlantic Gap Analysis Land Cover Map Using Airborne Videograph - PowerPoint PPT Presentation

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A Fuzzy Accuracy Assessment of the MidAtlantic Gap Analysis Land Cover Map Using Airborne Videograph

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A Fuzzy Accuracy Assessment of the Mid-Atlantic Gap Analysis Land Cover Map ... Gopal and Woodcock, 1994. Rasberry and McKerrow 2002. Linguistic Scale ... – PowerPoint PPT presentation

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Title: A Fuzzy Accuracy Assessment of the MidAtlantic Gap Analysis Land Cover Map Using Airborne Videograph


1
A Fuzzy Accuracy Assessment of the Mid-Atlantic
Gap Analysis Land Cover Map Using Airborne
Videography
Confusion, Fuzziness, and Random Thoughts
  • D. Ann Rasberry and Alexa J. McKerrow
  • 3 August 2002

2
Middle-Atlantic GAP
3
Example from Assateague/Chincoteague
  • Division Vegetated
  • Order Tree Dominated
  • Class Closed Tree Canopy
  • Subclass Evergreen
  • Group Temperate or Subpolar Needle-leaved
  • Subgroup Natural
  • Formation Rounded Crowns
  • Alliance Pinus taeda Forest
  • Association Pinus taeda/Symplocos tinctoria
    Myrica cerifera Vaccinum elliotti

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Video Flight Lines 1996-1997
7
Ancillary Data
NLCD93 NWI Elevation, Slope, Aspect STATSGO State
Natural Heritage
Program
Inventories DOQQs Data collected coincident with
video interpretation key development
8
Maryland, Delaware, New Jersey GAP Land Cover
Map 1993
9
Error Assessment
  • Conventional Confusion Matrix
  • Conditional Probability Accuracies
  • Fuzzy Set Assessment

10
Video Classes
Map Class
11


12
Conditional Probability
  • Weighting the accuracies based on the proportion
    of samples in a class and the proportion of the
    area represented by a class.
  • We used this to account for non-proportional
    sampling.
  • We attempted to get a minimum number of points
    per class and this adjusts for variation in the
    area represented by the assessment points.

13
Conditional Probability
  • (ni ) (ai)

nii
(N) (A)
where ni sample size in class i ai area
represented in class i nii samples correctly
classified in class i N total sample in the
study area A total area in the study area
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16
Fuzzy Set Assessment
  • Expands analysis of error, or uncertainty, in the
    map
  • Provides information about the nature, frequency,
    magnitude, and source of error in the thematic
    map
  • Improves the understanding of the relationships
    between classes in the map
  • Gopal and Woodcock, 1994

17
Linguistic Scale
  • Level 5 Absolutely correct
  • The mapped class exactly matches that which is on
    the ground, or that which has been assessed from
    the video
  • Level 4 Incorrect, but very similar
  • The mapped class does not match that which has
    been assessed, but the two are ecologically very
    similar, sharing the same dominant species, the
    same hydrological regime, comparable substrates,
    and like vegetation structure

18
Linguistic Scale
  • Level 3 Incorrect, but somewhat similar
  • The mapped class does not match that which was
    assessed, but the two are somewhat similar, in
    that they share the same hydrological regime and
    like vegetation structure
  • Level 2 Incorrect, but understandable
  • The mapped class does not match that which has
    been assessed, but the two share a similar
    vegetation structure, creating a plausible
    ecological explanation for the confusion
  • Level 1 Absolutely incorrect
  • The mapped class does not match that which has
    been assessed, and the confusion is ecologically
    inexplicable

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Potential Sources of Error
  • Time differential between imagery, video, and
    field work
  • Positional errors
  • Interpretation errors on videography
  • A single interpreter throughout the process
  • Error propagation from ancillary datasets
  • Assessing a polygon using a site
  • May not be a homogenous polygon

24
Conclusions
  • Using video works well for accuracy assessment
  • Need to develop a good interpretation key with
    ground photos
  • Make the key permanent
  • Invest the time in training the interpreter
    color, shape, size, texture, and juxtaposition
  • Not all projects will be able to fly such
    extensive video
  • Improvements in technology should greatly enhance
    this method and its utility to land cover mapping
  • We need to do some additional analysis to look at
    class confusion
  • Dont think this is necessarily a video issue
  • Learned ways to make better use of ancillary data

25
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