Combining Non-Parametric based models for multisource predictive forest mapping

@inproceedings{Huang2001CombiningNB,
  title={Combining Non-Parametric based models for multisource predictive forest mapping},
  author={Zhi Huang},
  year={2001}
}
Using a single model for forest type predictive mapping will not produce good estimates of confidence in the prediction of individual pixels, even with good overall accuracy. A new strategy which combines several models based on different philosophy could not only reduce the uncertainty of predictive modelling, but also improve the mapping accuracy. In our study, a Artificial Neural Networks, a Decision Trees, and a model of Dempster-Shafer’s Evidence Theory were individually applied to a… CONTINUE READING
15 Citations
6 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 15 extracted citations

References

Publications referenced by this paper.
Showing 1-6 of 6 references

A New Method for Predicting Vegetation Distributions using Decision Tree Analysis in a Geographic Information System

  • D. M. Moore, B. G. Lees, S. M. Davey
  • Environmental Management,
  • 1991
2 Excerpts

Decision-Tree and Rule-Induction Approach to Integration of Remotely Sensed and GIS Data in Mapping Vegetation in Disturbed or Hilly Enviroments

  • B. Lees, K. Ritman
  • Environmental Management,
  • 1991
1 Excerpt

Neural Network approaches versus statistical methods in classification of multisource remote sensing data

  • J. A. Benediktsson, P. H. Swain, O. K. Ersoy
  • IEEE Transactions on Geoscience and Remote…
  • 1990
1 Excerpt

Forest mapping accuracies are improved using a supervised nonparametric classifier with SPOT data, Photogrametric Engineering & Remote Sensing

  • A. K. Skidmore, B. J. Turner
  • Vol 54,
  • 1988
2 Excerpts

Similar Papers

Loading similar papers…