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- Title
Meta-Prediction of Bromus tectorum Invasion in Central Utah, United States.
- Authors
Clinton, Nicholas Etienne; Peng Gong; Zhenyu Jin; Bing Xu; Zhiliang Zhu
- Abstract
Cheatgrass (Bromus tectorum) is an invasive, exotic grass infesting the Western US. Multi-temporal Landsat TM imagery and ancillary topographic data were used for mapping this invasion over portions of Utah. Tobit, logit, probit, and Projection Adjustment by Contribution Estimation (PACE) regression, neural networks, and additive regression of regression trees were tested individually, and in an ensemble, Tobit regression had the best performance as an individual predictor. Tobit was most frequently the best predictor of zero cheatgrass coverage. A meta-predictor (classifier) to choose the best predictive model was implemented on a pixel-by-pixel basis. A J48 classification tree as a meta-predictor resulted in an increase in accuracy over the best performer in the ensemble. This study illustrated the potential for meta-prediction as a general technique for increasing accuracy from a collection of base predictors.
- Subjects
UTAH; CHEATGRASS brome; TOBITS; LOGITS; PROBITS; ARTIFICIAL neural networks; IMAGING systems
- Publication
Photogrammetric Engineering & Remote Sensing, 2009, Vol 75, Issue 6, p689
- ISSN
0099-1112
- Publication type
Article
- DOI
10.14358/PERS.75.6.689