A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

C. Huang; J.R.G. Townshend

doi:10.1080/01431160305001

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A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

C. Huang·J.R.G. Townshend

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Abstract

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

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BibTeX RIS

C. Huang, J.R.G. Townshend. 2010-11-26. A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover. https://doi.org/10.1080/01431160305001

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