ShapeSelectForest-package: Shape Selection for Landsat Time Series of Forest Dynamics

In ShapeSelectForest: Shape Selection for Landsat Time Series of Forest Dynamics

Shape Selection for Landsat Time Series of Forest Dynamics

Description

Given a scatterplot of (x_i, y_i), i = 1,\ldots,n, where \bold{x} could be a vector of years and \bold{y} could be a vector of Landsat signals, constrained least-squares spline fits are obtained for the following shapes:

• 1. flat

• 2. decreasing

• 3. one-jump, i.e., decreasing, jump up, decreasing

• 4. inverted vee (increasing then decreasing)

• 5. vee (decreasing then increasing)

• 6. linear increasing

• 7. double-jump, i.e., decreasing, jump up, decreasing, jump up, decreasing.

The shape with the smallest information criterion may be considered a "best" fit. This shape-selection problem was motivated by a need to identify types of disturbances to areas of forest, given Landsat signals over a number of years. The satellite signal is constant or slowly decreasing for a healthy forest, with a jump upward in the signal caused by mass destruction of trees.

The main routine to select the shape for a scatterplot is "shape". See shape for more details.

Author(s)

Mary C. Meyer, Xiyue Liao, Elizabeth Freeman, Gretchen G. Moisen

Maintainer: Xiyue Liao <[e-mail]>

References

Meyer, M. C. and Woodroofe M (2000) On the Degrees of Freedom in Shape-Restricted Regression. The Annals of Statistics 28, 1083–1104.

Meyer, M. C. (2013a) Semi-parametric additive constrained regression. Journal of Nonparametric Statistics 25(3), 715.

Meyer, M. C. (2013b) A simple new algorithm for quadratic programming with applications in statistics. Communications in Statistics 42(5), 1126–1139.

Liao, X. and M. C. Meyer (2014) coneproj: An R package for the primal or dual cone projections with routines for constrained regression. Journal of Statistical Software 61(12), 1–22.

ShapeSelectForest documentation built on Aug. 19, 2023, 5:11 p.m.