B34C-08 – Using Linear and Non-Linear Temporal Adjustments to Align Multiple Phenology Curves, Making Vegetation Status and Health Directly Comparable

Authors

William W. Hargrove
USDA Forest Service
Steve P. Norman
USDA Forest Service
Jitendra Kumar
Oak Ridge National Laboratory
Forrest M. Hoffman (forrest at climatemodeling dot org)
Oak Ridge National Laboratory

Session

Vegetation Phenology as Forcing and Response Across Diverse Biomes: Detection, Attribution, Prediction, and Implications III
Wednesday, December 13, 2017 17:45–18:00
New Orleans Ernest N. Morial Convention Center – 383–385

Abstract

National-scale polar analysis of MODIS NDVI allows quantification of degree of seasonality expressed by local vegetation, and also selects the most optimum start/end of a local “phenological year” that is empirically customized for the vegetation that is growing at each location. Interannual differences in timing of phenology make direct comparisons of vegetation health and performance between years difficult, whether at the same or different locations. By “sliding” the two phenologies in time using a Procrustean linear time shift, any particular phenological event or “completion milestone” can be synchronized, allowing direct comparison of differences in timing of other remaining milestones. Going beyond a simple linear translation, time can be “rubber-sheeted,” compressed or dilated. Considering one phenology curve to be a reference, the second phenology can be “rubber-sheeted” to fit that baseline as well as possible by stretching or shrinking time to match multiple control points, which can be any recognizable phenological events. Similar to “rubber sheeting” to georectify a map inside a GIS, rubber sheeting a phenology curve also yields a warping signature that shows at every time and every location how many days the adjusted phenology is ahead or behind the phenological development of the reference vegetation. Using such temporal methods to “adjust” phenologies may help to quantify vegetation impacts from frost, drought, wildfire, insects and diseases by permitting the most commensurate quantitative comparisons with unaffected vegetation.


Forrest M. Hoffman (forrest at climatemodeling dot org)