environmental disturbance
; forest canopy
; land cover
; Landsat
; mapping
; monitoring
; regression analysis
; time series
; United States
; Scolytinae
英文摘要:
We present a new methodology for fitting nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral band or index of choice in temporal Landsat data, our method delivers a smoothed rendition of the trajectory constrained to behave in an ecologically sensible manner, reflecting one of seven possible ‘shapes’. It also provides parameters summarizing the patterns of each change including year of onset, duration, magnitude, and pre- and postchange rates of growth or recovery. Through a case study featuring fire, harvest, and bark beetle outbreak, we illustrate how resultant fitted values and parameters can be fed into empirical models to map disturbance causal agent and tree canopy cover changes coincident with disturbance events through time. We provide our code in the r package ShapeSelectForest on the Comprehensive R Archival Network and describe our computational approaches for running the method over large geographic areas. We also discuss how this methodology is currently being used for forest disturbance and attribute mapping across the conterminous United States. Published 2016. This article is a U.S. Government work and is in the public domain in the USA
资助项目:
The authors would like to thank NASA's Carbon Cycle and Applied Science programs (grant numbers NNX11AJ78G and NNH14AY63I) as well as the very talented scientists and staff at NASA's Earth Exchange, University of Maryland, and the US Forest Service, FIA Program. Thanks also go out to the members of the LCMS team for their efforts pushing change mapping forward in the US. In addition, we are grateful to the anonymous reviewers whose thoughtful comments helped in our revisions.
Rocky Mountain Research Station, US Forest Service, 507 25th Street, Ogden, UT, United States; Department of Statistics, Colorado State University, 212 Statistics Building, Fort Collins, CO, United States; ASRC Federal InuTeq, U.S. Geological Survey, Earth Resources Observation and Science Center, Sioux Falls, SD, United States
Recommended Citation:
Moisen G.G.,Meyer M.C.,Schroeder T.A.,et al. Shape selection in Landsat time series: a tool for monitoring forest dynamics[J]. Global Change Biology,2016-01-01,22(10)