You are here: Home / Research Topics / Research Highlights / Individual Highlight

Research Highlights

Individual Highlight

Shape Selection in Landsat Time Series

Photo of Forest impacted by the mountain pine beetle. U.S. Department of Agriculture Forest Service.Forest impacted by the mountain pine beetle. U.S. Department of Agriculture Forest Service.Snapshot : Understanding trends in forest disturbance and their effects on forest parameters such as tree canopy cover and biomass is important for carbon assessments, as well as for forest management decisions and scientific investigations across the globe. Data from the Landsat suite of remote sensing satellites offer a historically robust collection of earth observations, which can be used to understand forest dynamics at a variety of spatial and temporal scales.

Principal Investigators(s) :
Moisen, Gretchen 
Research Location : Conterminous United States
Research Station : Rocky Mountain Research Station (RMRS)
Year : 2016
Highlight ID : 1043


Forest Service scientists developed 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, their 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 temporal pattern including year(s) of change, magnitude of change, and pre- and post- change 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.

Forest Service Partners

External Partners

  • Mary Meyer, Colorado State University
  • Todd Schroeder, U.S. Geological Survey
  • Xiyue Lio, Colorado State University