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Improving the precision of dynamic forest parameter estimates using Landsat

Formally Refereed
Authors: Evan B. Brooks, John W. Coulston, Randolph H. Wynne, Valerie A. Thomas
Year: 2016
Type: Scientific Journal
Station: Southern Research Station
Source: Remote Sensing of Environment


The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well
established.When reducing the variance of post-stratification estimates for forest change parameters such as forest
growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is
ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat
ThematicMapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery
and recent measurements. These trajectories, from1985 to 2010, are converted to harmonic regression coefficient
estimates and classified according to a hierarchical clustering algorithm from a training sample. The
resulting strata maps are then used in conjunction with measured plots to estimate forest status and change
parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to
calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around
or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being
more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging
the study area and (2) using the Landsat images closest to the time ofmeasurement for each plot. Multitemporal
satellite-derived strata maps show promise for improving the precision of change parameter estimates.


Harmonic regression, Hierarchical cluster analysis, FIA, Site index, Post-stratification


Brooks, Evan B.; Coulston, John W.; Wynne, Randolph H.; Thomas, Valerie A. 2016. Improving the precision of dynamic forest parameter estimates using Landsat. Remote Sensing of Environment, Vol. 179: 8 pages.: 162-169. DOI:10.1016/j.rse.2016.03.017