Improving the precision of dynamic forest parameter estimates using Landsat

Abstract

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

Citation

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