Paper evaluates use of remotely sensed information in national forest inventory systems

Budget shortfalls for national continuous forest inventory systems can lead to various proposals for reducing sampling costs, such as lengthening the period between re-measurement of inventory plots, reducing the number of plots, and reducing the number of variables measured.

A recent paper describes results from a study that used simulation to investigate the extent to which the detrimental effects of a reduced forest sampling effort can be ameliorated with information obtained from multi-year remotely-sensed images.

The authors conclude that information obtained from multi-year remotely-sensed images can contribute to significant improvement in estimation.

The abstract for the publication follows.

“Fiscal uncertainties can sometimes affect national continuous forest monitoring efforts. One solution of interest is to lengthen the time it takes to collect a “full set” of plot data from five to 10 years in order to reduce costs. Here, we investigate using ancillary information to partially offset this proposed solution’s negative effects. We focus our discussion on the corresponding number of years between measurements of each plot while we investigate how thoroughly the detrimental effects of the reduced sampling effort can be ameliorated with change estimates obtained from temporally-dense remotely-sensed images. We simulate measured plot data under four sampling error structures, and we simulate remotely-sensed change estimates under three reliability assumptions, integrated with assumptions about the additional unobserved growth resulting from the lengthened observation window. We investigate a number of estimation systems with respect to their ability to provide compatible annual estimates of the components of change during years spanned by at least half of the full set of plot observations. We show that auxiliary data with shorter observation intervals can contribute to a significant improvement in estimation.”


Roesch, F.A., J.W. Coulston, P.C. Van Deusen, and R. Podlaski. 2015. Evaluation of image-assisted forest monitoring: a simulation. Forests 6:2897 – 2917.