Model-assisted estimation of forest attributes to handle spatial under-coverage

What is it about?

Model-assisted estimation of forest wood volume is approached exploiting the wall-to-wall information available from satellite data and partial information achieved from airborne laser scanning (ALS) covering a portion of the survey area. If the portion covered by ALS is selected by a probabilistic sampling scheme, two-phase estimators are considered in which the two sources of information are exploited by means of linear and non-linear models. If the portion covered by ALS is fixed because purposively selected, the two sources of information are exploited by the double-calibration estimator.

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Why is it important?

We have improved on earlier work by Saarela et al. (2015) in a way that a novel possibility of including auxiliary data that does not come from a probabilistic sample is demonstrated. This may be worthwhile for forest inventory experts which often have the situation that they do not have control over the acquisition of additional remote sensing data due to budgetary constraints. Within this context, we have presented an alternative for utilizing such data that are often available from other programs.