Model-assisted estimation of forest attributes exploiting remote sensing information to handle spatial under-coverage
2021
Abstract 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. The performance of the proposed strategies is checked by a simulation study from two study areas in Southern and Northern Italy.
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