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.

Franceschi, S., Chirici, G., Fattorini, L., Giannetti, F., Corona, P. (2021). Model-assisted estimation of forest attributes exploiting remote sensing information to handle spatial under-coverage. SPATIAL STATISTICS, 41 [10.1016/j.spasta.2020.100472].

Model-assisted estimation of forest attributes exploiting remote sensing information to handle spatial under-coverage

Franceschi S.
;
Fattorini L.;
2021-01-01

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.
2021
Franceschi, S., Chirici, G., Fattorini, L., Giannetti, F., Corona, P. (2021). Model-assisted estimation of forest attributes exploiting remote sensing information to handle spatial under-coverage. SPATIAL STATISTICS, 41 [10.1016/j.spasta.2020.100472].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1126017