Integration of remote sensing-based bioenergy inventory data and optimal bucking for stand-level decision making |
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| Authors: | E Kotamaa T Tokola M Maltamo P Packalén M Kurttila A Mäkinen |
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| Institution: | (1) School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland;(2) Finnish Forest Research Institute, P.O. Box 68, 80101 Joensuu, Finland;(3) Faculty of Agriculture and Forestry, University of Helsinki, P.O. Box 62, 00014 Helsinki, Finland |
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| Abstract: | This paper tests the reliability of a biomass prediction procedure which combines aerial data collection, biometric models
and optimisation for forest management planning. Tree stock information is obtained by predicting species-specific diameter
and height distributions by a combination of field sampling, ALS data and aerial photographs. The subsequent steps in the
chain are (1) assignment of the plots to forestry operation classes by means of remote sensing-based tree stock estimates,
(2) estimation of the biomass components removed by simulating forestry operations, and (3) estimation of forest owners’ income
flow from optimised bucking of the species-specific diameter distributions. The error effects caused by these steps are analysed,
and the applicability of remote sensing–based data collection for biomass inventories and planning is assessed. The approach
used for assigning the plots to operation classes resulted in moderate accuracies (75%). The reliability estimates indicated
quite poor performance when predicting the biomass components removed in forest treatments, with RMSEs of 33.0–69.4% in the
case of final cutting and 76.9–228.0% in the case of thinning. The relative RMSEs of the above-ground biomass estimates of
the standing stock were about 19%. The relative bias for the biomasses removed was 10.0–88.6% and that for the standing stock
biomasses 0.0%. When optimising bucking, the bucked assortments were larger and the incomes enhanced with this estimation
method relative to the reference. This explains why the estimation of forest owner’s incomes in the energy wood thinning simulations
led to suboptimal decisions and income losses. |
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