Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 55, Issue 3, Pages 1671-1681Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2628960
Keywords
Estimation; forestry; laser radar; remote sensing
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Funding
- University of Eastern Finland
- Academy of Finland through the Finnish Centre of Excellence of Inverse Problems Research [250215, 270174, 273536, 257372]
- Academy of Finland (AKA) [257372, 257372] Funding Source: Academy of Finland (AKA)
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In this paper, we propose an approach to quantify the plot-level uncertainty in species-specific growing stock volume estimated from airborne laser scanning data and aerial imagery. This is accomplished by adopting the framework of Bayesian inference in the area-based estimation of stock volume. The results show that the proposed approach performs well in quantifying the estimate uncertainty and produces optimal interval estimates for species-specific volumes when sufficient training data are available. Also the point estimate accuracy is competitive with current state-of-the-art methods. Furthermore, we demonstrate how the quantified uncertainties of the stand attributes can be utilized to determine the uncertainty in classification done using the estimated stand attributes.
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