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Advisor(s)
Abstract(s)
The analysis of the diameter distribution is important for forest management since the
knowledge of tree density and growing stock by diameter classes is essential to define management
plans and to support operational decisions. The modeling of diameter distributions from airborne
laser scanning (ALS) data has been performed through the two-parameterWeibull probability density
function (PDF), but the more flexible PDF Johnson’s SB has never been tested for this purpose until
now. This study evaluated the performance of the Johnson’s SB to predict the diameter distributions
based on ALS data from two of the most common forest plantations in the northwest of the Iberian
Peninsula (Eucalyptus globulus Labill. and Pinus radiata D. Don). The Weibull PDF was taken as
a benchmark for the diameter distributions prediction and both PDFs were fitted with ALS data.
The results show that the SB presented a comparable performance to the Weibull for both forest
types. The SB presented a slightly better performance for the E. globulus, while theWeibull PDF had a
small advantage when applied to the P. radiata data. The Johnson’s SB PDF is more flexible but also
more sensitive to possible errors arising from the higher number of stand variables needed for the
estimation of the PDF parameters
Description
Keywords
probability density function LiDAR remote sensing forest horizontal structure
Pedagogical Context
Citation
Remote Sens. 2019, 11, 2792
Publisher
MDPI
