Comparing Johnson’s SB and Weibull Functions to Model the Diameter Distribution of Forest Plantations through ALS Data

Diogo Nepomuceno Cosenza,Juan Guerra-Hernández,Fernando Castedo-Dorado,Eduardo González-Ferreiro,Paula Soares,Luísa Pereira,Margarida Tomé

doi:10.3390/rs11232792

Diogo Nepomuceno Cosenza, Juan Guerra-Hernández + Show 5 more

Open Access

https://doi.org/10.3390/rs11232792

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Abstract

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-parameter Weibull 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 the Weibull 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.

Highlights

Forest inventory is essential in forest management by providing information to diagnose the stands, which supports decision-makers
One of the most common approaches to performing an airborne laser scanning (ALS) forest inventory is the area-based approach (ABA), where metrics are extracted from the normalized height of the light detection and ranging (LiDAR) data cloud (NHD) and used to predict the forest variables [2,3]
This study evaluated the ability of the SB probability density functions (PDF) to predict the diameter distribution of forest plantations through ALS data

Summary

Introduction

Forest inventory is essential in forest management by providing information to diagnose the stands, which supports decision-makers. One of the most common approaches to performing an ALS forest inventory is the area-based approach (ABA), where metrics are extracted from the normalized height of the LiDAR data cloud (NHD) and used to predict the forest variables [2,3]. The growing stock assessment is the most frequent target of the inventories, but effective forest management often requires information of the timber volume distributed through the diameter at the breast height (dbh, 1.30 m) classes [4] In this case, even though the ABA does not allow detecting tree diameters directly, it enables obtaining the forest stand structure indirectly by using the NHD metrics to estimate probability density functions (PDF) that describe diameter distributions [5]

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