研究目的
The objective of the present study is to better understand the relationship between tree characteristics and cone production in Mediterranean stone pine. This was achieved by quantifying the gain in using detailed crown metrics in estimating cone production at the individual tree level (number of cones per tree and average cone weight). Models based on traditional variables (tree size and stand characteristics) were compared to models that relied on crown metrics extracted from TLS data. The resulting models should help owners and managers to better predict cone production.
研究成果
The study concludes that models based on TLS metrics outperform inventory-based models in understanding cone production in stone pine. It emphasizes the importance of crown dimensions and density in assessing cone production and suggests that crown pruning could be a key practice to increase production. The study also highlights the potential of TLS and other remote sensing technologies for large-area estimates of cone production.
研究不足
The study acknowledges the challenges in visually assessing cone production due to the location of cones in the sun-exposed part of the crown and their coverage by vegetative shoots. It also notes the lack of previous studies using TLS data for modeling non-wood forest products.
1：Experimental Design and Method Selection
The study used terrestrial laser scanning (TLS) technology to assess cone production in stone pine trees. Data from 129 trees in 26 plots located in the Spanish Northern Plateau were analyzed. The study compared models using TLS-derived metrics with traditional inventory data to predict cone presence, number, and average weight.
2：Sample Selection and Data Sources
The study sites were located on the Spanish Northern Plateau, covering a range of site quality, stocking, and stand age. Data from 129 trees in 26 plots were used, with cone production data from 2016 and 2017.
3：List of Experimental Equipment and Materials
The scanner used was a Faro Focus 3D. Each scan was performed with a resolution angle of 0.036° in both horizontal and vertical planes. The study also used spherical targets for scan registration and software like Faro Scene 6.2.2.7, Computree, R, and CloudCompare for data processing.
4：Experimental Procedures and Operational Workflow
Plots were scanned from five positions to minimize occlusion of the target trees. The co-registered point clouds were segmented to isolate ground returns from tree points. Crown and micro-topographical metrics were extracted from the TLS data.
5：Data Analysis Methods
Principal component analysis (PCA) was used to evaluate correlations among TLS-derived variables. Zero-inflated negative binomial regression and linear mixed effect models were used to analyze the data, with model comparison based on Akaike’s information criterion (AIC).
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