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[Stand density estimation based on the measurement of spatial structure].

Hong-Xiang Wang, Gang-Ying Hui, Gong-Qiao Zhang

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    This study combined distance sampling with spatial structure analysis to estimate stem density. Results show different estimators perform best with specific forest patterns, offering improved forest inventory methods.

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    Area of Science:

    • Forestry
    • Ecology
    • Quantitative Science

    Background:

    • Accurate stem density estimation is crucial for forest management and ecological studies.
    • Traditional methods may be sensitive to forest spatial patterns.
    • Integrating spatial structure analysis with distance sampling offers a novel approach.

    Purpose of the Study:

    • To evaluate the statistical performance of different methods for estimating stem density.
    • To assess the influence of spatial distribution patterns on density estimators.
    • To determine the optimal combination of distance sampling and spatial structure investigation techniques.

    Main Methods:

    • The study combined distance sampling with stand spatial structure investigation techniques.
    • Two investigative methods (selecting the fourth- or sixth-nearest tree) were tested.
    • Three density estimators (Prodan, Persson, and Thompson) were evaluated.

    Main Results:

    • The performance of density estimators varied significantly with different spatial distribution patterns.
    • Prodan's estimator was unbiased for uniform patterns but biased for clustered patterns.
    • Persson's estimator showed bias for uniform and random patterns, while Thompson's was robust for random patterns.

    Conclusions:

    • No significant difference in performance was found between selecting the fourth- and sixth-nearest trees.
    • The choice of density estimator should consider the forest's spatial distribution pattern.
    • Combining distance sampling with spatial structure investigation techniques provides a viable method for stem density estimation.