Optimizing the Selection of Representative Samples in Forest Plots Using Fuzzy Algorithm Approaches

Statistical surveying in forests plays a vital role in natural resource management and sustainable forest planning; however, measuring all vegetation units in extensive areas is time-consuming and costly. This study presents a method based on fuzzy logic and Gaussian weighting to select representative samples from tree data, such that the maximum similarity to the entire dataset is maintained with fewer samples. The data used consisted of ۱۲ blocks, each with ۸۰ measurements, collected from the ۶۰-hectare educational and research forest of the Faculty of Natural Resources, University of Tehran (Source: pp. ۲۶-۲۷, Forest Biometry, Dr. Mahmoud Zobeiri). For each block, samples were selected using Gaussian weighting and comparison with the mean and dispersion of the entire data to determine the optimal sample size (n). Results showed that the Gaussian weighting method achieves higher accuracy than random sampling with fewer samples and effectively reflects the statistical structure of the blocks. This approach, while reducing the volume of data under consideration, enables scientific decision-making and optimal forest management and provides a solid basis for developing multi-feature sampling and applying it to data blocks of varying sizes.

کلیدواژه ها:

Representative sampling ، Fuzzy logic ، Gaussian weighting ، Forest surveying ، Forest resource management ، Optimal sample selection