کنفرانس بین المللی محاسبات نرم

صفحه اصلی / ششمین کنفرانس بین المللی محاسبات نرم

The Application of Clustering Algorithms for Partitioning Forest Trees into Representative Sections

نویسندگان :

Yashar Pourali Behzad¹ Arghavan Habibi Bibalani² Ghassem Habibi Bibalani³

1- دانشگاه تبریز 2- دانشگاه تبریز 3- دانشگاه آزاد اسلامی واحد

کلمات کلیدی :

Clustering algorithms،Forest surveying،Representative sampling،Agglomerative clustering،Kernel density estimation،Outlier detection

چکیده :

Accurate forest surveying is critical for sustainable resource management, but is often limited by time and cost constraints. This study investigates the application of multiple clustering algorithms—K-means, K-means with local DBSCAN refinement, hierarchical agglomerative clustering, hybrid hierarchical–DBSCAN clustering, and agglomerative clustering with KDE-based outlier detection—to partition tree data into representative sections. The goal is to enable efficient sampling that preserves the forest’s structural patterns. Each method was evaluated using internal clustering metrics and visual comparison to a heatmap of tree diameter distribution. Results indicate significant differences in cluster shape accuracy, noise detection, and interpretability. Among the tested approaches, agglomerative clustering with KDE-based outlier detection most closely reproduced the forest’s spatial structure, offering robust cluster delineation and principled noise handling. This method provides a reliable framework for representative sampling and informed forest management.