The assessment of a forest’s structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur substantial data acquisition costs. To address these limitations, this study introduces a cost-effective and innovative evaluation model that incorporates remote sensing imagery and machine learning algorithms. This model holistically considers the forest composition, the tree age structure, and spatial configuration. Using a comprehensive approach, the forest structure in Longquan City was evaluated at the stand level and categorized into three distinct categories: good, moderate, and poor. The construction of this evaluation model drew upon multiple data sources, namely Sentinel-2 imagery, digital elevation models (DEMs), and forest resource planning and design survey data. The model employed the Recursive Feature Elimination with Cross-Validation (RFECV) method for feature selection, alongside various machine learning algorithms. The key findings from this research are summarized as follows: The application of the RFECV method proved effective in eliminating irrelevant factors, reducing data dimensionality and, subsequently, enhancing the model’s generalizability; among the tested machine learning algorithms, the CatBoost model emerged as the most accurate and stable across all the datasets; specifically, the CatBoost model achieved an impressive overall accuracy of 88.07%, a kappa coefficient of 0.6833, and a recall rate of 76.86%. These results significantly surpass the classification precision of previous methods. The forest structure assessment of Longquan City revealed notable variations in the forest quality distribution. Notably, forests classified as “good” quality comprised 11.18% of the total, while “medium” quality forests constituted the majority at 76.77%. In contrast, “poor” quality forests accounted for a relatively minor proportion of the total, at 12.05%. The distribution findings provide valuable insights for targeted forest management and conservation strategies.

Site quality evaluation is an important foundation for decision-making and planning in forest management and provides scientific decision support and guidance for the sustainable development of forests and commercial plantations. Site index and site form models were constructed and subsequently compared utilizing fir (Cunninghamia lanceolata) plantations in Nanping City, Fujian Province, China. This papers aim was to construct a site quality classification model, conduct further analysis on the effects of different site factors on the quality of the site, and achieve an assessment of site quality for Chinese fir plantations. An algebraic difference approach was used to establish a site index model and a site form model for Chinese fir in Fujian Province. The suitability of the two models was compared using model accuracy analysis and partial correlation, and the optimal model was chosen for classifying the site quality of the stands. On this basis, a site quality classification model was established using the random forest algorithm, and the importance of each site factor was determined through importance ranking in terms of their impact on site quality. Within the study area, the R² of the site index model results was 0.581, and the R² values of the five site form models based on different reference breast diameters, ranked from high to low, were 0.894, 0.886, 0.884, 0.880, and 0.865. The bias correlation coefficient between site form and stand volume was 0.71, and the bias correlation coefficient between site index and stand volume was 0.52. The results confirmed that the site form model is better suited for evaluating the site quality of Chinese fir plantations. The random forest-based site form classification model had a high classification accuracy with a generalization accuracy of 0.87. The factors that had the greatest impact on site form were altitude, canopy closure, and slope gradient, whereas landform had the smallest impact on site form. These results can provide a reference for the evaluation of the site quality of plantations and natural forests in southern China to ensure the long-term sustainable use of forest resources.