The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method’s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm’s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes.

This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the dataset independently, for model training. The experimental results show that the F1-score of Sophora japonica, Pinus tabulaeformis, and Koelreuteria paniculata reached 87.4%, 85.7%, and 86.3%, respectively. Meanwhile, we tested for the study area with a total area of 146 ha, and 27,403 tree species were identified in nine categories, with a total crown projection area of 318,725 m². We also fitted a biomass calculation model for oil pine (Pinus tabulaeformis) based on field measurements and assessed 205,199.69 kg of carbon for this species across the study area. Additionally, we compared the model to U-net, and the results showed that BlendMask has strong crown-segmentation capabilities. This study demonstrates that BlendMask can effectively perform crown segmentation and forest inventory in large-scale complex forest areas, showing its great potential for forest resource management.