[Objective] An image segmentation method for large wood based on the segment anything model （SAM） was introduced， in order to provide theoretical support for its application in investigating and assessing large wood disasters. [Methods] The Zebagou area in Gongjue County， Chamdo City， Xizang Autonomous Region was selected as the study area， based on SAM， a segmentation method for large wood images—large wood SAM （LWSAM）-was developed by introducing a lightweight adapter， simplifying the mask decoder， designing a multi-task loss function， and adding an auxiliary classifier. During training， the parameters of the original image encoder and the prompt encoder were frozen to improve large wood segmentation performance at a low training cost. The model was trained and tested on two datasets， LW_CAM_dataset and LW_UAV_dataset， and compared with current state-of-the-art image segmentation models. [Results] ① The proposed multi-task loss function could optimize segmentation quality from different perspectives， effectively address the issues of sparse foreground and class imbalance in large wood recognition， and enhance the model’s adaptability to various large wood morphologies. ② Compared with the SAM method， under point prompt conditions， LWSAM achieved improvements of 15.9%， 15.9%， and 10.0% in MDice， MIoU， and F1 score， respectively， on the LW_CAM_dataset， and improvements of 21.6%， 29.6%， and 16.7% on the LW_UAV_dataset， respectively. ③ The performance of large wood segmentation was influenced by dataset quality， with models trained on higher-quality datasets achieving better segmentation results. [Conclusion] Using LWSAM for large wood image segmentation is feasible， and it demonstrates high accuracy and strong robustness in practical applications， enabling accurate segmentation of large wood images. This approach can be applied to large wood disaster investigations in small watersheds.