单木尺度的树种分布与生境信息是森林生态系统科学管理的重要基础，无人机可见光（RGB）影像具备采集时间灵活、空间分辨率高、获取成本低等优势，为精细尺度的森林监测提供了数据支撑。高空间分辨率影像完整记录了林木的精细轮廓与森林的背景生境，利用全景分割技术对其进行统一解译，能够同步获取森林全要素的提取结果。然而，高空间分辨率影像在高郁闭度森林场景下的全景分割主要存在两方面难点：一是不同树种仅依靠的光谱信息区分度有限；二是传统方法多采用语义与实例分割任务分离的架构，导致前景与背景上下文利用不足，容易引发像素归属冲突。针对上述问题，本研究提出了一种端到端全景分割模型FSC-Mask2Former（Frequency and Supervised Contrastive Mask2Former）。该模型在统一的掩膜分类范式下进行了两项核心改进：（1）引入频域纹理感知注意力模块，通过二维离散余弦变换在频域空间捕捉高频边缘信号，强化模型对树冠微观纹理的细粒度特征提取能力；（2）设计实例感知查询对比头，利用监督对比学习策略施加判别性约束，增加相似树种间的类间特征距离。在广西南宁高峰林场等多个研究区的验证结果表明，该模型的综合全景质量（all PQ）达到57.0%，其中反映目标边界拟合精度的分割质量（SQ）达到76.0%，反映类别区分能力的识别质量（RQ）提高至56.0%。研究结果表明，该模型能有效克服复杂森林场景下的树种混淆问题，实现单木个体与背景生境要素的高精度同步解译，为实现复杂生境下的森林全要素精细化制图提供了一套低成本、高效率的技术方案。

Objective: Accurate monitoring of forest resources at the individual tree level is fundamental for forest ecosystem management. Unmanned aerial vehicle (UAV) visible light (RGB) imagery provides a cost-effective and high-spatial-resolution data source for these wide-area monitoring tasks. High-spatial-resolution imagery comprehensively records the fine contours of trees and the background habitat of the forest. Utilizing panoptic segmentation technology for unified interpretation enables the synchronous extraction of all forest elements. Nevertheless, interpreting highly closed-canopy forest scenes remains a critical challenge. Traditional deep learning approaches often decouple semantic segmentation for background elements and instance segmentation for individual trees, leading to severe pixel-level classification conflicts and spatial topology inconsistencies. Furthermore, the limited spectral information in RGB imagery frequently causes severe spectral confusion among adjacent trees. To systematically address these challenges, this study proposes an end-to-end forest panoptic segmentation model named FSC-Mask2Former. Method: The proposed FSC-Mask2Former builds upon the Mask2Former baseline by introducing two core architectural improvements tailored to the unstructured features of forests. First, a Frequency-domain Texture Awareness (FTA) module is incorporated into the feature extraction pathway to compensate for the loss of micro-texture details caused by spatial downsampling, essentially functioning as a learnable high-pass filter in the feature space to retain critical edge gradients. Second, an Instance-aware Query Contrastive (IQC) head is integrated at the output of the Transformer decoder to maximize the inter-class feature distance between spectrally similar tree species, imposing an anisotropic constraint on the feature distribution to enlarge decision boundaries and fundamentally suppress category assignment conflicts. To evaluate the model, a densely annotated dataset was constructed using UAV RGB imagery from Gaofeng Forest Farm in the Guangxi Zhuang Autonomous Region, supplemented by data from Genhe City in the Inner Mongolia Autonomous Region, Jixi County in Anhui Province, and Hengzhou City in the Guangxi Zhuang Autonomous Region to validate model transferability. Result: Comprehensive experiments demonstrate that FSC-Mask2Former significantly outperforms existing mainstream networks. The model achieves an overall Panoptic Quality (PQ) of 57.0%, a substantial gain of 11.0 percentage points over the baseline. Most notably, the foreground Recognition Quality (RQ) reaches 56.0%, representing a 12.0 percentage point increase. Visualizations confirm that FSC-Mask2Former effectively separates touching instances in high-canopy-closure forest areas, precisely delineates boundaries for morphologically irregular canopies, and maintains the spatial coherence of background elements. Furthermore, multi-region experiments indicate robust generalization capabilities across different geographical and ecological conditions. Conclusion: The proposed FSC-Mask2Former successfully overcomes the bottlenecks of spectral homogeneity and task separation in UAV-based forest interpretation. This research proves that accurate full-element forest mapping can be realized using universally accessible UAV RGB imagery, providing a practical, robust, and highly cost-effective technical paradigm for modern forest resource monitoring.