基于LiDAR数据与光谱影像融合的单木提取方法

针对现有的机载数据单木分割方法对林型的普适度不高，尤其在高郁闭度阔叶林地带提取精度偏低的问题，选用海南省海口市热带阔叶林地带的光谱影像和LiDAR数据，先采用基于距离阈值的单木分割方法，利用高分光谱影像分割得到的树冠边缘，对初始探测树顶点进行位置约束。获得单木顶点的精确定位后，采用基于种子点的单木分割方法分割，完成了阔叶林的单木提取。结果显示，与已有的基于单木间相对间距单木分割方法相比，本研究通过选取最佳分割尺度结合光谱影像进行精确定位，改善了原有单一尺度分割方法导致的过分割现象，将单木识别精确率由0.67提升至0.92。该方法在使用遥感对森林单木进行分割工作中，可以更好地识别单木，对不同林型适用度较高，可以为后续的单木信息提取工作提供数据基础。

Single Wood Extraction Method Combining LiDAR Data and Spectral Images

Existing airborne data single-tree segmentation methods exhibit low universality for different forest types, particularly in areas with high canopy closure where the extraction accuracy is notably compromised. Spectral images and LiDAR data from the tropical broad-leaved forest region within the jurisdiction of Haikou City, Hainan Province, China, were employed. Initially, a distance threshold-based single-tree segmentation method was employed to extract tree crown edges from the high-resolution spectral image. Subsequently, the obtained positions of initial detected tree vertices were constrained using the segmented tree crown edges, and precise positioning of single-tree vertices was achieved. Following this, a seed-point-based single-tree segmentation method was applied for final tree extraction in the broad-leaved forest. The results indicated that compared with existing single-tree segmentation methods based on the relative distances between trees, by selecting the optimal segmentation scale in combination with spectral imagery for precise positioning, the issue of over-segmentation caused by traditional single-scale segmentation methods was ameliorated. The accuracy of single-tree identification was improved from 0.67 to 0.92. This method proved to be more effective in the segmentation of forest trees using remote sensing, demonstrating high applicability across various forest types. It established a solid data foundation for subsequent single-tree information extraction and held promising prospects for practical applications.