利用无人机可见光影像,探索自动提取平原造林地区林木空间分布和数量的方法。以河北省张家口市怀来县造林斑块为研究对象,构建单木树冠数据集,选用不同卷积层的Mask R-CNN模型(R50-FPN-1x、R50-FPN-3x、R101-FPN-3x)进行训练和预测,比较各模型在独立测试集的精度。结果表明:各模型均可对造林地区的单木树冠进行分割,其中R101-FPN-3x模型精度最高,为75.33%,召回率为73.23%;基于Mask R-CNN的无人机影像单木分割方法能够快速、精准地自动检测造林地区单木目标,有效地分割单木树冠绘制树冠轮廓,提高中小范围平原造林区域单木调查监测的效率。

This paper aims to investigate an automated approach for extracting spatial distribution and quantities of planted trees in plain areas by using of visible light imagery obtained from unmanned aerial vehicles.Specifically,the study focuses on analyzing afforestation patches located in Huailai,Zhangjiakou,Hebei Province.To achieve this,a manually constructed single-tree crown dataset was utilized to train and predict Mask R-CNN models with varying backbone networks(R50-FPN-1x,R50-FPN-3x,R101-FPN-3x),and their accuracies were compared on an independent test set.Results indicate that all backbone models were able to segment tree crowns in the afforested areas,with the R101-FPN-3x model achieving the highest accuracy of 75.33% and a recall rate of 73.23%.The results showed that the single tree segmentation method based on Mask R-CNN from UAV images could quickly and accurately detect single tree targets in afforestation areas automatically,and effectively segment single tree crown to map the crown contour,which could meet the needs of efficient investigation and monitoring in small and medium-sized plain afforestation areas.