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树冠是树木的重要组成,可以直接反映树木健康状况。高空间分辨率遥感影像和遥感技术为快速获取详细的树冠信息和实时监测林冠变化提供了有效的途径。因此,基于高空间分辨率遥感影像的单木树冠提取方法研究对现代森林管理具有重要意义。本文以黄河三角洲地区孤岛林场人工刺槐林和旱柳为研究对象,以Quick Bird影像为数据源,首先利用面向对象方法实现研究区林地和非林地分类;然后以林地为掩膜,提取出树冠分布范围;在此基础上,分别选取疏林区和密林区为试验区域,通过形态学开闭重建滤波,平滑图像,去除噪声;最后,利用标记控制分水岭分割方法分别对疏林区和密林区进行树冠提取。本文以人工勾绘结果为参考进行精度验证,结果显示疏林区F测度达到87.8%,密林区F测度达到65.5%,表明该提取方法简单易行,精度可靠。
Canopy is an important component of trees and can directly reflect the health status of trees. High spatial resolution remote sensing images and remote sensing technology provide an effective way to obtain detailed canopy information and monitor canopy changes in real time. Therefore, the study of single-tree canopy extraction based on high spatial resolution remote sensing images is of great significance to modern forest management. In this paper, the Artificial Robinia pseudoacacia and Hsiaoliu willows in Gudao Forest Farm in the Yellow River Delta were studied. Using Quick Bird images as data source, the object-oriented method was used to classify the forest land and non-forest land. Then, the forest canopy was used as the mask to extract the canopy Based on this, the sparse forest area and the dense forest area were chosen as the experimental area respectively, and the morphological opening and closing reconstruction filter was used to smooth the image to remove the noise. Finally, the marker-controlled watershed segmentation method was applied to the sparse forest area and the dense forest area respectively Crown extraction. In this paper, the results of artificial hook drawing are used for accuracy verification. The results show that the F measure in sparse forest area reaches 87.8% and the F measure in dense forest area reaches 65.5%, which shows that the extraction method is simple and reliable and the accuracy is reliable.