Canopy Density of Broad-leaved Forest by Watershed Segmentation Algorithm
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摘要: 针对现有的人为调查林区郁闭度耗时,费力且林区条件恶劣等问题,本研究实现了一种使用无人机来测定森林郁闭度的方法。通过无人机在面积为 50 m×70 m以内的 5个阔叶林为主的地块中,在不同间距和高度拍摄 4组图像并制作成正射影像图,通过对图像的灰度化以及滤波等预处理,使用改进的标记控制分水岭的分割算法来提取树冠,并与人工提取做比对,经过实际试验该算法有较高的准确度,所得误差在 5%左右,并对提取的树冠使用样线法计算郁闭度,结果表明对 0.5 ~ 0.9之间的郁闭度有较高的精度。Abstract: Four groups were photographed at different height and distance on 5 sample plots of broad-leaved forest with 50 m×70 m in Linan, Zhejiang province by unmanned aerial vehicle. They were made into orthophotoquad, which were pretreated by gray level and median filter, tree canopy was extracted by improved tag-control watershed segmentation algorithm, and compared with that by manual one. The experiment showed that algorithm had higher accuracy, and the error was about 5%. Canopy density was calculated by line transect method on canopy estrated, and the results demonstrated that the canopy density between 0.5 and 0.9 was accurate.
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