基于分水岭算法的阔叶林郁闭度提取方法的研究

Canopy Density of Broad-leaved Forest by Watershed Segmentation Algorithm

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。

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.