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    基于可见光影像对干旱荒漠区植被覆盖度提取的研究

    Research on the extraction of vegetation coverage in arid desert area based on visible light images

    • 摘要: 【目的】为了快速准确获取干旱荒漠区的植被覆盖度信息。【方法】本研究基于无人机(Phantom 4 Advanced)采集的内蒙古乌海市海勃湾区桌子山附近的可见光影像,采用监督分类与植被指数统计直方图相结合的方法确定分割阈值,以目视解译得到的植被信息为真实值进行精度验证。【结果】结果表明:(1)应用可见光植被指数提取研究区植被信息时像元统计直方图难以呈现双峰特征,归一化绿红差异指数(NGRDI)、红绿比值指数(RGRI)、植被指数(VEG)三种指数的灰度图中植被与裸地像元值存在较大重叠。(2)应用监督分类与植被指数统计直方图结合的方法,能够很好地解决植被指数直方图法应用于干旱区植被提取时难以呈现双峰特征的问题,能够提高植被的提取精度。(3)通过对各个植被指数提取精度验证,得到差异增强植被指数(DEVI)对于研究区的植被提取效果要优于其他植被指数,精度可达96.44%,且阈值稳定性较好。【结论】采用监督分类与植被指数直方图相结合确定阈值的方法提取干旱荒漠区植被信息效果较好,对于干旱荒漠区可见光影像植被信息提取应用方面具有现实意义。

       

      Abstract: Objective In order to quickly and accurately obtain the information of vegetation coverage in arid desert area. Method In this study, based on the visible light images collected by UAV ( Phantom 4 Advanced ) near Table Mountain in Haibowan District, Wuhai City, Inner Mongolia, the segmentation threshold was determined by the combination of supervised classification and vegetation index statistical histogram, and the vegetation information obtained by visual interpretation was used as the true value for accuracy verification. Results The results show that : ( 1 ) When the visible light vegetation index is used to extract the vegetation information in the study area, the pixel statistical histogram is difficult to present the bimodal characteristics. There is a large overlap between the vegetation and the bare land pixel values in the gray images of the normalized green-red difference index ( NGRDI ), red-green ratio index ( RGRI ) and vegetation index ( VEG ). ( 2 ) The combination of supervised classification and vegetation index statistical histogram can well solve the problem that the vegetation index histogram method is difficult to show bimodal characteristics when it is applied to vegetation extraction in arid areas, and can improve the extraction accuracy of vegetation. ( 3 ) Through the verification of the extraction accuracy of each vegetation index, the difference enhanced vegetation index ( DEVI ) is better than other vegetation indexes in the study area, and the accuracy can reach 96.44 %, and the threshold stability is better. Conclusion The method of combining supervised classification with vegetation index histogram to determine the threshold has a good effect on extracting vegetation information in arid desert area, which has practical significance for the application of visible light image vegetation information extraction in arid desert area.

       

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