New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)

文献类型： 外文期刊

第一作者： Zhang, Xianlong

作者： Zhang, Xianlong;Zhang, Fei;Qi, Yaxiao;Deng, Laifei;Zhang, Xianlong;Zhang, Fei;Qi, Yaxiao;Deng, Laifei;Zhang, Xianlong;Zhang, Fei;Qi, Yaxiao;Deng, Laifei;Wang, Xiaolong;Yang, Shengtian

作者机构：

关键词： Iterative threshold method; Unmanned aerial vehicle (UAV); Visible light images; Vegetation information extraction; Construction of NGRVI

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：5.933； 五年影响因子：6.225 ）

ISSN： 0303-2434

年卷期： 2019 年 78 卷

页码：

收录情况： SCI

摘要： Currently, many remote sensing images of the vegetation index being used have disadvantages, because of high cost, long cycles, and low resolution. Thus, it is difficult to extract and analyse vegetation information in the field. A vegetation index based on visible light images from an unmanned aerial vehicle (UAV) has the advantages of fast image acquisition and high ground resolution, which is superior to traditional remote sensing. However, the vegetation coverage in arid and semi-arid areas is low, and the soil background has a great impact on the common visible vegetation index. The real-time extraction and analysis of the index vegetation information can easily result in big errors. Therefore, according to the construction principle of the green-red vegetation index (GRVI) and modified green-red vegetation index (MGRVI), a new green-red vegetation index (NGRVI) is proposed in this study. First, the newly constructed index and several published indices are used to extract visible light images and generate greyscale images for each of the visible light vegetation indices. Then, the threshold of vegetation and non-vegetation pixel classification is established according to the method of iterative threshold, and the optimal threshold is used to extract the vegetation information from the greyscale images of each of the visible light vegetation indices. Finally, the accuracy difference in vegetation information extraction between the newly constructed and several published indices is compared. The results show that the precision of vegetation information extraction by NGRVI is higher than that of other visible light band vegetation indices; the kappa coefficient is 0.82, and the classification accuracy reaches near-complete consistency. To verify the accuracy of the NGRVI, one image from the same period was selected, and the vegetation information was extracted using the same method. The NGRVI based on UAV visible light images can accurately extract the vegetation information in arid and semi-arid areas, and the extraction accuracy can reach more than 90%. To summarize, NGRVI can accurately and effectively reflect the vegetation information in arid and semi-arid areas and become an important technical means for retrieving biological and physical parameters using visible light images.

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