Research on recognition of healthy situation of vegetation leaves based on multispectral LiDAR

Biwu Chen; Shuo Shi; Jia Sun; Wei Gong; Lin Du; Jian Yang; Shalei Song; Binhui Wang

doi:10.1117/12.2296282

15 November 2017 Research on recognition of healthy situation of vegetation leaves based on multispectral LiDAR

Biwu Chen, Shuo Shi, Jia Sun, Wei Gong, Lin Du, Jian Yang, Shalei Song, Binhui Wang

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106050U (2017) https://doi.org/10.1117/12.2296282
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

The healthy and withered situation of vegetation has important influence on its biological and chemical process. Neither single wavelength LiDAR (light detection and ranging) nor spectral image can capture the spatial and spectral information of vegetation simultaneously. However, the invention of multispectral LiDAR provided the new method for vegetation detection. There have been some researches on vegetation detection based on multispectral LiDAR, but the potential of multispectral LiDAR’s capability of recognition of healthy and withered vegetation leaves is not totally revealed. So, this research, based on multispectral LiDAR, classified the healthy and withered scindapsus leaves with SVM (support vector machine). And then we also compared the classification capability between the vegetation index and spectral reflectance. The results showed that, the multispectral LiDAR can classify the healthy and withered scindapsus leaves effectively: overall classification accuracy is 95.556%. Compared with spectral reflectance, vegetation index could help increase the classification accuracy: the producer accuracy of withered leaves increased from 23.272% to 70.507%.

Citation Download Citation

Biwu Chen, Shuo Shi, Jia Sun, Wei Gong, Lin Du, Jian Yang, Shalei Song, and Binhui Wang "Research on recognition of healthy situation of vegetation leaves based on multispectral LiDAR", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106050U (15 November 2017); https://doi.org/10.1117/12.2296282

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KEYWORDS

Vegetation

LIDAR

Reflectivity

Near infrared

Absorption

Remote sensing

Multispectral sensing

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