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%.