Biodiv Sci ›› 2021, Vol. 29 ›› Issue (5): 647-660. DOI: 10.17520/biods.2021013
• Reviews • Previous Articles Next Articles
Yan Xu1, Congling Zhang1, Ruijiao Jiang1, Zifei Wang1, Mengchen Zhu1, Guochun Shen1,*()
Received:
2021-01-12
Accepted:
2021-03-16
Online:
2021-05-20
Published:
2021-04-22
Contact:
Guochun Shen
Yan Xu, Congling Zhang, Ruijiao Jiang, Zifei Wang, Mengchen Zhu, Guochun Shen. UAV-based hyperspectral images and monitoring of canopy tree diversity[J]. Biodiv Sci, 2021, 29(5): 647-660.
Fig. 1 RGB image and hyperspectral image of typical forest canopy of the subtropical evergreen broad-leaved forest in Tiantong, Zhejiang Province. (A) Ordinary RGB images only contain three layers of information: red (620-760 nm), green (500-560 nm) and blue (430-470 nm). Therefore, the canopy of most evergreen tree species is almost the same green in RGB images, which makes it very difficult to identify the canopy species; (B) Three dimensional display of canopy hyperspectral image, x-axis is the scanning length, y-axis is the scanning width, z-axis is the spectral axis; (C) The spectral reflection curve of selected pixel, abscissa represents the wavelength, ordinate represents the band reflectance value, the pixel shows different reflectance values in different bands, forming a nearly continuous spectral curve.
Fig. 2 Comparison of canopy RGB image and canopy hyperspectral image processed by principal component analysis (PCA). (A) The canopy RGB image shows that the canopy of each tree species is similar in green; (B) Through PCA processing of the first three axes of the canopy hyperspectral image, the canopy hyperspectral image of different tree species shows different colors, which means that the hyperspectral images have full potential to reflect the subtle differences between different tree species.
Fig. 3 Individual canopy spectral characteristic curve. (A) In the hyperspectral images of forest canopy processed by principal component analysis (PCA), the numbers ①-⑤ represent different individuals; (B) Spectral reflectance curves of five canopy individuals. Different plants show different spectral reflectance curves because of their different chemical properties and structures, which is the basis of spectral species classification.
Fig. 4 Classification model based on deep learning network. The model includes input layer, hidden layer and output layer. Hidden layer is used to extract image features. The higher the number of layers is, the higher the features can be extracted by hidden layer.
Fig. 5 From 2000 to 2019, the statistical results of articles in the field of ecology using unmanned aerial vehicle (UAV), deep learning and hyperspectral, respectively
Fig. 6 The performance of deep learning and non deep learning algorithms in hyperspectral tree species classification. The statistical test results show that the deep learning algorithm has obvious advantages in species classification.
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