Hyperspectral differences between sunlit and shaded leaves in a Manchurian ash canopy in Northeast China

ABSTRACT

The spectral characteristics of sunlit and shaded leaves are critical to improving the utilization of remote sensing methodology to quantify forest physiology. However, spectral characteristics within the tree canopies, especially normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI), are poorly understood. Our study used an imaging observation platform to obtain hyperspectral imagery of a Manchurian Ash canopy on Changbai Mountain. A non-imaging spectrometer was employed for an assisted analysis. The study results of the corresponding spectrum obtained at two observation spatial scales were significantly different between sunlit and shaded leaves. For imaging spectral observations, there were significant differences in NDVI and PRI between sunlit and shaded leaves (P < 0.001). PRI near the petiole was significantly lower than in other parts of leaves (P = 0.049). Non-imaging spectral observations of the reflectance of sunlit and shaded leaves were different only in the visible light region. The PRI of the shaded leaves were higher than that of sunlit leaves, which was consistent with the imaging spectral observations. The complexity of light environment within the canopy, especially the differences in incident irradiance, contributed to the range of leaf attribute measurements, which resulted in the variability of spectral characteristics.

KEYWORDS:

• Forest canopy
• sunlit leaf
• shaded leaf
• imaging spectrum
• NDVI
• PRI
• Manchurian Ash

Acknowledgements

Thanks to Zhan Sheng, Yu Dongxue, Tian Lizheng, Dai Guanhua and Xu Hao for their kind help.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data does not reside on the web; person interested in the data can contact me by email.

Additional information

Funding

This work was jointly supported by the National Natural Science Foundation of China [Grant No.31800367] and the Natural Science Foundation of Shandong Province [Grant No. ZR2021MD090].