Dr. Quanzhou Yu Profile

Dr. Quanzhou Yu

at Liaocheng Univ

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Author

Publications (3)

Proceedings Article | 20 January 2023 Paper

Research on the spectral polarization characteristics of teeth in different age groups

Tianquan Liang, Xiaojie Duan, Xiangrong Du, Qingxin Tang, Quanzhou Yu

Proceedings Volume 12560, 1256003 (2023) https://doi.org/10.1117/12.2645731

KEYWORDS: Teeth, Polarization, Data modeling, Spectroscopy, Statistical modeling, Polarizers, Statistical analysis, Dental caries

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SPIE Journal Paper | 27 October 2022

Seasonal variations of canopy spectra and their indications to carbon fluxes in a temperate forest in northeast China

Jie Jiang, Quanzhou Yu, Yujie Liu, Tianquan Liang, Qingxin Tang, Lei Zhou, Hongli Zhang, Baohua Zhang, Shaoqiang Wang

JARS, Vol. 16, Issue 04, 048503, (October 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.048503

KEYWORDS: Carbon, Reflectivity, Vegetation, Molybdenum, Remote sensing, Ecosystems, Picosecond phenomena, Near infrared, Visible radiation, Nitrogen

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The seasonal variations of forest canopy spectral characteristics are critical to improving the utilization of remote sensing methodology to quantify forest physiology, especially forest carbon sink. However, the seasonal variations of forest canopy spectra are poorly understood. Combined field survey and EO-1 Hyperion imageries, we extracted the spectral curves of seven forest types of Changbai Mountain in China in seven periods. We also calculated various remote sensing indexes and analyzed their seasonal change of spectral characteristics among different forest types. Optimal indexes were selected to indicate the seasonal variation of forest carbon fluxes. Our results showed that there were differences in spectral curves among forest types. The reflectance of coniferous forests was lower than that of broad-leaved forests in growing season. Changbai Scotch pine forest owned the lowest spectral reflectance, whereas the reflectance of Mongolian oak forest was the highest, especially in the near-infrared region. The red edge slope (RES) of broad-leaved forest was higher than coniferous forest in spring and summer. The RES of broad-leaved and coniferous forests was similar in autumn. The red edge position of various forest types showed slight shift in different seasons. Four typical forest types showed different spectral characteristics with seasonal changes. The seasonal variation of coniferous forest spectral curves was not obvious. The seasonal variation of broad-leaved forest spectra was the largest. Most of the spectral indexes can indicate the seasonal variation characteristics of each forest type. Enhanced vegetation index (EVI) is better than normalized difference vegetation index (NDVI) to indicate the forest phenology. Seasonal curves of spectral indexes were different in all forest types. Spectral indexes of coniferous forests were most stable throughout the year. The curves of each index in broad-leaved forests showed significant difference in autumn, which may be influenced by the understory vegetation after their defoliation. For broad-leaved Korean (BK) pine forest, the scaled value of photochemical reflectance index (SPRI)*EVI owned the highest correlation with gross primary productivity (R = 0.99 and P < 0.01) and net ecosystem exchange (R = − 0.77 and P < 0.05), respectively. SPRI*NDVI showed the highest correlation with ecosystem respiration (R = 0.96 and P < 0.01). The seasonal variation of carbon fluxes of different forest types retrieved from the optimal remote sensing index were consistent, but their peaks occurred at different times.

SPIE Journal Paper | 10 November 2017

Investigation into the role of canopy structure traits and plant functional types in modulating the correlation between canopy nitrogen and reflectance in a temperate forest in northeast China

Quanzhou Yu, Shaoqiang Wang, Lei Zhou

JARS, Vol. 11, Issue 04, 046013, (November 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.046013

KEYWORDS: Near infrared, Reflectivity, Nitrogen, Pulmonary function tests, Remote sensing, Vegetation, Ecosystems, Statistical analysis, Modulation, Carbon

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A precise estimate of canopy leaf nitrogen concentration (CNC, based on dry mass) is important for researching the carbon assimilation capability of forest ecosystems. Hyperspectral remote sensing technology has been applied to estimate regional CNC, which can adjust forest photosynthetic capacity and carbon uptake. However, the relationship between forest CNC and canopy spectral reflectance as well as its mechanism is still poorly understood. Using measured CNC, canopy structure and species composition data, four vegetation indices (VIs), and near-infrared reflectance (NIR) derived from EO-1 Hyperion imagery, we investigated the role of canopy structure traits and plant functional types (PFTs) in modulating the correlation between CNC and canopy reflectance in a temperate forest in northeast China. A plot-scale forest structure indicator, named broad foliar dominance index (BFDI), was introduced to provide forest canopy structure and coniferous and broadleaf species composition. Then, we revealed the response of forest canopy reflectance spectrum to BFDI and CNC. Our results showed that leaf area index had no significant effect on NIR (P>0.05) but indicated that there was a significant correlation (R2=0.76, P<0.0001) between CNC and BFDI. NIR had a more significant correlation with BFDI than with CNC for all PFTs, but it had no obvious correlation with CNC for single PFT. Partial correlation analysis showed that four VIs had better correlations with BFDI than with CNC. When the effect of BFDI was removed, the partial correlation between CNC and NIR was insignificant (R=0.273, P>0.05). On the contrary, removing the CNC effect, the partial correlation between BFDI and NIR was positively significant (R=0.69, P<0.0001). These findings proved that canopy structure and coniferous and broadleaf species composition had a greater influence on the remote sensing signal than canopy nitrogen concentration. The functional convergence of plant traits resulted in the relation of CNC and canopy structure and determined the positive correlation between CNC and NIR. We maintain that the repeatable relationship between CNC and NIR can be used in the remote sensing retrieval of CNC during various forest types. Nevertheless, the relationship cannot be considered as a feasible approach of CNC estimation for a single PFT.

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