Development of a Method for the Determination of Human Skin Moisture Using a Portable Near-Infrared System

2001 ◽  
Vol 73 (20) ◽  
pp. 4964-4971 ◽  
Author(s):  
Young-Ah Woo ◽  
Jhii-Weon Ahn ◽  
In-Koo Chun ◽  
Hyo-Jin Kim
Keyword(s):  
Human Skin ◽  
Near Infrared ◽  
Skin Moisture

Enzymatic Determination of Hydroxysteroids in Human Skin Surface Lipids1

1963 ◽  
Vol 41 (5) ◽  
pp. 265-268 ◽  
Author(s):  
Thomas J Cook ◽  
Allan L Lorincz ◽  
Alan R Spector
Keyword(s):  
Human Skin ◽  
Skin Surface ◽  
Enzymatic Determination

Near Infrared Spectroscopy Used in Purification and Determination of Pharmaceutical Cocrystal

2013 ◽  
Vol 41 (12) ◽  
pp. 1928
Author(s):  
Zong-Liang CHI ◽  
Miao-Miao WANG ◽  
Xiao-Dong CONG ◽  
Shao-Guang LIU ◽  
Bao-Chang CAI
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Pharmaceutical Cocrystal

scholarly journals Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Important Variable ◽  
Field Station ◽  
Near Surface ◽  
Fractional Snow Cover ◽  
Rgb Images ◽  
Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

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