Hyperspectral Sensors and Applications

2004 ◽  
pp. 11-49 ◽  
Author(s):  
Richard Lucas ◽  
Aled Rowlands ◽  
Olaf Niemann ◽  
Ray Merton
Keyword(s):  
Hyperspectral Sensors

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

Vicarious calibration of airborne hyperspectral sensors in operational environments

2001 ◽  
Vol 76 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Jeff Secker ◽  
Karl Staenz ◽  
Robert P Gauthier ◽  
Paul Budkewitsch
Keyword(s):  
Vicarious Calibration ◽  
Hyperspectral Sensors

Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors

2012 ◽  
Vol 50 (8) ◽  
pp. 3066-3080 ◽  
Author(s):  
Richard J. Murphy ◽  
Sildomar T. Monteiro ◽  
Sven Schneider
Keyword(s):  
Classification Techniques ◽  
Hyperspectral Sensors

Airborne remote sensing in forestry: Sensors, analysis and applications

2000 ◽  
Vol 76 (6) ◽  
pp. 859-876 ◽  
Author(s):  
Douglas J. King
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Key Words ◽  
Analytical Techniques ◽  
Remote Sensing Image ◽  
Digital Cameras ◽  
Airborne Remote Sensing ◽  
Hyperspectral Sensors ◽  
Imaging Characteristics ◽  
Laser Remote Sensing

This paper discusses the aspects of airborne remote sensing that are critical to forestry applications, the imaging characteristics of the most common sensors currently available, and analytical techniques that make use of the great amount of information content in airborne imagery. As the first paper in the CIF technical meeting to which this issue of the Forestry Chronicle is devoted, the paper is intended to provide an overview and context for subsequent papers and not a presentation of specific research methods or results. Key words: airborne remote sensing, forestry, photography, digital cameras, hyperspectral sensors, radar, laser remote sensing, image analysis

Improved Temperature and Emissivity Separation Algorithm for Multispectral and Hyperspectral Sensors

2017 ◽  
Vol 55 (4) ◽  
pp. 1944-1953 ◽  
Author(s):  
Marek Pivovarnik ◽  
Siri Jodha Singh Khalsa ◽  
Juan Carlos Jimenez-Munoz ◽  
Frantisek Zemek
Keyword(s):  
Separation Algorithm ◽  
Hyperspectral Sensors

Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors

2011 ◽  
Vol 115 (11) ◽  
pp. 2931-2942 ◽  
Author(s):  
Matthew L. Clark ◽  
Dar A. Roberts ◽  
John J. Ewel ◽  
David B. Clark
Keyword(s):  
Aboveground Biomass ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Hyperspectral Sensors ◽  
Small Footprint ◽  
Forest Aboveground Biomass
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