19 December 2017 Agreement analysis and spatial sensitivity of multispectral and hyperspectral sensors in detecting vegetation stress at management scales
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Abstract
Remotely sensed imagery is commonly used to map and monitor large land areas based on the ability to detect vegetation stress. Many sensors are available, including both hyper- and multispectral, but have varying costs, convenience, and characteristics. There were two objectives in this study: (1) to compare a hyperspectral sensor to two multispectral sensors with regards to each sensor’s ability to detect vegetation stress indicators in the visible, red edge, near-infrared, and shortwave infrared portions of the spectrum and (2) to determine the ability of coarser-resolution sensors to detect stress indicators in areas, where a finer resolution sensor detected stress indicators. Pairwise agreements between the sensors were ∼80% in each case, but much of this agreement was a function of agreement where stress indicators were absent. Spatial sensitivity analysis supported a conclusion that coarser-resolution sensors were consistently able to detect stress indicators in areas much smaller than their pixel size.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Mikindra Morin, Rick Lawrence, Kevin S. Repasky, Tracy M. Sterling, Cooper McCann, Scott Powell, "Agreement analysis and spatial sensitivity of multispectral and hyperspectral sensors in detecting vegetation stress at management scales," Journal of Applied Remote Sensing 11(4), 046025 (19 December 2017). https://doi.org/10.1117/1.JRS.11.046025 . Submission: Received: 3 June 2017; Accepted: 17 October 2017
Received: 3 June 2017; Accepted: 17 October 2017; Published: 19 December 2017
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