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Remote Sens. 2014, 6(1), 20-41; doi:10.3390/rs6010020

A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method

Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD 57007, USA
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Received: 1 November 2013 / Revised: 10 December 2013 / Accepted: 10 December 2013 / Published: 19 December 2013
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Abstract

We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl > 40 mg/cm2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI. View Full-Text
Keywords: moment distance index (MDI); hyperspectral analysis; PROSPECT/SAIL models; vegetation indices moment distance index (MDI); hyperspectral analysis; PROSPECT/SAIL models; vegetation indices
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Salas, E.A.L.; Henebry, G.M. A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method. Remote Sens. 2014, 6, 20-41.

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