Document Type
Article
Publication Date
2-20-2014
Keywords
moment distance index (MDI), hyperspectral analysis, PROSPECT/SAIL models, vegetation indices
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.
Publication Title
Remote Sensing
Volume
6
First Page
20
Last Page
41
Pages
21
Type
text
Format
application/pdf
Language
en
DOI of Published Version
10.3390/rs6010020
Publisher
MDPI AG
Rights
© 1996-2016 MDPI AG
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Recommended Citation
Salas, E.A. L. and Henebry, G. M., "A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method" (2014). Natural Resource Management Faculty Publications. 14.
https://openprairie.sdstate.edu/nrm_pubs/14
Included in
Physical and Environmental Geography Commons, Remote Sensing Commons, Spatial Science Commons
Comments
This article was originally published in Remote Sensing 6(1): 20-41. Posted with permission.