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Sensors 2009, 9(1), 303-310; doi:10.3390/s90100303
Communication

Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales

1,2
Received: 4 December 2008; in revised form: 24 December 2008 / Accepted: 24 December 2008 / Published: 8 January 2009
(This article belongs to the Special Issue Sensor Algorithms)
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Abstract: Measuring heterogeneity in satellite imagery is an important task to deal with. Most measures of spectral diversity have been based on Shannon Information theory. However, this approach does not inherently address different scales, ranging from local (hereafter referred to alpha diversity) to global scales (gamma diversity). The aim of this paper is to propose a method for measuring spectral heterogeneity at multiple scales based on rarefaction curves. An algorithmic solution of rarefaction applied to image pixel values (Digital Numbers, DNs) is provided and discussed.
Keywords: algorithmic solution of rarefaction; rarefaction theory; satellite imagery; spectral heterogeneity algorithmic solution of rarefaction; rarefaction theory; satellite imagery; spectral heterogeneity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Rocchini, D. Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales. Sensors 2009, 9, 303-310.

AMA Style

Rocchini D. Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales. Sensors. 2009; 9(1):303-310.

Chicago/Turabian Style

Rocchini, Duccio. 2009. "Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales." Sensors 9, no. 1: 303-310.


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