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Proceedings Paper

Hyperspectral image-based methods for spectral diversity
Author(s): Alejandro Sotomayor; Ollantay Medina; J. Danilo Chinea; Vidya Manian
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Paper Abstract

Hyperspectral images are an important tool to assess ecosystem biodiversity. To obtain more precise analysis of biodiversity indicators that agree with indicators obtained using field data, analysis of spectral diversity calculated from images have to be validated with field based diversity estimates. The plant species richness is one of the most important indicators of biodiversity. This indicator can be measured in hyperspectral images considering the Spectral Variation Hypothesis (SVH) which states that the spectral heterogeneity is related to spatial heterogeneity and thus to species richness. The goal of this research is to capture spectral heterogeneity from hyperspectral images for a terrestrial neo tropical forest site using Vector Quantization (VQ) method and then use the result for prediction of plant species richness. The results are compared with that of Hierarchical Agglomerative Clustering (HAC). The validation of the process index is done calculating the Pearson correlation coefficient between the Shannon entropy from actual field data and the Shannon entropy computed in the images. One of the advantages of developing more accurate analysis tools would be the extension of the analysis to larger zones. Multispectral image with a lower spatial resolution has been evaluated as a prospective tool for spectral diversity.

Paper Details

Date Published: 21 May 2015
PDF: 11 pages
Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 947205 (21 May 2015); doi: 10.1117/12.2182506
Show Author Affiliations
Alejandro Sotomayor, Univ. of Puerto Rico at Mayagues (United States)
Ollantay Medina, Univ. de Puerto Rico Utuado (United States)
J. Danilo Chinea, Univ. de Puerto Rico Mayagüez (United States)
Vidya Manian, Univ. de Puerto Rico Mayagüez (United States)


Published in SPIE Proceedings Vol. 9472:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)

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