Biodiversity assessment using hierarchical clustering over hyperspectral images

Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agg...

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Bibliographic Details
Published in2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) pp. 1 - 4
Main Authors Medina, Ollantay, Manian, Vidya, Chinea, Danilo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Biodiversity
Correlation
Couplings
Entropy
Hierarchical Clustering
Hyperspectral Images
Hyperspectral imaging
Indexes
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Summary:Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should reflect species richness on a given zone. However, in a high spectral mixing scenario, an additional unmixing step, just before entropy computation, is required; cluster centroids are enough for the unmixing process. Entropies computed using the proposed method correlate well with the ones calculated directly from synthetic and field data.
ISSN:2158-6276
DOI:10.1109/WHISPERS.2013.8080733