Sensors 2013, 13(10), 13949-13959; doi:10.3390/s131013949
Article

Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images

1 Computing and Information Sciences and Engineering, University of Puerto Rico at Mayaguez, Call box 9000, Mayaguez 00681, Puerto Rico 2 Department of Electrical & Computer Engineering, University of Puerto Rico at Mayaguez, Call box 9000, Mayaguez 00681, Puerto Rico 3 Department of Biology, University of Puerto Rico at Mayaguez, Call box 9000, Mayaguez 00681, Puerto Rico
* Author to whom correspondence should be addressed.
Received: 7 August 2013; in revised form: 29 September 2013 / Accepted: 30 September 2013 / Published: 15 October 2013
(This article belongs to the Section Remote Sensors)
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Abstract: 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.
Keywords: hyperspectral images; biodiversity; hierarchical clustering

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MDPI and ACS Style

Medina, O.; Manian, V.; Chinea, J.D. Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images. Sensors 2013, 13, 13949-13959.

AMA Style

Medina O, Manian V, Chinea JD. Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images. Sensors. 2013; 13(10):13949-13959.

Chicago/Turabian Style

Medina, Ollantay; Manian, Vidya; Chinea, J. D. 2013. "Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images." Sensors 13, no. 10: 13949-13959.

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