Version 1
: Received: 25 October 2018 / Approved: 25 October 2018 / Online: 25 October 2018 (16:30:53 CEST)
How to cite:
Manian, V.; Sotomayor, A.; Medina, O. Hyperspectral Image based Biodiversity of Forest Canopy and Marine Benthic Species. Preprints2018, 2018100615. https://doi.org/10.20944/preprints201810.0615.v1
Manian, V.; Sotomayor, A.; Medina, O. Hyperspectral Image based Biodiversity of Forest Canopy and Marine Benthic Species. Preprints 2018, 2018100615. https://doi.org/10.20944/preprints201810.0615.v1
Manian, V.; Sotomayor, A.; Medina, O. Hyperspectral Image based Biodiversity of Forest Canopy and Marine Benthic Species. Preprints2018, 2018100615. https://doi.org/10.20944/preprints201810.0615.v1
APA Style
Manian, V., Sotomayor, A., & Medina, O. (2018). Hyperspectral Image based Biodiversity of Forest Canopy and Marine Benthic Species. Preprints. https://doi.org/10.20944/preprints201810.0615.v1
Chicago/Turabian Style
Manian, V., Alejandro Sotomayor and Ollantay Medina. 2018 "Hyperspectral Image based Biodiversity of Forest Canopy and Marine Benthic Species" Preprints. https://doi.org/10.20944/preprints201810.0615.v1
Abstract
Hyperspectral images are an important tool to assess ecosystem biodiversity both on terrestrial and benthic habitats. 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. Terrestrial dry forest and marine coastal hyperspectral images with different resolutions have been used for spectral diversity feature validation.
Environmental and Earth Sciences, Environmental Science
Copyright:
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.
