Colour Vision in Stomatopod Crustaceans: more questions than answers

Stomatopod crustaceans, or mantis shrimps, are known for their extensive range of spectral sensitivities but relatively poor spectral discrimination. Instead of the colour-opponent mechanism of other colour vision systems, the 12 narrow-band colour channels they possess may underlie a different method of colour processing. We investigated one hypothesis, in which the photoreceptors are proposed to act as individual wave-band detectors, interpreting colour as a parallel pattern of photoreceptor activation, rather than a ratiometric comparison of individual signals. This different form of colour detection has been used to explain previous behavioural tests in which low saturation blue was not discriminated from grey potentially because of similar activation patterns. Results here, however, indicate that the stomatopod, Haptosquilla trispinosa was able to easily distinguish several colours, including blue of both high and low saturation, from greys. The animals did show a decrease in performance over time in an artificially lit environment, indicating plasticity in colour discrimination ability. This rapid plasticity, most likely the result of a change in opsin (visual pigment) expression, has now been noted in several animal lineages (both invertebrate and vertebrate) and is a factor we suggest needing care and potential re-examination in any colour-based behavioural tests. As for stomatopods, it remains unclear why they achieve poor colour discrimination using the most comprehensive set of spectral sensitivities in the animal kingdom and also what form of colour processing they may utilise.

The necessity of ascertaining contamination by dust using soil spectral reflectance as its indicator

Pollution of the urban environment by human disturbances and activities is a negative externality of urbanization, therefore becoming a great concern due to the serious problems associated with human health. The mobilization of heavy metals into the biosphere by human activities has become an important process in the geochemical recycling of these metals. Though the risks of exposure to road dust have been reported to be higher for individuals than those in soil, little attention has been paid to the occurrence characteristics of heavy metals in dust and its associated health risks to the population. In the present studies, the physical, chemical and spectral signatures gained from dust and soil constituents would be differentiated based on their reflectance in specific bands of the electromagnetic spectrum. It is expected that the analysis of road dust and soil samples will indicate spectral signatures exhibiting differences in specific wavelengths of the spectrum, hence, indicating their spectral discrimination, as well as the presence of heavy metals showing its reflectance based on its concentration. The expected outcome of this study can be used to provide a theoretical basis for controlling the risk of heavy metals exposure to population.

Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets

In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.