Sergi Pons

Effect of spectral resolution in hyperspectral data analysis

The effect of hyperspectral data resolution on the results obtained using derivative spectroscopy is discussed in this article. A comparison was made between attenuation spectra measured using two different hyperspectral sensors with 3648 and 256 spectral bands, respectively. Smoothing and derivative algorithms were applied to both types of spectral data in order to assess qualitative information from the spectral features of several laboratory samples. In order to make an optimal application of the derivative analysis, a suitable selection of the smoothing and derivating parameters was done according to the resolution of each type of hyperspectral data. Second derivative spectra were obtained and peaks related to the absorption bands of pigments present in the considered samples were identified in both cases. Furthermore, interpolation techniques were applied so as to match spectral resolutions of data collected by the two sensors and negligible variations in the positions of the peaks were achieved in the derivative spectra.

Monolithic spectrometer for environmental monitoring applications

Hyperspectral data analysis has been shown to be a reliable technique to identify several water components. Currently, there are numerous commercially available miniature spectrometer systems as well as discrete components that are used by researchers in designing their own systems. We have developed a custom-designed hyperspectral sensor. Experimental results and potential applications are discussed on this article. It can be used as a low cost optical monitoring system to be employed on fish farming and aquaculture facilities.

Hyperspectral remote sensing of phytoplankton assemblages in the ocean: Effects of the vertical distribution

The increasing availability of hyperspectral technology on remote sensing observing platforms provides marine scientists the potential to better map the phytoplankton community composition in the ocean globally. In this study, this approach is examined using unsupervised cluster techniques applied to data sets of hyperspectral remote sensing reflectance obtained by radiative simulations. Different oceanic environments in terms of phytoplankton biodiversity were satisfactorily classified. Furthermore, an assessment of the effect of a variable stratified water column scenario on the remote sensing reflectance points out that vertical distributions of phytoplankton communities along the water column play an essential role in the accurate phytoplankton assemblages mapping by remote sensing.

Stray-light correction of in-water array spectroradiometers. Effects on underwater optical measurements

Two miniature hyperspectral sensors are being considered to become part of a marine observatory system. The effect of stray-light radiation on its response is discussed in this article. A new method to correct the unwanted spectral stray-light radiation has been developed and applied to both hyperspectral sensors. The correction method has been proved as effective for both considered miniature spectrographs using different calibration light sources. Furthermore, preliminary results of spectral stray-light corrections applied to underwater light field measurements point out that spectral stray-light correction plays an important role in accurate underwater optical hyperspectral measurements and its later spectral analysis.

Comparing the use of hyperspectral Irradiance Reflectance and Diffuse Attenuation Coeficient as indicators for algal presence in the water column

Remarkable advances are being achieved on spectroradiometric systems technology, allowing the development of spectrometers aimed at in-situ measurements. However, the use of numerical modelling of the ocean optical properties can help to understand how these properties react to changes in the water columns composition. In this work, HydroLight-EcoLight was used in order to simulate different scenarios to study how two Apparent Optical Properties (AOPs) such as Irradiance Reflectance (R) and Diffuse Attenuation Coefficient (Kd) could be used to detect the presence of a specific phytoplankton group in the water column.

Mapping marine phytoplankton assemblages from a hyperspectral and Artificial Intelligence perspective

The aim of this contribution is to demonstrate the feasibility of different processing techniques to identify phytoplankton assemblages when applied to oceanographic hyperspectral data sets (i.e. above surface measurements and vertical profiles). In order to address this issue and validate the proposed techniques, a simulated framework has been used based on the oceanic radiative transfer model Hydrolight-Ecolight 5.0. The potential offered by an unsupervised hierarchical cluster analysis technique and two Artificial Intelligence algorithms (i.e. Particle Swarm Optimization and Case-Based Reasoning) have been explored. Our results confirm their suitability to map phytoplanktons distribution from hyperspectral information given a variety of hypothetical oceanic environments.

Video-image processing applied to the analysis of the behaviour of deep-water lobsters (Nephrops norvegicus)

In this study, we show the results of applying two automated data processing methods to a set of videos previously used to manually monitor the period and the phase of activity rhythms of the deep-water Norway lobster, Nephrops norvegicus, a species of elevated commercial fishery value. Present data are consistent with published findings on Nephrops norvegicus activity rhythms both in the laboratory and the field