The ManTIS Field Spectro-Goniometer Movie - Presentation of the Instrument and Measurement Scheme

This article presents and technically describes a new field spectro-goniometer system for the ground-based characterization of the surface reflectance anisotropy under natural illumination conditions developed at the Alfred Wegener Institute (AWI). The spectro-goniometer consists of a Manual Transportable Instrument platform for ground-based Spectro-directional observations (ManTIS), and a hyperspectral sensor system. The presented measurement strategy shows that the AWI ManTIS field spectro-goniometer can deliver high quality hemispherical conical reflectance factor (HCRF) measurements with a pointing accuracy of ±6 cm within the constant observation center. The sampling of a ManTIS hemisphere (up to 30° viewing zenith, 360° viewing azimuth) needs approx. 18 min. The developed data processing chain in combination with the software used for the semi-automatic control provides a reliable method to reduce temporal effects during the measurements. The presented visualization and analysis approaches of the HCRF data of an Arctic low growing vegetation showcase prove the high quality of spectro-goniometer measurements. The patented low-cost and lightweight ManTIS instrument platform can be customized for various research needs and is available for purchase.

Abundance and biomass of phytoplankton in the Black Sea near Gelendzhik in 2002-2005

It is shown that in 2002-2005 mass development of coccolithofore Emiliania huxleyi on the Gelendzhik shelf (northeast Black Sea) occurred annually and in May-June its abundance reached 1500000 cells/l. In 2004-2005 bloom of E. huxleyi was accompanied by mass development of diatom alga Chaetoceros subtilis var. abnormis f. simplex (600000-900000 cells/l). For the first time it was registered as a dominating form of Black Sea phytoplankton. Small flagellates and picoplankton algae played a noticeable role in phytoplankton throughout the entire period of the studies. Meanwhile in the early summer period the bulk of biomass consisted of coccolithophores (50-60%), while in the late summer period diatomaceous algae dominated (50-70%). Among ecological factors that favor coccolithophore development one may note microstratification of the upper mixed layer at a high illumination level and high temperature in surface waters (18-21°C). Terrigenous runoff during the rainy period had a negative effect on E. huxleyi development, while storms dispersed the population over the upper mixed layer. A wind-induced near-shore upwelling stimulated development of diatoms.

Primary production of phytoplankton and chlorophyll a in the southeast Barents Sea in August-September 1998

A study was performed from August 11 to September 3, 1998 in the Pechora Sea, which covered the shallow-water southeastern Barents Sea. Chlorophyll a concentration in the surface layer (C_chls) ranged from 0.08 to 1.15 mg/m**3, while primary production in the water column (C_phs) Varied from 17 to 170 mg C/m**2/day, aver. 75 mg C/m**2/day. Transition from central deep-water (60-190 m) parts of the sea to coastal shallow-water (15-30 m) parts was accompanied by increase of average C_chls values 2.4 times (from 0.21 to 0.51 mg/m**3) and decrease in average C_phs 1.6 times (from 95 to 58 mg C/m**2/day); the latter, in turn, resulted from decrease in thickness of the photosynthetic layer (H_ph) from 55 to 12 m and its relative transparency (H) from 17 to 4 m. This sharp change in H value and absence of a positive feedback between C_chls and C_phs were most probably related to rapid increase in the role of yellow substance and suspended matter in absorption of solar radiation in coastal waters. In sea areas with depths greater than 30 m a deep chlorophyll maximum was observed; at most of stations it located in the 20-35 m deep layer during illumination in photosynthetic active radiation range comprising 0.8-1.5% of its surface value. Parameters of photosynthetic light curves in these regions indicate participation of shade-adapted flora in formation of the deep chlorophyll maximum. In coastal waters characterized by a relatively uniform chlorophyll distribution over the water column no light adaptation of phytoplankton to efficient utilization of low irradiation for photosynthesis was encountered. Thus, a conclusion was made that combination of extremely low values of C_phs and H_ph makes the pelagic ecosystem of the Pechora Sea coastal regions very sensitive to anthropogenic impacts that may increase water turbidity.

30 years of synoptic observations from Neumayer Station with links to datasets

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.