Search Results (5)

The ability to adapt to wide ranges of environmental conditions coupled with their long evolution has allowed cyanobacteria to colonize almost every habitat on Earth. Modern taxonomy tries to track not only this diversification process but also to assign individual cyanobacteria to specific niches. It was our aim to work out a potential niche concept for the genus Cyanocohniella in terms of salt tolerance. We used a strain based on the description of C. rudolphia sp. nov. isolated from a potash tailing pile (Germany) and for comparison C. crotaloides that was isolated from sandy beaches (The Netherlands). The taxonomic position of C. rudolphia sp. nov. was evaluated by phylogenetic analysis and morphological descriptions of its life cycle. Salt tolerance of C. rudolphia sp. nov. and C. crotaloides was monitored with cultivation assays in liquid medium and on sand under salt concentrations ranging from 0% to 12% (1500 mM) NaCl. Optimum growth conditions were detected for both strains at 4% (500 mM) NaCl based on morpho-anatomical and physiological criteria such as photosynthetic yield by chlorophyll a fluorescence measurements. Taking into consideration that all known strains of this genus colonize salty habitats supports our assumption that the genus might have a marine origin but also expands colonization to salty terrestrial habitats. This aspect is further discussed, including the ecological and biotechnological relevance of the data presented. Full article

In this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image object statistics. One of the methods utilizes only texture-based features, that is, features that are related to the spatial arrangement of image characteristics. The second method is similar, but relies on a wider set of image object features. The methods were developed and tested using a dataset from Norwegian waters, specifically the Røstbanken area off the coast of Lofoten. The classification results were compared to backscatter-based classification and to grab sample ground-reference data. The algorithm that performed the best was then also applied to a dataset from the Borkumer Stones area close to the island of Schiermonnikoog in Dutch waters. This allowed testing the applicability of the algorithm for different datasets. Because the algorithms that were developed do not require backscatter, the availability of which is much more scarce than bathymetry, and because of the low computational requirements, they could be applied to any area where high-resolution bathymetry and grab samples are available. Full article

The composition of benthic species communities in the nearshore zone is closely related to the hydrodynamic and morphodynamic conditions. Sustainable management of the coastal ecosystem requires knowledge about the natural dynamics as well as human-induced changes on the ecosystem. To improve our knowledge of the benthic species distribution along a dissipative sandy shore with multiple breaker bars, an extensive dataset was collected in the nearshore zone of the barrier islands Ameland and Schiermonnikoog in the Dutch North Sea. From 2010 to 2014, every year, approximately 180 grab samples along 18 cross-shore transects were collected and analyzed for sediment characteristics and macrobenthic species composition. Mixed-effect-models and partial redundancy analysis were used to analyze the importance of morphological features (i.e., slopes, bar crests, and troughs) as an explanatory variable for the benthic species distribution. The results indicate that the morphological features in themselves explain three times more variation than the environmental parameters used. This demonstrates the importance of morphological features as a factor in explaining the distribution of benthic species communities in the nearshore. Detailed information on morphological features is easy to obtain from bathymetry maps or visual inspection. Incorporating morphological features in species distribution models will therefore help to improve sustainable management of our valuable sandy coastal systems. Full article

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R²) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R² of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R² = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland. Full article

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R² (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R² = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale. Full article