Time-series of seagrass and land cover maps from 1972 to 2010, in South East Queensland, Australia, with links to GeoTIFFs

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Dominant pelagic amphipods collected as swimmers from a mooring time-series at AWI HAUSGARTEN

Time series length-frequency data are presented for Themisto amphipods collected as swimmers by moored sediment traps since 2000 at the AWI deep-sea observatory HAUSGARTEN (79°N/4°E) in the eastern Fram Strait. Amphipod occurrences increased significantly from 2000 to 2009 at 200-300 m depth, and the North Atlantic species Themisto compressa was continuously present in the samples starting in 2004. We present year-round records of large adult Themisto amphipods, including the appearance of Themisto libellula with a total body length of up to 56.7 mm and juveniles starting from 4.0 mm. The length of Themisto abyssorum ranged from 4.2 to 25.6 mm, whereas it varied for Themisto compressa from 8.8 to 24.4 mm. Length-frequency analysis indicated a life span of 2 years for T. abyssorum and at least 3 years for T. libellula. The absence of juveniles for T. compressa suggested its reproduction in southern subarctic areas and its occasional northward migration with warmer Atlantic water into the eastern Fram Strait. The seasonal and long-term size structures of the three pelagic species were consistent over the course of the study, indicating no changes occurred in cohort development due to increasing abundances or warming water temperatures.

Time series of zooplankton abundance at station L4 in the English Channel from 1988 to 2011

Ongoing zooplankton research at the Plymouth Marine Laboratory has established a time series of zooplankton species since 1988 at L4, a coastal station off Plymouth. Samples were collected by vertical net hauls (WP2 net, mesh 200 µm; UNESCO 1968) from the sea floor (approximately 50 m) to the surface and stored in 4% formalin. Much of the zooplankton analysis has been to the level of "major taxonomic groups" only, and a number of different analysts have participated over the years. The level of expertise has generally been consistent, but the user should be aware that levels of taxonomic discrimination may vary during the course of the dataset. The dominant calanoid copepods are generally well discriminated to species throughout. Calanus has not been routinely examined for species determination, the assumption being that the local population is entirely composed of Calanus helgolandicus. In certain years there has been a particular interest in Temora stylifera, Centropages cherchiae and other species reflected in the dataset. The lack of records in other previous years does not necessarily reflect species absence. We view it as essential for all users of L4 plankton data to establish and maintain contact with the nominated current data originators as well as fully consulting the metadata. While not impinging on free data access, this ensures that this large, species-rich but slightly complex species database is being used in the correct way, and any potential issues with the data are clarified. Furthermore, a proper dialogue with these local experts on the time series will enable where appropriate the most recent sampling timepoints to be used. The data can be downloaded from BODC or from doi:10.1594/PANGAEA.778092 as files for each year by searching for "L4 zooplankton". The most comprehensive dataset is the version downloadable directly from this page. The entire set of zooplankton samples is stored at the Plymouth Marine Laboratory in buffered formalin, and may be available for further taxonomic analysis on request.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

Time series of ZOOSCAN images and results of zooplankton samples at Villefranche from 1966 to 2003

ZooScan with ZooProcess and Plankton Identifier (PkID) software is an integrated analysis system for acquisition and classification of digital zooplankton images from preserved zooplankton samples. Zooplankton samples are digitized by the ZooScan and processed by ZooProcess and PkID in order to detect, enumerate, measure and classify the digitized objects. Here we present a semi-automatic approach that entails automated classification of images followed by manual validation, which allows rapid and accurate classification of zooplankton and abiotic objects. We demonstrate this approach with a biweekly zooplankton time series from the Bay of Villefranche-sur-mer, France. The classification approach proposed here provides a practical compromise between a fully automatic method with varying degrees of bias and a manual but accurate classification of zooplankton. We also evaluate the appropriate number of images to include in digital learning sets and compare the accuracy of six classification algorithms. We evaluate the accuracy of the ZooScan for automated measurements of body size and present relationships between machine measures of size and C and N content of selected zooplankton taxa. We demonstrate that the ZooScan system can produce useful measures of zooplankton abundance, biomass and size spectra, for a variety of ecological studies.