Seagrass time-series analysis products, in Moreton Bay, Australia, with links to GeoTIFFs

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (~200 km**2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

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.

Benthic and substrate cover data derived from a time series of photo-transect surveys for the Eastern Banks, Moreton Bay Australia, 2004-2015

This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.

Snow and ice surfaces on King George Island (Antarctic Peninsula) with high-resolution TerraSAR-X time series, with links to shapfiles and GeoTIFFs

Changes of glaciers and snow cover in polar regions affect a wide range of physical and ecosystem processes on land and in the adjacent marine environment. In this study, we investigate the potential of 11-day repeat high-resolution satellite image time series from the TerraSAR-X mission to derive glaciological and hydrological parameters on King George Island, Antarctica during the period Oct/25/2010 to Apr/19/2011. The spatial pattern and temporal evolution of snow cover extent on ice-free areas can be monitored using multi-temporal coherence images. SAR coherence is used to map glacier extent of land terminating glaciers with an average accuracy of 25 m. Multi-temporal SAR color composites identify the position of the late summer snow line at about 220 m above sea level. Glacier surface velocities are obtained from intensity feature-tracking. Surface velocities near the calving front of Fourcade Glacier were up to 1.8 ± 0.01 m/d. Using an intercept theorem based on fundamental geometric principles together with differential GPS field measurements, the ice discharge of Fourcade Glacier was estimated to 20700 ± 5500 m**3/d (corresponding to ~19 ± 5 kt/d). The rapidly changing surface conditions on King George Island and the lack of high-resolution digital elevation models for the region remain restrictions for the applicability of SAR data and the precision of derived products.

Primary production at time series station DYFAMED

Phytoplankton carbon assimilation has been measured near monthly using the 14C method at DYFAMED France JGOFS time-series station from 1993 to 1999. Data were obtained using the "LET GO" technique, which allowed in situ injection of bicarbonate and incubation in enclosures at 10 depths. Incubation duration was 4 h around noon, from which daily production was estimated. The seasonal variation of the depth-integrated carbon assimilation exhibits a marked cycle. Maximum values reach 1.8 g C/m**2/d in March or April; constant lower values were observed from August to January, in the range 100-300 mg C/m**2/d. The annual primary production vary in the range 86-232 g C/m**2/yr, in the upper range of older estimations. Primary production normalized to chlorophyll a shows maximum values in the period of oligotrophy. This increase of carbon assimilation rate per unit of chlorophyll a appears as linked to the period of phosphorus-limited ecosystem, and vertical distribution of taxonomic pigments suggests a possible role of cyanobacteria. Potential export production has been estimated from primary production data and Fp ratio based on pigments concentrations. These estimates (which imply biological steady state conditions) vary in a wide range, from 19 to 71 g C/m**2/yr. There is a decoupling between years with high potential export production and years with high measured particulate fluxes, which highlights the question of balance by resupply of the limiting nutrients and the role of dissolved organic carbon. A possible shift of primary production towards a more regeneration-dominated system is suggested for recent years.

Abundance and biomass of dinoflagellates and ciliates at time series station Helgoland Roads, North Sea, 2007-2009

A monitoring programme for microzooplankton was started at the long-term sampling station ''Kabeltonne'' at Helgoland Roads (54°11.30' N; 7°54.00' E) in January 2007 in order to provide more detailed knowledge on microzooplankton occurrence, composition and seasonality patterns at this site and to complement the existing plankton data series. Ciliate and dinoflagellate cell concentration and carbon biomass were recorded on a weekly basis. Heterotrophic dinoflagellates were considerably more important in terms of biomass than ciliates, especially during the summer months. However, in early spring, ciliates were the major group of microzooplankton grazers as they responded more quickly to phytoplankton food availability. Mixotrophic dinoflagellates played a secondary role in terms of biomass when compared to heterotrophic species; nevertheless, they made up an intense late summer bloom in 2007. The photosynthetic ciliate Myrionecta rubra bloomed at the end of the sampling period. Due to its high biomass when compared to crustacean plankton especially during the spring bloom, microzooplankton should be regarded as the more important phytoplankton grazer group at Helgoland Roads. Based on these results, analyses of biotic and abiotic factors driving microzooplankton composition and abundance are necessary for a full understanding of this important component of the plankton.

Robust trends of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks, with links to GeoTIFFs

Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the ~ 29,000 km**2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.

Atmospheric and seawater carbon dioxide at time series station DYFAMED

A time series of fCO2, SST, and fluorescence data was collected between 1995 and 1997 by a CARIOCA buoy moored at the DyFAMed station (Dynamique des Flux Atmospheriques en Mediterranée) located in the northwestern Mediterranean Sea. On seasonal timescales, the spring phytoplankton bloom decreases the surface water fCO2 to approximately 290 µatm, followed by summer heating and a strong increase in fCO2 to a maximum of approximately 510 µatm. While the DELTA fCO2 shows strong variations on seasonal timescales, the annual average air-sea disequilibrium is only 2 µatm. Temperature-normalized fCO2 shows a continued decrease in dissolved CO2 throughout the summer and fall at a rate of approximately 0.6 µatm/d. The calculated annual air-sea CO2 transfer rate is -0.10 to -0.15 moles CO2 m-2 y-1, with these low values reflecting the relatively weak wind speed regime and small annual air-sea fCO2 disequilibrium. Extrapolating this rate over the whole Mediterranean Sea would lead to a flux of approximately -3 * 10**12 to -4.5 * 10**12 grams C/y, in good agreement with other estimates. An analysis of the effects of sampling frequency on annual air-sea CO2 flux estimates showed that monthly sampling is adequate to resolve the annual CO2 flux to within approximately ±10 - 18% at this site. Annual flux estimates made using temperature-derived fCO2 based on the measured fCO2-SST correlations are in agreement with measurement-based calculations to within ± 7-10% (depending on the gas transfer parameterization used), and suggest that annual CO2 flux estimates may be reasonably well predicted in this region from satellite or model-derived SST and wind speed information.

Zooplankton biovolume time series at station STARESO (Bay of Calvi, Corsica)

A long-term time series of subsurface zooplankton is performed in the oligotrophic Bay of Calvi (Corsica, Ligurian Sea, NW Mediterranean) from 2003 onwards. It is carried out from the marine station STARESO and is based on weekly measurement of zooplankton biovolumes. The main objectives are (i) to determine the seasonal cycle and inter-annual variability of the organisms, (ii) to study the dynamics of the populations, and (iii) to understand zooplankton interactions with other co-sampled hydrographic, meteorological and phytoplankton variables at the site.

Long-term environmental time series of continuously collected data in hydroelectric reservoirs in Brazil

Long-term environmental time series of continuously collected data are fundamental to identify and classify pulses and determine their role in aquatic systems. This paper presents a web based archive for limnological and meteorological data collected by integrated system for environmental monitoring (SIMA). The environmental parameters that are measured by SIMA are: chlorophyll-a (µg/L), water surface temperature (ºC), water column temperature by a thermistor string (ºC), turbidity (NTU), pH, dissolved oxygen concentration (mg/L), electric conductivity (µS/cm), wind speed (m/s) and direction (º), relative humidity (%), short wave radiation (W/m**2), barometric pressure (hPa). The data are collected in preprogrammed time interval (1 hour) and are transmitted by satellite in quasi-real time for any user in a range of 2500 km from the acquisition point. So far 11 hydroelectric reservoirs being monitored using the SIMA buoy. A basic statistics (mean and standard deviation) for some parameters and an example of time series were displayed. The main observed problem are divided into sensors and satellite. The sensors problems is due to the environmental characteristics of each water body. In acid waters the sensors of water quality rapidly degrade, and the collected data are invalid. Another problem is the infestation of periphyton in the sensor. SIMA buoy makes the parameters readings every hour, or 24 readings per day. However, not always received all readings because the system requires satellites passing over the buoy antenna to complete the transfer and due to the satellite constellation position, some locations inland are not met as often as necessary to complete all transmissions. This is the more often causes for lack in the time series.

Absorption coefficients of particulate matter and chlorophyll a concentrations at time series station OLIPAC

Spectral absorption coefficients of total particulate matter ap (lambda) were determined using the in vitro filter technique. The present analysis deals with a set of 1166 spectra, determined in various oceanic (case 1) waters, with field chl a concentrations ([chl]) spanning 3 orders of magnitude (0.02-25 mg/m**3). As previously shown [Bricaud et al., 1995, doi:10.1029/95JC00463] for the absorption coefficients of living phytoplankton a phi (lamda), the ap (labda) coefficients also increase nonlinearly with [chl]. The relationships (power laws) that link ap (lambda) and a phi (lambda) to [chl] show striking similarities. Despite large fluctuations, the relative contribution of nonalgal particles to total absorption oscillates around an average value of 25-30% throughout the [chl] range. The spectral dependence of absorption by these nonalgal particles follows an exponential increase toward short wavelengths, with a weakly variable slope (0.011 ± 0.0025/nm). The empirical relationships linking ap (lambda) to ([chl]) can be used in bio-optical models. This parameterization based on in vitro measurements leads to a good agreement with a former modeling of the diffuse attenuation coefficient based on in situ measurements. This agreement is worth noting as independent methods and data sets are compared. It is stressed that for a given ([chl]), the ap (lambda) coefficients show large residual variability around the regression lines (for instance, by a factor of 3 at 440 nm). The consequences of such a variability, when predicting or interpreting the diffuse reflectance of the ocean, are examined, according to whether or not these variations in ap are associated with concomitant variations in particle scattering. In most situations the deviations in ap actually are not compensated by those in particle scattering, so that the amplitude of reflectance is affected by these variations.

Surface time series of nitrate, silicate and chlorophyll a (1999-2011) and CTD profiles (1999) at the PhytoCly station, Bay of Calvi, Corsica, Mediterranean Sea

This work is based on a long time series of data collected in the well-preserved Bay of Calvi (Corsica island, Ligurian Sea, NW Mediterranean) between 1979 and 2011, which include physical characteristics (31 years), chlorophyll a (chl a, 15 years), and inorganic nutrients (13 years). Because samples were collected at relatively high frequencies, which ranged from daily to biweekly during the winter-spring period, it was possible to (1) evidence the key role of two interacting physical variables, i.e. water temperature and wind intensity, on nutrient replenishment and phytoplankton dynamics during the winter-spring period, (2) determine critical values of physical factors that explained interannual variability in the replenishment of surface nutrients and the winter-spring phytoplankton bloom, and (3) identify previously unrecognized characteristics of the planktonic ecosystem. Over the >30 year observation period, the main driver of nutrient replenishment and phytoplankton (chl a) development was the number of wind events (mean daily wind speed >5 m s-1) during the cold-water period (subsurface water <13.5°C). According to winter intensity, there were strong differences in both the duration and intensity of nutrient fertilization and phytoplankton blooms (chl a). The trophic character of the Bay of Calvi changed according to years, and ranged from very oligotrophic (i.e. subtropical regime, characterized by low seasonal variability) to mesotrophic (i.e. temperate regime, with a well-marked increase in nutrient concentrations and chl a during the winter-spring period) during mild and moderate winters, respectively. A third regime occurred during severe winters characterized by specific wind conditions (i.e. high frequency of northeasterly winds), when Mediterranean "high nutrient - low chlorophyll" conditions occurred as a result of enhanced crossshore exchanges and associated offshore export of the nutrient-rich water. There was no long-term trend (e.g. climatic) in either nutrient replenishment or the winter-spring phytoplankton bloom between 1979 and 2011, but both nutrients and chl a reflected interannual and decadal changes in winter intensity.

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.