Effective environmental management and monitoring has become increasingly important as anthropogenic processes increasingly impact natural ecosystems. One locality that is under direct threat due to human activities is the Australian Great Barrier Reef (GBR). Marine benthic foraminifera represent an abundant and readily applicable tool that can be used in environmental studies to investigate an array of ecological parameters and assist in understanding ecosystem dynamics and influence management protocols. Initially, baseline knowledge of the taxonomic composition within the region must be established to facilitate comparative studies and monitor change to maximise understanding and management efficacy. A detailed taxonomic assessment is provided of 133 species of benthic foraminifera in 76 genera from Heron Island, One Tree Island, Wistari and Sykes Reefs, which form the core of the Capricorn Group (CG) at the southern end of the GBR. Of these 133 species, 46% belong to the order Miliolida, 34% to Rotaliida, 7% to Textulariida, 5% to Lagenida, 3% to Lituolida, 3% to Spirillinida, 1% to Loftusiida and 1% to Robertinida. Samples were collected from a variety of shallow shelf reef environments including reef flat, lagoonal and channel environments. This work establishes a platform from which future investigations can stem.

Land surface phenology (LSP) is associated with climate over space and time, and the monitoring of LSP help understandings of the terrestrial environmental changes. The LSP is often inferred by satellite observation, and long-term and regularly composite satellite imagery is now freely available. In this study, we demonstrate how LSP changes over space and time at the global scale over the last three decades by using GIMMS3g datasets. We focus on the magnitude and the timing of the peak of yearly phenological activity, estimated from a harmonic analysis. The first harmonic curve is regarded as a proxy of the overall productivity of vegetation and the second one is interpreted as a sensitive bimodal system changes. Results show the long-term trend of LSP changes; for example the peak of phenogical activity tend to be earlier in high-latitude regions. Land surface phenology (LSP) is associated with climate over space and time, and the monitoring of LSP help understandings of the terrestrial environmental changes. The LSP is often inferred by satellite observation, and long-term and regularly composite satellite imagery is now freely available. In this study, we demonstrate how LSP changes over space and time at the global scale over the last three decades by using GIMMS3g datasets. We focus on the magnitude and the timing of the peak of yearly phenological activity, estimated from a harmonic analysis. The first harmonic curve is regarded as a proxy of the overall productivity of vegetation and the second one is interpreted as a sensitive bimodal system changes. Results show the long-term trend of LSP changes; for example the peak of phenogical activity tend to be earlier in high-latitude regions.

Land surface phenology (LSP) characterizes the vegetated land surface and is practical to understand terrestrial environmentals at a global scale. Regularly observed remotely sensed data such as Landsat, MODIS, and AVHRR contributes to analyze LSP spatially. However, at least two main challenges should be addressed such that (i) the spatial resolution which attributes to the data source may significantly impact to LSP estimation, and (ii) the estimated LSP may not represent the vegetated land surface well due to the mixed land cover. Previous studies have shown that the estimation of LSP from different data is not consistent due to the spatial scale of data but yet fully linked with the mixed land cover problem. Thus, in this study, we attempt to analyze the impact of spatial scale issue to the estimated LSP in homogenous land cover areas. We use freely available remotely sensed data with different spatial resolution such as Landsat (30m), MODIS (250m, 500m, 1km), and GIMMS3g (8km) and estimate phenological indices for each. As land cover description differs among data products, land cover classes are aggregated into 12 classes globally from major global land cover producs (GLCC, GLC2000, and globcover), then spatially homogenuous land cover are only picked up. Phenological indices such as the magnitude and the peak of DOY are calculated by harmonic analysis to compare results among different spatial scales. The variability of phenological indices is explored according to the different spatial scale under the condition of homogenuous land cover. It is expected to model such variability to overcome the spatial scale impact and such characteristics depending on the spatial scale should be taken into account when considering LSP from satellite.