Several lagoons of the Eastern Tuamotu Atolls (French Polynesia) are characterized by enormous populations of the clam Tridacna maxima, a species considered as endangered in many locations worldwide. This unique resource is virtually intact, until recently being impacted only by local consumption. Increasing exports to Tahiti's market (up to 50 tonnes of wet matter y−1), combined with the relatively small size of these lagoons (<50 km2), have raised significant concerns for agencies charged with management of lagoonal resources. In order to evaluate whether the current harvesting pressure threatens long-term sustainability of this resource, it is necessary to estimate the total number of individual clams present and also the fraction of that stock that is currently targeted by fishers, who generally collect clams in very shallow waters (<1 m), walking on the reef edges. Here, we present results for a pilot study evaluating this resource at Fangatau Atoll. Using a combination of data collected in situ and three remotely sensed images with different spatial resolution (1.5, 5.6, and 30 m), we estimate that the shallowest lagoonal areas (4.05 km2 at depth <6 m) harbour five classes of benthic habitat with significantly different clam areal covers and densities. Considering the cover/density values for each habitat class, 23.65 ± 5.33 million clams (mean ± 95% confidence interval) inhabit these 4.05 km2. Assuming that current harvesting techniques will be maintained in the future, the commercially available stock represents 44% of the population located on 1.18 km2 of the shallow lagoon. A comparison of results from the three remote sensing platforms indicates that high resolution, broadband multispectral sensors (e.g. IKONOS, Quickbird) should provide the best existing platforms to conduct similar assessments elsewhere.
Research Containing: Earth Observation
The Level 2 research product algorithms for the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES)
This paper describes the algorithms of the level-2 research (L2r) processing chain developed for the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES). The chain has been developed in parallel to the operational chain for conducting researches on calibration and retrieval algorithms. L2r chain products are available to the scientific community. The objective of version 2 is the retrieval of the vertical distribution of trace gases in the altitude range of 18–90 km. An theoretical error analysis is conducted to estimate the retrieval feasibility of key parameters of the processing: line-of-sight elevation tangent altitudes (or angles), temperature and O3 profiles. The line-of-sight tangent altitudes are retrieved between 20 and 50 km from the strong ozone (O3) line at 625.371 GHz, with low correlation with the O3 volume-mixing ratio and temperature retrieved profiles. Neglecting the non-linearity of the radiometric gain in the calibration procedure is the main systematic error. It is large for the retrieved temperature (between 5–10 K). Therefore, atmospheric pressure can not be derived from the retrieved temperature, and, then, in the altitude range where the line-of-sight tangent altitudes are retrieved, the retrieved trace gases profiles are found to be better represented on pressure levels than on altitude levels. The error analysis for the retrieved HOCl profile demonstrates that best results for inverting weak lines can be obtained by using narrow spectral windows. Future versions of the L2r algorithms will improve the temperature/pressure retrievals and also provide information in the upper tropospheric/lower stratospheric region (e.g., water vapor, ice content, O3) and on stratospheric and mesospheric line-of-sight winds.
The Remote Atmospheric and Ionospheric Detection System (RAIDS) is a new NASA experiment studying the Earth's thermosphere and ionosphere from a vantage point on the International Space Station (ISS). The RAIDS mission focuses on the coupling and transition from the coldest part of the atmosphere, the mesopause near 85 kilometers, up to the hottest regions of the thermosphere, above 300 kilometers in altitude. Built jointly by the Naval Research Laboratory (NRL) and The Aerospace Corporation, RAIDS also is serving as a pathfinder experiment for atmospheric remote sensing aboard the ISS. RAIDS and a companion experiment, NRL's Hyperspectral Imager for the Coastal Ocean (HICO), make up the HICO-RAIDS Experiment Payload (HREP), the first U.S. payload on the Japanese Experiment Module—Exposed Facility (JEM-EF). The experience developing and operating RAIDS for this mission provides useful insights for utilizing the ISS as a platform for atmospheric science.
Coastal embayments are a broad category of an ecosystem type that may be loosely defined as an enclosed or semi-enclosed aquatic environment along a land-mass margin. Embayments are highly diverse, representing a spectrum of varying degrees of physical isolation from the open coast and hydrodynamic regime. Such systems include certain estuaries, lagoons, rías ("drowned estuaries"), firths, and fjords. At one extreme of the continuum, classic fjords, such as are found in Norway, Chile, and British Columbia, are typically deeply glaciated basins (often V-shaped) with steep sides, and are generally restricted to latitudes above 45° in both hemispheres. Fjordal water circulation patterns are characterized by surface outflow of buoyant freshwater, and an inward-bound compensation current, so-called estuarine circulation. Bottom water of fjords often tends to become anoxic, especially with the presence of a sill formed by excavated or scoured material accumulated towards the mouth of the inlet (Skjoldal et al., 1995) (Figure 1). By contrast, coastal lagoons, such as those found along the Mediterranean Sea (right), the Carolinas in the United States, and other parts of the Iberian coast, as well as in the tropics, are often shallow basins linked to the land's margin, but lack the stratification parameters and circulation of fjords, and typically have a soft-bottom substrate.
Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon, FL
Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, SlopeRED and SlopeNIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63?64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.