APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO REMOTE SENSING*

Remote sensing is a geographic analysis tool capable of producing large quantities of data in the spectral, temporal and spatial domains. Techniques for automating the image analysis process would be advanced by the inclusion of artificial intelligence techniques in the design of image processing systems. The remote sensing application which show promise for successful implementation of artificial intelligence techniques are intelligent onboard processing, advanced database interrogation, and the automated analysis of multispectral imagery.     

World Data Center-A for Paleoclimatology at the NOAA Paleoclimatology Program,Boulder, CO

Conclusion The World Data Center-A for Paleoclimatology, located in the NOAA/NGDC Paleoclimatology Program, is committed to providing the scientific community with easy access to all paleoenvironmental data. Efforts to make archived data readily available include international coordination of data acquisition, management, and distribution, sponsoring workshops and data cooperatives to facilitate the compilation of important data sets, development of a browse and visualization software package (PaleoVu), and dispersal of archived data on magnetic media or over ANONYMOUS FTP/INTERNET. The program publishes a semi-annual newsletter that highlights latest developments and accomplishments in the area of paleoenvironmental data for global change research. Contributions to the newsletter are welcome from researchers describing their efforts to coordinate the free flow of paleoclimate data throughout the international scientific community.For information on the program or to be added to the mailing list contact Mrs Mildred England (phone: 303-497-6227; Fax: 303 497-6513; e-mail: MKE@mail.ngdc.noaa.gov), NOAA National Geophysical Data Center, Paleoclimatology Program/World Data Center-A for Paleoclimatology, 325 Broadway, E/GC Boulder, CO 80303 USA     

Late Quaternary palaeolimnology of a tropical marl lake: Wallywash Great Pond,Jamaica

Wallywash Great Pond (17° 57 N, 77° 48 W, 7 m a.s.l.) is the largest perennial lake in Jamaica. It occupies a fault trough within the karstic White Limestone. The Great Pond is a hardwater lake with a pH of 8.2–8.6 and an alkalinity of 3.6–3.9 meq 1^–1. Its chemistry is strongly influenced by the spring discharge from the limestone. The lake water is subject to degassing, evaporation and bicarbonate assimilation by submerged plants and algae, resulting in marl precipitation. A 9.23 m core (WGP2), taken from a water depth of 2.8 m, was analysed for magnetic susceptibility, loss-on-ignition, carbonate content, mole % MgCO₃ in calcite, and stable isotopes in the fine carbonate fraction. The chronology is based on ten¹⁴C and four U/Th dates. Four main sediment types alternate in the core: marl; organic, calcareous mud; organic mud or peat; and earthy, brown, calcareous mud. The marls represent periods of wet/warm climate during sea-level highstands and the organic deposits, shallower, swampy conditions. In contrast, the brown, calcareous muds were laid down when the lake was dry or ephemeral. The last interglacial (120 000- 106 000 yr BP) is represented by three distinct marl units. After a dry interval, stable, wet/warm conditions set in from 106 000 to 93 000 yr BP. A dry/cool climate prevailed between 93 000 and at least 9500 yr BP. Three subsequent cycles of alternating wet and dry conditions culminated in flooding of the basin by the Black River during the late Holocene. These recent events cannot be accurately dated by¹⁴C due to significant and temporally-variable inputs of dead carbon from the springs.     

Last interglacial sea levels on the south coast of New South Wales

Remnants of the Last Interglacial shoreline occur at Middle Lagoon on the far south coast of New South Wales. Relict beach sediments can be traced to a height of at least +4.8 m and are indicative of a former mean sea level of about +3 m. Thermoluminescence (TL) ages of 126 ± 13 ka and 114 ± 15 ka were determined for beach and aeolian facies respectively. Sands in the lower part of an exposure on the adjacent Gillards Beach gave TL ages of 108 ± 13 ka, but sands in the upper part of that exposure gave an age of 19.9 ± 3.5 ka. This chronological evidence of a stratigraphic unconformity in what was initially taken as pedogenic differentiation at Gillards Beach is supported by contrasting electron traps and colour centres in crystal lattices of quartz grains in these two samples. No tectonic displacement is apparent. This site provides the first evidence of the Last Interglacial sea level for 1000 km along the coast between Gippsland and Newcastle.     

FINDING CAUSES OF OUTLIERS IN MULTIVARIATE ENVIRONMENTAL DATA

Multivariate outliers in environmental data sets are often caused by atypical measurement error in a singlevariable.From a quality assurance perspective it is important to identify these variables efficiently so thatcorrective actions may be performed.We demonstrate a procedure for using two multivariate tests toidentify which variable‘caused’each outlier.The procedure is tested with simulated data sets that havethe same correlation structure as selected water chemistry variables from a survey of lakes in the WesternUnited States.The success rates are evaluated for three of the variables for sample sizes of 50 and 100,significance levels of 0.01 and 0.05 and various amounts of mean shift.The procedure works best forhighly correlated variables.     

Shallow-marine sequences as the building blocks of stratigraphy: insights from numerical modelling

Two types of depositional sequences can be defined within the sequence stratigraphic framework: the parasequence and the high‐frequency sequence. Both sequences consist of stacked regressive and transgressive deposits. However, a parasequence forms under conditions of overall sea‐level rise, whereas a high‐frequency sequence forms as the sea level oscillates which results in typical forced regressive deposits during sea‐level fall. Both depositional sequences may develop over comparable temporal (10–100 kyr) and spatial (1–20 km wide and 1–40 m thick) scales. Numerical modelling is used to compare the architecture, preservation potential, internal volumes, bounding surfaces, condensed and expanded sections and facies assemblages of parasequences and high‐frequency sequences. Deposits originating from transgression are less pronounced than their regressive counterparts and consist of either preserved backbarrier deposits or shelf deposits. Shoreface deposits are not preserved during transgression. The second half of the paper evaluates in detail the preservation potential of backbarrier deposits and proposes a mechanism that explains the occurrence of both continuous and discontinuous barrier retreat in terms of varying rates of sea‐level rise and sediment supply. The key to this mechanism is the maximum washover capacity, which plays a part in both barrier shoreline retreat and backbarrier‐lagoonal shoreline retreat. If these two shorelines are not balanced, then the retreat of the coastal system as a whole is discontinuous and in time barrier overstep may take place.