5 resultados para Network Analysis Methods

em Publishing Network for Geoscientific


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Information on possible resource value of sea floor manganese nodule deposits in the eastern north Pacific has been obtained by a study of records and collections of the 1972 Sea Scope Expedition.

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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.

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We analyzed the abundance of Scots pine regeneration in a 257 ha wildfire in an inner-alpine forest. We sampled regeneration, percent soil cover by classes, physical and chemical properties of topsoils (A horizon, 0-5 cm) under four fire severity levels (unburned, moderate, moderate/high, high severity). 5 plots per severity level, circular (R= 3m). Analysis methods for soil properties as described in the paper.

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Ecological network analysis (ENA) was used to study the effects of Pomatoschistus microps on energy transport through the food web, its impact on other compartments and its possible role as a keystone species in the trophic webs of an Arenicola tidal flat ecosystem and a sparse Zostera noltii bed ecosystem. Three ENA models were constructed: (a) model 1 contains data of the original food web from prior research in the investigated area by Baird et al. (2007), (b) an updated model 2 which included biomass and diet data of P. microps from recent sampling, and (c) model 3 simulating a food web without P. microps. A comparison of energy transport between the different models revealed that more energy is transported from lower trophic levels up the food chain, in the presence of P. microps (models 1 and 2) than in its absence (model 3). Calculations of the keystone index (KSi) revealed the high overall impact (measured as eps_i) of this fish species on food webs. In model 1, P. microps was assigned a low KSi in the Arenicola flat and in the sparse Z. noltii bed. Calculations in model 2 ranked P. microps first for keystoneness and eps_i in both communities, the Arenicola flat and the sparse Z. noltii bed. Taken together, our results give insight into the role of P. microps when considering a whole food web and reveal direct and indirect trophic interactions of this small-sized fish species. These results might illustrate the impact and importance of abundant, widespread species in food webs and facilitate further investigations.

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A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.