44 research outputs found

    Landholder Typologies Used in the Development of Natural Resource Management Programs in Australia - A Review

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    This article reviews the literature on the identification of landholder typologies that can be used to assist the design and delivery of natural resource management (NRM) programs. Australian researchers have developed typologies of landholders based on a variety of criteria. The rationale for developing landholder typologies is first discussed before reviewing the various approaches that have been used by Australian researchers and comparing their findings. The methods employed have differed according to the theories used to guide the research and the 'clients' or 'sponsors' of the research. The landholder types they describe, however, have a number of similarities. These similarities suggest that the studies have identified the same fundamental divisions in the rural community, and that it may be possible to integrate landholder typologies for a variety of NRM and non-NRM applications. It is concluded that further research could usefully investigate whether concepts of social class or sub-cultures may be appropriate to define and describe the variations in landholder types

    Public participation and local anchorage in countryside planning

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    The ¤farmer as landscape manager

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    Enhancing the recovery of a temporal sequence of images using joint deconvolution

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    Abstract In this work, we address the reconstruction of spatial patterns that are encoded in light fields associated with a series of light pulses emitted by a laser source and imaged using photon-counting cameras, with an intrinsic response significantly longer than the pulse delay. Adopting a Bayesian approach, we propose and demonstrate experimentally a novel joint temporal deconvolution algorithm taking advantage of the fact that single pulses are observed simultaneously by different pixels. Using an intensified CCD camera with a 1000-ps gate, stepped with 10-ps increments, we show the ability to resolve images that are separated by a 10-ps delay, four time better compared to standard deconvolution techniques
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