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Byron Flora and Fauna Study 1999 - Byron Shire Council

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A GREENPRINT FOR A SUSTAINABLE FUTURE<br />

(e.g. identifying areas considered likely to provide habitat for Threatened flora <strong>and</strong> fauna species, such as<br />

remnant rainforest <strong>and</strong> old growth eucalypt forest). The mapping component provided data on extent of<br />

remnant <strong>and</strong> regrowth native vegetation, extent of Camphor Laurel cover <strong>and</strong> an indication of the areal<br />

extent of different vegetation associations present in the <strong>Shire</strong>.<br />

4.2 PREVIOUS STUDIES<br />

The vegetation of <strong>Byron</strong> <strong>Shire</strong> had not previously been mapped, although parts had been treated in studies<br />

using differing methodologies (Table 4.1). As some aspects of vegetation cover change relatively quickly,<br />

often it is only the most recent studies which are useful as an indication of current vegetation.<br />

In June 1996 L<strong>and</strong>mark Ecological Services Pty Ltd was engaged by <strong>Byron</strong> <strong>Shire</strong> <strong>Council</strong> to carry out a pilot<br />

vegetation mapping project as part of the planning for the major flora <strong>and</strong> fauna study of the <strong>Shire</strong> (Milledge<br />

<strong>and</strong> McKinley 1996). The principal objective was to design a vegetation mapping system incorporating<br />

structural <strong>and</strong> floristic attributes at a scale of 1:25,000 which would provide sufficient detail to be useful for<br />

l<strong>and</strong> use planning purposes. The area selected for the pilot project comprised the coast <strong>and</strong> hinterl<strong>and</strong><br />

about Cape <strong>Byron</strong> <strong>and</strong> <strong>Byron</strong> Bay.<br />

4.3 RECOMMENDATIONS FOR BYRON FLORA AND FAUNA STUDY<br />

(based on Milledge <strong>and</strong> McKinley 1996)<br />

A multi-attribute approach with vegetation structure (following Walker <strong>and</strong> Hopkins 1990) as the primary<br />

classification variable was initially used for vegetation mapping but this resulted in an extremely complex<br />

map, difficult to interpret <strong>and</strong> of limited use for l<strong>and</strong>-use planning. The complexity was primarily due to the<br />

highly disturbed nature of the pilot project area, which has resulted in a mosaic of regrowth communities<br />

with rapidly changing structural attributes. For example, major differences were apparent in the height <strong>and</strong><br />

density of some st<strong>and</strong>s as depicted in the 1991 aerial photographs <strong>and</strong> as they presently exist. Even if<br />

mapped in current form, it is likely that these st<strong>and</strong>s would be structurally different again in a few years.<br />

While structure may change rapidly, floristic changes tend to be slower. Mapping based on floristics produced<br />

a more homogeneous map, which allowed easier interpretation <strong>and</strong> provided useful information more<br />

readily.<br />

The Pilot <strong>Study</strong> was intended as a multi-attribute approach with structure as the primary classifying variable<br />

but the method proved to be impractical <strong>and</strong> was ab<strong>and</strong>oned in favour of a mapping system using floristics<br />

as the primary classification variable (Milledge <strong>and</strong> McKinley 1996). The latter method adopted floristic<br />

communities <strong>and</strong> associations as mapping units identified by dominant species in the upper stratum, although<br />

structural attributes were also included in the classification as a secondary variable, identified by a coding<br />

system.<br />

4.4 METHODS<br />

4.4.1 Aerial Photographic Interpretation (API)<br />

Interpretation of aerial photographs for the purpose of vegetation identification is a cost effective <strong>and</strong><br />

proven method for mapping large areas of vegetation.<br />

Identification of various vegetation types is undertaken in the field <strong>and</strong> identified via the use of a stereoscope,<br />

with the corresponding features on the photo. Field observations are then extrapolated <strong>and</strong> marked onto<br />

plastic overlays on the photo.<br />

Accuracy in API is dependent on several factors. These include the age <strong>and</strong> quality of photographs, the<br />

accessibility of areas for fieldwork, <strong>and</strong> the amount of resources <strong>and</strong> therefore time available for both the<br />

fieldwork <strong>and</strong> the mapping.<br />

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