A framework for mapping vegetation over broad spatial extents : a technique to aid land management across jurisdictional boundaries

Haslem, Angie, Callister, Kate E., Avitabile, Sarah C., Griffioen, Peter A., Kelly, Luke T., Nimmo. Dale G., Spence-Bailey, Lisa M., Taylor, Rick S., Watson, Simon J., Brown, Lauren, Bennett, Andrew F. and Clarke, Michael F. 2010, A framework for mapping vegetation over broad spatial extents : a technique to aid land management across jurisdictional boundaries, Landscape and urban planning, vol. 97, no. 4, pp. 296-305.

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Title A framework for mapping vegetation over broad spatial extents : a technique to aid land management across jurisdictional boundaries
Author(s) Haslem, Angie
Callister, Kate E.
Avitabile, Sarah C.
Griffioen, Peter A.
Kelly, Luke T.
Nimmo. Dale G.
Spence-Bailey, Lisa M.
Taylor, Rick S.
Watson, Simon J.
Brown, Lauren
Bennett, Andrew F.
Clarke, Michael F.
Journal name Landscape and urban planning
Volume number 97
Issue number 4
Start page 296
End page 305
Publisher Elsevier B.V.
Place of publication Amsterdam, The Netherlands
Publication date 2010-09-30
ISSN 0169-2046
1872-6062
Keyword(s) Semi-arid ecosystems
Mallee vegetation
Remote sensing
Neural network classification models
Ecosystem management
Australia
Summary

Mismatches in boundaries between natural ecosystems and land governance units often complicate an ecosystem approach to management and conservation. For example, information used to guide management, such as vegetation maps, may not be available or consistent across entire ecosystems. This study was undertaken within a single biogeographic region (the Murray Mallee) spanning three Australian states. Existing vegetation maps could not be used as vegetation classifications differed between states. Our aim was to describe and map ‘tree mallee’ vegetation consistently across a 104 000km2 area of this region. Hierarchical cluster analyses, incorporating floristic data from 713 sites, were employed to identify distinct vegetation types. Neural network classification models were used to map these vegetation types across the region, with additional data from 634 validation sites providing a measure of map accuracy. Four distinct vegetation types were recognised: Triodia Mallee, Heathy Mallee, Chenopod Mallee and Shrubby Mallee. Neural network models predicted the occurrence of three of them with 79% accuracy. Validation results identified that map accuracy was 67% (kappa = 0.42) when using independent data. The framework employed provides a simple approach to describing and mapping vegetation consistently across broad spatial extents. Specific outcomes include: (1) a system of vegetation classification suitable for use across this biogeographic region; (2) a consistent vegetationmapto inform land-use planning and biodiversity management at local and regional scales; and (3) a quantification of map accuracy using independent data. This approach is applicable to other regions facing similar challenges associated with integrating vegetation data across jurisdictional boundaries.

Language eng
Field of Research 050209 Natural Resource Management
Socio Economic Objective 960805 Flora, Fauna and Biodiversity at Regional or Larger Scales
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032326

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