A Framework for Mapping Vegetation Over Broad Spatial Extents: a Technique to Aid Land Management Across Jurisdictional Boundaries
Landscape and Urban Planning 97 (2010) 296–305 Contents lists available at ScienceDirect Landscape and Urban Planning journal homepage: www.elsevier.com/locate/landurbplan A framework for mapping vegetation over broad spatial extents: A technique to aid land management across jurisdictional boundaries Angie Haslem a,b,∗, Kate E. Callister a, Sarah C. Avitabile a, Peter A. Griffioen c, Luke T. Kelly b, Dale G. Nimmo b, Lisa M. Spence-Bailey a, Rick S. Taylor a, Simon J. Watson b, Lauren Brown a, Andrew F. Bennett b, Michael F. Clarke a a Department of Zoology, La Trobe University, Bundoora, Victoria 3086, Australia b School of Life and Environmental Sciences, Deakin University, Burwood, Victoria 3125, Australia c Peter Griffioen Consulting, Ivanhoe, Victoria 3079, Australia article info abstract Article history: Mismatches in boundaries between natural ecosystems and land governance units often complicate an Received 2 October 2009 ecosystem approach to management and conservation. For example, information used to guide man- Received in revised form 25 June 2010 agement, such as vegetation maps, may not be available or consistent across entire ecosystems. This Accepted 5 July 2010 study was undertaken within a single biogeographic region (the Murray Mallee) spanning three Aus- Available online 7 August 2010 tralian 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 104 000 km2 area Keywords: of this region. Hierarchical cluster analyses, incorporating floristic data from 713 sites, were employed Semi-arid ecosystems Mallee vegetation to identify distinct vegetation types. Neural network classification models were used to map these veg- Remote sensing etation types across the region, with additional data from 634 validation sites providing a measure of Neural network classification models map accuracy.
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