Creating a large area landcover dataset for public land monitoring and reporting

Farmer, E, Jones, S, Clarke, M, Buxton, L, Soto-Berelov, M, Page, S, Mellor, A and Haywood, A 2013, 'Creating a large area landcover dataset for public land monitoring and reporting', in C. Arrowsmith, C. Bellman, W. Cartwright, S. Jones and M. Shortis (ed.) Progress in Geospatial Science Research, Melbourne, Australia, 12-14 December 2011, pp. 116-132.


Document type: Conference Paper
Collection: Conference Papers

Title Creating a large area landcover dataset for public land monitoring and reporting
Author(s) Farmer, E
Jones, S
Clarke, M
Buxton, L
Soto-Berelov, M
Page, S
Mellor, A
Haywood, A
Year 2013
Conference name Geospatial Science Research Symposium (GSR_1)
Conference location Melbourne, Australia
Conference dates 12-14 December 2011
Proceedings title Progress in Geospatial Science Research
Editor(s) C. Arrowsmith, C. Bellman, W. Cartwright, S. Jones and M. Shortis
Publisher School of Mathematical and Geospatial Sciences RMIT University
Place of publication Melbourne, Australia
Start page 116
End page 132
Total pages 17
Abstract Enhanced up-to-date information on the extent and quality of woody vegetation systems is required to inform sustainable land management. Government and land management agencies require tools to generate consistent and scientifically robust large area (i.e. landscape level) woody vegetation spatial features – with which to conduct environmental monitoring and assessment, under a number of regulatory environmental monitoring frameworks (e.g. UN Framework Convention on Climate Change, State of the Forest reporting - Victoria, National Forest Inventory – Australia). This paper presents a method for the creation of a partial sample land cover baseline to complement existing on-ground forest monitoring plots and the total sample mapping using moderate-resolution remote sensing imagery. The land cover baseline was created via non-stereo, on-screen aerial photographic interpretation (API). Land cover maps were produced from 790 2x2 km digital (georectified) high-resolution (≤ 50 cm) colour aerial photographs (photoplots) located across a large area sampling grid (stratified by IBRA1 Bioregion), with an average of 72 photoplots in each Bioregion. Forest type and structural components (based on the National Vegetation Information System – NVIS) and land cover information in non-forest areas (based on the UN FAO Land Cover Classification System - LCCS) were derived using a semi-automated API (air photo interpretation) methodology. Initially, landscape elements (image objects) were delineated using automated segmentation algorithms and these were tuned to ensure resultant image objects approximated the land cover classes of interest given the stated minimum mapping unit (0.5 ha). Image objects were subsequently verified and classified, using operator knowledge, into hierarchical land cover categories (including dominant species, height, canopy cover, LCCS class - and where appropriate, whether the area had been fire affected). Modelling to a baseline year was required to account for landcover change events and non-synchronous image capture. Continuous improvement and validation of the dataset was built into the methodology so as to provide on-going feedback to the API team and report accuracy across all bioregions. This case study demonstrates the operational capability of semi-automated API to support large area (state-wide) environmental monitoring and assessment.
Subjects Geomatic Engineering not elsewhere classified
Keyword(s) multi-scale
vegetation
monitoring and reporting
api
automation
Copyright notice © School of Mathematical and Geospatial Sciences RMIT University 2013
ISBN 9781921488276
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