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Remote Sens. 2012, 4(8), 2236-2255; doi:10.3390/rs4082236
Article

An Approach to Mapping Forest Growth Stages in Queensland, Australia through Integration of ALOS PALSAR and Landsat Sensor Data

1,* , 1
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1 Institute of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion SY23 3DB, UK 2 Queensland Herbarium, Queensland Department of Science, Information Technology, Innovation and the Arts, Brisbane Botanic Gardens Mt Coot-tha Rd Toowong, Brisbane, QLD 4066, Australia 3 Remote Sensing Centre, Queensland Department of Science, Information Technology, Innovation and the Arts, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia 4 School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, QLD 4072, Australia 5 School of Plant Biology, The University of Western Australia (M084), 35 Stirling Highway, Crawley, WA 6009, Australia 6 Tropical Research Institute (IICT), Travessa do Conde da Ribeira, 9 1300-142 Lisbon, Portugal 7 Department of Electrical Engineering, University of Southern California, Powell Hall of Engineering, 3737 Watt Way, Los Angeles, CA 90089, USA
* Author to whom correspondence should be addressed.
Received: 15 June 2012 / Revised: 24 July 2012 / Accepted: 24 July 2012 / Published: 2 August 2012
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
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Abstract

Whilst extensive clearance of forests in the eastern Australian Brigalow Belt Bioregion (BBB) has occurred since European settlement, appropriate management of those that are regenerating can facilitate restoration of biomass (carbon) and biodiversity to levels typical of relatively undisturbed or remnant formations. However, maps of forests are different stages of regeneration are needed to facilitate restoration planning, including prevention of further re-clearing. Focusing on the Tara Downs subregion of the BBB and on forests with brigalow (Acacia harpophylla) as a component, this research establishes a method for differentiating and mapping early, intermediate and remnant growth stages from Japan Aerospace Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phased-Array L-band Synthetic Aperture Radar (PALSAR) Fine Beam Dual (FBD) L-band HH- and HV-polarisation backscatter and Landsat-derived Foliage Projective Cover (FPC). Using inventory data collected from 74 plots, located in the Tara Downs subregion, forests were assigned to one of three regrowth stages based on their height and cover relative to that of undisturbed stands. The image data were then segmented into objects with each assigned to a growth stage by comparing the distributions of L-band HV and HH polarisation backscatter and FPC to that of reference distributions using a z-test. Comparison with independent assessments of growth stage, based on time-series analysis of aerial photography and SPOT images, established an overall accuracy of > 70%, with this increasing to 90% when intermediate regrowth was excluded and only early-stage regrowth and remnant classes were considered. The proposed method can be adapted to respond to amendments to user-definitions of growth stage and, as regional mosaics of ALOS PALSAR and Landsat FPC are available for Queensland, has application across the state.
Keywords: synthetic aperture radar; regrowth mapping; land cover change; Queensland; Australia; classification; brigalow; ALOS PALSAR; foliage projective cover synthetic aperture radar; regrowth mapping; land cover change; Queensland; Australia; classification; brigalow; ALOS PALSAR; foliage projective cover
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Clewley, D.; Lucas, R.; Accad, A.; Armston, J.; Bowen, M.; Dwyer, J.; Pollock, S.; Bunting, P.; McAlpine, C.; Eyre, T.; Kelly, A.; Carreiras, J.; Moghaddam, M. An Approach to Mapping Forest Growth Stages in Queensland, Australia through Integration of ALOS PALSAR and Landsat Sensor Data. Remote Sens. 2012, 4, 2236-2255.

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