Perera, Kithsiri and Moore, David and Apan, Armando and McDougall, Kevin (2010) Applying the global standard FAO LCCS to map rural Queensland land cover. In: Queensland Surveying and Spatial Conference 2010 (QSSC 2010), 1-3 Sept 2010, Brisbane, Queensland.
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Official URL: http://www.spatialsciences.org/images/QLD/QSSC/Program/kithsiri_perera_usq_fao_lccs_submit_format_150810.pdf
Abstract
With the introduction of satellite images, land cover map production developed rapidly. However it faced a common challenge to adopt an internationally accepted classification scheme. Most of the classification schemes were tailored to match local demands without flexibility to apply in other parts of the world. Land cover mapping in Australia is also facing the same dilemma, 'the lack of standard classification system' to classify its massive land mass and compare internally and internationally. As a solution, in 2000, the Food and Agriculture Organization (FAO) produced a widely acceptable land cover classification system (FAO LCCS) which is based on priori (pre-decided) approach, to match with any region of the world. In this study we classified rural Queensland land cover, using the hierarchical and the priori method used by FAO LCCS. Under the priori approach, all classes are set before the classification, to maintain the standardization of categories. Then, a hierarchical dichotomous approach (divide into sub-categories) follows to achieve classes without having conflict between any two land cover types. We classified two rural Queensland regions, Hughenden grasslands and semi-arid Mt Isa. After classifying regions with level 1 to level 3 (FAO pre-set classes), classifiers based on spectral values and field investigations were implemented to build the level 4. Primarily, the classification used SPOT 10m data, other available information were utilized for the classification. Field investigation was carried out to verify uncertainties in spectral values and to collect ground information. Results of the study rendered well-classified two maps at 10m resolution for each area with over 80% overall accuracy. The most significant outcome of the study was the successful integration of FAO LCCS into local conditions of Queensland, which could serve as a guideline to map other regions in Queensland and other states of Australia.
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