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Applying the global standard FAO LCCS to map land cover of rural Queensland

机译:应用全球标准FAO LCCS绘制昆士兰农村的土地覆盖图

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摘要

Production of land cover maps has developed rapidly with the introduction of satellite images. However, these mapping tasks face a common challenge in adopting an internationally accepted classification scheme. Classification schemes were generally tailored to match local conditions without a 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. To address this issue, the Food and Agriculture Organization (FAO) produced a widely acceptable land cover classification system (FAO LCCS) in year 2000, based on an a priori (pre-decided) approach to classify the land to match with any region of the world. In this study we classified rural Queensland land cover, using the hierarchical and the a priori method used in FAO LCCS. Under the a priori approach, all classes were determined before the classification start to maintain the standardization of categories. The hierarchical dichotomous approach was (divide into subcategories) applied afterward, to obtain classes without having any conflict between two given land cover types. We classified satellite images of two rural Queensland regions, Hughenden grasslands and semi-arid Mt Isa. After classifying regions into 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, SPOT 10m images were classified for land cover maps, however, all other available information were utilized for the classification process. Field investigations were carried out to verify uncertainties in spectral values and to collect ground truth information. Results of the study rendered well-classified two maps at 10m resolution with over 80% overall accuracy. The most significant outcome of the study is the successful implementation of FAO LCCS approach to local conditions of Queensland, which could serve as a guideline to map other regions in Queensland and other states of Australia.
机译:随着卫星图像的引入,土地覆盖图的制作迅速发展。但是,这些映射任务在采用国际公认的分类方案时面临着共同的挑战。分类方案通常是为适应当地条件而量身定制的,没有灵活性,无法应用于世界其他地区。澳大利亚的土地覆盖制图也面临着同样的难题,即“缺乏标准分类系统”,无法对其庞大的土地面积进行分类,并进行内部和国际比较。为了解决这个问题,粮食及农业组织(FAO)在2000年基于一种先验(预先确定)的方法对土地进行分类以使其与任何地区的土地相匹配,从而建立了一个广为接受的土地覆盖分类系统(FAO LCCS)。世界。在这项研究中,我们使用粮农组织LCCS中使用的分层方法和先验方法对昆士兰州农村土地覆盖进行了分类。在先验方法下,在分类开始之前确定所有类别,以保持类别的标准化。之后采用分级二分法(分为子类),以获取类别,而两种给定的土地覆被类型之间没有任何冲突。我们对昆士兰州的两个农村地区(Hughenden草原和半干旱的伊萨山)的卫星图像进行了分类。在将区域分为1级到3级(FAO预设类别)之后,基于光谱值和田野调查的分类器被实施为构建4级。最初,对SPOT 10m图像进行了土地覆盖图分类,但是,所有其他可用方法信息用于分类过程。进行了现场调查,以验证光谱值的不确定性并收集地面真实信息。研究结果以10m的分辨率对两张地图进行了很好的分类,总体精度超过80%。该研究的最重要成果是成功实施了粮农组织对昆士兰州当地条件的LCCS方法,该方法可以作为绘制昆士兰州其他地区和澳大利亚其他州的指南。

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