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Disaggregation of land types using terrain analysis, expert knowledge and GIS methods

机译:使用地形分析,专家知识和GIS方法对土地类型进行分类

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Soil maps' value is increasingly recognised for enabling the optimal management of ecosystems. Digital soil mapping (DSN!) can overcome the cost constraints of traditional mapping methods, but requires local area-specific research. As South Africa isblessed with the land type survey, local DSM research should start with the disaggregation of this resource. This paper shows how two land types (Ea34 and Ca11) near Newcastle in KwaZulu-Natal were disaggregated using DSM methods. A series of soil maps were created. With each map, more information was incorporated when creating the map. For Map 1 only the land type inventory and terrain analysis were used. A reconnaissance field visit with the land type surveyor was added for the second map. Field workand a simplified soil association legend improved the map accuracy for Maps 3 and 4, which were created using 30% and 60%, respectively, of the observation points as training data. The accuracy of the maps increased when more information was utilised. Map 1 reached an accuracy of 35%, whereas Map 4 achieved a commendable accuracy of 67%. Thus DSM methods can be used to disaggregate land types into accurate soil association maps. Emerging principles include that lithology rather than hard geology shouldbe used as parent material input, field work is critical to obtain acceptable results, and simplifying the map legend into soil associations improves the accuracy of the map.
机译:土壤图的价值日益得到认可,可以实现生态系统的最佳管理。数字土壤制图(DSN!)可以克服传统制图方法的成本限制,但需要针对特定​​区域进行研究。由于南非拥有丰富的土地类型调查,因此本地DSM研究应从对该资源的分类开始。本文说明了如何使用DSM方法对夸祖鲁-纳塔尔省纽卡斯尔附近的两种土地类型(Ea34和Ca11)进行分类。创建了一系列土壤图。对于每张地图,创建地图时都包含了更多信息。对于地图1,仅使用土地类型清单和地形分析。在第二张地图中添加了与土地类型测量师的侦察实地访问。现场工作和简化的土壤关联图例提高了地图3和4的地图准确性,分别使用30%和60%的观测点作为训练数据来创建地图。当使用更多信息时,地图的准确性会提高。地图1的准确度为35%,而地图4的准确度为67%。因此,DSM方法可用于将土地类型分解为准确的土壤关联图。新兴的原则包括应使用岩性而不是硬地质作为母体材料输入,现场工作对于获得可接受的结果至关重要,并且将图例简化为土壤关联可以提高图的准确性。

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