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Aligning New Zealand digital soil mapping with the global soil mapping project

机译:与全球土壤映射项目对齐新西兰数字土壤映射

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New Zealand is undertaking a new phase of soil mapping (S-map) with the goal of national soil map coverage at 1:50 000 scale. One S-map objective is to develop a national capability in digital soil mapping (DSM). Concurrent development of the global soil map (GSM) project provides an opportunity for the S-map project to align its work with the GSM initiative both to achieve an enhanced national soil map and contribute New Zealand data to the GSM project. Consequently we have applied GSM method guidelines nationally. We matched the generic GSM methods to 193 New Zealand soilscapes. Soilscapes with similar levels of legacy soil information were grouped into eight soilscape groups, and DSM methods applied to them. Four soilscape groups were in low relief land where intensification of land use generates high demand for new soil information. The other four method classes were in rolling, hilly and mountainous land. The most extensive method in low relief land requires establishment of better environmental co varíate layers followed by soil map reference area extrapolation, and the most extensive method in high relief land involves sampling programmes and scorpan analysis. The proposed inference engines are Bayesian belief networks, SoLIM, and Random Forests.
机译:新西兰正在进行新阶段的土壤映射(S-MAP),其目标是1:50 000规模的国家土壤地图覆盖。一个S-MAP目标是在数字土壤映射(DSM)中制定国家能力。全球土壤图(GSM)项目的并发开发为S-MAP项目提供了一个机会,使其与GSM计划协调,以实现增强的国家土壤图并将新西兰数据贡献给GSM项目。因此,我们已在全国范围内应用GSM方法指导。我们将通用GSM方法与193个新西兰的土壤漂亮相匹配。具有类似遗留土壤信息水平的土壤景观分为八个土壤景观组,申请DSM方法。四个土壤景观群体在低浮雕土地,土地利用的强化产生了对新土壤信息的高需求。另外四种方法类是滚动,丘陵和山地。低浮雕土地中最广泛的方法需要建立更好的环境COvaríate层,然后是土壤图参考面积推断,以及高浮雕土地中最广泛的方法涉及采样计划和蝎子分析。所提出的推理引擎是贝叶斯信仰网络,孤独和随机森林。

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