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首页> 外文期刊>Soil Science Society of America Journal >A Technique for Low Cost Soil Mapping and Validation Using Expert Knowledge on a Watershed in Minas Gerais, Brazil
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A Technique for Low Cost Soil Mapping and Validation Using Expert Knowledge on a Watershed in Minas Gerais, Brazil

机译：利用巴西米纳斯吉拉斯州一个流域上的专家知识进行低成本土壤制图和验证的技术

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Understanding the soil attributes and types occurring within a region is critical for providing the best land-use decisions. Soils vary in their ability to clean and store water, provide water for plant growth, and many other ecosystem services. Soil variability is dependent on climate, parent material, organisms, time, and topography. When only topography varies within an area, the topography and redistribution of water should be the main drivers for soils differentiation. Digital soil mapping (DSM) has advantages due to computational tools and easily accessible digital elevation models (DEMs) at multiple resolutions. Terrain attributes (e. g., slope, wetness index, and profile curvature) are derived from the DEM and, in association with a soil expert, knowledge-based models can be applied to predict soil variability. The objective of this study was to create and validate a predicted Cambisol (Inceptisol) solum depth map for Lavrinha Creek Watershed (LCW) in Minas Gerais, Brazil, by applying DSM techniques for the Brazilian soil landscapes. The best available 30-m DEM was used to derive the terrain derivatives. A set of rules were formulated according to the terrain attributes, limited data, and expert knowledge to predict the solum depth behavior throughout the watershed. Conditioned Latin hypercube sampling scheme was used for allocating the validation points. In this study, 20 out of the 25 validating samples were correctly classified yielding a Kappa index of 0.616. Soil expert knowledge and Digital Soil Mapping techniques can be employed for mapping areas, especially in countries where there is limited data available, which will provide a useful soil map for planning while saving time and investments.
机译：了解区域内发生的土壤属性和类型对于提供最佳的土地利用决策至关重要。土壤清洁和储存水，为植物生长提供水以及许多其他生态系统服务的能力各不相同。土壤变异性取决于气候，母体材料，生物，时间和地形。当一个地区内只有地形变化时，水的地形和重新分配应该是土壤分化的主要驱动力。数字土壤制图（DSM）具有计算工具和易于访问的多种分辨率的数字高程模型（DEM）的优势。地形属性（例如，坡度，湿度指数和剖面曲率）是从DEM导出的，并且与土壤专家一起，可以将基于知识的模型应用于预测土壤变异性。这项研究的目的是通过对巴西土壤景观应用DSM技术，为巴西Minas Gerais的Lavrinha Creek流域（LCW）创建并验证预测的坎比索（Inceptisol）贫民窟深度图。可用的最佳30米DEM用于得出地形导数。根据地形属性，有限的数据和专家知识制定了一组规则，以预测整个流域的贫民窟深度行为。条件拉丁超立方体采样方案用于分配验证点。在这项研究中，对25个验证样本中的20个进行了正确分类，得出的Kappa指数为0.616。土壤专家知识和数字土壤测绘技术可用于制图区域，尤其是在可用数据有限的国家中，这将为规划提供有用的土壤图，同时节省时间和投资。

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