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Object-oriented soil erosion modelling: A possible paradigm shift from potential to actual risk assessments in agricultural environments

机译:面向对象的土壤侵蚀建模:可能的范式从农业环境中实际风险评估的潜力转变

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Over the last 2 decades, geospatial technologies such as Geographic Information System and spatial interpolation methods have facilitated the development of increasingly accurate spatially explicit assessments of soil erosion. Despite these advances, current modelling approaches rest on (a) an insufficient definition of the proportion of arable land that is exploited for crop production and (b) a neglect of the intra-annual variability of soil cover conditions in arable land. To overcome these inaccuracies, this study introduces a novel spatio-temporal approach to compute an enhanced cover-management factor (C) for revised universal soil loss equation-based models. It combines highly accurate agricultural parcel information contained in the Land Parcel Identification System with an object-oriented Landsat imagery classification technique to assess spatial conditions and interannual variability of soil cover conditions at field scale. With its strong link to Land Parcel Identification System and Earth observation satellite data, the approach documents an unprecedented representation of farming operations. This opens the door for the transition from the currently used potential soil erosion risk assessments towards the assessment of the actual soil erosion risk. Testing this method in a medium-size catchment located in the Swiss Plateau (Upper Enziwigger River Catchment), this study lays an important foundation for the application of the very same methods for large-scale or even pan-European applications. Soil loss rates modelled in this study were compared with the insights gained from emerging techniques to differentiate sediment source contribution through compound-specific isotope analysis on river sediments. The presented technique is adaptable beyond revised universal soil loss equation-type soil erosion models.
机译:在过去的二十年中,地理学空间技术如地理信息系统和空间插值方法都促进了日益准确的土壤侵蚀性评估的发展。尽管有这些进展,但目前的建模方法休息(a)耕地的比例不足,该耕地生产的比例和(b)忽视了耕地土壤覆盖条件的年度覆盖条件的内部变异性。为了克服这些不准确性,本研究介绍了一种新的时空方法,用于计算用于修正的普遍土壤损失方程式模型的增强覆盖管理因子(c)。它将具有面向对象的Landsat Imagery分类技术的地块识别系统中包含的高准确的农业包信息与面向对象的Landsat意象的分类技术相结合,以评估现场规模的空间条件和土壤覆盖条件的际变化。凭借其陆地包裹识别系统和地球观测卫星数据的强烈联系,该方法文件记录了营运业务的前所未有的代表。这为从目前使用的潜在土壤侵蚀风险评估开始评估实际土壤侵蚀风险的潜在土壤侵蚀风险的过渡。在位于瑞士高原(上埃兹威格河集水区)的中型集水区中测试此方法,该研究为应用了相同的大规模甚至泛欧应用的方法奠定了重要的基础。将本研究中建模的土壤损失率与来自新兴技术中获得的见解进行了比较,以通过河流沉积物的复合特异性同位素分析区分沉积物来源贡献。呈现的技术适应于修正的通用土壤损失方程式土壤侵蚀模型。

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