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首页> 外文期刊>Environmental Monitoring and Assessment >Quantification And Site-specification Of The Support Practice Factor When Mapping Soil Erosion Risk Associated With Olive Plantations In The Mediterranean Island Of Crete
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Quantification And Site-specification Of The Support Practice Factor When Mapping Soil Erosion Risk Associated With Olive Plantations In The Mediterranean Island Of Crete

机译:在绘制地中海克里特岛上与橄榄园相关的土壤侵蚀风险时,对支持措施因素的量化和现场确定

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Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using object-oriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.
机译:由于不适当的农业管理做法,水土流失已成为地中海地区许多橄榄种植区最危险的土壤退化形式之一,导致土壤肥力和单产大幅下降。为了防止土壤进一步退化,必须在当地实施适当的措施。从这个角度来看,遥感数据集空间精度的提高和先进的图像分析是在小规模上绘制土壤侵蚀风险所必需和有效的重要工具。在这项研究中,使用GIS在空间域中实施了修订的通用土壤流失方程(RUSLE),而高分辨率的卫星图像(即QuickBird图像)用于推导覆盖管理(C)和支持实践(P)这些因素,以便绘制出希腊克里特岛典型的橄榄种植区Kolymvari的土壤侵蚀风险。结果包括统一采用P因子时(常规方法)的土壤侵蚀风险图和使用面向对象图像分析对P因子进行位置定量时的​​风险图。结果表明,QuickBird图像对于获得P因子的位点特异性并因此支持橄榄种植区(如克里特岛的Kolymvari之一)土壤侵蚀风险的精细标度绘制是必要的。提高QB图像分类的准确性将进一步改善最终的土壤侵蚀图。

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