Karydas Christos G, Sekuloska Tijana, Silleos Georgios N
Department of Environmental Management, Mediterranean Agronomic Institute of Chania, P.O. Box 85, 73100 Chania, Greece.
Environ Monit Assess. 2009 Feb;149(1-4):19-28. doi: 10.1007/s10661-008-0179-8. Epub 2008 Mar 5.
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.
由于农业管理措施不当，土壤侵蚀正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，导致土壤肥力和产量大幅下降。为防止土壤进一步退化，有必要在当地实施适当措施。从这个角度来看，提高遥感数据集的空间精度和先进的图像分析是在精细尺度上绘制土壤侵蚀风险所需且有效的重要工具。在本研究中，利用地理信息系统（GIS）在空间域实施修订的通用土壤流失方程（RUSLE），同时使用一幅高分辨率卫星图像，即快鸟（QuickBird）图像，来推导植被覆盖管理（C）和支撑措施（P）因子，以便绘制希腊克里特岛典型橄榄种植区科林瓦里（Kolymvari）的土壤侵蚀风险图。结果包括P因子采用统一值（传统方法）时的土壤侵蚀风险图，以及使用面向对象图像分析对P因子进行特定地点量化时的风险图。结果表明，快鸟图像对于实现P因子的特定地点性是必要的，因此有助于在橄榄种植区（如克里特岛的科林瓦里地区）进行土壤侵蚀风险的精细尺度制图。提高快鸟图像分类的精度将进一步改善土壤侵蚀制图结果。
