首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >Soil surface infiltration capacity classification based on the bi-directional reflectance distribution function sampled by aerial photographs. The case of vineyards in a Mediterranean area.
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Soil surface infiltration capacity classification based on the bi-directional reflectance distribution function sampled by aerial photographs. The case of vineyards in a Mediterranean area.

机译:基于航空照片采样的双向反射率分布函数的土壤表面入渗能力分类。以地中海地区的葡萄园为例。

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Spatially distributed hydrological modelling is required to understand and predict erosion, flooding and pollution risks that affect the vine cultivated Mediterranean environment. Previous field studies have demonstrated the dominant influence of soil surface features on overland flow and they therefore constitute an essential input to the hydrological model. In this paper we propose a remote sensing based method to map vineyard soil surface features with a spatial and temporal resolution appropriate for integration into the model. Our goal was to classify each soil surface portion in accordance with a pre-established, field measured infiltration capacity based typology. The radiometric characteristics of the classes of this typology were measured in the field and their Bi-directional Reflectance Distribution Function (BRDF) was modelled. Vineyard sunlit soil surface pixels were automatically extracted from high spatial resolution scanned aerial colour photographs Wassenaar et al., 2001 and Wassenaar et al., 2002. These pixels are radiometrically classified by comparison of their reflectance with BRDF-based reflectance predictions of each soil surface type for the specific illumination and viewing geometry of the pixel. The results show that most hydrological soil surface classes have distinct bi-directional radiometric properties. For one given geometric configuration however, the predicted reflectance ranges of some classes can considerably overlap (tilled soils and stone layers for example), while others can always unambiguously be identified (bare soil crusts, surfaces covered for more than 50% by weed or litter). We conclude that our fuzzy classification approach and the simple radiometric information used, allow us to identify the majority of the hydrological surface types. The method can easily be transposed in time and space. Its performance quite strongly depends on the radiometric and geometric accuracy of the input data. Significant improvements in soil surface classification precision are expected from considering spatial context information and monitoring the soil surface evolution..
机译:需要空间分布的水文模型来理解和预测影响葡萄树栽培的地中海环境的侵蚀,洪水和污染风险。先前的田间研究已经证明了土壤表面特征对陆上水流的主要影响,因此它们构成了水文模型的重要输入。在本文中,我们提出了一种基于遥感的方法来绘制具有适合集成到模型中的时空分辨率的葡萄园土壤表面特征的地图。我们的目标是根据预先建立的,基于现场测量的入渗能力的类型对每个土壤表面部分进行分类。在野外测量了这类分类的辐射特征,并对它们的双向反射分布函数(BRDF)进行了建模。从高空间分辨率扫描的航空彩色照片中自动提取葡萄园阳光照射下的土壤表面像素Wassenaar等人,2001和Wassenaar等人,2002。通过比较每个像素的反射率与基于BRDF的每个土壤表面的反射率预测,对这些像素进行放射线分类像素的特定照明和观看几何图形的类型。结果表明,大多数水文土壤表层具有明显的双向辐射特性。但是,对于一种给定的几何结构,某些类别的预测反射率范围可能会大大重叠(例如,倾斜的土壤和石材层),而其他类别的预测反射率范围则始终可以明确地确定(裸露的土壤结皮,杂草或垃圾覆盖的表面超过50%) )。我们得出的结论是,我们的模糊分类方法和所使用的简单放射线信息使我们能够识别大多数水文表面类型。该方法可以轻松地在时间和空间上进行转换。其性能在很大程度上取决于输入数据的辐射度和几何精度。通过考虑空间上下文信息和监视土壤表面演变,可以预期土壤表面分类精度的显着提高。

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