Drăguţ Lucian, Eisank Clemens, Strasser Thomas
Department of Geography and Geology, University of Salzburg, Hellbrunnerstraße 34, Salzburg 5020, Austria.
Geomorphology (Amst). 2011 Jul 15;130(3-4):162-172. doi: 10.1016/j.geomorph.2011.03.011.
Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements.
高分辨率数字高程模型（DEM）的可用性不断提高，正在引发地貌测量学中尺度问题的范式转变，促使人们寻求新的解决方案来应对多尺度分析和特征尺度的检测。我们测试了最初为图像分析开发的局部方差（LV）方法在地貌测量学多尺度分析中的适用性。该方法包括：1）对从DEM导出的地表参数进行尺度上推；2）对于每个尺度级别，在3×3移动窗口内计算LV作为平均标准差（SD）；3）计算LV从一个级别到另一个级别的变化率（ROC-LV），以及4）将如此获得的值相对于尺度级别进行绘图。我们将ROC-LV图中的峰值解释为尺度级别的标记，在这些尺度级别上，像元或线段与以（相对）同等程度的同质性为特征的模式元素类型相匹配。所提出的方法已应用于两个粗糙度不同的测试区域的LiDAR DEM，分别是低起伏区域和山区。对于每个测试区域，通过重采样（基于像元）或图像分割（基于对象）以恒定增量生成坡度、平面曲率和剖面曲率的尺度级别。视觉评估揭示了均匀区域，这些区域令人信服地关联成跨尺度有明显差异的地表参数模式。我们发现，与通过重采样进行尺度上推相比，LV方法在通过分割生成的尺度级别上表现更好。结果表明，将多尺度模式分析与形态测量基元的描绘相结合是可能的。这种方法可进一步用于开发地形要素的层次分类。
