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Semi-automated Technique to Extract Boundary of Valley/mountain Glaciers using Glaciomorphological Information from Digital Elevation Model

机译:半自动技术利用数字海拔模型使用冰川晶体信息提取谷/山冰川边界

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A semi automated technique has been developed to extract the spatial extension of valleys and mountain glaciers. The method is based on morphological properties of glaciated area extracted from Digital Elevation Model (DEM). Identification of glacial boundary based on spectral information from optical remote sensing imageries produces errors due to misclassification of debris-covered ablation area with surrounding rocky terrain and perennially snow-covered slope with debris free glaciated area. Elevation information DEM of Shuttle Radar Topography Mission (SRTM), CartoDEM and ASTER DEM have been used. A part of western Himalayas was selected as the study area that contains large glaciated basins, e.g., Bhagirathi, Baspa, Chandra basin. First order derivatives, slope aspect, and second order derivatives like, profile and plan curvatures are computed from the DEM. The derivatives are used to quantify and characterise the morphological aspects of the glaciated area and used in the decision rule models to generate the glacial boundaries. The ridge lines of the study areas are also generated from the plan curvature and used in the model to delineate the catchments areas of the glaciers. The slope based boundary is checked for consistency with the boundary from profile curvature and combined manually to generate the final glacier boundary. Area and length under the derived boundary of Gangotri glacier of Bhagirathi catchments are 90.25 sq km and 30.5 km. The result has been checked with high resolution optical data. This objective approach is important to delineate glaciated area, measure the length, width and area and generate glacial hypsometry, concentration factor of the glaciers. Accuracy of the result depends up on the quality of the DEM. DEM generated by SAR interferometric technique is found superior over DEM generated from other interpolation techniques.
机译:已经开发出半自动技术来提取山谷和山冰川的空间延伸。该方法基于从数字仰卧模型(DEM)中提取的冰川区域的形态学性质。基于光学遥感成像的光谱信息识别冰川边界产生的损坏导致碎片覆盖的消融区域的错误分类,周围的岩石地形和常年冰雪覆盖的坡度,碎片无宽冰川区域。已经使用了海绵雷达形貌任务(SRTM),Cardodem和Aster DEM的海拔信息。选中西方喜马拉雅山的一部分被选为含有大冰川盆地的研究区,例如Bhagirathi,Baspa,Chandra盆地。从DEM计算的第一阶衍生物,斜率方面和二阶衍生物等二阶衍生物。衍生物用于量化和表征冰川区域的形态方面,并用于决策规则模型以产生冰川边界。研究区域的脊线也从平面曲率产生并在模型中使用以描绘冰川的集水区区域。检查基于斜率的边界与来自轮廓曲率的边界的一致性,并手动组合以产生最终的冰川边界。 Bhagirathi集水区甘蓝冰川衍生边界下的面积和长度为90.25平方公里,30.5公里。结果已通过高分辨率光学数据进行检查。这种目标方法对于描绘冰川区域来说是重要的,测量长度,宽度和面积并产生冰川低位测量,冰川的浓度因子。结果的准确性取决于DEM的质量。由SAR干涉测量技术产生的DEM在其他插值技术产生的DEM上被发现优越。

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