Orengo Hector A, Petrie Cameron A
McDonald Institute of Archaeological Research University of Cambridge Cambridge UK.
Department of Archaeology and Anthropology University of Cambridge Cambridge UK.
Earth Surf Process Landf. 2018 May;43(6):1361-1369. doi: 10.1002/esp.4317. Epub 2018 Feb 5.
Morphological analysis of landforms has traditionally relied on the interpretation of imagery. Although imagery provides a natural view of an area of interest (AOI) images are largely hindered by the environmental conditions at the time of image acquisition, the quality of the image and, mainly, the lack of topographical information, which is an essential factor for a correct understanding of the AOI's geomorphology. More recently digital surface models (DSMs) have been incorporated into the analytical toolbox of geomorphologists. These are usually high-resolution models derived from digital photogrammetric processes or LiDAR data. However, these are restricted to relatively small areas and are expensive or complex to acquire, which limits widespread implementation. In this paper, we present the multi-scale relief model (MSRM), which is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software. The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM. The code, written in Google Earth Engine's implementation of JavaScript, is provided as Supporting Information for its use in any other AOI without particular technical knowledge or access to topographical data. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
传统上，地形地貌的形态分析依赖于对图像的解读。尽管图像能提供感兴趣区域（AOI）的自然视图，但图像在很大程度上受到图像采集时的环境条件、图像质量的限制，主要还受到缺乏地形信息的影响，而地形信息是正确理解AOI地貌的关键因素。近年来，数字表面模型（DSM）已被纳入地貌学家的分析工具箱。这些模型通常是通过数字摄影测量过程或激光雷达数据生成的高分辨率模型。然而，这些模型仅限于相对较小的区域，获取成本高昂或过程复杂，这限制了其广泛应用。在本文中，我们介绍了多尺度地形模型（MSRM），这是一种利用DSM进行地貌视觉解释的新算法。这种新方法的重要意义在于它能够从高分辨率和低分辨率的DSM中提取地貌形态，而不受所研究地貌的形状或尺度的影响。因此，与以前的方法相比，该方法具有重要优势：（1）允许使用全球中等分辨率模型，如航天飞机雷达地形测绘任务（SRTM）、先进星载热发射和反射辐射计全球数字高程模型（ASTER GDEM）、先进陆地观测卫星（ALOS）和TanDEM-X；（2）为传统的照片解读提供了一种替代方法，不依赖于所使用图像的质量、环境条件及其采集时间；（3）使用传统的地理信息系统/遥感（GIS/RS）软件，能够轻松地在大面积区域实施。该算法在印度西北部的萨特莱杰河—亚穆纳河河间地进行了测试，这里是一个非常大的低起伏冲积平原，利用MSRM已绘制出10000公里的古河道。该代码是用谷歌地球引擎（Google Earth Engine）的JavaScript实现编写的，作为支持信息提供，以便在任何其他AOI中使用，无需特定的技术知识或获取地形数据。©2017作者。由约翰·威利父子有限公司出版的《地表过程与地貌》
