Characterization of different landscape surfaces in arid environments by the use of Sentinel-1 SAR data

使用 Sentinel-1 SAR 数据表征干旱环境中的不同景观表面

• 批准号: 430973477
• 负责人: Privatdozent Dr. Georg Stauch
• 金额: --
• 依托单位: Institut für Geographie und Geologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/430973477?language=en
• 关键词: Characterization; different; landscape; surfaces; arid

The arid regions of the earth were and are particularly susceptible to climatic changes. At the same time, they are the habitat of more than 2 billion people. Due to the size of the areas, the poor infrastructure in many parts and the often adverse environmental conditions, there are still large gaps in the state of research on the rates of landscape change. For several decades, optical remote sensing data have been used intensively to monitor arid areas. However, optical remote sensing methods often face problems to identify geomorphological processes and process rates due to their coarse resolution and the low spectral diversity. Within the framework of this project, the suitability of Synthetic Aperture Radar (SAR) data for the characterization of surfaces will be investigated. With the Sentinel-1 mission of the ESA (European Space Agency), a modern SAR system, offering spatial resolution of less than 15 m per pixel, is operating. It delivers remote sensing imagery free of charge and suited to build temporal dense time series. Due to the sparse vegetation cover, arid regions are particularly suitable for the use of radar remote sensing because the influence of vegetation, leading to volume scattering and thus unwanted distortion, is low. The project will investigate the SAR intensities and the interferometric coherences in order to evaluate the capacities of Sentinel-1 for the spatial-temporal characterization of the land surface. The Orog-Nuur Basin in southern Mongolia was selected as a suited study area. The region is characterized by a variety of different land surfaces and geomorphological processes. These include former lake sediments modified by periglacial processes, dunes, large gravelly beach ridges and, in particular, a large number of different alluvial fan surfaces of various ages. The different surfaces will be geomorphologically recorded and described in detail during the fieldwork. Special emphasis during the field work will be put on the creation of high-precision orthophotos and digital terrain models from drone imagery. Since surface roughness strongly influences the backscatter of the SAR system, roughness analyses on different spatial scales will be carried out on these terrain models. Additionally, these models will be suited for the characterization of micro- and mesoscale landform elements. Finally, the results from the field work and the morphometric analyses will be compared with the SAR data in order to achieve an accurate characterization of the different surfaces and landforms on different scales. If the suitability of the SAR data for a detailed characterisation of the surfaces in arid environment is successfully determined, a new method for a detailed monitoring of these sensitive landscapes is offered for future research. Especially in areas with a high variability, detailed and temporally frequent observations are of high relevance.

地球的干旱地区尤其容易受到气候变化的影响。同时，它们是超过20亿人的栖息地。由于该地区的规模，许多部分的基础设施和通常不利的环境条件，在景观变化率的研究状态下仍然存在很大的差距。几十年来，光学遥感数据已被密集地用于监测干旱区域。但是，光学遥感方法通常面临问题，以确定由于其粗分辨率和低光谱多样性而导致的地貌过程和过程速率。在该项目的框架内，将研究合成孔径雷达（SAR）数据以表面表征的适用性。随着ESA（欧洲航天局）的Sentinel-1任务，现代SAR系统，每个像素的空间分辨率少于15 m。它可以免费提供遥感图像，并适合构建时间密集的时间序列。由于植被的覆盖率稀疏，干旱地区特别适合使用雷达遥感，因为植被的影响，导致体积散射并因此不必要的失真较低。该项目将研究SAR强度和干涉相干性，以评估Sentinel-1的能力对于土地表面的时空表征。蒙古南部的Orog-nuur盆地被选为合适的研究区。该地区的特征是各种不同的土地表面和地貌过程。其中包括以前的湖泊沉积物，通过冰山冰期过程，沙丘，大砾石山脊，尤其是各种年龄的大量不同冲积风扇表面。在现场工作期间，将对不同的表面进行地貌记录和详细描述。在现场工作期间的特殊重点将放在无人机图像中的高精度正射击和数字地形模型上的创建。由于表面粗糙度强烈影响SAR系统的反向散射，因此将在这些地形模型上对不同空间尺度进行粗糙度分析。此外，这些模型将适用于微观和中尺度地形元素的表征。最后，将将现场工作和形态分析的结果与SAR数据进行比较，以便在不同尺度上准确地表征不同表面和地形。如果成功确定了SAR数据对干旱环境中表面的详细表征的适用性，则为将来的研究提供了一种详细监视这些敏感景观的新方法。尤其是在变异性较高的领域，详细且在时间上频繁的观察值很高。