【摘要】 将卫星雷达遥感应用于滑坡灾害的探测与监测,不仅可以从空间尺度上大范围捕捉到滑坡信号,而且可以从时间尺度上以较长周期追踪滑坡的运动状态。但是,卫星雷达遥感本身的局限性和滑坡所处的复杂地形环境使这一应用面临一些挑战。对卫星雷达遥感技术的4个主要挑战进行了总结与分析,同时给出了相应的解决方案:①通过提高卫星雷达影像的空间、时间分辨率,使用较长波段雷达信号或采用增强型时间序列分析技术,可降低密集植被覆盖对相干性的影响。另外,采用像素点偏移量追踪或距离向分频干涉测量方法,可克服传统干涉测量中大梯度形变引起的相位失相干。②大气延迟对卫星遥感的影响较大,尤其是地处山区的滑坡探测和监测,利用通用型卫星雷达大气改正系统可显著减弱干涉影像的大气信号并进一步简化时间序列分析,提高缓慢运动滑坡的探测和监测质量。③对于中等分辨率的雷达影像而言,利用数字高程模型可提前量化分析雷达几何畸变(如叠掩、阴影等)引发的滑坡探测监测的适用性;而对于高分辨率的雷达影像而言,利用机器学习方法无需外部高分辨率数字高程模型即可精确识别雷达影像的阴影和叠掩区并进行掩膜,从而大幅度提高数据处理效率。④针对高坡度地区残余的地形相位引起的解缠误差,可通过基线线性组合的方法予以减弱。此外,提出了一个基于多源对地观测的滑坡探测/监测系统框架,综合卫星雷达遥感与其他对地观测数据(如地基雷达、激光雷达、全球导航定位系统),搭建了一个自动化滑坡探测与监测系统。该研究旨在阐明卫星雷达遥感的优缺点,进一步深化其在滑坡灾害监测方面的应用和推广,引出未来侧重发展方向的思考与探讨。更多还原

【Abstract】 Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed: ①The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar(SAR) imagery with a long radar wavelength(e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution(e.g. 1 m or even higher), and/or advanced interferometric SAR(InSAR) time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry techniques.②Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service(GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model(DEM) for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM.④Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations(e.g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.更多还原