Integrated coastal subsidence analysis using InSAR, LiDAR, and land cover data
Integrated coastal subsidence analysis using InSAR, LiDAR, and land cover data
复制标题
DOI：
10.1016/j.rse.2022.113297
复制
发表时间：
2022-12
影响因子：
13.5
通讯作者：
W. Zhong;T. Chu;P. Tissot;Zhenming Wu;Jie Chen;Hua Zhang
中科院分区：
文献类型：
--
作者：
W. Zhong;T. Chu;P. Tissot;Zhenming Wu;Jie Chen;Hua Zhang
地面沉降是墨西哥湾沿岸沿着海平面相对上升的一个重要原因。目前缺乏高精度和高空间分辨率的海岸沉降有效监测，以改善海岸风险评估和缓解。本研究首次尝试将卫星干涉合成孔径雷达（干涉合成孔径雷达）和机载光探测和测距（LiDAR）方法结合起来，以调查海岸沉降的时空模式。该研究区域位于德克萨斯州的鹰点附近，该地区以近几十年来相对海平面上升速度快而闻名。从2006年到2011年，根据ALOS-1和PALSAR-1的上升图像，视线速度高达-33毫米/年。从2016年到2021年，根据Sentinel-1的上升和下降图像，垂直速度高达-34毫米/年。沉降模式的其他细节，揭示了将来自1米机载激光雷达结果的表面差异。在不同的空间层次上，结合土地覆盖格局和地形，对图像时间序列中的国际合成孔径雷达导出的速度和延时观测中的激光雷达导出的地表变化进行了比较。结果表明，当地的沉降率可能会显着低于干涉合成孔径雷达结果的空间分辨率，这表明机载LiDAR结果在扩展干涉合成孔径雷达结果到地块和建筑物水平，并解释子像素的不确定性有价值的作用。此外，沉降似乎是更强的植被地区比发达地区和负相关的表面不透水性。沉降的幅度与沿沿着选定的样带线的海拔高度无关。总体而言，这项研究表明，结合干涉合成孔径雷达的结果与其他地理空间数据集，以表征沿海沉降的好处。特别是，干涉合成孔径雷达结果的高垂直精度和机载激光雷达结果的高空间分辨率可以相互补充，突出了多分辨率数据融合的必要性，以支持对沿海洪水脆弱性、基础设施可靠性和侵蚀控制的研究。
Land subsidence is an important cause of relative sea-level rise along the Gulf Coast. There is a lack of effective monitoring of coastal subsidence with high accuracy and high spatial resolution for improving coastal risk assessment and mitigation. This study is the first attempt to integrate satellite interferometric synthetic aperture radar (InSAR) and airborne light detection and ranging (LiDAR) methods to investigate the spatiotemporal pattern of coastal subsidence. The study area is around Eagle Point, Texas, a region known for its fast rate of relative sea-level rise in recent decades. From 2006 to 2011, the line-of-sight velocities were up to −33 mm/year based on ascending ALOS-1 PALSAR-1 images. From 2016 to 2021, the vertical velocities were up to −34 mm/year based on ascending and descending Sentinel-1 images. Additional details of the subsidence pattern were revealed by incorporating the surface difference derived from 1-m airborne LiDAR results. Comparisons of the InSAR-derived velocities from image time series and the LiDAR-derived surface changes from time-lapsed observations were conducted at different spatial levels with linkages to land cover patterns and topography. The results showed that local subsidence rates could vary significantly below the spatial resolution of InSAR results, indicating a valuable role of airborne LiDAR results in extending InSAR results to parcel and building levels and explaining subpixel uncertainties. Also, subsidence appeared to be stronger in vegetated areas than in developed areas and negatively correlated with surface imperviousness. The magnitude of subsidence was not correlated with elevation along selected transect lines. Overall, this study demonstrated the benefits of combining InSAR results with other geospatial datasets to characterize coastal subsidence. In particular, the high vertical accuracy of InSAR results and the high spatial resolution of airborne LiDAR results could be complementary, highlighting the necessity of multi-resolution data fusion to support studies on coastal flood vulnerability, infrastructure reliability, and erosion control.