数字高程模型(Digital Elevation Model,DEM)是开展青藏高原冰川研究的重要基础数据.随着国产立体测图卫星的快速发展,自主可控地获取青藏高原冰川区高精度DEM成为可能.该研究综合采用资源三号、高分七号卫星的立体影像和激光测高数据,分别生成冰川区域5m和2m格网的DEM,并选择岗钦及普若岗 日等两处冰川为实验区,将国产卫星DEM与国外的AW3D、SRTM、TanDEM、HMA DEM等多种开源数字高程模型进行对比分析,并采用ICESat-2星载激光测高数据开展DEM绝对高程精度验证.结果表明:与中等空间分辨率的开源DEM相比,基于国产立体测图卫星影像生产的DEM高程精度更优,且格网更精细、更能详细描述冰川末端纹理特征;与高空间分辨率数据集HMA DEM对比高程精度,资源三号DEM略差、高分七号DEM更优,且在覆盖完整性方面国产卫星DEM均优于HMADEM.综上所述,基于国产立体测图卫星可以实现冰川区高精度DEM的获取,能够为青藏高原冰川研究提供自主可控、精度可靠的地形参考数据.

精确测量森林生物量对分析全球碳循环有重要意义,星载激光雷达能大范围、高精度获取森林冠层结构和林下地形信息,为森林生物量和碳汇估算提供支撑.日本宇宙航空研究开发机构(Japan Aerospace Exploration Agency,JAXA)最新提出多足印观测激光雷达和成像仪计划(Multi-footprint Observation LiDAR and Imager,MO-LI),旨在结合激光测高仪和光学相机准确估算全球碳储量.本文对MOLI系统的研制计划、任务目标、仪器参数、数据产品等方面进行了详细梳理,并根据其特点对我国后续激光测高载荷提出若干建议.

高分七号卫星是国内首个亚米级双线阵立体成像卫星,同时配有两套激光测高仪和激光足印相机,可同期获取多源遥感数据.文中采用高分七号卫星获取的多源遥感数据进行平面和高程精度优化,利用激光测高数据对立体影像密集匹配的DSM进行偏度、中值、线性和二阶多项式模型和高程优化评估,利用足印影像对DOM进行一阶仿射变换方法和平面优化评估,并利用外业控制点对无控平面高程、激光高程优化、足印-激光平面高程优化、外业-激光平面高程优化等不同优化模型的结果进行精度评估.实验结果表明,利用激光测高数据可明显优化DSM高程精度,无控DSM高程误差平均值为-4.268 m,中误差为4.518 m,经过中值模型优化后的DSM高程误差平均值提升为-0.272 m,中误差提升为1.508 m,经过线性模型优化后的DSM高程误差平均值提升为-0.320 m,中误差提升为1.351 m;利用足印影像可改善DOM的平面精度,平面误差平均值从13.606m提升到5.341 m,中误差从13.626m提升到5.495 m.

The GF-7 satellite is China's first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite.

新型星载光子计数雷达可获取地面及地面目标的高精度三维信息,但是其测量精度受噪声影响较大.针对在背景噪声不一致及坡度较大区域自动化提取单光子激光数据信号较为困难的难题,文中提出基于多特征自适应的单光子点云去噪算法,有别于传统圆形或椭圆形滤波核,选择更加符合单光子点云数据特征的平行四边形滤波核,分别通过坡度、空间密度、噪声率等多特征自适应识别信号.选择位于青藏高原冰川区域坡度较大且地形破碎的ICESat-2单光子点云数据,开展点云去噪试验和验证,通过与ATL03、ATL08官方去噪结果对比,文中算法在背景噪声水平不一致和大坡度区域具有更优的性能.

Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing.

Crevasse is an important characteristic of ice shelf internal structure, and also an important index to measure the stability of ice shelf. This study aims to detect crevasses from ICESat-2 data and obtain three-dimensional features of the crevasse. First, the change of along-track surface slope and the depth threshold is combined to obtain the crevasse bottom points and the crevasse edge points. Second, the distance between the crevasse bottom points and the fitted ice shelf surface by crevasse edge points is used as the crevasse bottom points depth. Third, the crevasse direction is judged by the strong and weak laser positions and the width is calculated by the crevasse edge points and crevasse direction. The Amery ice shelf (AIS) is chosen as the test area to validate the method by ATL06 data, combined with the Landsat-8 optical image. The depth of the crevasses in the AIS is about ranges from 2.0 to 60.0 m, and the width is mainly 400 to 1600 m, and the width and length of the L3 rift have dramatically changed, which is an indicator that the AIS is in a new calving cycle. The method in this article can accurately obtain three-dimensional information of the crevasses, find crevasses with abnormal changes in depth and width, and provide effective help for predicting the calving of the ice shelf.

The laser altimeter loaded on the GaoFen-7(GF-7) satellite is designed to record the full waveform data and footprint image, which can obtain high-precision elevation control points for stereo image. The footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls, which can be used to judge whether the laser is affected by the cloud. At the same time, the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing stability. In this manuscript, a data quality analysis scheme of laser altimetry based on footprint image is presented. Firstly, the cloud detection of footprint image is realized based on deep learning. The fusion result of the model is about 5% better than that of the traditional cloud detection algorithm, which can quickly and accurately determine whether the laser spot is affected by cloud. Secondly, according to the characteristics of footprint image, a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period lasers. Based on the above method, the change of laser spot centroid since GF-7 satellite was put into operation is analyzed, and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.