Monitoring soil surface roughness under growing winter wheat with low-altitude UAV sensing: Potential and limitations

Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high-resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high-resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation-induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley & Sons, Ltd.     

自适应卡尔曼滤波在航空重力异常解算的应用研究

依据航空重力测量基本原理,构建了航空重力异常解算的卡尔曼滤波模型,将新息自适应卡尔曼滤波器(IAE,Innovation based Adaptive Estimation)应用于量测噪声未知的航空重力异常解算.针对IAE滤波器滑动窗口宽度难以准确确定的问题,通过对多个不同滑动窗口新息协方差估计的加权平均,获得改进的IAE滤波器,该IAE滤波器不仅具有量测噪声自适应估计能力,还能实现滑动采样窗口的优化选取.试验结果表明,IAE滤波器可以降低因量测噪声统计信息不明引起的解算误差,改进IAE解算的重力异常误差约为1mGal.     

相邻历元误差相关的抗差卡尔曼滤波算法分析

根据卡尔曼滤波理论以及抗差理论,本文推导出了相邻历元误差相关的抗差卡尔曼滤波模型,其对观测值中含有粗差有良好的抗差性。通过对含有粗差的变形监测数据分析,与相邻历元误差相关的卡尔曼滤波模型进行比较,采用本文构造的抗差卡尔曼滤波模型处理数据,无论是否有粗差存在观测值里,变形计算结果与实际结果大体一致,粗差对计算结果的影响不敏感。在对变形监测数据分析时,可得出卡尔曼滤波方法估计的状态向量,没有寄存大量的以往观测数据,而是使用最近的观测数据,经过不断的预测和改正,把新的状态展示在系统中。     

航空重力测量中牛顿中心差分器对确定载体加速度的影响

讨论不同点数的牛顿中心差分器对航空重力测量中确定载体加速度的影响。理论分析表明,当航空重力测量的有效频段位于牛顿中心差分器的通带时,不同点数的牛顿中心差分器所得结果经低通滤波后几乎没有差异。实验表明,当采样间隔为1 s、载体平均速度为155 m/s、所需重力数据的分辨率为10 km时,低通滤波后不同点数的牛顿中心差分器输出结果之间的较差优于2.1 mGal。     

城市CORS坐标时间序列噪声模型建立与分析

以GAMIT/GLOBK处理得到高精度常州CZCORS坐标时间序列,用奇异谱分析方法对其进行空间滤波,使用最大似然估计方法对剔除共性误差的坐标时间序列进行噪声特征分析,得出最优白噪声模型为“WH”,N、U分量最优有色噪声模型为“WH+FN”,E分量最优有色噪声模型为“WH+FN+RWN”。根据最优有色噪声模型估计出的CZCORS站速度场表现为东南方向整体运动的趋势,其整体位移变化值及运动方向与周围IGS站基本一致。     

基于单程波偏移算子的地表相关多次波成像

在常规地震资料处理中,多次反射波被视为噪声并从地震数据中去除,以免在之后的地震资料解释中造成误解.而事实上,多次波也是地震信号,是照明波场的一部分,能够对地下构造成像的精度做出贡献.本文分析了多次波在传统单程波叠前深度偏移中产生构造假象的机制和表现,为实现基于单程波偏移算子的多次波成像,修改了单程波叠前深度偏移的边界条件,即将输入的震源波场用包含多次波的记录来替代,输入的记录波场用预测出的表层相关多次波来替代,实现了基于单程波偏移算子的地表相关多次波成像,并从理论上给出了其成像依据.通过基于二范式最小能量差原则求取的匹配因子,将多次波成像结果与一次波成像结果进行匹配叠加,应用多次波成像来弥补一次波成像的不足.简单模型验证了基于单程波偏移算子的多次波成像方法的有效性,最后对Sigsbee2B模型进行了一次波与多次波联合成像试算,盐边界高陡构造成像质量得到了明显改善.     

Generalized diffraction‐stack migration and filtering of coherent noise

We reformulate the equation of reverse‐time migration so that it can be interpreted as summing data along a series of hyperbola‐like curves, each one representing a different type of event such as a reflection or multiple. This is a generalization of the familiar diffraction‐stack migration algorithm where the migration image at a point is computed by the sum of trace amplitudes along an appropriate hyperbola‐like curve. Instead of summing along the curve associated with the primary reflection, the sum is over all scattering events and so this method is named generalized diffraction‐stack migration. This formulation leads to filters that can be applied to the generalized diffraction‐stack migration operator to mitigate coherent migration artefacts due to, e.g., crosstalk and aliasing. Results with both synthetic and field data show that generalized diffraction‐stack migration images have fewer artefacts than those computed by the standard reverse‐time migration algorithm. The main drawback is that generalized diffraction‐stack migration is much more memory intensive and I/O limited than the standard reverse‐time migration method.     

傅里叶变换在深州井资料处理中的应用

深州井水位日常观测中,经常会因为仪器供电不稳、雷击等原因出现一些幅度很大的脉冲、突跳及数据缺测,严重影响了观测资料的内在质量。本文采用数字信号处理技术[1]对深州井水位资料进行分析处理,首先使用分段线性插值法对缺测数据进行插值处理,然后用快速傅里叶变换对水位数据进行频谱分析,绘制振幅谱,分析水位数据中的不同周期成分,选取恰当的截止频率进行滤波处理,去除了干扰,获得了井水位的变化动态,提高了资料质量。最后应用滤波后的水位数据做一些震例分析。     

用于变形监测的最小二乘滤波数据处理

随着全站仪在测量工作中的广泛使用,以坐标为观测值的数据处理成为内业工作的主要内容。根据最小二乘滤波原理,将工程岩土体变形监测中所获得的坐标观测值及其他常规观测值进行联合平差。结果表明,滤波后变形点的点位精度最大可提高1.35 mm,最弱点亦提高0.94 mm,均明显高于直接观测结果,而且各类观测值之间所形成的约束条件进一步增强了平差结果的可靠性。     

分布式虚拟地理环境中时空一致性研究

为了保证分布式虚拟地理环境的时空一致性和解决共享虚拟场景过程中的同步和一致,本文提出了在虚拟地理环境中通过统一的空间基准和环境描述来保证空间一致性,通过具体的减少数据传输延迟的技术方案来保证实时性.