与抽样相结合的县级作物遥感面积估算应用实例

为了探索遥感卫星影像和无人机调查在实际遥感面积估算中的调查效果和适用性,以广东省阳春市晚稻为例,采用卫星影像与无人机调查结果相结合,使用两种面积估算方法进行2013年晚稻种植面积估算。实验结果表明,比估计和回归估计方法的估算结果分别为22 501.1hm2和22 781.1hm2,二者的CV分别为8.84%和1.03%。研究结果表明:1无人机调查可获得高质量的面积测量信息;2优化后的样本能够支持面积估算过程,但其理论意义需要更进一步地讨论与证明;3卫星数据与无人机调查数据相结合的方法可以提供满足统计精度要求的面积测量结果,具有良好的应用前景。     

基于.NET三层架构的数字油田生产管理系统的设计与实现

针对数字油田生产管理需求特点,提出一种基于ASP.NET三层架构的总体设计方案,给出系统的功能构成和业务流程,并以油井辅助分析,产液量计算功能模块为实例,设计其三层架构,给出各层具体实现的关键技术。     

移动天气雷达系统的卫星通信

为了把移动雷达实时数据传输到北京市气象局,通过对比多种无线通信方式以及卫星通信的优缺点等方法建立了一套卫星通信系统。结果表明：本系统在应急、网络速率、安全等方面都能满足移动雷达系统对数据通信的要求。这套系统的设计将对应急系统的建立具有一定的指导意义。     

Temporal persistence and stability of surface soil moisture in a semi-arid watershed

Satellite soil moisture products, such as those from Advanced Microwave Scanning Radiometer (AMSR), require diverse landscapes for validation. Semi-arid landscapes present a particular challenge to satellite remote sensing validation using traditional techniques because of the high spatial variability and potentially rapid rates of temporal change in moisture conditions. In this study, temporal stability analysis and spatial sampling techniques are used to investigate the representativeness of ground observations at satellite scale soil moisture in a semi-arid watershed for a long study period (March 1, 2002 to September 13, 2005). The watershed utilized, the Walnut Gulch Experimental Watershed, has a dense network of 19 soil moisture sensors, distributed over a 150 km² study region. In conjunction with this monitoring network, intensive gravimetric soil moisture sampling conducted as part of the Soil Moisture Experiment in 2004 (SMEX04), contributed to the calibration of the network for large-scale estimation during the North American Monsoon System (NAMS). The sensor network is shown to be an excellent estimator of the watershed average with an accuracy of approximately 0.01 m³/m³ soil moisture. However, temporal stability analysis indicated that while much of the network is stable, the soil moisture spatial pattern, as represented by mean relative difference, is not replicated by the network mean relative difference pattern. Rather, the network is composed of statistical samples. Geophysical aspects of the watershed, including topography and soil type are also examined for their influence on the soil moisture variability and stability. Soil type, as characterized by bulk density, clay and sand content, was responsible for nearly 50% of the temporal stability. Topographic effects were less important in defining representativeness and stability.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

空间光通信中轨道短时预报

瞄准、捕获和跟踪(PAT)技术是星间光通信的关键技术之一,高精度卫星轨道短时预报能有效实现PAT.首先在EGM96地球引力场模型下建立了卫星状态动力方程和预报方程,然后改进基于数值算法的扩展卡尔曼滤波算法对卫星轨道短时预报,最后以champ卫星星载GPS实时定轨数据为卡尔曼滤波器观测数据进行仿真实验:预报卫星位置误差约亚米级,速度误差约0.05m/s;卫星位置和速度的均方差估计趋于稳定,在一定程度上能很好地克服离散误差和模型误差对轨道估计精度的影响.PAT瞄准精度约1.6 μrad.预报轨道精度能满足空间光通信PAT技术要求.     

Attacking smart card systems: Theory and practice

Smart card technology has evolved over the last few years following notable improvements in the underlying hardware and software platforms. Advanced smart card microprocessors, along with robust smart card operating systems and platforms, contribute towards a broader acceptance of the technology. These improvements have eliminated some of the traditional smart card security concerns. However, researchers and hackers are constantly looking for new issues and vulnerabilities. In this article we provide a brief overview of the main smart card attack categories and their corresponding countermeasures. We also provide examples of well-documented attacks on systems that use smart card technology (e.g. satellite TV, EMV, proximity identification) in an attempt to highlight the importance of the security of the overall system rather than just the smart card.     

Help from space: grant-free massive access for satellite-based IoT in the 6G era

The Sixth-Generation (6G) standard for wireless communications is expected to realize ubiquitous coverage for massive Internet of Things (IoT) networks by 2030. Satellite-based communications are recognized as a highly promising technical enabler to satisfy IoT service requirements in the 6G era. This study analyzes multiple access technologies, which are essential for the effective deployment of satellite-based IoT. First, we thoroughly investigate the existing research related to massive access, including information-theory considerations as well as Non-Orthogonal Multiple Access (NOMA) and Random Access (RA) technologies.Then, we explore the influence of the satellite transmission environment on multiple access technologies. Based on this study, a Non-orthogonal Massive Grant-Free Access (NoMaGFA) scheme, which reaps the joint benefits of RA and NOMA, is proposed for asynchronous transmissions in satellite-based IoT to achieve improved system throughput and enhance the system robustness under varying traffics. Finally, we identify some important and interesting future developments for satellite-based IoT, including waveform design, transceiver design, resource allocation, and artificial intelligence-enhanced design.     

原油价格与油田经济开发期

介绍了油田经济开发期的概念,给出了原油价格与经济开发期之间的定量关系,在不同的原油价格下计算了某油田经济开发期,说明原油价格直接影响油田的经济开发期。     

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1-18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%-2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.