作物胁迫无人机遥感监测研究评述

作物胁迫是全球农业发展的一个重要制约因素,实现快速、大范围、实时的作物胁迫监测对于农业生产具有重要意义。传统的作物胁迫监测方式,如田间调查、理化检测和卫星遥感监测总是受到各种田间条件或大气条件的制约。随着无人机和各种轻量化传感器的快速发展,其凭借高频、迅捷等优势为各种作物胁迫监测提供了一套全新的解决方案。本文在介绍了目前主流的多种无人机和传感器的基础上,首先对目前无人机遥感用于作物监测的主要胁迫类型进行了梳理,然后重点阐述了基于光谱成像和热红外传感器进行作物胁迫无人机遥感监测的应用和技术方法,最后提出了作物胁迫无人机遥感监测尚需解决的关键问题,并展望了未来无人机遥感用于作物胁迫监测的前景。     

长白山景区旅游安全风险动态评价研究

以长白山景区旅游安全为研究对象,以鱼骨图、动态贝叶斯、GIS技术等为基本研究方法,从研究区自然环境、社会环境及责任人为3个方面出发,筛选景区致险因子,构建景区旅游安全风险危险性评价指标体系,利用动态贝叶斯方法综合构建景区旅游安全风险动态评价模型;并以实测数据及景区统计数据为依据,划分景区旅游安全风险评价的4个动态时段,综合实现景区旅游安全风险动态风险评价。研究结果表明：① 中等以上风险区域呈条带状分布;② 高风险区域与主要景点重合;③ 长白山景区安全风险发生高概率的时段发生在第三个时段（12:00~14:00）;较高概率发生分别在第二个时段（10:00~12:00）与第四个时段（14:00~16:00）;中等概率发生较高的时段在第四个时段（14:00~16:00）;较低概率发生在第一个时段（8:00~10:00）。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.     

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

船载海水温度观测研究

通过研究国家相关海洋观测标准对海水温度观测的规约,结合当前船载水温观测的现状,提出了基于总线的船载磁吸附温度链式观测方法,并对其在结构设计、理论模型、算法等多方面进行了阐述.通过多个竖直分布的温度传感器实时观测,采用自适应参数化算法,有效减少了风浪、海流、热辐射等方面的影响,具有观测层深相对固定、精度高等特点,可实现船...     

三杯风速传感器非水平风场测量性能

为解决三杯风速传感器在计量检定条件下与观测场景中环境差异所导致的测量数据误差,致力于研究空气流速计量标准在量值传递过程中的真实性、准确性和一致性,为新一代三杯风速传感器作为计量器具的新产品型式评价提供思路和参考指标,依据杯式测风仪测量方法与自动气象站风速风向传感器检定规程,并在实验中加入了主体由角度编码器构成的自动化转盘系统,设计了三杯风速传感器在非水平风场内测量性能水平实验。通过调整三杯风速传感器在风洞试验段内的倾斜角度,模拟其在自然界非水平风场中的测量状态,同步采集风洞的标准指示风速、三杯风速传感器的实测风速以及其相应的倾斜角度,计算示值误差,利用方差分析、趋势分析、相关性分析和线性回归分析等统计方法,对不同倾斜角度下三杯风速传感器示值误差进行研究,得出了三杯风速传感器在风洞试验段内的示值误差与实测风速和倾斜角度之间的相关关系,提出了三杯风速传感器在非水平风场下的测量性能指标。研究了三杯风速传感器在非水平风场中实测风速与标准风速和倾斜角度的回归关系,提出了三杯风速传感器在计量环境下非水平风场中数据的量值传递修正算法。     

面向应用的CBERS-02星高分辨率相机成像数值模拟技术框架

技术框架研究的目标是准确仿真卫星高分辨率相机成像过程中辐射和几何质量的下降情况,从应用方的角度建立评价高分辨率卫星相机在国土资源领域的应用潜力和技术框架,并以CBERS-02B星HR相机在轨成像模拟为例进行了原理验证,同时对今后以国土资源应用评价为目标的高分辨率相机在轨成像模拟改进方案提出了建议。     

凸包插值算法的改进与实现

研究了凸包插值算法在n维欧拉空间构造Delaunay三角网的应用,提出了其三角剖分不惟一的重大缺陷,采用了限制区域生长法解决了这一缺陷。     

航向测量仪在车载三维信息采集系统中的应用

航向测量仪(Vector Sensor)的作用就是为车载惯导系统初始化提供所必须的航向角;根据不同的应用环境和特性,用航向测量仪获取在各种条件下的航向角数据,通过对这些数据在精度、稳定性以及解算时间等性能上的对比分析,得到该仪器的最佳工作状态,进而获取该状态下的航向数据;通过实际的实验测试,此状态下的数据在精度和稳定性方面都能满足惯导系统的需求。