青藏高原和亚洲夏季风动力学研究的新进展

亚洲夏季风环流受海陆和伊朗高原—青藏高原大地形的热力作用调控.亚洲季风所释放的巨大潜热又对大气环流形成反馈.这种相互反馈过程十分复杂,揭示其物理过程对理解气候变化格局的形成和变化以及提高天气预报及气候预测的准确率十分重要.夏季北半球副热带对流层上层环流的主要特征是存在庞大的南亚高压(SAH)以及强大的对流层上层温度暖中心(UTTM).本文介绍了温度—加热垂直梯度(T-Q_Z)理论的发展,并用以揭示SAH和UTTM的形成机制.指出沿副热带欧亚大陆东部的季风对流潜热加热及其中西部的表面感热加热和高层长波辐射冷却是导致SAH和UTTM在南亚上空发展的原因.文中还介绍了Gill模型用于上部对流层研究的局限性及解决的办法.     

利用引力梯度数据计算径向梯度的优化方法

根据重力梯度观测各分量的方差及协方差信息,提出了利用GOCE梯度数据计算径向重力梯度的优化方法。首先给出了径向重力梯度的计算方法,并深入分析了误差传播规律,通过建立相应的条件极值问题,给出了计算径向重力梯度最优组合因子的方法;通过模拟数据验证了本文所提出的优化因子的优越性。实际数据计算表明:相对于传统方法,采用优化组合因子可使反演所得引力位模型的累积大地水准面精度在250阶时提高约2 cm。由于径向重力梯度不仅可以用于地球引力场模型的求解,也可直接应用于地球物理问题的讨论,因此本文所提出的优化方法也可对部分地球动力学问题的讨论提供方便。     

纹理特征向量与最大化熵法相结合的SAR影像非监督变化检测

合成孔径雷达(SAR)影像具有明显的斑点噪声,在变化检测中,一般需要考虑空间邻域信息。本文结合SAR影像丰富的纹理信息,提出一种考虑空间邻域信息的高分辨率SAR影像非监督变化检测方法,用基于灰度共生矩阵(GLCM)的32维纹理特征向量构造差异影像。通过最大化熵法自动选取阈值,对精度指标随窗口大小的变化进行回归分析,得到适合于变化检测的窗口为11×11。试验表明,本文方法优于马尔科夫随机场法,可以减小斑点噪声的影响,有效提高高分辨率SAR影像变化检测的精度。     

结合投影变换和相位相关的海冰运动监测

为提高对渤海海冰旋转和平移运动的监测精度,提出一种基于投影变换的相位相关跟踪方法。选取连续8景静止水色成像仪(GOCI)图像序列,根据特征图像窗口的投影变换构造辅助函数,通过寻求函数最优解得到旋转角度集合,选择修正相关系数确定最佳旋转角度,同时根据相位信息实现海冰样本间亚像素级别的平移跟踪,消除传统相位相关法中因忽略图像相频特性所造成的匹配误差。实验结果表明,以手动测量的旋转角度为基准,该方法和传统相位相关法的旋转监测均方根误差的最大值/平均值分别为0.59/0.50与1.41/0.94,跟踪速度提高了50.6%;海冰平移运动的速度矢量与辽东湾的现场实测数据及历史资料记录数据基本一致。该方法对渤海海冰旋转和平移运动具有较好的跟踪效果。     

低空大比例尺地形图航测生产关键技术

针对传统航空摄影测量生产工艺在生产过程中遇到的影响大比例尺测图精度的问题,该文探讨了低空航测生产大比例尺地形图的关键技术。通过选用宽角相机、低航高飞行、强化影像匹配、野外布设标志控制点、优选平差模型、精化测图操作等改进方式,以提高大比例尺航测精度;组建无人飞艇低空航测系统,对提出的技术方法予以实现;最后给出了山西境内近30个县市建成区1∶500测绘生产实践的部分验证成果。     

云蒙湖流域不同坡度的土壤侵蚀特征

针对定量分析土壤侵蚀在各坡度等级上的空间分布研究较少的现状,该文选用通用的土壤流失预报方程,对云蒙湖流域1986—2010年间的土壤水力侵蚀状况进行了定量的估算,以探讨不同坡度上的土壤侵蚀特征,并进一步分析了土壤侵蚀变化与人类活动的关系。分析得出:土壤侵蚀强度发生在人类活动比较频繁的区域上(8~25°坡度)更为严重;2010年比1986年强度以上所占比例在15°坡度等级上相对更低,在15°坡度等级上有所增加;云蒙湖流域主要土壤侵蚀量发生在25°坡度上;2010年比1986年耕地面积减少、林地和居民用地面积增加是土壤侵蚀降低的主要因素。     

一种基于灰度块匹配的视野监测算法

针对视野移动方向的判断问题,该文介绍了运动补偿的常用方法及应用,提出了基于灰度块匹配的视野移动监测算法。通过对比各种算法计算复杂度和计算时间,发现灰度投影算法准确率低、计算量较小;块匹配搜索算法中的穷尽搜索算法准确率高,但是计算量较大。为了发挥以上两者算法的优势,将两者结合形成了灰度块匹配算法,并通过实验证实了灰度块匹配算法在实时监测系统中的可行性。     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

Modeling the risk of spread and establishment for Asian longhorned beetle (Anoplophora glabripennis) in Massachusetts from 2008-2009

Land managers responsible for invasive species removal in the USA require tools to prevent the Asian longhorned beetle (Anoplophora glabripennis) (ALB) from decimating the maple-dominant hardwood forests of Massachusetts and New England. Species distribution models (SDMs) and spread models have been applied individually to predict the invasion distribution and rate of spread, but the combination of both models can increase the accuracy of predictions of species spread over time when habitat suitability is heterogeneous across landscapes. First, a SDM was fit to 2008 ALB presence-only locations. Then, a stratified spread model was generated to measure the probability of spread due to natural and human causes. Finally, the SDM and spread models were combined to evaluate the risk of ALB spread in Central Massachusetts in 2008–2009. The SDM predicted many urban locations in Central Massachusetts as having suitable environments for species establishment. The combined model shows the greatest risk of spread and establishment in suitable locations immediately surrounding the epicentre of the ALB outbreak in Northern Worcester with lower risk areas in suitable locations only accessible through long-range dispersal from access to human transportation networks. The risk map achieved an accuracy of 67% using 2009 ALB locations for model validation. This model framework can effectively provide risk managers with valuable information concerning the timing and spatial extent of spread/establishment risk of ALB and potential strategies needed for effective future risk management efforts.     

CMIP5模式对中国近海海表温度的模拟及预估

基于观测和再分析资料;利用多种指标和方法评估了国际耦合模式比较计划（CMIP5）中21个模式对中国近海海温的月、季节和年际变化模拟能力。多模式集合能够再现气候平均意义下近海海温的空间分布特征;但量值上存在一定的低估。在渤海和黄海;集合平均与观测差别比较明显。在年际尺度上;与观测数据对比;模式模拟海温与Niño3指数相关性较小。中国近海海表面温度在1960-2002年有明显的升高趋势;从2003年开始增温趋缓。评估结果表明;ACCESS1.0、BCC-CSM1.1、HadGEM2-ES、IPSL-CM5A-MR、CMCC-CM、FGOALS-g2、CNRM-CM5-2、INMCM4八个模式对中国近海海温的变化有较好的模拟能力。利用ACCESS1.0、INMCM4、BCC-CSM1.1、IPSL-CM5A-MR、CMCC-CM这5个模式结果对中国近海海温未来的变化进行了预估。在RCP4.5、RCP8.5情景下;未来近100年中国近海海温有明显升高趋势;最优模式多模式集合平均增温分别可达到1.5℃、3.3℃;净热通量变化和平流变化共同促进了东海升温。