利用Sentinel-1影像监测土地回填导致的地表形变

在机场填海区地表稳定散射的范围内,本文运用合成孔径雷达干涉测量(InSAR)技术对机场地表形变安全性进行了监测分析,反演获得了研究区域范围内的年形变速率数据结果和高分辨率形变时序结果,并以此分析了香港国际机场的地表形变区在监测周期内的形变时空分布特征.为证明监测结果的精度和可靠性,本文采用全球导航卫星系统(GNSS)观...     

利用AIRS产品分析中国地区近地面甲烷浓度时空特性

甲烷(CH4)作为仅次于二氧化碳(CO2)的第二大温室气体,不仅在全球和区域尺度光化学反应中起着重要作用,而且在能量收支平衡及气候变化方面有着重要的影响。2013年,Auqa/EOS的大气红外探测仪(AIRS)热红外近地表CH4产品在AIRS Version6.0产品中发布,其结果尚未在中国进行验证,利用热红外传感器分析中国近地表CH4浓度的时空分布还处于初始阶段。本文利用中国青海瓦里关(WLG)、中国台湾鹿林山(LLN)及蒙古乌兰乌勒(UUM)地基观测资料对AIRS V6.0近地面CH4浓度产品进行了验证,误差在2%以内,二者相关系数r分别为0.68、0.5、0.69,变化趋势一致,进而从地域、季节变化和年际变化3个方面探讨了2003年—2013年中国地区近地面CH4浓度的时空分布特征。结果表明:CH4浓度最低值位于西藏地区(1800 ppbv),高值区位于新疆维吾尔自治区北部、内蒙古自治区及黑龙江北部(1920 ppbv);夏季高,冬季低,季节性变化明显,年际变化基本上呈增长趋势。     

基于时空主题模型的微博主题提取

目的 已有地理主题模型没有考虑不同区域对微博主题影响程度的差异性,同时他们将时间要素离散化,难以得到连续时间上的微博主题强度。提出了一种顾及连续时间及区域影响力因素的时空主题模型。该方法将城市划分为多个区域,依据各兴趣点类型及数量对区域赋予权重以表达区域社会功能对微博主题的影响程度,基于稀疏增量式生成模型表达微博主题分布,利用 Beta分布描述主题在连续时间中的强度,最终通过Gibbs采样得到时空主题模型各参数。实验表明,本文方法能发现连续时间上微博主题的演变,与已有地理主题模型相比,能更加准确地提取微博主题。     

无光照条件下CMOS图像传感器噪声分析

在无光照条件下,对采用CMOS图像传感器的尼康D90相机在不同的曝光时间下进行拍摄实验,分析CMOS图像传感器在不同曝光时间下的暗电流噪声以及系统噪声在传感器像面上的空间分布。得出如下结论：系统噪声并没有随曝光时间增加而增大的趋势,因此,系统的暗电流噪声比较小;而整体噪声在不同的曝光时间下,都有在空间分布上有像面外围较大而像面中间较小的情况,根据该分布给出计算不同位置像素系统噪声大小的公式。     

我国陆地边疆人口空间分布与地形关系

以我国国境线内50km缓冲区为研究区域,分析我国陆地边疆地区人口总体分布,结果符合黑河-腾冲线的规律,再叠加分析我国陆地边疆地区人口密度曲线与地形剖面线,总体探究了人口空间分布与地形的关系,最后分别统计分析人口与高程、坡度、地形起伏度及坡向之间的关系,研究结果对进一步探究我国陆地边疆人口空间分布规律的内在机理有一定帮助...     

基于高性能计算平台的天宫一号遥感数据地面预处理系统

针对天宫一号传感器特点,设计并实现了基于高性能并行集群环境的遥感数据地面预处理系统,可完成对高并发、海量遥感数据处理任务的快速、高效处理.该系统可通过传感器的星上定标参数完成系统辐射校正、系统几何校正等处理过程,生产出高光谱热红外谱段和高光谱数据的1级、2级遥感产品.同时,在数据产品封装与输出分系统中,采用了较为通用的GeoTiff与HDF作为产品的主要输出格式,可满足领域内多种用户的需求.     

模糊度固定与弧段长度对区域站定轨的影响分析

我国北斗卫星导航系统难以实现境外全球布站,采用区域站观测值是实现高精度定轨的主要手段。本文分析了整网模糊度固定、有效定轨弧段选择对提高区域定轨精度的作用。采用陆态网GPS观测数据进行区域精密定轨仿真验证,首先论证了不同测站分布下,整网模糊度固定对区域定轨精度的效果,结果表明:相同测站条件下,固定解定轨精度比浮点解精度提高30%以上;仅采用国内7个站的固定解三维定轨精度即达到20 cm左右,优于27站的浮点解精度。另外,从星座的构型与地面站分布的可视范围方面,分析了不同观测时长对定轨精度影响,实测数据论证表明:当中国区域站观测时间大于48 h,总能获取不小于24 h的区域有效定轨弧段,并且各卫星最佳的24 h观测弧段三维定轨精度均可达到0.3 m左右。     

A generalized additive model for the spatial distribution of snowpack in the Spanish Pyrenees

A generalized additive model (GAM) was used to model the spatial distribution of snow depth in the central Spanish Pyrenees. Statistically significant non‐linear relationships were found between distinct location and topographical variables and the average depth of the April snowpack at 76 snow poles from 1985 to 2000. The joint effect of the predictor variables explained more than 73% of the variance of the dependent variable. The performance of the model was assessed by applying a number of quantitative approaches to the residuals from a cross‐validation test. The relatively low estimated errors and the possibility of understanding the processes that control snow accumulation, through the response curves of each independent variable, indicate that GAMs may be a useful tool for interpolating local snow depth or other climate parameters. Copyright © 2005 John Wiley & Sons, Ltd.     

On seasonal approach to flood frequency modelling. Part II: flood frequency analysis of Polish rivers

The annual peak flow series of Polish rivers are mixtures of summer and winter flows. As Part II of a sequence of two papers, practical aspects of applicability of seasonal approach to flood frequency analysis (FFA) of Polish rivers are discussed. Taking A Two‐Component Extreme Value (TCEV1) model as an example it was shown in the first part that regardless of estimation method, the seasonal approach can give profit in terms of upper quantile estimation accuracy that rises with the return period of the quantile and is the greatest for no seasonal variation. In this part, an assessment of annual maxima (AM) versus seasonal maxima (SM) approach to FFA was carried out with respect to seasonal and annual peak flow series of 38 Polish gauging stations. First, the assumption of mutual independence of the seasonal maxima has been tested. The smoothness of SM and AM empirical probability distribution functions was analysed and compared. The TCEV1 model with seasonally estimated parameters was found to be not appropriate for most Polish data as it considerably underrates the skewness of AM distributions and upper quantile values as well. Consequently, the discrepancies between the SM and AM estimates of TCEV1 are observed. Taking SM and TCEV1 distribution, the dominating season in AM series was confronted with predominant season for extreme floods. The key argument for presumptive superiority of SM approach that SM samples are more statistically homogeneous than AM samples has not been confirmed by the data. An analysis of fitness to SM and AM of Polish datasets made for seven distributions pointed to Pearson (3) distribution as the best for AM and Summer Maxima, whereas it was impossible to select a single best model for winter samples. In the multi‐model approach to FFA, the tree functions, i.e., Pe(3), CD3 and LN3, should be involved for both SM and AM. As the case study, Warsaw gauge on the Vistula River was selected. While most of AM elements are here from winter season, the prevailing majority of extreme annual floods are the summer maxima. The upper quantile estimates got by means of classical annual and two‐season methods happen to be fairly close; what's more they are nearly equal to the quantiles calculated just for the season of dominating extreme floods. Copyright © 2011 John Wiley & Sons, Ltd.