中巴资源卫星(CBERS-02B)遥感影像数据融合研究

在阐述和分析中巴资源卫星(CBERS-02B)数据特点的基础上,对像素级融合的方法进行分析,重点针对CBERS-02B星上的多光谱和全色数据进行融合算法的实验,分别采用主成分变换和小波变换融合,然后使用PCA与小波分解相结合的融合方法,最后对不同融合算法结果从均值、标准方差、信息熵、扭曲度等指标进行了比较分析与评价.     

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Historical analysis of heavy metal pollution in three estuaries on the north coast of Galicia (NW Spain)

This research focuses on the development of metal pollution in sediment cores from three estuaries in Northwest Spain: Viveiro, Ortigueira and Barqueiro. Pb, Cu, Co, Cr, Cd and Zn and total organic carbon were assessed using principal component analysis (PCA) in order to obtain background values, measure pollution levels and identify pollution sources. Results were interpreted by considering the local industrial history, grain size and C/N relationship. The pollution levels obtained bear a strong resemblance to those documented for of a moderately industrialised area. PCA identifies factors that reflect mainly temporal associations with metals. Sedimentation rates between 0.9 and 1.1 cm/year were determined. In Viveiro core levels of Cr pollution are associated with tanneries. In Ortigueira, high core levels of Cu and Co are linked to mining, and Cr levels to adjacent ultramafic rocks. Erosion of Holocene sediment causes high values of Co and Cr in the Barqueiro core. Cu increase in the three estuaries is related to fungicide use since 1910. Sea level rise appears to be affecting the marine characteristics of the sediments in Barqueiro. In Viveiro, the nature of the sediment reflects engineering work and land reclamation.     

基于最小噪声分离变换的遥感影像融合方法

针对主成分分析（PCA）融合算法的不足和最小噪声分离（MNF）变换的优势，以IKONOS新型高分辨率观测卫星提供的全色和多光谱数据为实验数据，提出了基于最小噪声分离变换的遥感影像融合方法，并与其它融合方法进行定量和视觉比较，结果表明该方法能得到更好的融合效果。     

SHALLOW SEAWATER DEPTH RETRIEVAL BASED ON BOTTOM CLASSIFICATION FROM REMOTE SENSING IMAGERY

Remote sensing technique, replacing conventional sonar bathymetry technique, has become an effective complementary method of mapping submarine terrain where special conditions make the sonar technique difficult to be carried out. At the same time, as one kind of data set, multispectral remote sensing data has the disadvantage of being influenced by the variable bottom types in shallow seawater, when it is applied in bathymetry. This paper puts forward a new method to extract water depth information fi‘om multispectral data, considering the bottom classification and the true water depth accuracy. That is the Principal Component Analysis (PCA) technique based on the bottom classification. By the least square regression with significance, the experiment near Qingdao City has obtained more satisfactory bathymetry accuracy than that of the traditional single-band method, with the mean absolute error about 2.57m.     

基于PCA的井间地震偏振分析方法及其应用

基于主成分分析(principal component analysis, PCA)方法构建理论模型进行三分量地震偏振分析试算,获得较准确的检波器的水平方位角和垂直倾角,然后对地震响应进行坐标旋转,从而校正检波器方向。将该方法应用于跨孔井间弹性波波速测试,对实测资料进行偏振分析可知,初至P波与初至S波分别在H_P分量和R分量中得到加强,不仅有利于初至起跳的拾取,还可以获得较准确的波速资料,为岩土工程设计提供更精确的动力学参数。     

南洛河流域洛南盆地表土孢粉与植被的关系(英文)

The catchment of South Luohe River in Central China is an important region for investigating modern pollen-environment relationship, because it is located in the transitional zone between south and north China, an environment which is sensitive to climate changes. In this study, 40 surface samples under ten vegetation types were collected to reveal the relationship between pollen assemblages and vegetation. The results show that the surface pollen assemblages reflect the vegetation quite well. In forest topsoils, the average of arboreal pollen content is greater than 40%, and the Selaginella sinensis spore is high. As to sparse forest grassland and shrub community, the average arboreal pollen is 13.2% and 16.6% respectively, and the shrub pollen is relatively higher than that of grassland samples. The grassland and farmland are characterized by low percentage of tree and shrub pollen(<10% and <1%), and high percentage of herbs(>80%). Pinus, Quercus and some other arboreal pollen can indicate the regional vegetation because of their dispersal ability. Quercus pollen is under-representative and so is Pinus. Artemisia pollen is significantly over-represented, has poor correlation with the plant coverage, and may reflect human disturbance. Gramineae can indicate plant quite well, but with low representation. High content of Chenopodiaceae probably suggests human impact. Predominant Selaginella sinensis can be used as an indicator of forest environment. Cluster analysis and principal components analysis of pollen assemblages can distinguish forest and non-forest vegetation well. The former method is better at separating pine and mixed forests, while the latter is more stable and could better differentiate farmland and other non-forest area. The first axis of PCA mainly reflects the humidity.     

1993—2011年北大西洋与北太平洋的波浪变化及其原因探究

基于1993—2011年的7套卫星高度计数据,分析北大西洋和北太平洋的波浪变化趋势;利用NCEP/NCAR提供的月平均风场及SLP(Surface Level Pressure)数据,分析风场和SLP的空间变化特征,并通过主成分分析进一步考察了波浪变化与SLP的相关关系。结果表明:北大西洋波浪变化趋势的空间分布呈偶极子形态,西南区域为增大趋势,东北区域为减小趋势;而北太平洋波浪变化的主要特征是在中纬度带呈明显的减小趋势;比较而言,波浪变化趋势的空间形态与SLP变化的空间分布特征更接近,波浪主要模态的空间分布及时间系数与SLP距平场的震荡特征十分吻合,大尺度背景场SLP的变化对波浪的长期变化趋势有重要的影响。     

基于SPEI的1960—2018年赣江流域干旱特征

基于赣江流域39个气象站点逐月降水和气温数据,计算不同时间尺度标准化降水蒸散发指数(SPEI),采用Mann-Kendall突变检验、主成分分析(PCA)等方法,分析了赣江流域1960—2018年干旱时空变化特征.研究表明:不同时间尺度SPEI均有微弱升高的趋势,干旱形势有所缓解,SPEI能够较好地表征赣江流域旱涝情况.赣江流域中部的轻旱和特旱发生频率要高于其他地区,中旱主要高发地区主要分布在南部和西部区域,重旱主要集中在东部和北部.赣江流域干旱的空间分布具有较好的一致性,旱涝变化整体保持一致,南部与北部旱涝状态存在相反的纵向差异,且中部与南部、北部旱状况涝存在空间差异.     

地质灾害预警预报模型设计与应用

基于地质灾害的多样性及其变化的随机性和非稳定性特点,本文综合利用数学算法和GIS技术,设计了地质灾害预警模型,分析了诱发地质灾害的地形、地貌、气象、水文等几方面的因素,并根据这些因素的特点,选取不同的样本对其进行量化处理,运用主成分分析法,计算了各个因素导致地质灾害发生的权重指数及各监测站点的易发级别。在此基础上,基于GIS技术、采用泰森多边形法等将站点的易发级别转换成面的易发级别,运用日降雨量、降雨强度等指标计算出地质灾害发生的概率,通过区域化易发等级图与降雨图的叠加分析,采用地质气象偶合方法,实现了地质灾害预测。在某省案例区的研究表明,本研究设计的地质灾害预警模型具有较高的预警精度(预警结果80%符合实际情况)。