基于庞卡莱代数边界理论的三维地籍TEN模型

针对空间权属单元排他性特征,本文对不规则四面体格网的拓扑关系进行有效性筛分,细化出9种有效拓扑关系并对其进行描述,同时,建立相应拓扑规则,完成TEN模型改进并应用于三维地籍建模。基于庞卡莱边界代数和理论,推演了空间权属单元的合并算法,使其聚合为多胞元复合体,用以模拟权属空间。通过分析,提出10个剖分规则,实现了TEN复合体的分割。在VC++开发环境下,对合并与剖分进行了编程实现。实验证明,改进后的地籍TEN模型具有拓扑关系精练、易于实现的优点,能有效构建真三维权属空间,不失为一种简单高效的三维地籍建模方法。     

Sedimentary record of climate change in a high latitude fjord——Kongsfjord

The sedimentary record of climate change in the Arctic region is useful for understanding global warming.Kongsfjord is located in the subpolar region of the Arctic and is a suitable site for studying climate change.Glacier retreat is occurring in this region due to climate change,leading to an increase in meltwater outflow with a high debris content.In August 2017,we collected a sediment Core Z3 from the central fjord near the Yellow River Station.Then,we used the widely used chronology method o...     

华北平原典型工业城市秋、冬季大气细颗粒物污染特征

济宁市是位于华北平原大气污染传输通道上的工业城市,为研究其秋、冬季细颗粒物(PM2.5)的污染特征,在市区的3个站点进行了PM2.5的同步滤膜采样.采样期为2018年10月15日至2019年1月31日,涵盖非采暖期和采暖期(自2019年11月15日始),共270个小流量滤膜样品.研究结果表明,济宁市秋、冬季PM2.5平...     

青藏高原雪冰中碳质气溶胶含量变化

文中采用供氧两步加热的方法对过滤到石英膜上的雪冰中碳质气溶胶含量进行分析,其中有机碳(OC)和元素碳(EC)分别在340和650℃的条件下进行热解、氧化分离,生成的CO2转化成CH4并由气相色谱仪氢火焰离子化检测器(FID)检测其含量。空白测试表明,该系统的OC本底值为(0·50±0·04)(1σ)μgC,EC为(0·38±0·04)(1σ)μgC。利用这套分析系统对青藏高原8条冰川的34个雪冰和降水样品中OC和EC的含量进行了测试。结果表明,在青藏高原雪冰中OC和EC含量自东向西、自北向南呈明显的下降趋势(西昆仑除外)。在高原东北部EC的质量分数相对较高,平均为79·2ng·g-1;在喜马拉雅西段EC的质量分数最低,平均为4·3ng·g-1。在冰川表面,雪的融化使雪冰中碳质气溶胶聚集,并导致其含量明显升高,该过程降低了雪表面的反照率,加速了冰川的消融。     

结合mRMR选择和IFCM聚类的遥感影像分类算法

为解决高分影像特征间相关性大冗余度高、FCM聚类稳健性差带来的分类精度不佳问题,提出一种基于mRMR选择和改进FCM聚类的影像分类算法。首先基于对象置信度指标（OC）进行影像分割,然后利用mRMR算法实现特征选择,解决特征冗余问题,最后将提取的特征输入分类器通过IFCM聚类,得到最终分类结果。试验结果表明,本文算法能减少特征间相关性,降低冗余,并有效提高影像分类精度。     

Spatial variations of small phytoplankton contributions in the Northern Bering Sea and the Southern Chukchi Sea

Phytoplankton size classes (hereafter, PSCs) were derived from satellite ocean color data using a present phytoplankton abundance-based optical algorithm in the northern Bering and southern Chukchi Seas to characterize the spatial and seasonal variations of the different PSC and investigate the contributions of small phytoplankton to the total phytoplankton biomass. The comparison results showed that the phytoplankton abundance-based method approach could reasonably classify the three PSCs (pico-, nano-, and micro phytoplankton). The satellite maps of the dominant PSCs were derived using long-term satellite ocean color data. The general spatial distribution showed that the large (micro-) phytoplankton were dominant in the coastal waters and the west side of the Bering strait, while the small size (nano- or pico-) phytoplankton were dominant in the open ocean waters. Nano- and microphytoplankton were dominant in May and October in most of the study area, while pico-phytoplankton were dominant in the summer months in the open ocean waters. The annual variation in small phytoplankton dominance had a strong positive relationship with the annual mean sea surface temperature (SST), which is consistent with the increasing dominance of small phytoplankton biomass as water temperature increases. Microphytoplankton have an apparent increasing trend in the southeastern Chukchi Sea but slightly decreasing trends in Chirikov and St. Lawrence Island Polynya (SLIP). In contrast, there were increasing trends in picophytoplankton in Chirikov and SLIP, which seems to be related to increasing annual SST. It is crucial to monitor changes in dominant groups of phytoplankton community in the Bering and Chukchi Seas as important biological hotspots responding to the recent changes in environmental conditions.