南极夏季南设德兰群岛东部海域南极大磷虾的摄食策略

Euphausia superba is a key species in the Southern Ocean that serves as a link between primary production and higher trophic levels. To investigate the feeding strategies of E. superba from the eastern South Shetland Islands,fatty acid biomarkers, stable isotope signatures, and an incubation experiment were conducted. The results of the incubation experiment proved that adult E. superba mainly fed on 2–20 μm particles, demonstrating the importance of nanoplankton in their diet. Moreover, significant positive relationships between δ15 N and body size demonstrated that size-related dietary shifts were present in E. superba. Evidence from principal component analysis and the C16:1ω7/C18:4ω3 ratio showed that juveniles preferentially fed on dinoflagellates and adults were more likely to feed on diatoms. Fatty acid profiles in adult E. superba roughly mirrored the different trophic conditions and feeding strategies between stations. Adult E. superba at Stas D2-07, D5-07, DA-01 and DA-02 exhibited elevated levels of C16:1ω7, C18:4ω3, C18:1ω9 and C18:1ω9/C18:1ω7, indicating higher levels of feeding on both phytoplankton and higher trophic diets. In contrast, adult E. superba at Stas D1-03 and D1-04 were characterized by high levels of polyunsaturated fatty acids/saturated fatty acids ratios and low levels of C16:1ω7,C18:1ω7, C18:4ω3, C18:1ω9 and total fatty acids. We inferred that adult krill at Stas D1-03 and D1-04 still suffered from difficult dietary conditions after overwintering. The different dietary conditions between stations suggest a highly plastic feeding strategy of E. superba in the eastern South Shetland Islands.     

基于建筑物卫星图像的子像素角点优化方法

提出一种通过建立优化函数进行子像素角点坐标优化的新方法。引入超分辨的方法及建筑物的高程信息,建立优化模型,将角点坐标从像素级精确到子像素级,提高了建筑物角点坐标的提取精度和对应点的高程值解算精度。     

基于混合智能的地图自动审核技术框架

地图审核是国家测绘地理信息行政主管部门的主要职责。当前我国的地图审核依赖人工目视判别,成本高、效率低。如何从高度依赖专家经验的人工检测走向混合智能的自动判断,是我国地图审核工作面临的技术难点和痛点问题。提出以知识为引导、算法为基础的地图自动审核的混合智能方法,设计了基于混合智能的地图自动审核技术框架,并指出需要重点突破的3项关键技术;并结合典型的“问题地图”智能化审核案例,给出了地图智能化审核的技术实现途径。     

FY-3B/D星NDVI与MODIS NDVI对作物长势监测对比

风云三号卫星（FY-3）NDVI数据是中国对全球用户开放可进行全球尺度作物长势监测的主要卫星遥感数据源,客观评估其进行作物长势监测的真实状况和能力,对业务产品应用推广、产品算法和流程改进以及长时序标准数据生产等具有重要意义。采用国际通用的年际差值比较模型、植被状况指数模型（VCI）以及基于NDVI的作物生长过程评估了中国风云三号B星（FY-3B）和D星（FY-3D）NDVI旬、月合成业务产品与同期MODIS MOD13A1和MOD13A3 NDVI数据对作物长势的监测情况。年际比较模型结果表明,FY-3 NDVI的监测动态范围比MODIS NDVI的窄约0.1—0.15,两者差值在-0.02—0.02,均方根误差在0.08—0.09,在长势分级评估时可忽略,但动态范围偏窄提示有必要在分级评估时使用窄一些的阈值范围。植被状况指数模型结果表明,FY-3B NDVI和MODIS NDVI的监测结果高度一致,因此FY-3B NDVI和MODIS NDVI一样可准确反映作物生长的年际差异。作物生长过程动态监测结果表明,FY-3和MODIS月合成NDVI的年际比较结果总体一致,但在作物快速生长阶段...