Enhanced orbit determination for BeiDou satellites with FengYun-3C onboard GNSS data

GPS Solutions - A key limitation for precise orbit determination of BeiDou satellites, particularly for satellites in geostationary orbit (GEO), is the relative weak geometry of ground stations....     

国家环境遥感监测体系研究与实现

随着国家空间基础设施的建设和发展,中国遥感监测体系研究与建设取得了重大突破,从无到有形成了国家环境遥感监测能力,并业务化实现了主要环境遥感监测要素产品的生产与服务,为新时期环境管理工作提供了有力支撑。在分析国内外环境遥感发展及中国环境遥感监测需求的基础上,阐述了中国环境监测体系建设的必要性,研究了国家环境遥感监测体系的建立与业务化运行,提出了中国环境遥感监测体系发展思路与下一步重点任务。     

北川羌族自治县国际旅游城镇建设研究

5.12汶川大地震后,北川县城易地重建,并将以建设国际旅游城镇为重要目标之一。分析了北川国际旅游城镇建设所面临的新的约束条件,从资源禀赋结构变化、地域文化重建、旅游产业先导、旅游环境安全等方面揭示其特殊性。从供需两个角度以大旅游产业的视角,构建并拓展了国际旅游城镇建设的一般要素模型。在此基础上,探讨了北川须重点发展的关键竞争力要素,提出四大精品国际旅游产品的开发思路,以及国际旅游城镇建设的必要支撑措施。     

空间视角下城乡物流网络的内涵、构成要素与研究框架

地理学视角的物流网络研究已成为物流科学领域的热点方向。从地理学的空间视角出发,在明确城乡物流网络理论来源和相关概念的基础上,对城乡物流网络的内涵、构成及研究框架进行梳理。基于研究,首先给出了城乡物流网络的定义,认为城乡物流网络的本质是旧的城市物流网络和乡村物流网络在空间上的延伸、衔接和提效。其次,从空间视角将城乡物流网络分解为节点、连线和域面3要素,指出网络各要素均出现了功能强化和更新,如节点的管理、衔接和经济社会功能强化;连线的联结功能和平衡功能出现,域面的协同组织、动态调整功能突出。最后,遵循地理学“格局-过程-机理-决策”的科学逻辑,提出“数据采集与融合、要素映射与解析、时空比较与分析、机理探究与优化”4个模块的城乡物流网络分析框架,为深刻揭示城乡物流网络结构及其演化机理提供基础。进一步指出未来应探讨城乡物流节点、连线、域面及网络的地区类型、区域适宜性及要素间相互作用机制,提炼适应不同区域、能有效提升要素流通效应的多尺度空间组织模式,加强对城乡要素流通系统的科学认知,科学支撑城乡地域融合发展实践。     

高师地理师范专业隐性课程体系的重构研究——地理学特性视域

高师地理师范专业的隐性课程体系弱化是人才培养课程体系中主要问题之一。当前高师地理师范专业隐性课程普遍存在比重偏低、综合性较弱、碎片化明显、特色不突出、动态性不足的问题。要实现隐性课程在课程体系中的深度融合,可从隐性课程特性与地理学特性角度对隐性课程体系进行重构,调整结构,优化比重,实现隐性课程的协调化;协同整合,整体推进,形成隐性课程的综合化;重构融合,完善体系,实现隐性课程的系统化;凝练内涵,突出特色,形成隐性课程内容的差异化;更新调整,继承吸收,实现隐性课程内涵的动态化。     

负压对四肢瘢痕切除创面成纤维细胞的生长环境影响

目的 探究持续封闭负压引流技术（VSD）对瘢痕切除创面成纤维细胞生长环境的影响。方法 回顾性分析2014年1月~2015年11月于笔者医院行植皮手术的四肢瘢痕切除术后患者60例,根据植皮手术前是否接受VSD治疗分为负压组（n=30）和包扎组（n=30）,负压组均于一期植皮前接受VSD治疗,包扎组未接受VSD治疗。回顾性分析对比两组患者并发症发生率、首次植皮存活率、创面感染率、住院时间之间的差异。并用TUNEL染色、CD34染色法对比瘢痕标本中成纤维细胞凋亡指数及血管生成情况。结果 负压组菌培养阳性率明显低于包扎组（P<0.05）,植皮存活率明显高于包扎组（P<0.05）。两组并发症发生率、住院时间、疼痛评分、随访VSS评分间比较,负压组均明显较低（P<0.05）。负压组成纤维细胞凋亡指数明显较低,微血管计数明显较高（P<0.05）。结论 负压封闭引流可以减轻瘢痕切除创面感染率,有效减轻炎性水肿,且可以通过改变局部血液循环微环境以及机械应力作用促进成纤维细胞增殖分裂,提高创面恢复效果。     

Inversion of Vegetation Components Based on the Spectral Mixture Analysis Using Hyperion Data

Hyper spectral remote sensing is widely used to identify ground objects as a result of the advantages of ground radiation intensity characteristics and spectral position characteristics, in which inversion of vegetation components is the difficult point and hotspot. In this study, Huma county of Heilongjiang Province was selected as the study area, the canopy spectra of four types of typical vegetation were measured in situ firstly, including mongolian oak, cotton grass, lespedeza and white birch. Then, on the basis of analyzing the canopy spectral characteristics and their parameterization, the spectral differences of different vegetations were located, and the parameterization method of characteristics identification was determined. Finally, Hyperion data were used to calculate the canopy albedos based on the bidirectional reflectance model of vegetation canopies, and to map the vegetation components in the study area by use of linear spectral mixture model. The results showed that inversion of vegetation components in high vegetation-covered area was accurate using the canopy albedos and liner spectral mixture model, and was identical with the field sampling, which validated the feasibility of canopy albedos and liner spectral mixture model for the inversion of vegetation components.     

Variations in the Ice Phenology and Water Level of Ayakekumu Lake,Tibetan Plateau,Derived from MODIS and Satellite Altimetry Data

The alpine lakes on the Tibetan Plateau (TP) are highly sensitive to variations in climate changes, and the lake ice phenology and water level are considered to be direct indicators of regional climate variability. In this study, we first used 14 years of moderate resolution imaging spectroradiometer snow cover products to analyse the freeze dates, ablation dates, and ice coverage durations. The lake level changes during 2002–2015 were estimated, derived from satellite altimetry and Hydroweb data. Unexpectedly, the freeze dates of lake ice greatly advanced, and the ablation dates were markedly delayed. The complete freezing duration lengthened by approximately 77 days. As a result of the warm-wet climate in the northern TP, the lake area expanded from 770 to 995 km² during 2002–2015, and the water levels rose by 4.2 m in total, at a rate of 0.3 m/year. The progressive expansion of Ayakekumu Lake profoundly affected the ice phenology. Larger water volume with larger thermal capacity likely led to the delaying of ablation dates, with the freezing point depression caused by decreasing salinity. Some new narrow and shallow bays located in southern and eastern Ayakekumu Lake were conducive to early freezing of ice. Additionally, the changes in air temperature, precipitation, potential evaporation, and sunshine duration may be related to the prolonged ice cover duration since 2002. In sum, accurate measurements of lake ice and water levels are critical for understanding the water resource balance and hydrologic cycle in arid or semi-arid regions of China.     

Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations

With the improvement in resolution, more and more useful information is contained in the space of remote sensing images, which makes the processing of remote sensing data become more complex, and it is easy to cause the curse of dimensionality and the poor recognition effect. In this paper, a remote target recognition approach named AJRC is proposed, which uses joint feature dictionary for sparse representation based on different feature information for adaptive weighting. Firstly, the features of the images are extracted to calculate the contribution weight of each eigenvalue in sparse representation, and each eigenvalue contribution weight is calculated in sparse representation. Through the adaptive method, the contribution ability of each feature value in sparse representation is strengthened, and new atoms are formed to construct feature dictionary, which makes the dictionary more discriminative. Then, the common features of each category image and the private features of a single image are extracted from the feature vector, and a joint dictionary is formed to represent the test image sparse and recognize the output of the target. Aiming at the problem that the target visual contrast difference, the low resolution and the rotation of the target with different angles, the experiment is carried out by different feature extraction methods. At the same time, we use the PCA method to reduce the feature dictionary in order to avoid dimensionality. Experiments show that compared with the existing SRC method and JSM method, this method has better recognition rate.