河口最大浑浊带研究的回顾与展望

最大浑浊带是河口近岸泥沙含量显著高于上下游,并在一定范围内有规律迁移的浑浊水体,是河流入海泥沙输移过程中的特有现象.它显著体现了河流、海洋及物质能量的耦合作用,其发展动态、研究进展备受关注.本文通过回顾国内外河口最大浑浊带80多年来的研究现状和历程,总结经验、展望未来.既有研究已对河口最大浑浊带的泥沙含量、物质来源及组成等基本特征进行了较为全面的总结,认识到不同类型河口最大浑浊带的形成发育存在不同的主导动力过程和主控影响因素;此外,河口最大浑浊带在中短尺度上的时空变化也得到了比较充分的分析和讨论.相关研究在多方面都取得了瞩目的成果和进展,具有重要的科学价值与现实意义.但是,分析认为仍亟待继续开展并进一步加强最大浑浊带的精准定量识别方法研究、空间立体分布整体特性研究,特别是人类活动加剧和全球自然环境显著变化影响下的长期时空动态特征研究,以促进对河口最大浑浊带现象及其时空演变规律和趋势更加全面、更加深刻的认识和理解,助力社会经济和自然环境健康可持续发展.

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources.Various sources of imagery are known for their differences in spectral,spatial,radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping.Generally,it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.Then,correlations of the vegetation types(communities or species)within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified.These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process,which is also called image processing.This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically,this paper focuses on the comparisons of popular remote sensing sensors,commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts,available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced,analyzed and compared.The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures,which can be utilized to study vegetation cover from remote sensed images.

Cytotoxicity and autophagy induction by graphene quantum dots with different functional groups

Graphene quantum dots(GQDs) possess great potential in various applications due to their superior physicochemical properties and wide array of available surface modifications.However, the toxicity of GQDs has not been systematically assessed, thus hindered their further development; especially, the risk of surface modifications of GQDs is largely unknown. In this study, we employed a lung carcinoma A549 cells as the model to investigate the cytotoxicity and autophagy induction of three types GQDs, including cGQDs(COOH-GQDs), hGQDs(OH-GQDs), and aGQDs(NH_2-GQDs). The results showed hGQDs was the most toxic, as significant cell death was induced at the concentration of 100 μg/mL,determining by WST-1 assay as well as Annexin-V-FITC/PI apoptosis analysis, whereas cGQDs and aGQDs were non-cytotoxic within the measured concentration. Autophagy detection was performed by TEM examination, LC3 fluorescence tracking, and Westernblot. Both aGQDs and hGQDs induced cellular autophagy to various degrees except for cGQDs. Further analysis on autophagy pathways indicated all GQDs significantly activated p-p38 MAPK; p-ERK1/2 was inhibited by aGQDs and hGQDs but activated by c GQDs. p-JNK was inhibited by aGQDs and c GQDs, while activated by hGQDs. Simultaneously, Akt was activated by hGQDs but inhibited by aGQDs. Inhibition of autophagy by 3-MA significantly increased the cytotoxicity of GQDs, suggesting that autophagy played a protective role against the toxicity of GQDs. In conclusion, c GQDs showed excellent biocompatibility and may be considered for biological applications. Autophagy induction may be included in the health risk assessment of GQDs as it reflects the stress status which may eventually lead to diseases.

无人机遥感在植物生态学中的应用与挑战

无人机为获取高时空分辨率的遥感数据提供了经济灵活的工具,为植物生态学家开展从个体到区域尺度的生态学研究提供了新的机遇和手段。但作为一种新兴的技术手段,当前无人机遥感在植物生态学中的应用仍充满了挑战,植物生态学的科研需求与无人机遥感的生态应用需要更为深入的融合。本文综述了无人机遥感技术在植物生态学中的应用,展望了无人机在植物生态学研究中的应用前景。在所综述的400篇文献中,59%的文章发表于非植物生态学领域的遥感类期刊,遥感学者与生态学者的关注点存在较大差异。当前的研究集中在无人机遥感的技术层面,如数据处理和遥感反演方法等,对生态学问题本身的关注较少。综述的文献中,61%的研究案例集中在群落尺度,可见光(RGB)相机和多光谱相机是最常用的传感器类型(75%)。无人机遥感数据中蕴藏着诸多待挖掘的、有重要意义的生态参数,这些参数有助于我们识别林窗的几何特征,构建林冠的化学组合,更好地了解林冠表面的不规则性和群落的异质性。无人机遥感技术在植物生态学研究中的深入应用,需要集植物生态学家和遥感专家之合力共同推进。