Dynamics of iron and aluminum storages in a subtropical forest headwater stream

The forest headwater streams are important hubs for connecting terrestrial and aquatic ecosystems,with plant litter and sediments as the major carriers for material migrations;however,until now we knew little about the dynamics of trace elements such as iron(Fe)and aluminum(Al)in forest headwater streams.Here,we quantitatively identified the spatiotemporal dynamics of Fe and Al storages in plant litter and sediments and their influencing factors in a subtropical forest headwater stream,and assessed the potential pollution risk.The results showed that:(1)the mean concentrations of Fe and Al in plant litter(sediments)were 5.48 and 8.46(7.39 and 47.47)g·kg^(-1),and the mean storages of Fe and Al in plant litter(sediments)were 0.26 and 0.43(749.04 and 5030.90)g·m^(-2),respectively;(2)the storages of Fe and Al in plant litter and sediments significantly fluctuated from January to December,and showed a decreasing pattern from the source to mouth;and(3)storages of Fe and Al had no significant correlation with riparian forest type and the present of tributary and the Fe and Al storages in plant litter were mainly affected by water temperature and water alkalinity,and their storages in sediments were mainly affected by water temperature and frequency of rainfall;and(4)there were no anthropogenic pollution in Fe and Al in the forest headwater stream.Our study revealed the primary factors of concentrations and storages of Fe and Al in plant litter and sediments in a forest headwater stream,which will improve our understanding of the role of headwater streams in forest nutrient storage and cycling along with hydrological processes.

基于LOF-FCM算法的船舶航行数据识别

针对传统船舶自动识别系统数据在清洗异常数据和提取停留数据时分别采用不同的识别方式、类型判断阈值需要人为设定、识别效率不佳的局限性,首次提出了一种船舶航行轨迹中停留及异常数据的一体化检测方法。通过分析航行路线的3种数据(停留、异常和航行)异常因子特征,提出基于LOF-FCM的船舶航行数据、停留数据和异常数据一体化检测算法。实验对3类数据进行了识别,模型识别准确率达到了92.69%,有效提高了异常、停留、航行数据的识别能力。结果表明所提方法可一次性实现AIS数据中3种数据的检测,能高效分离出正常船舶航行数据,具有良好的工程应用价值。

澜沧江下游景观破碎化时空动态及成因分析

景观破碎化深刻影响着景观格局和过程。以澜沧江下游城市——景洪市为研究区域,选取2000、2005和2010年的景观类型数据与对应的SPOT-NDVI遥感数据,对比了移动窗口法和空间自相关法在指示景观破碎化时的精确性和可应用性。采用300 m的移动窗口边长,选取蔓延度指数(CONTAG)、景观形状指数(LSI)、斑块密度(PD)、边缘密度(ED)、最大斑块指数(LPI)和香农多样性指数(SHDI),利用移动窗口法将景观指数空间可视化以指示景观破碎化特征。研究表明,不同景观类型的NDVI值均在0.6以上;2000年、2005年和2010年,Moran’s I值均在0.78以上;进一步分析了NDVI的局部空间自相关指数Moran’s I,结果表明NDVI呈"高—高"自相关的地区大多为常绿阔叶林地带,而NDVI呈"低—低"自相关的地区大多为建筑用地和耕地分布广泛的区域。统计各景观类型的景观指数和空间自相关指数后发现,对于常绿阔叶林和未利用地等景观类型,局部自相关指数高的区域,CONTAG值也高,而LSI值较低,这表明该类景观类型斑块较少且完整,破碎化不明显。通过比较不同景观类型的景观指数和空间自相关系数,发现两种方法在指示景观破碎化上具有良好的可比性。同时,提取了海拔、坡度、坡向、地形起伏度和地面粗糙度等地形因子,分析了NDVI和地形因子之间的局部空间自相关指数Moran’s I,结果表明研究区植被分布在一定的程度上由海拔、坡度、地形起伏度和地面粗糙度所决定。本研究为综合景观指数与空间统计分析揭示景观格局变化和空间异质性提供了参考,并通过景观格局与地形因子的相关性分析进一步解释了景观格局变化与环境变化的生态意义。