ANALYSIS OF ENVIRONMENTAL FEATURES OF RED TIDE IN THE WESTERN WATERS OF XIAMEN BAY

This article reports main results of investigations on red tide in western waters of Xiamen Bay from Jan. to Dec. 1987, and elaborates on the relationship between the change of phytoplanktonic number and main environmental factors there. The cause and the method for judging and monitoring of red tide are discussed.     

The recognition of ocean red tide with hyper-spectral-image based on EMD

A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.     

矿山地面灾害精准监测地学传感网系统

 研究矿山地面灾害监测传感网的理论和技术体系,建立矿山地面灾害监测地学传感网系统,进行矿区地面灾害精准监测及预报工作,对矿山及时准确科学预报、预防和减小地面灾害的损失,具有重要意义和作用。地学传感网主要是指采用GPS、测量机器人、倾斜传感器、位移传感器、温度和气压传感器、全景相机、CCD相机、视频传感器、RFID传感器、地面3D激光扫描仪和地基SAR等测地型传感器和测地辅助传感器,应用计算机网络和无线通信技术,通过传感器和数据库管理软件构建的地学传感网。矿山地面灾害精准监测地学传感网的理论,主要包括:矿山时空监测基准、矿山地面灾害监测地学传感网数据整合、监测空间数据聚类分析、矿山监测功能分区、矿山区域动态形变场理论和矿山地学传感网地理信息系统模型理论。 技术体系主要包括:监测空间数据库管理技术、数据通信传输技术和传感网地理信息系统平台技术。     

STUDY OF RED TIDE PREDICTION MODEL FOR THE CHANGJIANG ESTUARY

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基于多层神经网络与Sentinel-2数据的大豆种植区识别方法

大豆作为全球最重要的油料作物,是中国进口的大宗农产品,对其种植区的精准识别是决策制定、种植结构调整基础,对国家粮食安全有重要意义。本文利用Sentinel-2作为数据源,利用多层神经网络方法与对大豆进行提取,并与随机森林、决策树、支持向量机等机器学习进行对比,发现F1-Socre指标显示多层神经网络的分类精度最高,为93.53%,其次为随机森林、支持向量机、决策树。将神经网络分类结果与SLIC面向对象分割聚合之后,结果既忽略了同一地块的微小差别,又区分出了不同地块的作物差异,很好的体现了大豆的分布。Sentinel-2数据是进行大尺度大豆种植监测的绝佳数据源,大豆与玉米等其他作物在第二个红边波段的反射率有较为明显的差异。多层神经网络方法在图像分类任务中表现出色,结合图像分割算法精度可达到95.51%,可以满足大豆种植面积监测的需求。     

MODIS遥感数据提取赤潮信息方法与应用——以珠江口为例

近年来,中国沿海近岸二类水体中的赤潮频发,对海洋环境、社会经济和公众健康都造成较大危害,因此,针对近岸二类水体中较小范围赤潮的遥感监测分析有重要意义。本文的工作主要集中在:(1)分析典型赤潮海水、泥沙浑浊海水、赤潮泥沙混合海水,以及清洁海水的MODIS影像光谱特征;(2)分析叶绿素a法,温度法,荧光法和波段比值法,在近岸小范围赤潮识别应用中的优势和缺点;(3)提出监督分类的赤潮信息提取方法,其中典型赤潮样本的选取基于蓝绿波段反射率比值和MODIS 1,4,3波段。利用本方法,对珠江口海域2006年2月赤潮进行了提取,取得良好的效果。     

基于移动对象轨迹的室内导航网络构建方法

室内导航网络是行人导航、信息推荐和商业分析的基础。传统人工测绘或半自动提取的室内三维导航网络无法满足复杂室内空间结构高频变化需求。随着室内定位技术的不断发展,室内移动对象轨迹数据爆发式增长,为室内导航网络快速构建与变化监测更新提供了可能。本文提出一种基于移动对象轨迹的室内导航网络构建方法,在基于ST-DBSCAN的轨迹简化预处理基础上,提出了室内轨迹自适应栅格化算法,减弱栅格图像分辨率对导航网络提取的影响,有效避免廊道轨迹密度差异造成的导航网络拓扑连通失效,并通过CFSFDP自适应聚类算法自动识别楼层之间连通点,实现室内导航网络的快速构建。实验数据来源于上海图聚智能科技股份有限公司提供的某商城真实的室内移动对象轨迹数据,实验结果表明,与普适栅格化方法相比,本文提出的方法将导航网络构建准确率平均提高2.43%,拓扑正确度提高12.8%。     

基于BDS信噪比的潮位监测精度分析与评估

基于非洲东海岸MAYG站2020年年积日268～366的信噪比（SNR）数据开展GNSS-IR（global navigation satellite system interferometric reflectometry）监测海平面高度研究,并与Dzaoudzi验潮站的实测潮位进行对比。结果显示,BDS-GEO不适用于岸基测高;BDS-MEO的监测精度优于BDS-IGSO和GPS。     

湛江港附近海域赤潮生物的种类调查

以光学显微镜对在湛江港附近海域采集的浮游生物进行观察,初步鉴定出赤潮生物——— 硅藻31种、甲藻10种、蓝藻1种,简要介绍了几种主要的赤潮生物的习性、分布和形成赤潮后对渔 业生产造成的损失。     

基于AIS数据的船舶行为特征挖掘与预测:研究进展与展望

船舶行为特征挖掘与预测是水上智能交通系统的重要研究内容,也是交通运输工程领域的关键科学问题。为系统研究基于船舶自动识别系统（Automatic Identification System, AIS）数据的船舶行为特征挖掘与预测的研究现状与发展趋势,本文首先针对Web of Science(WOS)和中国知网（China National Knowledge Infrastructure, CNKI）收录的文献,用知识图谱分析软件VOSviewer对文献关键词进行处理,从文献计量学的角度生成高频关键词的聚类图谱和趋势演化。然后对基于AIS数据的水上交通要素挖掘、船舶行为聚类和船舶行为预测3个主题的研究内容、方法、存在问题进行了系统分析和展望,研究结果表明：① 在基于AIS的水上交通要素挖掘方面,主要集中在对AIS数据中表征船舶行为空间特征和交通流的时间特征单独挖掘分析,缺乏对AIS数据的时间、空间以及环境因素特征的关联挖掘,对于如何进行交通要素的关联融合挖掘研究还有待深入探索;② 在船舶行为聚类方面,研究主要是运用无监督聚类方法研究船舶航迹点和航迹段聚类,得到船舶航行行为模式的时空分布和船舶操纵意图辨识模型,然而融合多维特征的船舶轨迹的相似性计算方法、聚类参数的自适应选取以及船舶行为的语义特征建模有待进一步研究;③ 在船舶行为预测方面,主要集中在基于动力学方程、传统智能算法和深度循环神经网络的船舶行为预测研究,考虑船舶行为的随机性、多样性和耦合性的特点,运用混合神经网络模型以及神经网络与向量机、注意力机制相结合的模型实现多维的船舶航行行为特征的实时预测将是新的研究方向。最后提出了基于语义模型的船舶行为特征挖掘、基于深度卷积神经网络的船舶行为的预测和基于知识图谱的船舶行为特征挖掘和预测结果可视化等有待进一步研究的方向。     

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遗传算法优化支持向量机在大坝变形预测中的应用

建立大坝变形预测的支持向量机模型,并用遗传算法对支持向量机模型的核函数参数、惩罚参数和损失参数进行优化。将同一优化方法不同支持向量机核函数、不同优化方法同种支持向量机核函数进行横向对比,将BP神经网络、自回归AR(p)模型、多元回归分析法和周期函数拟合法进行纵向对比。结果表明,该GA-SVM(RBF)模型不仅能较好地预测大坝的变形趋势,而且能大幅提高预测精度。     

Automatic cell object extraction of red tide algae in microscopic images

Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.     

Research on red tide occurrences using enclosed experimental ecosystems in west Xiamen Harbor, China—Relationship between various factors and red tide occurrences

A series of enclosed ecosystem experiments were conducted in a land-based tank near the seaside of West Xiamen Harbor. The results of experiments conducted in different seasons and years showed a repeatable phytoplankton succession. In this relatively stable ecosystem with added nutrients and trace metals, diatoms dominated initially, dinoflagellates dominated in the later stage, and dinoflagellate red tides eventually occurred. Vitamin B₁₂ enrichment may speed up this succession process. Stirring the water column could stop this process. Soluble Mn at a level of 3–4 μg/L in seawater, which also is the existing concentration of soluble Mn in Xiamen Harbor seawater, is sufficient for the multiplication of algae and occurrence of red tide. The present study showed that excessive soluble Mn in Xiamen Harbor cannot cause red tide, and that Fe was one of the important factors causing diatiom red tide in this present study. Project 39570145 supported by NSFC.     

一种新的固体潮观测数据特征量提取方法

将经验模态分解方法应用于固体潮观测数据的处理,通过对模态分量从频域上以及从固体潮调和分析结果上与原始观测数据进行对比证明：经验模态分解可以将固体潮观测数据完整地分离成5种特征量：潮汐观测高频信号、半日波信号、周日波信号、潮汐观测低频信号和观测数据的长趋势变化。     

固体潮观测序列的AR建模方法

证明了某一波群的固体潮理论值加上一个白噪声可用一个ＡＲ（２） 过程表示,固体潮观测序列可用高阶的ＡＲ模型逼近。给出了由ＡＲ 模型参数求解观测序列功率谱的方法,并用九江台资料作了实例分析。该方法可用于对短观测序列进行谱分析,以便跟踪地震短临前兆。     

Nutrient and chlorophyll a anomaly in red-tide periods of 2003-2008 in Sishili Bay, China

Sishili Bay is the most important aquiculture and tourism area for the city of Yantai, China; however, red tides occurred frequently and have caused huge economic losses in this bay in recent years. To gain a better understanding of the local ecological environments in the bay, we conducted this research between 2003 and 2008 to analyze variations in nutrients and chlorophyll (chl-a) during high frequency red tide period (May to September). The results show that the chl-a concentration increased from 2.70 in 2003 to 7.26 mg/m3 in 2008, while the concentration of total inorganic nitrogen (TIN) and silicate (SiO3-Si) increased lineally from 5.18 and 1.45 μmol/L in 2003 to 18.57 and 9.52 μmol/L in 2008, respectively, and the annual phosphate (PO4-P) varied between 0.15 and 0.46 μmol/L. Special attention was given to a red tide in August 2007 occurred when water temperature was high and nutrient concentrations increased sharply because of a heavy rainfall. Overall, the results show the P limitation in Sishili Bay, and reveal that red tides were caused by eutrophication from terrestrial inputs and local warm weather, particularly during rainy periods. Therefore, to control red tide, greater efforts should be made to reduce sewage discharges into Sishili Bay, particularly during rainfall seasons.     

APPLICATION OF CLAYS TO REMOVAL OF RED TIDE ORGANISMS Ⅲ.THE COAGULATION OF KAOLIN ON RED TIDE ORGANISMS

This article reports studies on the coagulation of kaolin(from Wuxian,Jiangsu,China)on various red tide organisms,and the observation for the first time that the coagulation of kaolin is much greaterthan that of montmorillonite so that kaolin is a more effective clay for removing red tide organisms.The authors’theoretical explanation and analysis by a mathematical-physical model prove that comparedto montmorillonite,kaolin has greater attraction for organism cells and therefore greater coagulation capabil-ity.This project’s studies on the effects of pH and acid-treatment show that the acid-treatment does not have much influence on the kaolin system;whereas the effect of pH on the kaolin system is the same asthat on the montmonrillonite system.     

井水固体潮的调和分析方法及京14井调和分析结果

本文系统地论述了井水水位固体潮的调和分析方法,介绍了自动排除水位记录中短期扰动影响的预处理方法。对京14井水位观测资料进行分析后,获得了包括长周期波M_1在内的各类波的调和分析结果。其结果令人满意,表明调和分析和预处理方法可行,也说明京14井的井孔-含水层系统的封闭性和抗干扰性好,具有较强的记录井水固体潮的能力。     

Optimization of groundwater level monitoring network using GIS-based geostatistical method and multi-parameter analysis: A case study in Wainganga Sub-basin,India

Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation (GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology (LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System (GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.