基于SPE与SPATT的水体中麻痹性贝类毒素检测方法构建与应用   总被引：1，自引：0，他引：1  

张亚亚  闫国旺  吴海燕  谭志军  张志华  江涛  李玉 《海洋与湖沼》2020,51(2):298-306

为实现麻痹性贝类毒素(paralytic shellfish poisoning,PSP)的实时监控与提前预警,本研究构建了基于固相萃取技术(solid phase extraction,SPE)与固相吸附毒素跟踪技术(solid phase adsorption toxin tracking,SPATT)的水体中PSP检测方法,重点优化了吸附材料及前处理方法,评价了回收率、检出限等指标,并将方法应用于2019年春季秦皇岛山海关海域PSP消长过程的监测中,比较评估了两种方法的监控预警效果。结果表明:SPE方法选用ENVI-Carb 500mg/6mL固相萃取柱,过样体积为50mL,13种PSP组分的平均回收率为82.2%±10.0%、检出限为4.0-20.0ng/L;SPATT方法选用SP207大孔吸附树脂,洗脱时间为静置Id最佳,整体回收率约为9.2%;在实际应用中,结合产毒藻密度及贻贝富集毒素含量的变化,发现SPE方法的检测结果可实时表征海域PSP风险状况,对于贻贝中PSP的预警效果也显著优于SAPTT方法,后者不仅因监控方式相对滞后一个监测周期,且灵敏度及准确性均较差。对于秦皇岛海域,当SPE方法检测结果达到100ng STX eq/L时,该海域贻贝中PSP残留将具有潜在的食用安全风险,跟踪过程表明这一阈值可提前两周预警贻贝富集毒素含量超出我国限量标准(800μg STX eq/kg),这对于强化风险警示并制定防范措施具有积极作用。  相似文献   

How forest gaps shaped plant diversity along an elevational gradient in Wolong National Nature Reserve?     

Chen  Li  Han  Wangya  Liu  Dan  Liu  Guohua 《地理学报(英文版)》2019,29(7):1081-1097

Journal of Geographical Sciences - Understanding the underlying ecological processes that control plant diversity within (α-diversity) and among (β-diversity) forest gaps is important for...  相似文献   

Measured and modelled above‐ and below‐canopy turbulent fluxes for a snow‐dominated mountain forest using GEOtop     

Andrew T. Fullhart  Thijs J. Kelleners  Heather N. Speckman  Daniel Beverly  Brent E. Ewers  John M. Frank  William J. Massman 《水文研究》2019,33(18):2464-2480

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深水海底管道铺设安全辅助系统     

高爽  王法承  汤珂  李斌  李杨 《海洋工程》2018,36(2):127-134

此次研发的铺管安全辅助系统是一套自动化、简易化、旨在提高安装经济性和安全性的实时运行安全辅助系统,通过实时数据采集及处理,达到对安装期海管强度进行实时监测目的。系统组成包括设备系统和监测软件两部分。设备为系统提供辅助决策数据源;监测软件采用C++语言编写,用于数据接收、解析,并通过OrcaFlex API调用商业有限元软件OrcaFlex为分析内核进行实时分析计算,以可视化界面输出管道应力、应变、触地点等信息,给予船上作业人员安全指导,及时作出决策。  相似文献   

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《地学前缘(英文版)》2020,11(3):871-883

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   

随机森林模型在遥感水深反演中的应用     

邱耀炜  沈蔚  纪茜 《海洋技术学报》2019,38(5)

随着我国浅海测绘需求的日益增长,文中利用四波段的WorldView-2高分辨率遥感影像,选取我国南海西沙群岛中的甘泉岛和台湾南湾地区作为典型试验区,开展水深反演研究。引入随机森林算法构建了随机森林水深反演模型,并同常用的3种水深反演模型进行精度对比。结果表明,在甘泉岛和南湾地区随机森林模型反演的水深值和真实水深值的RMSE分别为0.85 m和1.59 m,MRE分别为8%和12%,均优于其他3种模型。  相似文献   

地表覆盖分类成果质量特征分析   总被引：1，自引：1，他引：0  

商建伟 《测绘通报》2020,(9):159-161

自我国全面建成地理国情普查成果库之后，工作重心由全面普查变为重点监测。不论普查还是监测，准确地对地表覆盖进行分类一直是工作的重点和难点。在常态化监测阶段，把握地表覆盖分类成果的主要质量指标，归纳其诸如变化率、变化区域分布、变化类型，分析影响其成果质量的主要因素，对监测生产组织及质量控制具有非常重要的作用。  相似文献   

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa     

《International Journal of Applied Earth Observation and Geoinformation》2020

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.  相似文献   

复杂环境下地基SAR粗差探测及应用     

马金玉  龙四春  童爱霞  吴文豪  祝传广 《测绘通报》2020,(2):43

雷达视线受施工机械等的间断遮挡导致部分影像产生相位奇异值，从而造成解缠错误及误差传递，简单的相关影像处理难以识别受遮挡影像。本文提出了改进的基于小波变换的信号奇异性检测方法，通过对地基SAR PS点时序相位特征进行分析，将遮挡影像识别转化为粗差探测问题；由PS点相位序列与影像位置关系，根据测区PS点相位序列的奇异点集合得到受遮挡影像集；最后将受遮挡影像剔除后得到的地基SAR监测结果与精密全站仪、水准与游标卡尺数据进行对比分析。结果表明：本文提出的方法用于受遮挡影像的识别是可行的，解决了地基SAR在实际工程应用中可能存在的影像遮挡带来的测量数据含有粗差的问题，提高了监测区域伪影像检测的效率与准确性。  相似文献   

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery     

《International Journal of Applied Earth Observation and Geoinformation》2020

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.  相似文献