海南省农村多维贫困及影响因素的空间分异

贫困具有多维属性,根据不同社会群体和背景从多维视角定义贫困已成为贫困问题研究的共识。依据Alkire-Foster多维贫困框架,拓展精准扶贫的“两不愁,三保障”识别标准,建立了涵盖教育、健康、居住、生活和收入指标的海南省农户多维贫困评估指标体系,基于海南省70个乡镇、134个贫困村3924户入户调查数据,采用双重临界值法评估了农户及村域多维贫困状况,进而运用地理加权回归(Geographically Weighted Regression,GWR)模型,分析了村域多维贫困影响因素的空间分异。结果显示,调查农户多维贫困率达18.22%,多维贫困程度严重的村多维贫困发生率不一定高,“两不愁、三保障”及收入指标对多维贫困指数的贡献率低。中、西部连片贫困地区多维贫困主要表现为较差的资产状况、不清洁的炊事燃料、较高的家庭成员患病率和较低的家庭成员最高学历。GWR模型分析表明,作为多维贫困最重要的影响因素,户主性别、户主受教育水平、女性劳动力占比和抚养比4个变量估计系数的空间分异明显。总体上,女性户主和低学历户主为主的地区倾向于更易发生多维贫困,二者的影响分别表现为从东到西、从北到南有所增强。女性劳动力占比为负向影响,抚养比为正向影响,呈现出自北向南增强的趋势,体现了海南贫困地区劳动力弱、女性相对更勤劳等典型地域特征。     

长江三角洲全新世沉积物光释光测年研究

长江三角洲是研究古海岸沉积环境演化的理想区域。选取长江三角洲平原一支长岩芯钻孔上部55 m层位的沉积物进行光释光(optically stimulated luminescence,OSL)测年研究。根据沉积物粒度特性,在实验前处理过程中,提取100~200 μm或63~100 μm粗颗粒石英矿物进行OSL测年。测年样品的预热坪与剂量恢复实验表明选择180℃作为预热条件较为合适,石英OSL信号衰退曲线以快组分为主。条件实验结果、等效剂量分布以及各测片的循环比、热转移等方法学层面的实验结果表明,OSL测年技术对该孔的沉积物测年具有适应性和可靠性。通过年代—深度关系模型,建立该段地层中全新世以来的年龄框架。依据该孔的年代地层序列并结合前人工作,探讨全新世以来长江三角洲的沉积演化。     

Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

We investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two‐layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions. Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AIC_C] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them. A classical variance‐based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two‐layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.     

填充墙刚度影响的实测高层建筑增量动力分析

高层结构中的填充墙在地震作用下与周围结构构件之间的相互作用十分复杂,对建筑结构的整体抗震性能具有较大影响。然而我国规范在设计阶段通常不考虑填充墙对结构抗震性能的影响,统一采用周期折减系数来考虑其刚度变化引起的内力变化,因此准确评估填充墙对结构抗震性能的影响具有重要意义。本文在对一栋框-剪结构和一栋剪力墙高层建筑进行随机振动测试的基础上,利用Perform-3D对每栋高层分别建立了3种分析模型。其中对未考虑填充墙作用的结构模型,分别采取规范建议值和实测结果值两种方式进行周期折减。对通过添加斜撑单元来考虑填充墙作用的结构模型,利用环境激励测试识别获得的结构模态信息进行模型修正。对该3种模型进行了增量动力分析,探讨潜在危险性水平地震作用下填充墙对高层建筑抗震性能的影响。结果表明,填充墙增加了结构在弹性阶段的整体初始刚度,但随着地震动强度的增加逐渐丧失对结构刚度的贡献作用。相比考虑了填充墙作用的模型计算结果,规范建议的周期折减系数较为保守。同时研究发现,填充墙对高层框-剪结构的影响程度要比剪力墙结构大。     

Using visibility analysis to improve point density and processing time of SfM-MVS techniques for 3D reconstruction of landforms

Image network geometry, including the number and orientation of images, impacts the error, coverage, and processing time of 3D terrain mapping performed using structure-from-motion and multiview-stereo (SfM-MVS). Few studies have quantified trade-offs in error and processing time or ways to optimize image acquisition in diverse topographic conditions. Here, we determine suitable camera locations for image acquisition by minimizing the occlusion produced by topography. Viewshed analysis is used to select the suitable images, which requires a preliminary digital elevation model (DEM), potential camera locations, and sensor parameters. One aerial and two ground-based image collections were used to analyse differences between SfM-MVS models produced using: (1) all available images (ALL); (2) images selected using conventional methods (CON); and (3) images selected using the viewshed analysis (VIEW). The resulting models were compared with benchmark point clouds acquired by a terrestrial laser scanner (TLS) and TLS-derived DEMs. The VIEW datasets produced denser point clouds (28–32% more points) and DEMs with up to 66% reduction in error compared with CON datasets due to reduction of gaps in the DEM. VIEW datasets reduced processing time by 37–76% compared with ALL, with no reduction in coverage or increase in error. DEMs produced with ALL and VIEW datasets had similar slope and roughness, while slight differences that may be locally important were observed for the CON dataset. The new method helps optimize SfM-MVS image collection strategies that significantly reduce the number of images required with minimal loss in coverage or accuracy over complex surfaces. © 2020 John Wiley & Sons, Ltd.     

利用InSAR数据的格尔木市地表沉降监测

InSAR及其相关技术在城市地面沉降监测中具有独特优势。本文采用2015年12月4日至2018年1月10日间的37景Sentinel-1卫星SAR影像数据，基于InSAR时间序列分析技术，对地处高原的格尔木市市区及其周边进行了地表形变监测。结果表明，基于相干性等指标共提取了252 889个相干点，每平方千米平均有501个相干点，后续经相干点分析计算后掩模去误差较大的点后，实际使用相干点252 035个；格尔木市及其周边郊区的整体沉降速率均在5 mm/a以内，未发现明显的沉降区域或大面积的沉降带，市区南部的沉降速率低于北部。     

基于深度学习理论的无人机影像分类

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。     

基于D-InSAR技术的金沙江地区滑坡形变监测与分析

滑坡是发生在我国山区的主要地质灾害类型,金沙江地区由于地势较高、地形复杂、多云多雨的特点,给传统的滑坡监测增加了难度。合成孔径雷达差分干涉测量技术(Differential interferometry synthetic aperture radar,D-InSAR)已在滑坡地面沉降监测中得到了广泛应用。本文选取金沙江上游沿岸作为研究区域,基于2018年8月11日与9月28日的Sentinel-1A影像及SRTM1数据,利用GAMMA软件及D-InSAR技术监测到金沙江地区的地表形变,成功识别出金沙江右岸的一处滑坡灾害。研究结果显示,在此滑坡的坡顶部分出现了约2.5 cm的沉降,而在坡底部分由于崩塌物的累积,地面出现了约3 cm的抬升。从实验结果可以得出,InSAR技术是一种有效的滑坡变形监测手段,利用Sentinel-1A卫星的SAR数据对滑坡区域进行形变监测,可以得到较好的干涉结果。     

长江口邻近海域夏季底栖纤毛虫的多样性与群落结构特点

采用Ludox-QPS方法,研究了2011年8月采自长江口邻近海域9个站位沉积物中纤毛虫的群落结构及分布特点,并结合沉积环境进行综合分析。结果表明,表层8cm沉积物中底栖纤毛虫的平均丰度为(2782±1493)cells/10cm2,生物量为(10.06±6.41)μgC/10cm2。长江口海域北部站位的丰度和生物量呈从近岸向外海增加,南部的站位呈现相反的分布趋势。在垂直分布上, 62%的底栖纤毛虫分布在表层2cm, 12%分布在5—8 cm。本研究共检获纤毛虫106种,隶属于15纲/亚纲, 24目, 69属,前口纲在丰度及生物量上均为最优势类群(丰度占45.5%,生物量占56.4%),核残迹纲在生物量上居第二位。就食性来看,肉食性纤毛虫物种数最多(44种),其丰度和生物量所占比例也最高(40.3%,66.8%),但在长江口外站位(M1站),菌食性纤毛虫为最优势摄食类群。分析表明,研究海域底栖纤毛虫群落结构与底层水盐度最相关。聚类分析结果显示,由于较多量的伪钟虫属(Pseudovorticella)和原领毛虫属(Prototrachelocerca)种类出现导致长江口北部L1站的纤毛虫群落结构不同于其他站位。本研究所获底栖纤毛虫的丰度和生物量较东海离岸海域已有研究结果均高,表层8cm沉积物中纤毛虫的丰度约是上层30m水柱中浮游纤毛虫的116倍,生物量约是后者的150倍。基于目前黄东海有关底栖纤毛虫物种多样性的已有报道,其多样性在长江口离岸海域高于近岸潮间带,且在离岸海域东海低于黄海。