基于广义回归神经网络的全球表层海水1°×1°二氧化碳分压数据推演

表层海水二氧化碳分压是评估海洋碳源汇强度的关键参数,但其实测数据较少、时空分布极不均匀,导致二氧化碳交换通量的估算有很大的不确定性,海洋源汇特征就不能确切获取。为了解决这个难题,在收集的表层大洋二氧化碳地图(Surface Ocean CO₂ Atlas,SOCAT)实测数据集基础上,运用广义回归神经网络建立二氧化碳分压与经纬度、时间、温度、盐度和叶绿素浓度间的非线性关系,构建了1998?2018年间全球1°×1°经纬度的表层海水二氧化碳分压格点数据,其标准误差为16.93 μatm,平均相对误差为2.97%,优于现有研究中的前反馈神经网络、自组织映射神经网络和机器学习算法等方法。根据构建的数据所绘制的全球表层海水二氧化碳分压的分布与现有研究有较好的一致性。     

基于二三维一体化的智慧警务地理信息平台建设与研究

"智慧警务"是"智慧城市"的重要组成部分,也是社会治安防控体系发展的必然趋势,应提高认识,积极探索和论证建设"智慧警务"的模式和方法。本文阐述了基于二三维一体化的智慧警务地理信息平台建设内容和总体框架,并详细地介绍了警用地理信息系统及智慧警务应用系统建设内容和技术路线。     

基于与Jason-2数据比对的Jason-3卫星高度计全球数据质量评估

Jason-3卫星高度计于2016年1月17日成功发射,2016年2月12日进入预定轨道,与Jason-2高度计同轨进入编队飞行阶段,并落后Jason-2高度计约1分20秒,两者相距约560 km。2016年9月1日,Jason-2高度计变换轨道,编队飞行阶段结束,两高度计进入平行轨道,以增加卫星高度计对地观测的空间覆盖。本研究主要开展了Jason-3高度计的数据质量的评估与检验,包括Jason-3高度计数据可用性和有效性的验证,以及Jason-3高度计和校正辐射计各参数的数据质量监测。重点开展了Jason-2与Jason-3高度计各项参数的综合比较,利用Jason-2与Jason-3高度计编队飞行阶段的数据精确评估了两高度计参数的一致性,并从全球数据角度分析了Jason-3高度计获取各参数的能力以及稳定性;通过与Jason-2互交叉点比较分析评估Jason-3高度计海面高度数据质量情况,验证Jason-3高度计数据精度。结果表明,Jason-3高度计的数据质量满足高度计测高的要求,具有与Jason-1、Jason-2、T/P等高度计相同或更高的测高精度以监测全球海平面变化,此外,Jason-3有效波高参数数据质量明显优于Jason-2高度计。     

羧乙基壳聚糖蒙脱石吸附养殖水体中Cu2+的应用

为了寻求性能良好、环保吸附剂,用于去除养殖水体中重金属Cu~(2+),作者利用蒙脱石负载羧乙基壳聚糖制备成复合吸附剂,并利用IR、SEM、XRD等手段分析其表面性能,将其用于处理Cu~(2+)溶液,考察了环境因子对其吸附性能的影响,并从吸附动力学和吸附热力学角度分析吸附剂对Cu~(2+)的吸附机理,最后考察其再生利用效果。结果表明:羧乙基壳聚糖成功进入到蒙脱石层间;在羧乙基壳聚糖与蒙脱石质量比为1︰25、40℃恒温水浴搅拌60min、pH=6.0、最佳投加量为4.0g/L、处理浓度不超过30 mg/L Cu~(2+)溶液时,复合吸附剂对Cu~(2+)去除率可以达到96.23%,将其用于淡水养殖鱼塘水体中, Cu~(2+)去除后可达到《渔业水质标准》规定;吸附剂对Cu~(2+)吸附热力学实验结果表明,符合Langmuir模型,反应过程为自发、吸热反应;吸附动力学结果表明该吸附符合准二级动力学方程,反应属于化学吸附;再生实验中NaOH的再生效果优于HCl。     

琼胶/SiO2复合纤维的制备及性能研究

利用湿法纺丝技术制备了琼胶/SiO₂复合纤维,对琼胶分子在溶液中的分散性、纺丝液流变性、纤维的形貌、化学结构分别用DLS、旋转黏度计、SEM、FTIR和XRD进行了表征,并对纤维的力学性能、热性能和吸湿性能进行了测定。研究结果表明：琼胶分子在溶液中呈纳米尺度分布,纺丝液具有良好的流动性;琼胶/纳米SiO₂复合纤维具有良好的形态。随着纳米SiO₂的添加量逐渐提高,复合纤维的力学拉伸强度先增强后降低,复合纤维的吸水性降低,复合纤维的热稳定性逐渐增强。结合复合纤维综合性能,纳米SiO₂的最佳添加质量分数为0.5%。     

基于核函数NPSSB模型的HY-2A卫星雷达高度计海况偏差研究

利用机器学习的方法,对14个周期HY-2A卫星高度计数据:风速、有效波高和海面高度差值进行训练,探究海况偏差和风速、有效波高之间的关系,创建海况偏差核函数非参数模型(NPSSB),并与参数模型中具有代表性的BM3、BM4模型进行对比。研究表明:(1)核函数NPSSB模型能够很好的反映SSB与U、SWH之间的关系,SSB与U呈二次函数关系,SSB与SWH呈反比例函数关系;(2)核函数NPSSB模型对SSB的模拟能力与训练数据集相关,数据量越多,模拟能力越好;(3)核函数NPSSB模型与BM3、BM4模型都存在0^-0.03 m的差值,随着风速和有效波高的增加,差值的绝对值越大。     

Cu2+对序批式反应器中活性污泥胞外聚合物产量及其组分的影响

研究了长期暴露条件下Cu2+对序批式反应器(SBR)性能及其活性污泥胞外聚合物(EPS)特性的影响。结果表明,进水中加入10 mg·L-1的Cu2+后,在SBR运行的第16~55天,COD和NH+4-N的去除率保持稳定;在第56~75天,COD和NH+4-N的平均去除率与进水Cu2+浓度为0 mg·L-1时相比分别下降了3.88%和6.41%。浓度为10 mg·L-1的Cu2+长期作用下,活性污泥中EPS、松散附着EPS(LB-EPS)和紧密附着EPS(TB-EPS)产量及LB-EPS和TB-EPS中蛋白质(PN)含量增加。傅里叶变换红外光谱分析表明10 mg·L-1 Cu2+的长期暴露导致TB-EPS中PN的C=O键、N-H键和C-O键相对含量降低。X射线光电子能谱(XPS)测试结果显示在10 mg·L-1 Cu2+长期暴露条件下,LB-EPS和TB-EPS中元素Cu和O百分含量增加。     

Targeting rare earth element bearing mine tailings on Bangka Island,Indonesia, with Sentinel-2 MSI

A laboratory study on rare earth element bearing mine tailings, collected from Bangka Island, Indonesia, reported a new spectral absorption feature at 674 nm associated with Erbium. The present study aims to evaluate the capability of the European Space Agency’s Sentinel-2 MSI sensors to detect this absorption feature from space. An arithmetic band operation is performed on selected visible and near-infrared spectral bands of a Sentinel-2 image. The results show that Sentinel-2 MSI is capable of detecting the 674 nm Erbium-related absorption feature within the particular environmental setting of the study area.     

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

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.     

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

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.