基于遥感技术的龙门山前山带烃类微渗漏信息提取

通过对龙门山前山带含油区与非含油区的岩石、土壤、植被等进行测试,认为烃类微渗漏会造成地表热异常、红层褪色、低价铁富集以及粘土矿化等现象,并对研究区的粘土矿物、Fe3+、Fe2+与烃类的波普曲线特征进行了分析对比。以ETM卫星图像为主要信息源,选择了TM2/TM3、TM3/TM4和TM5/TM7进行比值处理,从光谱数据中直接提取出碳酸盐化、粘土矿化、红层褪色等油气蚀变信息,并在对研究区遥感地质解译的基础上,指出烃渗漏信息异常晕主要沿断裂带分布,特别在不同走向断裂的交汇区域分布面积和强度较大,而在已知油气圈闭区域,信息晕主要呈碎片状或斑点状零散分布,且以低值异常为主。根据烃渗漏异常的分布特征,提取出石板滩、中坝、孝泉、聚源、鸭子河以及邛崃等遥感信息异常区块。     

Spatial patterns and environmental associations of deep scattering layers in the northwestern subtropical Pacific Ocean

The mesopelagic communities are important for food web and carbon pump in ocean, but the large-scale studies of them are still limited until now because of the difficulties on sampling and analyzing of mesopelagic organisms. Mesopelagic organisms, especially micronekton, can form acoustic deep scattering layers (DSLs) and DSLs are widely observed. To explore the spatial patterns of DSLs and their possible influencing factors, the DSLs during daytime (10:00–14:00) were investigated in the subtropical northwestern Pacific Ocean (13°–23.5°N, 153°–163°E) using a shipboard acoustic Doppler current profiler at 38 kHz. The study area was divided into three parts using k-means cluster analysis: the northern part (NP, 22°–24°N), the central part (CP, 17°–22°N), and the southern part (SP, 12°–17°N). The characteristics of DSLs varied widely with latitudinal gradient. Deepest core DSLs (523.5 m±17.4 m), largest nautical area scattering coefficient (NASC) (130.8 m2/n mile2±41.0 m2/n mile2), and most concentrated DSLs (mesopelagic organisms gathering level, 6.7%±0.7%) were observed in NP. The proportion of migration was also stronger in NP (39.7%) than those in other parts (18.6% in CP and 21.5% in SP) for mesopelagic organisms. The latitudinal variation of DSLs was probably caused by changes in oxygen concentration and light intensity of mesopelagic zones. A positive relationship between NASC and primary productivity was identified. A four-months lag was seemed to exist. This study provides the first basin-scale baselines information of mesopelagic communities in the northwest Pacific with acoustic approach. Further researches are suggested to gain understandings of seasonal and annual variations of DSLs in the region.     

Distribution and invasion of Spartina alterniflora within the Jiaozhou Bay monitored by remote sensing image

Spartina alterniflora as an alien invasive plant, poses a serious threat to the ecological functions of the coastal wetland of the Jiaozhou Bay. As of 2019, the distribution area of S. alterniflora in the Jiaozhou Bay has reached more than 500 hm². For this reason, combined with field surveys, remote sensing monitoring of the invasion S.alterniflora in the Jiaozhou Bay has been carried out. To accurately identify S. alterniflora within the Jiaozhou Bay coastal wetland, we used a new m...     

基于深度学习的循环水养殖鳗鲡(Anguilla)计数研究

鳗鲡(Anguilla)作为我国优质水产养殖种类,精准掌握其数量对高效养殖有重要意义。为实现对循环水养殖鳗鲡的准确计数,提出了一种基于深度学习的改进Faster RCNN模型。针对检测目标即鳗鲡头部尺寸小的问题,选择在特征提取网络ResNet50中加入FPN结构来作为模型的骨干网络,以提取并融合多尺度的特征;针对原模型锚框都是基于人工经验设置的,并不适用于鳗鲡数据集的问题,使用k-means聚类算法对训练集中标注的鳗鲡头部检测框进行聚类分析,获得了适合鳗鲡数据集的15种不同尺度的锚框;针对图像中存在鳗鲡头部重叠的问题,选择使用Soft-NMS算法替代原NMS算法对RPN部分生成的候选框进行筛选,以减少模型对鳗鲡重叠部分的漏检情况。试验结果表明:改进后的Faster RCNN模型对鳗鲡头部的检测精度(mAP^0.5)高达96.5%,较原Faster RCNN模型(Backbone为ResNet50)显著提升了14%,与SSD300和YOLOV3模型相比分别显著提升了24.9%和15%;在鳗鲡计数上,利用改进后的Faster RCNN模型检测结果进行计数,计数准确率达到90%以上,提升了模型对鳗鲡的检测识别能力。     

基于卷积神经网络的ICESat-2光子点云去噪分类

ICESat-2（Ice, Cloud, and land Elevation Satellite-2）是美国NASA（National Aeronautics and Space Administration）在2018年发射的激光测高卫星,其上搭载的激光测高系统ATLAS（Advanced Topographic Laser Altimeter System）采用微脉冲多波束光子计数激光雷达系统,因其低能耗、高探测灵敏度、高重复频率的特性极大改善了沿轨采样密度,但也使获取的数据中包含大量的噪声,如何有效实现光子点云去噪分类成为后续应用的关键,也是当前研究的热点和难点,为此本文提出一种基于卷积神经网络的光子点云去噪和分类算法。首先将光子点云按照沿轨和高程方向划分格网,去除明显的噪声光子,并将每个粗信号光子点栅格化为影像;然后基于少量样本构建的卷积神经网络分类模型实现光子点云精去噪和分类;最后利用机载激光雷达数据进行验证,并与ATL08产品的去噪分类结果进行对比。结果表明,对于裸地和森林区域,卷积神经网络算法均能有效去除噪声光子,特别对于森林区域,可同时实现去噪和分类;其中,裸地区域地表计算的R²和RMSE分别为1.0和0.72 m,森林区域地表和树冠计算的R²分别为1.0和0.70, RMSE分别为1.11 m和4.99 m。本文利用深度学习算法实现光子点云去噪分类,在裸地和森林区域均取得了较好的结果,为后续光子点云数据处理提供了参考。     

基于“数字城市”地理空间框架的国土资源档案管理系统的设计与实现

"数字城市"地理空间框架建设中发布的地理信息数据具有丰富性、多样性及准确性等诸多明显优势,基于该框架开发的国土资源档案管理系统,不仅可以使各类档案实现在线信息化管理,并且对地籍档案、建设用地档案及利用规划类档案实现在线位置查询及浏览具有很大作用,可以极大地提高档案信息化管理和应用水平。     

山东省栖霞市笏山-西陡崖金矿地质特征及深部找矿远景

笏山-西陡崖金矿为台前-陡崖成矿带2012年新提交的大型金矿床,该金矿严格受台前-陡崖断裂带控制,-675m标高以上累计探明资源储量金金属量30t,通过对其成矿地质背景、矿床地质特征及矿床成因、找矿标志、成矿规律的总结分析,指出了深部-1 000m标高以深的找矿远景,为进一步开展深部及走向上继续"攻深找盲"提供了依据。     

基于SOA云服务的济宁市电子政务服务平台研究

基于政务网的政府各部门横向与纵向信息共享及业务协同的需求十分迫切,传统的单向共享模式的应用瓶颈日益突出,结合济宁市政务信息平台建设的实际环境及需求,设计了基于SOA云服务的政府部门间信息资源共享及在线业务协同平台的框架,实现了政府各部门之间数据有序交换和资源共享,建立了数据建设规范和业务协同机制,极大地提升了政务办公自动化水平。     

Making a Mark: a computational and visual analysis of one researcher’s intellectual domain

Dr. David Mark is widely regarded as a path-breaking researcher in geographic information science. What are the structural and temporal characteristics of his intellectual contributions, as seen through the eyes of the broader academic community? Aiming to answer that question, this article presents a scientometric analysis of publications that have been cited alongside David Mark’s papers. In deliberate contrast to the widespread focus on using citation data to condense scientific impact into a handful of indicators, the methodological contribution of this study lies in its mix of computational and visualization approaches. In the search for latent domain structures, state-of-the-art practices in information science, bibliometrics, and network visualization are combined and extended. An initial network of 50,000+ publications and 4,000,000+ document co-citations undergoes a series of transformations reducing it to 9000 publications that are then clustered in a two-stage process, leading to 678 communities whose co-citation linkages are used to delineate 19 super-communities. To enable replication of this approach for other studies, much focus in this article is on detailed discussion of that workflow as well as on highlighting the reasoning behind the choices made among data sources and analytical methods. The topical evolution of David Mark’s domain of influence is explored in some detail, based on tabular and graphic representations of extracted community structures. Results confirm not only the enormous overall breadth of his influence but also how lasting and recurrent it has been in some areas.     

An agent-integrated irregular automata model of urban land-use dynamics

Urban growth models are useful tools to understand the patterns and processes of urbanization. In recent years, the bottom-up approach of geo-computation, such as cellular automata and agent-based modeling, is commonly used to simulate urban land-use dynamics. This study has developed an integrated model of urban growth called agent-integrated irregular automata (AIIA) by using vector geographic information system environment (i.e. both the data model and operations). The model was tested for the city of San Marcos, Texas to simulate two scenarios of urban growth. Specifically, the study aimed to answer whether incorporating commercial, industrial and institutional agents in the model and using social theories (e.g. utility functions) improves the conventional urban growth modeling. By validating against empirical land-use data, the results suggest that a holistic framework such as AIIA performs better than the existing irregular-automata-based urban growth modeling.