基于地理信息系统的数字环境模型研究

本文提出了以遥感与地理信息系统为技术手段,用有序数值阵列的方法建立数字环境模型的新思路,具体阐述了建立数字环境模型的意义、方法和应用范围。并在建立数字环境模型的基础上,对西藏“一江两河”地区的环境综合状况进行了定量评价。     

基于BP神经网络的昆明天顶湿延迟模型

为了满足昆明市卫星定位综合服务系统(KMCORS)对高精度天顶湿延迟(ZWD)的需要,本文开发了适用于昆明地区的ZWD模型KM。KM模型是根据昆明探空站2015-2018年的探空资料,基于误差反向传播(BP)神经网络建立的,同时采用2019年的探空数据,验证了KM模型的预测性能。测试结果表明,与广泛使用的SA模型相比,KM模型的RMSE由4.0 cm降至2.2 cm,精度提升了45%;KM和SA模型的Bias分别为0和-3.1 cm。该结果表明KM模型对ZWD估计具有无偏性,而SA模型在高原区存在过度估计的问题,KM模型具有比SA经验模型更优的预测性能,其应用将有助于提升KMCORS的服务质量。     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

多维时空场数据的多模式张量表达模型

对数据量及复杂度日益激增的多维时空场数据进行有效的管理与分析是当前GIS必须解决的重要瓶颈。本文通过引入具有多维统一与坐标无关特性的张量数学结构,研究了多维时空场数据的多模式表达模型,实现了地学数据的张量表达以及面向分析的多维张量数据流与任务流构建。针对不同类型的数据管理与分析需求,通过定义基于原始张量、张量分解以及层次张量分解的三类张量数据表达及应用模式,实现了面向不同应用场景的多维时空场数据的管理与分析。在此基础上,设计了基于张量的多模式数据分析框架以及应用分析业务流模板。对上述关键技术进行算法实现并基于卫星测高数据对相关性能进行了测试与验证。结果显示,基于张量的新型海量时空场数据的多模式表达模型可以很好地支撑海量时空场数据的管理、分析与存储,并在存储空间占用,检索与分析效率上具有明显优势。     

多元遥感技术在重庆市露天矿恢复治理动态监测中的应用

为在露天矿山恢复治理过程监督管理中提供技术支撑,通过文献阅读,结合重庆实际,提出了基于多源遥感技术的露天矿山恢复治理综合监测方法,主要包括：利用高分辨率遥感影像结合矿山遥感样本特征以及其他专题数据,识别矿山损毁土地范围;利用中分辨率遥感影像（Landsat数据、高分数据）对植被光谱的响应特征,构建植被指数进行矿山恢复治理植被动态监测;利用星载雷达数据（哨兵数据）通过干涉分析,对矿山范围内地表形变动态监测。此外,还可结合无人机采集数据,开展矿山恢复治理倾斜摄影数据查看和分析。     

贵州省国土资源执法监察监管系统建设与应用

针对贵州省山地居多、生态环境脆弱、交通通讯不发达,人力资金相对不足等现状,研究建设了在线、离线相结合,内业、外业相协同,"省、市、县、乡"四级一体化应用,涵盖"线索发现、问题分析、外业核查、立案查处"全流程的国土资源执法监察监管系统。系统突破了高效、高精度、多模式的国土资源执法监察监管的系列关键技术方法,建立了国土资源监察监管系统与"一张图"平台一体化集成应用的新模式,已经部署应用到贵州省国土资源执法局和9个市州国土局和若干区县国土局。实践表明系统可以节省大量的国土资源执法人力、物力和时间,有效提升贵州省国土资源执法监察监管的效率和水平。     

多核并行环境中矢量数据格式的转换算法

针对目前对VCT格式转换的并行算法研究较少的现状,该文以VCT向shapefile格式转换为例,探索多核并行环境中进行VCT格式向其他矢量数据格式快速转换的方法:在深入探索VCT与shapefile格式特点的基础上,通过构建索引、数据解析转换,完成由VCT向shapefile格式的转换;并设计出一种顾及负载均衡的数据划分方法,通过评估不同类型图层的复杂度实现不同类型矢量数据的均衡划分,实现了VCT向shapefile格式转换的并行算法。最后利用某地土地利用VCT文件进行算法测试,结果表明该算法转换结果正确,取得了良好的加速比,并基本达到负载均衡。     

图像蒙版的三维场景专题信息表达

本文提出利用图像蒙版技术表现场景专题信息:通过将重要的场景专题信息以单一图像的形式罩于地形表面,形成地形"蒙版",使用户只关注蒙版即可获取所需信息,用户可以在最短时间内形成对场景专题信息的总体把握与正确认识。这种方法在一定程度上提高了用户对场景的认知效率与深度。     

Scale Effects on Chlorophyll-A Concentration Retrieved: Assessment and Validation Using Indian Remote Sensing Satellite

To understand the scale effects on chlorophyll-a (chl-a) concentration retrieved from satellite images, the two-band algorithm (TA) and three-band algorithm (TBA) were constructed for estimating chl-a from satellite images. Two synchronous images of Advanced Wide-Field Sensor (AWiFS) and Linear Imaging Self-Scanner (LISS) of Indian remote sensing satellite were used to assess and validate the scale errors of these two algorithms. They were collected at local time 02:55:46:471 and 02:58:25:053 on October 8, 2005 in Yellow River Estuary, and their spatial resolutions are 24 m and 56 m, respectively. From the results of this study, it was found that: (1) the relative scale error (RSE) of TA and TBA, caused by scale changing from LISS to AWiFS, varied from 0% to 100%; (2) the RSE was correlated with the spatial non-homogeneous degree of chl-a distribution; and (3) using TBA to estimate chl-a concentration in Yellow River Estuary decreased 2.55% of model uncertainty, but increased 4.97% of scale errors, in comparison with TA. Additionally, the study indicated that the performance of algorithms for chl-a estimation was greatly affected by the scale error. If the scale effects of chl-a retrieval algorithm were taken into consideration, TA had a superior performance to the TBA in this study.     

一种改进的点与多边形关系的叉乘判别法

本文针对叉乘判别法只适用于凸多边形的缺陷提出了改进,将其推广至凹多边形和自相交多边形,改进方法利用叉乘值的正负变化,选择对点的积分路径,从而判断是否有闭合的路径包围被测点。并通过检测多边形是否环绕被测点,判断点与多边形的关系,属于非零环绕数法。经过实验,改进方法被证明有较快的速度和较强的适应性。