乌鲁木齐市土地利用景观格局变化研究

运用GIS、RS技术及景观生态学理论和方法,基于乌鲁木齐市2006年和2010年遥感影像解译数据,对乌鲁木齐市5年间土地利用景观格局变化特征进行分析,分别从斑块类型和景观水平2个方面对研究区土地利用景观类型进行动态分析。揭示其变化原因和内在规律,为研究区生态治理提供数据支撑,对景观的规划与管理、资源的有效利用和生物多样性保护具有重要意义。     

大连市臭氧污染特征及典型污染日成因

通过对大连市区10个空气监测子站的监测数据进行分析,探讨了大连市臭氧污染的时空分布、气象条件对臭氧污染的影响,对臭氧污染日进行了归类分析。结果表明,大连市臭氧污染主要出现在4—10月。在强紫外辐射、高温、低湿、低压和低风速的气象条件下,监测点位的臭氧浓度较高。臭氧污染日的日变化分为单峰型、双峰型和夜间持续升高型3种类型。通过对2015年的一次高浓度臭氧污染过程的气象条件、污染物浓度和污染气团轨迹进行分析,发现臭氧浓度在夜间持续升高现象与区域输送密切相关。     

基于CA-Markov模型的艾比湖流域平原区景观格局动态模拟预测

利用“3S”技术及CA-Markov模型,按照是否实施跨流域调水工程2种预案情况,以2005年为起始时刻,对新疆艾比湖流域平原区2020年景观格局进行模拟预测。结果显示：在调水工程未实施的情况下,2020年研究区的生态环境将进一步恶化,其突出表现为艾比湖湖泊水面将持续萎缩、裸露湖底盐漠面积进一步扩大,水资源短缺及生态环境恶化的结果将严重制约研究区社会经济的发展;在调水工程实施的情况下,2020年研究区内艾比湖湖泊水面将稳定增加至800 km^2以上、裸露湖底盐漠面积相应有所减少,生态环境恶化趋势得到改善,区域水资源短缺问题将有所缓解,可有效地促进研究区内社会经济的发展。     

中国人口集疏格局与形成机制研究

人口集聚和疏散是人口空间分布格局最直观和最集中的体现,基于人口集聚度的中国人口集疏的空间格局和形成机制的研究,有助于准确把握中国人口空间分布的基本脉络,具有重要的学术价值和实践意义。研究采用了人口集聚度分级评价的方法,依据人口集聚度的不同,将各个地区划分为不同等级的人口集聚区,并对其分别讨论。在此基础上,结合中国人口分布格局、自然条件的空间分布格局、人居环境自然适宜性评价结果以及经济发展格局和城市化格局,对中国的人口集疏的空间格局进行了归纳,并进一步对相应的形成机制进行了探讨,得出了我国人口分布的空间不平衡日益加剧,并呈现沿海、沿江、沿线集聚的态势的基本结论,并对我国人口分布集疏格局的形成进行了探讨,提出了自然因素奠定我国人口分布的基本格局,而经济发展不平衡与城市化成为人口集聚的动力的观点。     

应用隶属函数的条件概率法综合判别雹云

本文提出应用隶属函数的条件概率法综合判别雹云。该方法把雹云和非雹云视作参数的模糊集合,因而在综合判别式中,每个参数对雹云和非雹云都有一个隶属度。利用成都市1982～1987年天气雷达回波历史资料,分别建立了全年、4～6月和7～8月的雹云等强对流天气的综合判别式,并用建立的模式对成都市1990年4～6月的7个强对流天气个例作试报检验。结果表明用该方法建立不同地区和不同季节的雹云等强对流天气综合判别式是可行的。     

镇江市综合防灾系统中消防信息的输入与管理

本文以镇江市消防地图及其相关信息为对象,分析了消防信息的特点和现有的输入方法,以及作者在设计与建造镇江市消防信息数据库的实践中采用的技术路线和具体方法。     

提高交通安全——实现智能汽车的研究

主要介绍了通过智能汽车的研究 ,提高城市交通安全的必要性 ;综述了发达国家关于智能汽车研究的现状与趋势 ,并通过国外智能汽车发展的趋势 ;进一步论证智能汽车的研究是汽车工业发展的方向 ,也是提高交通安全的重要手段之一。此外 ,论文提出利用模式识别技术的智能汽车的结构框架 ;指出神经网络方法应用与模式识别技术结合的优势。最后 ,论文在阐述我国需要发展智能汽车的同时 ,提出应结合中国国情相应研究相关理论 ,为今后的实施奠定基础的近期目标 ,并提出展望     

DEVELOPMENT OF WATERSIIED MODELS FOR TWO SIERRA NEVADA BASINS USING A GEOGRAPHIC INFORMATION SYSTEM1

ABSTRACT: Techniques were developed using vector and raster data in a geographic information system (GIS) to define the spatial variability of watershed characteristics in the north-central Sierra Nevada of California and Nevada and to assist in computing model input parameters. The U.S. Geological Survey's Precipitation-Runoff Modeling System, a physically based, distributed-parameter watershed model, simulates runoff for a basin by partitioning a watershed into areas that each have a homogeneous hydrologic response to precipitation or snowmelt. These land units, known as hydrologic-response units (HRU's), are characterized according to physical properties, such as altitude, slope, aspect, land cover, soils, and geology, and climate patterns. Digital data were used to develop a GIS data base and HRIJ classification for the American River and Carson River basins. The following criteria are used in delineating HRU's: (1) Data layers are hydrologically significant and have a resolution appropriate to the watershed's natural spatial variability, (2) the technique for delineating HRU's accommodates different classification criteria and is reproducible, and (3) HRU's are not limited by hydrographic-subbasin boundaries. HRU's so defined are spatially noncontiguous. The result is an objective, efficient methodology for characterizing a watershed and for delineating HRU's. Also, digital data can be analyzed and transformed to assist in defining parameters and in calibrating the model.     

区域化变量理论在历史洪涝灾害空间格局重建中的应用──以长江流域1736-1911年洪涝灾害为例

通过构造一个例子,简介了区域化变量理论的基本原理、计算过程及其在洪涝灾害空间格局重建中的优点。依据区域化变量理论,以长江流域1736～1911年洪涝灾害为实例,重建了长江流域历史时期洪涝灾害的基本空间格局．     

The concept of degraded images applied to hazard recognition training in mining for reduction of lost-time injuries

Introduction: This paper discusses the application of a training intervention that uses degraded images for improving the hazard recognition skills of miners. Method: NIOSH researchers, in an extensive literature review, identified fundamental psychological principles on perception that may be employed to enhance the ability of miners to recognize and respond to hazards in their dangerous work environment. Three studies were conducted to evaluate the effectiveness of the degraded image training intervention. A model of hazard recognition was developed to guide the study. Results: In the first study, miners from Pennsylvania, West Virginia and Alabama, who were taught with the aid of degraded images, scored significantly better on follow-up hazard recognition performance measures than those trained using traditional instructional methodologies. The second and third studies investigated the effectiveness of the training intervention at two mining companies. Data collected over a 3-year period showed that lost-time injuries at mines in Alabama and Illinois declined soon after the training intervention was instituted. Impact on Industry: Further exploration of the hazard recognition model and the development of other interventions based on the model could support the validity of the steps in the hazard recognition model.