平原地区洪灾风险评价的GIS方法研究 --以荆州6县市为例

平原地区因地势低平。水网发达而常遭到洪水的威胁。这种威胁既有来自过境洪水的洪灾．也有来自本地因暴雨引起的涝灾。如何考虑它们两者的相互影响以及洪水灾害的自然与社会经济属性，进行洪水风险评价是一个比较困难的问题。在地理信息系统软件ArcGIS8．1支持下，既考虑过境洪水与本地涝灾，也考虑承灾体的易损性(包括人口、交通线与经济)，还考虑抗灾能力，以GRID模块的地图代数作为运算手段，用AHP法确定因子的权重，建立了评估平原地区的洪灾风险的计算模型和评价方法，该模型运用于长江中游洪灾风险比较严重的地区——荆州地区的6县市．取得了比较好的效果。     

结构风灾经济损失模型在GIS中的应用

在城市抗风防灾研究中，采用GIS技术已成为一种必要的手段，但现有结构风灾经济损失模型参数多，不易采集，造成建立GIS数据库具有一定的困难。因此，简化模型，降低数据采集难度的工作量是十分必要的，本文采用样本计算，制成表格供查询使用，在保证计算精度的前提下，使参数由12个减为2个，大大降低了数据采集和GIS数据库建立的难度。     

基于GIS和SPSS的湖北省可持续农业发展能力研究

本文基于可持续农业发展内涵构建其能力评价指标体系 ,应用GIS技术、因子分析模型和模糊数学方法对湖北省可持续农业发展能力进行了评价与聚类分区 ,并对影响湖北省可持续农业发展能力的主因子和各分区作了简要评述。旨在揭示湖北可持续农业发展存在的问题及其地域分异规律 ,为政府农业决策提供参考依据。     

基于组件式GIS的重大危险源管理系统的设计与开发

基于组件式GIS技术建立城市重大危险源管理系统。概述了组件式GIS的技术特点 ,结合当前城市重大危险源管理的要求 ,利用该技术从系统结构、系统功能、数据库结构等方面完成城市重大危险源管理信息系统的设计。实现了反映城市重大危险源数据的图层的建立以及重大危险源的评价分级功能 ,并提出了危险源的专题图分析和危险事故分析方法 ,完成了城市重大危险源管理系统的开发。     

遥感与GIS支持下的城市绿地信息提取方法研究--以深圳市为例

城市绿地作为城市结构中的自然生产力主体，在城市系统中起着重要作用。传统的城市绿地调查多采用人工普查并结合统计学方法进行城市绿地调查，需要投入大量的人力和资金，但得到的数据精度和现时性都较差，而采用高分辨率遥感卫星数据与地理信息系统相结合的方法，可以达到对城市绿地遥感数据的快速特征提取和准确的空间统计分析。文章采用基于遥感和GIS相结合的方法，并以深圳市为例，进行了城市绿地特征提取方法的研究和应用分析。     

GIS技术在区域土壤有机碳储量估算方面的应用--以柴达木盆地为例

目前在估算土壤有机碳储量方面的方法较多,并且都有其各自的特点所在。地理信息系统(GIS)技术作为当今一种在地学统计分析方面具有强大空间分析功能的计算机应用技术,在地学及相关研究领域得到了广泛的应用。文章首次尝试了利用该技术估算柴达木盆地土壤有机碳储量,该方法与其它计算方法相比,有简单、易操作、可视性好等优点。经计算,柴达木盆地的土壤有机碳储量约为62754.442 t,土壤平均碳面密度为6.105 kgm-2。     

GIS在城市给水排水中的应用

阐述了GIS在城市给水排水领域的应用和发展趋势，包括水资源管理与分析、给水排水管网设计与管理、城市污水处理厂设计与管理、管网应急预警系统分析、水质污染状况和趋势分析等，并以大庆市给水排水管网信息管理系统实例，证明GIS可以及时准确完整的提供所需信息，更好的为市政管理者辅助决策提供依据。     

Ecological modelling of nitrate pollution in small river basins by spreadsheets and GIS

With the increasing importance and awareness of water quality in the small basins, the modelling system is developed for monitoring and predictions of surface water pollution. The compartment model deals with basin characteristics extended by land cover attributes. The parameters of the model are estimated by experimental data of water quality together with land cover types that serve as nutrient detention media or transformers. The study examines methodology to determine the potential areas of nitrate pollution from point and non-point sources by remote sensing techniques. Classification of water, agricultural, forest and urban areas is processed with satellite images (LANDSAT 7). Whereas the agricultural and urban areas act as sources of pollution, forest zones operate as sinks. The nitrate levels are decreased downstream, if the proportion of the forest inside contribution zones increases. The modelling system is used to simulate amounts of nitrates in each compartment of the stream during the monitored period of one year. The number of compartments and their lengths are estimated on the basis of morphology of the basin. Simulation of the dynamic model is carried out with the TabSim. Geographic information system (GIS) and remote sensing tools are used to manage and estimate nitrate inflows from point and non-point sources of pollution. The article presents the spatial and time variation of the nitrate (NO₃⁻) within the basin of the stream Rakovnický (the west part of Bohemia, the whole area of 368 km²). It is shown that the model approach extended by the GIS and remote sensing can support decision-making process for better management practices in the basins.     

Model-based combination of spatial information for stream networks

Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial support is of vital importance to environmental science. In this paper we develop a hierarchical spatial statistical model for environmental indicators of stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic units and remote sensing information. These models are used to estimate the indicators throughout the riverine system based on information from multiple sources and aggregate scales. The analysis is based on information underlying the Landscape Atlas of the mid-Atlantic region produced by the US Environmental Monitoring and Assessment Program (EMAP). We also combine information from two overlapping separate monitoring surveys, the EMAP Stream and River Survey and the Maryland Biological Streams Survey. We present a general framework for comparative distributional analysis based on the concept of a relative spatial distribution. As an application, the spatial model is used to predict spatial distributions and relative spatial distributions for a watershed.