INTEGRATION OF WATER QUALITY MODELING,REMOTE SENSING,AND GIS1

ABSTRACT: Water quality modeling has been developed for more than three quarters of a century, but is limited to the study of trends instead of making accurate short-term forecasts. A major barrier to water quality forecasting is the lack of an efficient system for water quality monitoring. Traditional water quality sampling is time-consuming, expensive, and can only be taken for small sizes. Remote sensing provides a new technique to monitor water quality repetitively for a large area. The objective of this research is to use remotely sensed data in a water quality model - QUAL2E - in a case study of the Te-Chi Reservoir in Taiwan. The water quality variables developed from the simulations are displayed in map form. The developed forecasting system is designed to predict water quality variables using remote sensing data as an input to initialize and update water quality conditions.     

热带海洋大气环境氯离子浓度立体化观测技术研究

目的 对热带海洋环境氯离子开展立体化观测,掌握热带海洋环境下大气氯离子浓度在空间上的分布规律,为海洋大气环境下装备产品和重大工程服役环境研究提供立体化观测数据支撑。方法 在观测海岸线附近科学选点,采用无人机搭载远程控制大气采样器开展大气氯离子浓度立体化观测,对不同空间高度氯离子浓度分布规律进行分析。结果 成功采集万宁某海岸线附近1 km×300 m×100 m区域1 a以上氯离子浓度数据,通过GIS技术分析并建立了氯离子浓度分布三维场,实现了氯离子浓度可视化,并随离海距离、离地高度剖切显示。结论 海南万宁某区域海岸线附近氯离子浓度在冬季最大,夏季最低,且在一定观测范围内,随观测高度增加而先增大、后降低,随离海距离增大而逐渐降低。     

Population Distribution and Influencing Factors Based on ESDA

Based on the exploratory spatial data analysis(ESDA) technique and geographic information system(GIS) platform,with statistic data of counties in 2005,this paper confirms that there is a large population density gap between counties in 2005 because the Gini coefficient is 0.55.Population distribution does not change a lot during the past decades,and the southeast China is still much more densely populated than the northwest China.The global spatial autocorrelation of population distribution is obvious because Moran’s I scores 0.42 and local spatial autocorrelation is partly significant.Climate and elevation are still the main natural influencing factors.Meanwhile industrial structure and transportation significantly influence population distribution.Different combinations of natural factors have different effects on population distribution.For a long term,climate and terrain factor stability affect population distribution.But its influence will be weakened by progress of technology.Economic development is the main factor that changes population distribution for a short term.     

浙江省灾害预防与经济协调发展的系统动态学模型

本文运用系统工程原理和系统动态学方法,构造了浙江省灾害预防与社会经济协调发展的系统动态学模型,并运用该模型对浙江省进行了初步的仿真研究,以寻求适宜的防灾政策。     

走向未来的一个战略问题——山东省人口、资源、环境协调发展的回顾与展望

山东是人口和资源大省,当前面临着人口、资源与环境三大问题,为了谋求经济社会持续、稳定、协调发展,要提高对人口、资源、环境三者关系再认识,将它作为复杂的巨系统进行分析、研究,并制定相应的政策和措施,各级领导必须强化人口、资源与环境意识和目标责任制,提高管理水平,以促进未来发展。     

基于GIS空间关联规则的挖掘综述

空间关联规则是将空间数据库访问技术与事物数据库中常用的关联规则挖掘算法相结合,能有效地挖掘出隐藏在数据背后的知识、规则。介绍了空间关联规则挖掘的现状,给出了空间关联规则及相关的术语定义,对基于地理信息科学(GIS)的空间关联规则挖掘方法进行了归纳与分析。通过对近年空间关联规则的发展研究,结合GIS的发展现状,提出了基于GIS的空间关联规则挖掘的发展趋势。     

Optimization of tourism impacts within protected areas by means of genetic algorithms

The search for a balance between nature conservation and tourism development within protected areas is becoming an increasingly multifaceted problem worldwide, as outlined by an increasing number of authors and highlighted at several international events. Since it is unlikely that all management objectives will reach their optimum values simultaneously, an optimization approach is required to meet multiple, conflicting goals and to obtain an overall trade-off in terms of the conceived objectives.     

Classification and spatial mapping of riparian habitat with applications toward management of streams impacted by nonpoint source pollution

Management of riparian habitats has been recognized for its importance in reducing instream effects of agricultural nonpoint source pollution. By serving as a buffer, well structured riparian habitats can reduce nonpoint source impacts by filtering surface runoff from field to stream. A system has been developed where key characteristics of riparian habitat, vegetation type, height, width, riparian and shoreline bank slope, and land use are classified as discrete categorical units. This classification system recognizes seven riparian vegetation types, which are determined by dominant plant type. Riparian and shoreline bank slope, in addition to riparian width and height, each consist of five categories. Classification by discrete units allows for ready digitizing of information for production of spatial maps using a geographic information system (GIS). The classification system was tested for field efficiency on Tom Beall Creek watershed, an agriculturally impacted third-order stream in the Clearwater River drainage, Nez Perce County, Idaho, USA. The classification system was simple to use during field applications and provided a good inventory of riparian habitat. After successful field tests, spatial maps were produced for each component using the Professional Map Analysis Package (pMAP), a GIS program. With pMAP, a map describing general riparian habitat condition was produced by combining the maps of components of riparian habitat, and the condition map was integrated with a map of soil erosion potential in order to determine areas along the stream that are susceptible to nonpoint source pollution inputs. Integration of spatial maps of riparian classification and watershed characteristics has great potential as a tool for aiding in making management decisions for mitigating off-site impacts of agricultural nonpoint source pollution.     

Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments

The integration of Landsat TM and environmental GIS data sets using artificial intelligence rule-induction and decision-tree analysis is shown to facilitate the production of vegetation maps with both floristic and structural information. This technique is particularly suited to vegetation mapping in disturbed or hilly environments that are unsuited to either conventional remote sensing methods or GIS modeling using environmental data bases.