The Use of Remote Sensing and GIS in the Sustainable Management of Tropical Coastal Ecosystems

The sustainable use and management of important tropical coastal ecosystems (mangrove forests, seagrass beds and coral reefs) cannot be done without understanding the direct and indirect impacts of man. The ecosystem's resilience and recovery capacity following such impacts must be determined. The efficacy of mitigation measures must also be considered. Remote sensing and geographic information systems (GIS) are excellent tools to use in such studies. This paper reviews the state of the art and application of these tools in tropical coastal zones, and illustrates their relevance in sustainable development. It highlights a selected number of remote sensing case-studies on land cover patterns, population structure and dynamics, and stand characteristics from South-East Asia, Africa and South-America, with a particular emphasis on mangroves. It further shows how remote sensing technology and other scientific tools can be integrated in long-term studies, both retrospective and predictive, in order to anticipate degradation and to take mitigating measures at an early stage. The paper also highlights the guidelines for sustainable management that can result from remote sensing and GIS studies, and identifies existent gaps and research priorities.There is a need for more comprehensive approaches that deal with new remote sensing technologies and analysis in a GIS-environment, and that integrate findings collected over longer periods with the aim of prediction. It is also imperative to collect and integrate data from different disciplines. These are essential in the spirit of sustainable development and management, particularly in developing countries, which are often more vulnerable to environmental degradation.     

Risk attitudes and mitigation among gold miners and others in the Suriname rainforest

This article analyses the question: do attitudes towards risk influence participation in small‐scale gold mining, a hazardous activity that generates uncertain income? This question is examined by measuring and comparing the risk attitudes of gold miners and non‐mining community members in the rainforest of Suriname, South America. The author presents a multivariate model to predict the duration of work in mining areas as a function of risk tolerance, age, education, and household demographics. The results suggest that a greater tolerance to risk increases the duration of a person's mining career. However, attitudes explain only a fraction of the variation in occupational choices. Qualitative data suggest that these choices are primarily shaped by local barriers to human capital development and by national economic volatility. Given their marginal position in society and the multitude of mining risk mitigation strategies, it is questionable whether gold mining exposes Suriname forest peoples to greater risks than other subsistence alternatives. The author argues that sensitivity to local historical and cultural conditions would improve the efficiency of policies aimed at developing a more sustainable mining industry. By zooming in on the daily lives of miners, anthropology can complement macro‐scale analyses and contribute to policy interventions in the small‐scale mining sector.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

浙江省酸雨的空间分布格局及其未来变化趋势预测

利用浙江省1992、2002年的酸雨监测数据,并利用RAINS-ASIA模型模拟了1990-2030年浙江省酸雨前体物(SO2)的排放量,以及酸沉降超临界负荷面积占研究区域面积比例的变化情况.结果表明,浙江省的酸雨分布范围不断扩大、危害程度不断加重;未来一个时期内,SO2排放量都会维持在较高的水平;酸沉降超临界负荷面积所占比例在经历较快的增长阶段之后,会维持在较为恒定的水平,并呈现出明显的时滞性.     

Spatial sampling and the environment: some issues and directions

In this paper we summarize research issues for spatial environmental sampling stemming from a NISS/USEPA workshop held on 21-22 September 1994 at Chapel Hill, NC.     

安徽省市级区域发展的空间结构演变分析

以市级区域为空间结构研究对象,分析安徽省20世纪80年代以来区域经济空间结构的演化过程。结果表明：①安徽省各市级区域在近20年的发展中,区域差异空间格局整体上变化不大,20世纪80年代初期经济发达的地区现在仍为发达区,原先落后的地区仍落后;②1980年以来,工业化进程的空间差异与区域社会经济发展的空间差异具有很大的相似性,表明工业化是推动区域经济发展的重要力量;③通过分析区域发展差异的空间机制得出：轴线的地位差异显著,以贯穿南北的中轴线和沿江轴线为主,形成经济实力中心,而其它地区相对落后,形成了明显的中心-边缘模式。     

Risk assessment and prioritisation of contaminated sites on the catchment scale

Contaminated sites pose a significant threat to groundwater resources worldwide. Due to limited available resources a risk-based prioritisation of the remediation efforts is essential. Existing risk assessment tools are unsuitable for this purpose, because they consider each contaminated site separately and on a local scale, which makes it difficult to compare the impact from different sites. Hence a modelling tool for risk assessment of contaminated sites on the catchment scale has been developed. The CatchRisk screening tool evaluates the risk associated with each site in terms of its ability to contaminate abstracted groundwater in the catchment. The tool considers both the local scale and the catchment scale. At the local scale, a flexible, site specific leaching model that can be adjusted to the actual data availability is used to estimate the mass flux over time from identified sites. At the catchment scale, a transport model that utilises the source flux and a groundwater model covering the catchment is used to estimate the transient impact on the supply well. The CatchRisk model was tested on a groundwater catchment for a waterworks north of Copenhagen, Denmark. Even though data scarcity limited the application of the model, the sites that most likely caused the observed contamination at the waterworks were identified. The method was found to be valuable as a basis for prioritising point sources according to their impact on groundwater quality. The tool can also be used as a framework for testing hypotheses on the origin of contamination in the catchment and for identification of unknown contaminant sources.     

Semi-parametric statistical approaches for space-time process prediction

The problem of estimation and prediction of a spatial-temporal stochastic process, observed at regular times and irregularly in space, is considered. A mixed formulation involving a non- parametric component, accounting for a deterministic trend and the effect of exogenous variables, and a parametric component representing the purely spatio-temporal random variation is proposed. Correspondingly, a two-step procedure, first addressing the estimation of the non- parametric component, and then the estimation of the parametric component is developed from the residual series obtained, with spatial-temporal prediction being performed in terms of suitable spatial interpolation of the temporal variation structure. The proposed model formula-tion, together with the estimation and prediction procedure, are applied using a Gaussian ARMA structure for temporal modelling to space-time forecasting from real data of air pollution concentration levels in the region surrounding a power station in northwest Spain.     

宁东能源化工基地核心区表层土壤酚类污染物的污染评价、空间分布及来源解析

采集了宁东能源化工基地核心区的146个表层土壤样品,检测了6种优控酚类污染物.运用单因子污染指数、内梅罗综合污染指数和致癌风险指数评价了酚类污染物的污染程度及健康风险.借助主成分分析和绝对主成分分数-多元线性回归受体模型,结合地统计学方法,对酚类污染物的空间分布及来源进行了分析.结果显示:2,4-二氯酚和2,4,6-三...     

基于GIS的安徽省土壤空间分布的分形特征研究

分维数和稳定性指数是土壤空间分布的重要指数。通过建立安徽省土壤的空间数据库,运用分形理论,获得安徽省土壤类型周长与面积的关系,进而计算出各类土属在空间上的分维数和稳定性指数大小,得出如下结论:①安徽省土壤图斑的周长与面积之间存在双对数函数关系,相关性系数大部分在0.9以上,证明了安徽省土壤空间分形的客观存在。②安徽省各类土壤空间分维数大小在1.1375—1.9948之间,平均值为1.6138,最大的是泥质岩砾质土,最小的是脱青湖泥田。分维数在空间分布上存在差异,总的趋势是从西北向东南逐渐变大。③各种土属的稳定性指数在1.0252—0.0124之间,平均值约为0.2324,最大的是湿潮土,最小的是砂礓黄土。