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.     

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.     

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.     

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。     

农村污染防治工作已迫在眉睫

针对目前农村经济发展过程中出现的主要环境问题,结合农村的实际情况,提出了解决农村环境污染问题的几项措施。并着重分析了规模化畜禽养殖业中的环境污染问题,提出了综合防治对策。     

管式电化学反应器中试处理高浓度含氰废水与经济分析

基于对高浓度含氰废水处理的重大需求和现有破氰技术的共性缺点,采用管式电化学反应器工艺对西部某化工厂生产过程的高浓度含氰废水进行预处理的中试研究,并与次钠氯碱法和ClO₂氧化法进行了对比。以Ti/RuO₂为阳极的管式电化学反应器相比于其他工艺有最佳的处理效果,在20 mA·cm⁻²处理4 h后,对废水中TCN、COD和间苯二腈的去除率分别可以达到81.74%、57.71%和81.33%,长期运行效果也处于最佳。此外,尽管管式电化学反应器的建设成本较高,单位能耗高,但由于该工艺无需加药,其运行成本低廉,仅为次钠氯碱法的13.10%,故总体运行成本较低。同时,还对管式电化学反应器的运行过程进行了参数优化及机理探究。在综合考虑建设、运行和折旧,管式电化学反应器具有良好的应用前景。     

Impact of leakage location and downwind storage tank on the gas dispersion in a typical chemical tank storage area

Toxic gas leakage in a tank area can have catastrophic consequences. Storage tank leakage location (particularly for high leakage) and downwind storage tanks potentially influence gas diffusion in tank areas. In this study, we developed a numerical and experimental method to investigate the impact of a high leakage location and downwind storage tank on gas diffusion based on three (1.05H, 0.90H, and 0.77H, H was the tank height, 22m) leakage field experiments on the leeward side of storage tank, which have been not conducted before. The experiments revealed an unexpected phenomenon: the maximum ground concentration first decreased and then increased with increasing leakage height. The simulations illustrated that the differences in micrometeorological conditions caused the maximum ground concentration of gas emitted from the roof to be higher than that emitted from the tank wall near the storage tank height. The downwind storage tank 1) had little influence on the entire diffusion direction but altered the local diffusion pattern; 2) reduced the maximum ground concentration (∼18.7%) and the distance from the emission source (approximately a storage tank diameter); and 3) had strong influences on the concentration, velocity, turbulence, and pressure on the leeward side. The concentration negatively correlated with the velocity, pressure, and turbulence in the middle of the two storage tanks on wind centerline. Our results can improve understanding of gas dispersion in tank areas and provide references for mitigating loss and protecting lives during emergency response processes.     

城郊农业区小流域土地利用结构对氮素输出的影响

以位于湖南省长沙市城郊农业区的金脱河流域为研究区域,通过解译2009年的Spot-5卫星遥感图获取了小流域的土地利用图;结合流域的DEM图,利用ArcGIS 9.3的水文、空间分析模块将流域划分为若干个子流域,分析各子流域的土地利用结构.从2009年12月～2010年11月在各子流域出口处采取水样,分析其TN、NH4+-N、NO3--N输出浓度、输出量的时空变化特征以及与土地利用结构、施肥等因素之间的关系.结果表明,流域径流氮素污染严重,其TN、NH4+-N输出浓度呈明显的季节变化特征,为冬季>春季>夏、秋季,而NO3--N的输出浓度的季节差异较小.流域内的土地利用结构对NO3--N的输出浓度起着重要的控制作用,林地、水面表现为源作用,水田、旱地、居民地为汇作用;不同的时间尺度上土地利用结构对其影响不同,年度尺度上对NO3--N输出影响最大的为旱地,春夏季为林地,秋冬季为旱地.TN、NH4+-N的输出浓度与土地利用结构的相关性较差,各形态氮素的输出量与纯氮施用量、人口数、养猪量表现为极显著正相关.