云南星云湖南岸生态整治规划

阐述了高原湖泊星云湖的自然条件、污染根源和景观特点,在此基础上通过本项目星云湖南岸(一期)生态整治规划,尝试在整个流域范围内运用GIS分析生态湿地建设、农业面源污染控制和重现滨水空间等,改善星云湖的水质,提高星云湖及周边的生态旅游价值.规划采用顶端控制和底端治理相结合的策略,恢复星云湖流域的生态功能,解决星云湖流域现有...     

基于GIS的煤矿区景观格局时空变化分析——以呼伦贝尔市某煤矿为例

在GIS平台和Fragstats 4．1软件的支持下,本文以1989年和1999年的TM影像,2012年的资源三号卫星影像为数据源,利用景观／斑块类型面积、斑块所占景观面积的比例、斑块密度、最大斑块指数、总边缘长度、边缘密度、香农多样性指数、蔓延度指数等景观特征指标,对呼伦贝尔市某煤矿区的景观特征动态变化进行分析。结果显示：该煤矿区20多年来景观格局有了很大变化,景观类型由1989年的水域、草地、未利用地为主导变为2012年以未利用地、草地、耕地为主导,其中因人类工业活动和人工复垦导致工矿景观、道路景观、林地景观和耕地景观面积增加,而原来占绝对优势的水域景观面积则大幅减小。     

EM菌处理垃圾渗滤液的微生物生态学及机理分析研究

通过DNA提取,PCR扩增和DGGE分离,用BLAST程序进行相似性比较分析体系中微生物群落多样性。所有序列与数据库中16S rDNA序列的相似性在94%~100%之间。从整个挂膜过程考虑,挂膜时间应在8天左右。渗滤液中污染物的去除与投加EM有很大的关系,但是渗滤液中原始菌种的贡献同样不可忽视。投加EM菌剂促进了渗滤液中群落结构演替和功能优化,并最终通过EM菌剂和渗滤液中原始菌群的共同作用实现渗滤液处理系统效能提高和完善。     

基于《环境影响评价》的研究性系列实验设计与教学实践研究

为提高高校本科生实验及实践类课程教学水平,基于笔者十余年实践类课程教学实践,介绍了以课程形式新建的具有研究性特质的系列本科生教学实验,实验素材主要源于“理论课程中的经典研究方法、学科前沿的科学研究实验和成熟的社会应用技术方法和规范”,最后基于具体实践教学案例,详细阐述了上述系列研究性实验的教学细节和流程.希望本研究的探索工作能够为中国高校实验教学的深入改革,以及建立基于学科的国家级基础实验教学示范中心提供基础和借鉴.     

生态环境变化遥感评价指数的应用研究——以敖江流域为例

利用1994年5月12日与2009年6月6日的Landsat TM和2001年5月23日的Landsat ETM＋卫星影像,选用遥感生态指数（RSEI）,结合流域的植被覆盖度状况,定量评价了15年间敖江流域的生态环境变化与植被覆盖度变化情况,并对二者的关系进行了简要分析.结果表明：15年间,敖江流域生态等级为优所占的面积比例从13.48％上升到24.90％,增加了304.29 km2;植被覆盖度等级为极高的面积增加比例为29.31％.总体看来,敖江流域生态环境状况和植被覆盖状况均有明显的提高,两者具有较好的对应关系.     

维修时间不确定性下配电网灾后维修队安排的鲁棒恢复研究*

为最小化灾后配电网损失量,准确描述完整维修队工作时间（分为路途时间与具体维修时间）,依据台风路径对维修队所需路途时间进行分类,并利用期望概率描述具体维修时间的不确定性。建立2阶段分布式鲁棒优化模型,采用CCG算法分析国内某地区配电网算例发现:考虑维修时间不确定性可以有效减少配电网损失量。     

河道生态环境保护与治理工作

现阶段,随着社会经济的不断发展,国家越来越重视河道整治工作。目前,依然存在着部分河道污染严重的问题。本文将对河道生态环境保护与治理工作进行分析研究,希望为相关工作人员提供参考。     

Formulaic Expert Method to Integrate Evaluation and Valuation of Heritage Trees in Compact City

Urban trees serve important environmental, social and economic functions, but similar to other natural endowments they are not customarily depicted in monetary terms. The needs to augment protection, funding and community support for urban greening call for proper valuation. Heritage trees (HTs), the cream of urban-tree stock, deserve special attention. Existing assessment methods do not give justice to outstanding trees in compact cities deficient in high-caliber greenery, and to their social-cultural-historical importance. They artificially separate evaluation from valuation, which should be a natural progression from the former. Review of tree valuation methods suggested the formula approach to be more suitable than contingent valuation and hedonic pricing, and provided hints on their strengths and weaknesses. This study develops an alternative formulaic expert method (FEM) that integrates evaluation and valuation, maximizes objectivity, broadly encompasses the key tree, tree-environment and tree-human traits, and accords realistic monetary value to HTs. Six primary criteria (dimension, species, tree, condition, location, and outstanding consideration) branched into 45 secondary criteria, each allocated numerical marks. Each primary criterion was standardized to carry equal weight, and a tree's maximum aggregate score is capped at 100. A Monetary Assignment Factor (MAF) to consign dollar value to each score unit was derived from three-year average per m² sale price of medium-sized residential flats. The applicability of FEM was tested on selected HTs in compact Hong Kong. The aggregate score of a tree multiplied by MAF yielded monetary value, which was on average 66 times higher than the result from the commonly-adopted Council of Tree and Landscape Appraisers method. The computed tree values could be publicized together with multiple tree benefits to raise understanding and awareness and rally support to protect HTs. The property-linked FEM could be flexibly applied to other cities, especially to assess HTs in compact developing cities.     

基于CA的城市形态扩展多解模拟——以北京市东部平原区情景分析为例

构建了一个基于人工神经网络的约束型城市扩展CA模型。利用该模型,预测了北京市东部平原区在3种情景规划之下的未来50年的城市形态,为不同的城市发展模式之间的比较分析奠定基础。然后以2024年的北京东部平原区模拟城市形态为基准,从自然生态功效、社会服务功效、经济利益功效3个方面对3种规划模式进行了情景分析,从而系统的比较了3种规划模式的复合生态功效,为城市规划决策提供有力的支持。     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.