黑河流域中上游湿地生态功能评价

从湿地生态评价体系和湿地生态评分标准入手,基于层次分析法(AHP)对黑河流域中上游具有代表性的8个较大湿地区域进行生态功能综合评价,结果表明:黑河流域中上游湿地生态功能依次为:祁连冰川湿地功能最好;高山灌丛草甸湿地次之;黑河干支流沿岸湿地区、肃南明海湿地、临泽双泉湖草本沼泽、民乐永固沼泽湿地的生态功能有所退化;甘州区城郊芦苇(Phragmites australis)湿地、高台盐田湿地功能退化较为严重。这一结论可为黑河流域湿地资源恢复、保护和可持续发展提供科学依据。     

甘肃省文县观音坝古地震事件的研究

通过野外调查在甘肃陇南的文县阳山金矿东北磨坝—观音坝一带首次发现一大套震碎砾岩,这套砾岩由于覆盖在白垩纪红色砾岩之下,故而一直把其当作白垩纪砾岩处理。通过作者参加该项目的研究后认为,是一套震碎角砾岩,这种震碎角砾岩,在国内外尚未见公开报道。它是地质历史上一次浅源强烈地震的岩石记录,反映了该地区一次重要的地震构造运动,同时本文对该地区的震积岩进行了沉积序列及岩石类型划分,这种观点对促进陕甘川地震历史的认知有重要的补充作用。     

GPS与GIS集成初论

GPS的高精度定位特性使其成为GIS系统中重要的数据来源,在现实生活和科研中有广泛的应用。本文介绍用VB,VC等通用编程工具,利用DLL,DDE,OCX等计算机技术,通过串口编程,在GIS软件中实现GPS功能的几种不同实现手段。     

江苏省矿山地质环境质量的模糊评价

矿山地质环境质量评价是一项复杂的系统工作,由于影响因素较多,难以用统一的标准进行度量,至今仍未形成一套标准的评价方法。文章选取影响矿山地质环境的地形地貌、岩土体工程地质性质、开采矿种、开采方式、对自然景观、人文景观及地质遗迹的破坏、对水资源的破坏、占用破坏土地、地质灾害易发程度、地质灾害危害程度、矿产资源开发利用规划、矿山生态恢复治理难易程度等11个因素作为评价因子,应用模糊评价的原理与方法,通过建立模糊数学模型和隶属度矩阵,将江苏省的矿山地质环境质量划分为良好、较好、较差及差4个等级,评价结果为江苏省矿山地质环境保护和整治规划提供了依据。     

30年来呼伦贝尔地区草地植被对气候变化的响应(英文)

Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world’s three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is used as an indicator to monitor vegetation changes. GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation changes in Hulun Buir Grassland. The responses of vegetation coverage to climatic change on the yearly, seasonal and monthly scales were analyzed combined with temperature and precipitation data of seven meteorological sites. In the past 30 years, vegetation coverage was more correlated with climatic factors, and the correlations were dependent on the time scales. On an inter-annual scale, vegetation change was better correlated with precipitation, suggesting that rainfall was the main factor for driving vegetation changes. On a seasonal-interannual scale, correlations between vegetation coverage change and climatic factors showed that the sensitivity of vegetation growth to the aqueous and thermal condition changes was different in different seasons. The sensitivity of vegetation growth to temperature in summers was higher than in the other seasons, while its sensitivity to rainfall in both summers and autumns was higher, especially in summers. On a monthly-interannual scale, correlations between vegetation coverage change and climatic factors during growth seasons showed that the response of vegetation changes to temperature in both April and May was stronger. This indicates that the temperature effect occurs in the early stage of vegetation growth. Correlations between vegetation growth and precipitation of the month before the current month, were better from May to August, showing a hysteresis response of vegetation growth to rainfall. Grasses get green and begin to grow in April, and the impacts of temperature on grass growth are obvious. The increase of NDVI in April may be due to climatic warming that leads to an advanced growth season. In summary, relationships between monthly-interannual variations of vegetation coverage and climatic factors represent the temporal rhythm controls of temperature and precipitation on grass growth largely.