Saraswati Nadi in Haryana and its linkage with the Vedic Saraswati River — Integrated study based on satellite images and ground based information

Detailed studies on the status of Saraswati Nadi of northern Haryana have been carried out using multi date and multi resolution satellite images, GIS techniques and ground data. Palaeochannels have been delineated using remote sensing techniques and validated using discovered archaeological sites, sedimentological data from drilled wells and water quality data. Detailed analysis of hydrological data (rainfall and stream discharge), catchment area and petrographic analysis of rock samples have been done to decipher the dwindling state of Saraswati Nadi. Likelihood of Adi Badri as the place of origin of Saraswati Nadi and its possible linkage with the Vedic Saraswati River is discussed. Suggestions have been given for safeguarding and revival of Saraswati Nadi as a national heritage.     

高分辨率卫星数据在矿山开发状况及环境监测中的应用效果比较

以著名钨矿生产基地———江西省崇义县为实验区,对QUICKBIRD和SPOT-5数据在南方高植被覆盖山区矿产资源开发状况及其引发的环境问题的监测效果和与性能作了系统比较,为类似地区矿山监测时数据源的选择提供参考。     

北京山区植被覆被率遥感制图与景观格局分析——以门头沟区为例

准确、快速地获取植被覆盖状况信息是北山区生态恢复和建设的关键与重点.本文在FCD Mapping model的原理和方法基础上,利用TM图像对北京门头沟区植被覆被率展开调查和制图应用研究,并利用FRAGSTATS软件对所得的植被覆盖等级图从斑块大小、斑块数、斑块密度、斑块平均面积、分形维数和聚集度等角度进行了空间格局的剖析.     

西藏班公湖-怒江板块结合带岩金遥感找矿模式

西藏班公湖-怒江板块结合带地质构造复杂,成矿条件优越,具备形成金矿的地质背景条件,是西藏最具潜力和规模巨大的贵金属成矿带.但由于该区地质工作程度较低和对成矿规律认识不清,至今没有一个可供开发的岩金矿产地.通过班公湖-怒江板块结合带砂金、岩金矿(化)点,地球化学异常和重砂异常的分布并进行控矿因子分析,建立了该带两种岩金遥感找矿模式,通过验证发现该区具有大型远景规模的岩金矿产地,并认为蚀变岩型金矿和穆龙套型金矿是该带最重要的两种矿床类型,具有巨大的找矿潜力.     

ASTER遥感数据解读与应用

ASTER遥感数据可以在地球表面自然过程与全球变化等多个研究领域得到广泛应用.本文首先对ASTER数据格式进行了分析,并对解读关键技术进行了研究,在此基础上,通过计算机编程实现了ASTER数据的读取;最后结合ASTER数据的特点给出了ASTER数据在几个主要研究领域的应用.     

高分辨率遥感影像提取道路的方法综述与思考

 介绍了高分辨率遥感影像上道路的特征、模型和道路提取的基本思想，对已有的道路提取方法进行了分析，并对道路提取问题进行思考，提出下一步的研究方向。     

遥感影像制图综合的智能化研究

本文以人工智能为基础,用计算机模拟一般制图人员对遥感图像进行制图综合的过程,用框架的知识表示方法建立了一个实验性的知识库,并设计了一个推理机。对蒙古人民共和国乌布苏淖尔地区的ＴＭ分类图进行了实验,取得了较好的效果。     

图象分割中随机噪音的影响

该文在生成的灰度图象上加上随机噪音，用最大后验（ＭＡＰ）估计方法、模糊Ｃ－均值方法和Ｏｔｓｕ阈值方法进行了分割计算，对结果进行了对比分析，绘出了分割随噪音的标准差变化曲线，对噪音图象使用了不同的滤波方法处理之后，并进行计算。     

天文观测科学实践中心技术实现

天文观测科学实践中心是一个以天文观测为核心的网络实践体验中心,以网站的形式向公众提供服务。网站建设使用HTML、CSS、JavaScript和JSP/JaveBean等技术;基于B/S结构实现望远镜远程控制;采用Windows Media技术实现视频发布。建成的网站为用户提供了全新的天文观测实践体验机会,达到了传播天文知识的目的。     

Assessing protected area’s carbon stocks and ecological structure at regional-scale using GEDI lidar

Protected areas (PAs) serve as a critical strategy for protecting natural resources, conserving biodiversity, and mitigating climate change. While there is a critical need to guide area-based conservation efforts, a systematic assessment of PA effectiveness for storing carbon stocks has not been possible due to the lack of globally consistent forest biomass data. In this study, we present a new methodology utilizing forest structural information and aboveground biomass density (AGBD) obtained from the Global Ecosystem Dynamics Investigation (GEDI) mission. We compare PAs with similar, unprotected forests obtained through statistical matching to assess differences in carbon storage and forest structure. We also assess matching outcomes for a robust and minimally biased way to quantify PA efficacy. We find that all analyzed PAs in Tanzania possess higher biomass densities than their unprotected counterfactuals (24.4% higher on average). This is also true for other forest structure metrics, including tree height, canopy cover, and plant area index (PAI). We also find that community-governed PAs are the most effective category of PAs at preserving forest structure and AGBD – often outperforming those managed by international or national entities. In addition, PAs designated under more than one entity perform better than the PAs with a single designation, especially those with multiple international designations. Finally, our findings suggest that smaller PAs may be more effective for conservation, depending on levels of connectivity. Taken together, these findings support the designation of PAs as an effective means for forest management with considerable potential to protect forest ecosystems and achieve long-term climate goals.