试论中国NSDI建设的若干问题   总被引：2，自引：0，他引：2  

陈军 《遥感学报》1999,3(2):94-97

国家空间数据基础设施是“数字地球”的基础。要发展中国数字地球及其应用,就必须加在ＮＳＤＩ的建设力度,文中主要从（地球）空间数据框架,空间数据标准,空间数据交换网络和空间数据协调管理机构４个方面,介绍了中国ＮＳＤＩ的有关进展,分析了其中存在的一些问题,讨论了发展方向。  相似文献   

南麂列岛自然保护区药用海藻资源及其应用   总被引：1，自引：0，他引：1  

朱根海 《海洋学研究》1998,16(2):63-68

本文报道了南麂列岛国家海洋自然保护区药用海藻的种类组成及其应用。经初步鉴定南麂列岛共有海藻类４００余种,其中已知具有药用功效的海藻１１１种（小于２０μｍ的微型藻类为１６种,２０～２００μｍ的小型藻类为９种,大于２００μｍ的大型海藻类为８６种）,隶属于红藻门４９种、褐藻门２０种、硅藻门１５种、绿藻门１４种、蓝藻门８种和甲藻门５种。根据藻类的药理、生理特性及药用功效,可划分为９类：（１）抗菌药（ＡＦ）;（２）抗凝血、止血药（ＡＢ）;（３）抗病毒药（ＡＤ）;（４）抗高血压药（ＡＨ）;（５）清热解毒药（ＡＰＴ）;（６）驱虫药（ＡＩ）;（７）抗肿瘤药（ＡＴ）;（８）抗心脑血管药（ＢＣ）;（９）抗艾滋病药（ＡＩＤＳ）。  相似文献   

南麂列岛国家海洋自然保护区几种海洋动物胃含物中的微、小型藻类组成分析     

朱根海  王春生  高爱根  王旭 《海洋学研究》1998,(2)

首次对南麂列岛国家海洋自然保护区不同生境的主要动物的食性进行分析。结果表明,在常见的２０种动物胃含物中,共鉴定出微、小型藻类８３种。不同生境的动物食物的优势种类组成是不相同的。各动物胃含物种类组成和数量与其海区生境中的优势微、小型藻类种类组成和数量基本相吻合。海洋动物摄食微、小型藻类具有季节变化,夏季虾类、鱼类的胃含物中均有出现的微、小型藻类种类高于其它季节。藤壶、贝类胃含物中均有出现的微、小型藻类种类数春季高于夏、秋、冬季。  相似文献   

大连金石滩旅游度假区居住用地的空间分异     

杨俊  赵洪丹  席建超  葛全胜  李雪铭 《地理研究》2015,34(1)

以2003-2012年三个时相的SPOT5和卫星遥感影像数据为基础,以大连金石滩旅游度假区为研究区域。在行政村尺度下,利用GIS技术结合分异指数D和多组群分异测度模型D(m),研究金石滩旅游度假区居住用地的空间分异过程、分布特征及其驱动机制。研究表明:12003-2012年金石滩旅游度假区各类居住用地分异度变化不一。农村住宅分异度逐渐增加,其余各类住宅分异度均呈下降趋势,花园洋房分异度数值范围为0.06~0.65,波动幅度最剧烈;2在传统住宅逐渐被新型住宅所替代的大背景下,花园洋房、商品住宅和普通住宅的占地面积与日俱增,分布范围越来越广,主要是沿海岸线和景色优美地区分布。农村住宅越来越稀少,住宅用地占地面积显著增加;3在住宅产业发展的三个阶段,政府决策、社会分化、市场机制和个人选择等因素既相互促进又相互制约,共同作用于旅游地产在金石滩旅游度假区内的发展。  相似文献   

Evaluating the benefits of national adaptation to reduce climate risks and contribute to the Sustainable Development Goals     

《Global Environmental Change》2022

Scaling up national climate adaptation under the Paris Agreement is critical not only to reduce risk, but also to contribute to a nation’s development. Traditional adaptation assessments are aimed at evaluating adaptation to cost-effectively reduce risk and do not capture the far-reaching benefits of adaptation in the context of development and the global Sustainable Development Goals (SDGs). By grounding adaptation planning in an SDG vision, we propose and demonstrate a methodological process that for the first time allows national decision-makers to: i) quantify the adaptation that is needed to safeguard SDG target progress, and ii) evaluate strategies of stakeholder-driven adaptation options to meet those needs whilst delivering additional SDG target co-benefits. This methodological process is spatially applied to a national adaptation assessment in Ghana. In the face of the country’s risk from floods and landslides, this analysis identifies which energy and transport assets to prioritise in order to make the greatest contribution to safeguarding development progress. Three strategies (‘built’, ‘nature-based’, ‘combined SDG strategy’) were formulated through a multi-stakeholder partnership involving government, the private sector, and academia as a means to protect Ghana’s prioritised assets against climate risk. Evaluating these adaptation strategies in terms of their ability to deliver on SDG targets, we find that the combined SDG strategy maximises SDG co-benefits across 116 targets. The proposed methodological process for integrating SDG targets in adaptation assessments is transferable to other climate-vulnerable nations, and can provide decision-makers with spatially-explicit evidence for implementing sustainable adaptation in alignment with the global agendas.  相似文献   

Multi-hazard susceptibility mapping based on Convolutional Neural Networks     

《地学前缘(英文版)》2022,13(5):101425

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.  相似文献