基于Google Earth的巴尔喀什湖流域中下游水资源开发利用研究     

雪克来提·  巴斯托夫  龙爱华  邓铭江  李湘权  章毅 《干旱区地理》2012,35(3):388-398

 常规的信息收集渠道难以获取国际河流研究所需的有效信息,探索新的信息收集方式对资料稀缺区开展水文水资源研究十分必要且关键。在Internet海量信息中,Google Earth（GE）平台的出现,为资源环境、交通规划等诸多领域提供了“身临其境”的远距离信息检索模式。通过基于GE平台的资料稀缺区水资源开发利用状况评价的应用框架和评价方法,并以巴尔喀什湖流域中下游为例开展了应用研究。结果表明：GE平台可成为资料稀缺区水资源开发利用研究的有效信息收集手段,文中所提出的框架、应用流程和评价方法具有较好的可操作性;研究区在1990年初就形成了以伊犁河中下游为主体、准噶尔区与巴尔喀什湖北部诸小河为重要用水区的基本格局;随着研究区经济社会发展的剧烈波动,其用水量经历了“增长-顶峰-剧减-恢复性增长”的演变格局,目前其用水量预计已恢复至20世纪80年代中期的水平,即65×108 m3左右。  相似文献   

Preliminary mapping of high-resolution rural population distribution based on imagery from Google Earth: A case study in the Lake Tai basin, eastern China   总被引：2，自引：0，他引：2  

Xiaoying Yang  Geng-Ming Jiang 《Applied geography (Sevenoaks, England)》2012,32(2):221-227

There is a large rural population in China. Throughout the country, sewage from rural households is mostly discharged with minimal treatment. Knowledge of rural population distribution is essential to study the rural households’ impacts on their surrounding environment. However, high-resolution spatial datasets on rural population distribution are rarely available in China. This study explores the feasibility of using the image from Google Earth to derive a high-resolution map on rural population distribution for a town in the Lake Tai basin of eastern China. Study results show that texture analysis in conjunction with other processing procedures can extract man-made building features from the image reasonably, which can then serve as the basis for the preliminary mapping of rural population distribution in the study region. This may prove to be a promising alternative for deriving spatial datasets on rural population distribution in many parts of the world.  相似文献   

Synoptic Typing and Its Application for Evaluating Climatic Impact on Air Quality in Urumqi          下载免费PDF全文

WANG Li-Li  WANG Yue-Si  LI Yuan-Yuan 《大气和海洋科学快报》2010,3(6):312-318

An automated procedure employing principal-component analysis and a two-stage cluster analysis was developed to classify the synoptic meteorological conditions prevailing over Urumqi, one of the most heavily polluted cities in the world. Six clusters representing different circulation patterns and air-mass characteristics were classified using surface- and upper-meteorological variables during the heating period from 2001 to 2008, and the relationships between synoptic clusters and air quality were evaluated. The heaviest air-pollution episodes occurred when Urumqi was in either an extremely cold, strong anticyclone or at the front of a migrating cyclone, both with light winds, wet surface air, and relatively dry upper air. Moderate pollution was seen when Urumqi was in the pre-cold/cold frontal passages with lower temperatures and light winds or moderate anticyclone with relatively warmer, drier air. When Urumqi was at the front of a migrating anticyclone or in a weak anticyclone with moderate winds and most warm, dry air, or in the cold/post-cold frontal passages with relatively strongly northerly airflows and precipitation, relatively good air quality could be seen. These results suggest that air pollution in Urumqi is very closely related to the synoptic meteorological conditions, which provides an important basis for not only the prediction and control of urban air-quality problems here but also for the analysis of the differential impacts of weather and pollution on human morbidity.  相似文献   

利用Google earth调查气象台站环境   总被引：2，自引：0，他引：2  

王一姝  高晓东 《河南气象》2009,(1):88-90

利用Google earth地图软件,可查看台站周围的环境,测定干扰源距地面观测场围栏的距离,并分析干扰源与观测状况。该软件可以帮助了解台站周围土地使用情况,登记周围障碍物,绘制台站和四周情况平面示意图。  相似文献   

Sentinel-2密集时间序列数据和Google Earth Engine的潮间带湿地快速自动分类     

程丽娜  钟才荣  李晓燕  贾明明  王宗明  毛德华 《遥感学报》2022,26(2):348-357

潮间带湿地是滨海湿地的重要组成部分,具有维持生物多样性、促进碳汇等重要生态功能.及时、准确地掌握潮间带湿地现状是实现潮间带湿地可持续管理目标的基础.先前的潮间带湿地分类研究依赖于训练样本、人工设定阈值或后处理等,本研究基于GEE (Google Earth Engine)平台开发一种自动、快速、高精度的潮间带湿地分类方...  相似文献   

基于Google Earth的1∶50000地质地理底图更新     

王宏俊 《山东地质》2010,(11):43-45

数字地理底图是地质环境调查不可缺少的组成部分,但现有数据大多现势性较差,如何更新值得探讨。文中提出利用Google Earth进行地理底图更新的方法,并介绍了更新的步骤和精度,该方法简单快捷、可操作性较强,具有一定的实用价值。  相似文献   

截拼谷歌地球影像图     

蒋国才 《贵州地质》2010,27(4):317-319,271

介绍从"谷歌地球"上截取并拼接正射影像图的方法及注意事项。  相似文献   

基于HATEOAS约束的地理空间信息RESTful API设计探索     

刘明青  朱国宾  陈西亮 《地理空间信息》2022,20(1):42-45

随着地理信息行业的迅速发展,地理空间信息在各领域发挥着越来越重要的作用.在数据共享过程中,基于REST架构风格的API起着至关重要的作用.现有的地理空间信息RESTful API均存在一定的缺陷.HATEOAS是REST架构风格中最成熟、最复杂的约定.以遥感影像的Web应用开发为例,提出了一种遵循HATEOAS约束的地...  相似文献   

MapSource与GoogleEarth在测量中的整合应用     

裴国林  于德泳 《测绘与空间地理信息》2009,32(3):170-172

GPS和航空影像在现代测量中已成为一种不可或缺的技术手段,如何能更好地利用现有的我们能掌控的技术资源,让它为我们的测量服务呢?在这里我根据以往的工作经验及对GPS手持机处理软件MapSource和Google Earth的使用情况,初步探讨这两种软件的整合及给我们测量工作带来的益处,尚需说明的是本文只是想起到一个抛砖引玉的作用,希望能给大家带来一点有益的思考.  相似文献   

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud   总被引：1，自引：0，他引：1  

Murali Krishna Gumma  Pardhasaradhi G. Teluguntla  Adam Oliphant  Jun Xiong  Chandra Giri 《地理信息系统科学与遥感》2020,57(3):302-322

ABSTRACT

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three time-periods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/.  相似文献