基于Google Maps API的船舶自动站监控Web设计

简要介绍了Google Maps JavaScript API技术,并着重阐述了其作为主流的WebGIS技术在船舶自动气象站监控页面设计中的应用,包括：通过JavaScript和AJAX技术请求远程数据服务器获取气象数据、状态数据及GIS信息等,并将返回的JOSN格式数据加载到Google Map上,同时通过Google Maps JavaScript定制化船舶图标并对加载的船舶轨迹进行渲染,使用户需要的监控信息更加直观地显示在监控页面上。本文结果已成功应用到广东省气象局设备全网监控系统之中。     

An Apollonian appreciation of Google Earth

This short article reviews and critically appraises Kingsbury and Jones’ (2009)Geoforum article entitled “Walter Benjamin’s Dionysian Adventures on Google Earth”, and finds it lacking in several regards. First, the use of Nietzsche’s Dionysus/Apollo dichotomy is in fact a double binary, in which Apollonian logic is constructed as a binary within the dichotomy, while the Dionysian logic is ‘bacchanalian’. Second, Walter Benjamin as exemplar of Dionysian logic is shown to be problematic and more a distraction from the flawed Nietzschean logic than a useful construct in itself. Lastly, the educational potential of Google Earth is overlooked by Kingsbury and Jones, in their haste to see Google Earth through ‘Google goggles’, in which everything Google appears through Dionysian-tinted lenses.     

基于Google Earth Engine和NDVI时序差异指数的作物种植区提取

为提高农作物种植信息遥感监测的效率,扩展数据适用范围,本文提出了一种基于时间序列NDVI差异指数的作物种植区提取方法。随着海量遥感与云计算的发展,Google Earth Engine作为一个全球尺度地理空间分析云平台,弥补了单机计算耗时长的不足,为快速遥感分类带来了新机遇。基于Google Earth Engine平台,以河南省开封市杞县为研究区,以2019—2020年杞县地区多时相Sentinel-2影像为数据源,结合物候信息,根据不同作物在时间序列NDVI曲线上的差异构建NDVI时序差异指数,从而提取作物种植区,区分不同作物类型,并与其他方法进行了精度验证和对比。结果表明：① NDVI时序差异指数法以作物物候信息为基础,与GEE高性能的计算能力相结合,形成了作物种植信息快速提取框架,可以方便快捷地进行作物种植区提取,较本地处理具有明显优势;② 杞县冬小麦和大蒜种植区有明显的空间分异性,冬小麦种植区主要集中在研究区西北部以及南部的农村居民点周围,而杞县大蒜则由于产品流通需要,主要集中在研究区中部以及东北部,居民点较为密集,交通便利的城市周边;③ 与时间序列支持向量机法和最大似然法相比较, NDVI时序差异指数进行作物种植区提取的总体精度达到83.72%, Kappa系数为0.67,分别比最大似然法提高了10.02%和0.21,比支持向量机法提高了4.18%和0.09,表明该方法能更高效率,更高精度地提取作物种植信息,实现区域作物种植信息的高效准确监测。总体来看,该方法在一定程度上可拓展遥感数据在农业领域的应用范围,具有推广价值。     

浅谈利用Google Earth结合MapGis软件制作地形图方法

利用Google Earth的三维模拟功能,通过不同海拔平面与地表的接触线来获得等高线,结合MapGis的编辑功能即可形成矢量化的地形图。经与实测地形图对比,两者山头、山谷、山脊、鞍部、高程及等高线形态基本相同,可做为野外工作及其它对地形图精度要求不高工作的参考资料。     

基于贝叶斯层次时空模型的甘肃省土地利用程度演变分析

以甘肃省为例,在基于Google Earth Engine (GEE)平台实现1995年、2000年、2005年、2010年、2015年和2020年土地变化监测的基础上,利用贝叶斯层次时空模型(BHM)分析土地利用程度的时空变化特征。结果表明：① 研究期间内甘肃省土地利用程度呈增长趋势,其中1995―2000年和2010―2015年增长速度较明显;② 土地利用程度空间格局“东高西低”,热点区域主要分布在陇中、陇东和陇南地区;③ 土地利用程度局部变化呈现明显区域差异,整体表现为“东弱西强”,局部变化热点区域主要分布在河西地区;④ 影响土地利用程度变化的主要因素是经济规模和产业结构,其中经济因素影响程度最高。     

基于Google Earth API的典型GIS功能开发方法

探讨了基于Google Earth API开发部分典型GIS功能的方法,并进行了原型系统开发。该方法在原始Google Earth API基础上扩展实现了几何查询、地图要素的创建和编辑、矢量栅格数据浏览等典型GIS功能,能够满足用户对地图数据的操作需求,用户体验良好。     

Google Earth和天地图在林地变更中的应用比较和优化

在森林资源监测过程中,特别是涉及林地变更工作时,遥感影像发挥了重要的作用。在实际工作中,上级部门下发的标准影像存在部分地区影像分辨率不高的问题,影响了林地变更工作的正常进行。为了拓展影像资源,使林地变更工作顺利进行,该文对Google Earth和天地图影像及校正配准后的精度进行比较研究,结果表明,配准前的Google Earth影像精度明显优于天地图影像;面积在1万hm^2及其以下的乡镇配准后的影像精度较高。     

基于Google Earth的地质调查数据管理和三维显示

Google Earth作为功能十分强大的虚拟地球软件,已经广泛应用于多个行业。以Google Earth为平台,将通过GPS坐标集成化的地质信息转化为GE支持的KML格式,然后加载到Google Earth。充分利用Google Earth的海量数据管理能力和三维显示能力,提升地质调查工作的效率和成果的质量,更好地为地质调查工作服务。     

‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts

New sources of data such as ‘big data’ and computational analytics have stimulated innovative pedestrian oriented research. Current studies, however, are still limited and subjective with regard to the use of Google Street View and other online sources for environment audits or pedestrian counts because of the manual information extraction and compilation, especially for large areas. This study aims to provide future research an alternative method to conduct large scale data collection more consistently and objectively on pedestrian counts and possibly for environment audits and stimulate discussion of the use of ‘big data’ and recent computational advances for planning and design. We explore and report information needed to automatically download and assemble Google Street View images, as well as other image parameters for a wide range of analysis and visualization, and explore extracting pedestrian count data based on these images using machine vision and learning technology. The reliability tests results based on pedestrian information collected from over 200 street segments in Buffalo, NY, Washington, D.C., and Boston, MA respectively suggested that the image detection method used in this study are capable of determining the presence of pedestrian with a reasonable level of accuracy. The limitation and potential improvement of the proposed method is also discussed.     

Multitemporal settlement and population mapping from Landsat using Google Earth Engine

As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.