Visualizing dynamic geosciences phenomena using an octree-based view-dependent LOD strategy within virtual globes

Geoscientists build dynamic models to simulate various natural phenomena for a better understanding of our planet. Interactive visualizations of these geoscience models and their outputs through virtual globes on the Internet can help the public understand the dynamic phenomena related to the Earth more intuitively. However, challenges arise when the volume of four-dimensional data (4D), 3D in space plus time, is huge for rendering. Datasets loaded from geographically distributed data servers require synchronization between ingesting and rendering data. Also the visualization capability of display clients varies significantly in such an online visualization environment; some may not have high-end graphic cards. To enhance the efficiency of visualizing dynamic volumetric data in virtual globes, this paper proposes a systematic framework, in which an octree-based multiresolution data structure is implemented to organize time series 3D geospatial data to be used in virtual globe environments. This framework includes a view-dependent continuous level of detail (LOD) strategy formulated as a synchronized part of the virtual globe rendering process. Through the octree-based data retrieval process, the LOD strategy enables the rendering of the 4D simulation at a consistent and acceptable frame rate. To demonstrate the capabilities of this framework, data of a simulated dust storm event are rendered in World Wind, an open source virtual globe. The rendering performances with and without the octree-based LOD strategy are compared. The experimental results show that using the proposed data structure and processing strategy significantly enhances the visualization performance when rendering dynamic geospatial phenomena in virtual globes.     

Automatic geospatial metadata generation for earth science virtual data products

Recent advances in Semantic Web and Web Service technologies has shown promise for automatically deriving geospatial information and knowledge from Earth science data distributed over the Web. In a service-oriented environment, the data, information, and knowledge are often consumed or produced by complex, distributed geoscientific workflows or service chains. In order for the chaining results to be consumable, sufficient metadata for data products to be delivered by service chains must be provided. This paper proposes automatic generation of geospatial metadata for Earth science virtual data products. A virtual data product is represented using process models, and can be materialized on demand by dynamically binding and chaining archived data and services, as opposed to requiring that Earth science data products be physically archived. Semantics-enabled geospatial metadata is generated, validated, and propagated during the materialization of a virtual data product. The generated metadata not only provides a context in which end-users can interpret data products before intensive execution of service chains, but also assures semantic consistency of the service chains.     

Situation awareness and virtual globes: Applications for disaster management

This paper presents research on the use of virtual globes to support the development of disaster event situation awareness in humans via open source information analysis and visualization. The key technology used for this research is the Context Discovery Application (CDA), which is a geovisual analytic environment designed to integrate implicit geographic information with Google Earth™. A case study of humanitarian disaster management is used to demonstrate the unique abilities of the CDA and Google Earth^TM to support situation awareness. The paper provides some of the first empirical evidence on the utility of the virtual globes to support situation awareness for disaster management using implicit geographic information. The evidence presented was derived from evaluations by disaster management practitioners at the United Nations (UN) ReliefWeb project, an extremely relevant, yet difficult group to access for conducting academic disaster management research. Finally, ideas for future research on developing virtual globe applications to support situation awareness are described.     

Visualizing and analyzing dynamic meteorological data with virtual globes: A case study of tropical cyclones

Visualization is an important component of the evaluation of meteorological models, forecasting research, and other applications. With advances in computing power, the volume of meteorological data generated by geoscience and climate researchers has been steadily increasing. The emerging technique of virtual globes has been regarded as an ideal platform for visualizing larger geospatial data over the Internet. To visualize and analyze meteorological data with the new virtual globes, this paper proposes a systematic meteorological data visualization (MDV) framework in World Wind, an open-source virtual globe. The key technologies, including a hierarchical octree-based multiresolution data organization, data scheduling, level of detail (LOD) and rendering are described in detail. The framework is then applied to a practical tropical cyclone simulation, including flow vectors, particle tracking, cross-sectional analysis, streamlines, pathway animation, and volume rendering. The results show that virtual globes are effective tools for meteorological data visualization and analysis.     

Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Sciences

With the rapid development and large integration of global informatization and industrialization since the 21st century,the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience,environmental science and related disciplines.However,the traditional approaches for storing,managing and analyzing massive data on the local platform,which take up lots of resources,time and energy,have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure,and it combines a large number of geospatial datasets and satellite imagery,in which the datasets could be processing,analyzing as well as visualizing on a global scale.Meanwhile,it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change,vegetation degradation,food security and water resource shortages.Firstly,an introduction of GEE cloud platform has been given.Secondly,recent researches that using GEE cloud platform were reviewed.Thirdly,GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands,shrub grasslands and croplands.Finally,after a rough calculation,GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency,improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing,but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences,environment sciences and related disciplines.     

Rapid Land Cover Classification Using Landsat Time Series based on the Google Earth Engine

Accurate maps of land cover at high spatial resolution are fundamental to many researchs on carbon cycle, climate change monitoring and soil degradation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. It offer opportunities for generating land cover maps designed to meet the increasingly detailed information needs for science,monitoring, and reporting.In this study, we classified the land cover types in Shanxi using Landsat time series data based on the Google Earth Engine Platform. We selected 1 580 sample points be visual interpretation of the original fine spatial resolution images along with Google Earth historical images over six different cover types. We defined training data by randomly sampling 60% of the sample points. The remaining 40% was used for validation. We generated two diffirent types of Landsat composite: (1) one based on median values which is used as the input image for single-date classification; (2)one based on percentile values which is used as input images for time series classification. Random forest classification was performed with two different types of Landsat composites. Random forest classification was performed with two different types of Landsat composites.We visually compared the single-date based to the time series based cover maps of 1990, 2000, 2010 and 2017 in five local areas, and we future compared the results of time series to other products. We aslo performed an accuracy assessment on the land cover classification products. The results shown: (1) The results of time series classification had an overall accuracy of 84%~94%. The time series results improved overall accuracy by 5%~10% compared to single-date results; (2) The result of time series achieves the classification accuracy of products such as CNLUCC, GlobeLand30 and FROM-GLC.The following conclusions were drawn: (1) Cloud computing and archived Landsat data in the GEE has many advantages for land cover classification at a large geographic scale, such as s strong timeliness, short time cycle and low cost; (2) The statistics metrics from Landsat time series is a viable means for discrimination of land cover types, which is particularly useful for the time series classification.     

Evaluation of MODIS Snow Products in Southwestern Xinjiang Using the Google Earth Engine

Google Earth Engine(GEE) is a cloud\|based geospatial processing platform that can analyze geospatial data to achieve parallel processing of massive remote sensing data on a global scale,providing support for remote sensing big data and large\|area research.MODIS snow cover mapping is a global snow cover product established using MODIS data and has been widely used in regional and global climate and environmental monitoring.In the GEE,millions of remote sensing images are stored,including MODIS daily snow products MOD10A1 V5 data and Landsat data.Taking the three research areas in southwestern Xinjiang as examples,the Landsat stored by the GEE were selected,and the NDSI was used to extract the snow cover as the true value of the land cover to evaluate the MOD10A1 accuracy.The results show that the average overall accuracy of MOD10A1 in the snow cover season in southwestern Xinjiang during the period from 2000 to 2016 is 82%,the average misjudgment rate is 2.9%,and the average missed rate is 58.8%.The overall accuracy of MOD10A1 can reach 98% under the clear sky conditions.The accuracy of MOD10A1 is effected by the terrain conditions and cloud cover in different regions.Therefore,the GEE can quickly and effectively filter high quality cloudless Landsat images,and evaluate the accuracy of the MOD10A1 in the snow area around the global regions,displaying intuitively the misjudgment and missed areas in the form of online maps.Meanwhile,GEE provides the Landsat simple cloud score function to calculate the regional cloud cover,which makes the influence of cloud cover on the MOD10A1 accuracy assessment more regionally representative.     

影像的土地覆被快速分类

精确的土地覆盖信息是进行碳循环、气候变化监测、土壤退化等相关科学研究的基础。随着云计算技术的不断成熟,一些高效算法与平台被不断提出,用来充分挖掘遥感数据所包含的海量信息。基于Google Earth Engine(GEE)云平台,利用随机森林监督分类法对1990、2000、2010、2017年的山西省土地覆被进行了分类。参考Google Earth高清影像选择的1580个样本点,对分类结果进行了验证;同时将分类结果与CNLUCC、GlobeLand30、FROM-GLC等现有土地覆被分类产品进行比较。验证和对比发现时间序列分类结果的总体精度达到86%~94%,比同期单时相分类总体精度提高了5%~10%;本文时间序列结果达到了CNLUCC、GlobeLand30、FROM-GLC等产品的分类精度。结果表明:①在快速准确土地覆被分类方面,时间序列影像与云平台结合,显示出时效性强、时间周期短、成本低等优势;②时间序列百分位数指标能有效地区分不同土地覆被类型的物候差别,在进行土地覆被分类方面显示出简单、易用、高效等特点。该方法对于深入研究大区域尺度的土地覆被变化过程具有重要的参考价值。     

A tropical cyclone application for virtual globes

Within the past ten years, a wide variety of publicly available environmental satellite-based data have become available to users and gained popular exposure in meteorological applications. For example, the Naval Research Laboratory (NRL) has maintained a well accepted web-based tropical cyclone (TC) website (NRL TC-Web) with a diverse selection of environmental satellite imagery and products covering worldwide tropical cyclones extending back to 1997. The rapid development of virtual globe technologies provides for an effective framework to efficiently demonstrate meteorological and oceanographic concepts to not only specialized weather forecasters but also to students and the general public. With their emphasis upon geolocated data, virtual globes represent the next evolution beyond the traditional web browser by allowing one to define how, where, and when various data are displayed and dynamically updated.In this article, we describe a virtual globe implementation of the NRL TC-Web satellite data processing system. The resulting NRL Tropical Cyclones on Earth (TC-Earth) application is designed to exploit the capabilities of virtual globe technology to facilitate the display, animation, and layering of multiple environmental satellite imaging and sounding sensors for effective visualization of tropical cyclone evolution. As with the NRL TC-Web, the TC-Earth application is a dynamic, realtime application, driven by the locations of active and historical tropical cyclones. TC-Earth has a simple interface that is designed around a series of placemarks that follow the storm track history. The position coordinates along the storm track are used to map-register imagery and subset other types of information, allowing the user a wide range of freedom to choose data types, overlay combinations, and animations with a minimum number of clicks. TC-Earth enables the user to quickly select and navigate to the storm of interest from the multiple TCs active at anytime around the world or to peruse data from archived storms.     

Crusta: A new virtual globe for real-time visualization of sub-meter digital topography at planetary scales

Virtual globes are becoming ubiquitous in the visualization of planetary bodies and Earth specifically. While many of the current virtual globes have proven to be quite useful for remote geologic investigation, they were never designed for the purpose of serving as virtual geologic instruments. Their shortcomings have become more obvious as earth scientists struggle to visualize recently released digital elevation models of very high spatial resolution (0.5-1 m²/sample) and extent (>2000 km²). We developed Crusta as an alternative virtual globe that allows users to easily visualize their custom imagery and more importantly their custom topography. Crusta represents the globe as a 30-sided polyhedron to avoid distortion of the display, in particular the singularities at the poles characteristic of other projections. This polyhedron defines 30 “base patches,” each being a four-sided region that can be subdivided to an arbitrarily fine grid on the surface of the globe to accommodate input data of arbitrary resolution, from global (BlueMarble) to local (tripod LiDAR), all in the same visualization. We designed Crusta to be dynamic with the shading of the terrain surface computed on-the-fly when a user manipulates his point-of-view. In a similarly interactive fashion the globe's surface can be exaggerated vertically. The combination of the two effects greatly improves the perception of shape. A convenient pre-processing tool based on the GDAL library facilitates importing a number of data formats into the Crusta-specific multi-scale hierarchies that enable interactive visualization on a range of platforms from laptops to immersive geowalls and caves. The main scientific user community for Crusta is earth scientists, and their needs have been driving the development.     

Visualization of A-Train vertical profiles using Google Earth

Online tools, such as those pioneered by Google Earth (GE), are changing the way in which scientists and the general public interact with three-dimensional geospatial data in a virtual environment. However, while GE provides a number of features to facilitate geospatial data visualization, there is currently no readily available method for rendering vertical geospatial data derived from Earth—viewing remote sensing satellites as an orbit curtain seen from above. Here, a solution (one of many possible) is demonstrated to render vertical profiles of atmospheric data from the A-Train satellite formation in GE, using as a proof-of-concept data from one of the instruments—the NASA CloudSat satellite. CloudSat carries a nadir-viewing Cloud Profiling Radar that produces data revealing the vertical distribution of cloud characteristics along the satellite track. These data are first rendered into a long vertical image for a user-selected spatial range through the NASA Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) system (http://giovanni.gsfc.nasa.gov/). The vertical image is then chopped into small slices representing 15 s of satellite time (～103 km long ground distance). Each small piece, as a texture, is fed into a generalized COLLAborative Design Activity (COLLADA) three-dimensional (3-D) model. Using the satellite orbit coordinates, the repeated 15 s “3-D model slices” are spliced together to form a vertical “curtain” image in Keyhole Markup Language (KML) format. Each model slice is geolocated along the CloudSat orbit path based on its size, scale and angle with the longitude line that are precisely calculated on the fly. The resulting vertical cloud data can be viewed in GE, either transparently or opaquely, superimposed above the Earth's surface with an exaggerated vertical scale. Since CloudSat is just a part of the A-Train formation, the full utility of this tool can be explored within the context of the A-Train Data Depot (ATDD, http://disc.gsfc.nasa.gov/atdd/) and the corresponding Giovanni instance (http://disc1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=atrain). The latter portal allows scientists and the general public to access and visualize complex A-Train datasets without having to delve into data formats specific to a given mission.     

Interactive 3D content modeling for Digital Earth

Digital Earth is a global reference model for integrating, processing and visualizing geospatial datasets. In this reference model, various data-types, including Digital Elevation Models (DEM) and imagery (orthophotos), are universally and openly available for the entire globe. However, 3D content such as detailed terrains with features, man-made structures, 3D water bodies and 3D vegetation are not commonly available in Digital Earth. In this paper, we present an interactive system for the rapid creation and integration of these types of 3D content to augment Digital Earth. The inputs to our system include available data sources, such as DEM and imagery information depicting landscapes and urban environments. The proposed system employs sketch-based and image-assisted tools to support interactive creation of textured 3D content. For adding terrain features visible in orthophotos, and also the basin of water bodies, we use a multiscale least square surface fitting to generate an adaptive triangular subdivision. For modeling forests and vegetation, we use image-based techniques and take advantage of visible regions and colors of forests in orthophotos. For 3D man-made structures, starting from a single photograph, we provide a simple image-assisted sketching tool to extract these objects, correct for perspective distortion and place them into desired locations.     

Development of the Arctic Research Mapping Application (ARMAP): Interoperability challenges and solutions

Ensuring interoperability between WebGIS applications is essential for maximizing access to data, data sharing, and data manipulation. Interoperability is maximized through the adoption of best practices, use of open standards, and utilization of spatial data infrastructure (SDI). While many of the interoperability challenges like infrastructure, data exchange, and file formats are common between applications, some regions like the Arctic present specific challenges including the need for presenting data in one or more polar projections. This paper describes the Arctic Research Mapping Application (ARMAP) suite of online interactive maps, web services, and virtual globes (the ARMAP suite; http://armap.org/) and several of the interoperability challenges and solutions encountered in development to date. ARMAP is a unique science and logistic tool supporting United States and international Arctic science by providing users with the ability to access, query, and browse information and data. Access to data services include a text-based search utility, an Internet Map Server client (ArcIMS), a lightweight Flex client, ArcGIS Explorer and Google Earth virtual globes, and Open Geospatial Consortium (OGC) compliant web services, such as Web Map Service (WMS) and Web Feature Service (WFS). Through the ARMAP suite, users can view a variety of Arctic map layers and explore pertinent information about United States Arctic research efforts. The Arctic Research Logistics Support Service (ARLSS) database is the informational underpinning of ARMAP. Avoiding duplication of effort has been a key priority in the development of the ARMAP applications. The ARMAP suite incorporates best practices that facilitate interoperability such as Federal Geographic Data Committee (FGDC) metadata standards, web services for embedding external data and serving framework layers, and open standards such as Open Geospatial Consortium (OGC) compliant web services. Many of the features and capabilities of ARMAP are expected to greatly enhance the development of an Arctic SDI.     

融合Google Earth的船舶监控与应急系统的设计

Google Earth的高分辨率卫星地图资源、三维虚拟环境和人机界面,可以为船舶监控与应急系统提供丰富的地理信息资源。在Google Earth的KML技术及其COMAPI的研究和扩展开发的基础上,对海洋船舶的位置监控、信息融合和救援服务地理信息系统进行分层设计,整体提升船舶信息管理水平。该系统应用在浙江省的渔政应急救援系统项目中,收到良好效果。     

Using NASA's World Wind virtual globe for interactive internet visualization of the global MODIS burned area product

Three‐dimensional virtual globes are radically changing the way geographic information is perceived by the public. This article describes how NASA World Wind, an open source virtual globe, is currently being used for visualization of the MODIS burned area product. The procedures adopted for converting the product into a format compatible with World Wind, as well as the spatial generalization of these data at different scales, are described. Directions to instructions on how to obtain the MODIS burned area product visualization imagery and use it in World Wind are included. This article highlights the potential benefits of integrating the visualization capability of virtual globes into the next generation of remotely sensed product internet analysis and distribution systems.     

Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)

Google Earth Engine (GEE) has been increasingly used in environmental and urban studies due to its cloud-based geospatial processing capability and accessibility to a large collection of geospatial datasets like Landsat, Modis, etc. However, at present, ecological and urban modeling efforts based on GEE are facing three grave challenges: current illustrations of GEE are to a large extent “straightforward mapping” applications; technical complexities that ecological or urban modelers have to overcome in order to effectively and easily use GEE to develop image processing based environmental models; and the majority of ecological and urban modelers are not aware of new analytical approaches that are becoming available because of the unprecedent geospatial processing capability and large collection of big geospatial datasets GEE has brought to them. The great potential of GEE to support ecological and urban modeling is less explored. In this study, we augmented GEE functions with a few sets of user-customized functions for improving image classification accuracy, estimating ecosystem services, and modeling urban growth sustainability. The paper is the first effort of modeling urban sustainability based on the concept of ecosystem service value (ESV) and in the cloud with GEE; is the first application of classifying GEE Landsat time-series images to compute yearly ESV; and creates the first set of cloud tools to augment GEE for ecologists and urban modelers to model urban sustainability from GEE and ESV. The paper also chose Hohhot City, Inner Mongolia as a case study to model urban sustainability in a time-series 12 years (2005–2016). The case study successfully estimated ecosystem service values and analyzed urban growth sustainability. It also revealed spatial disparities and temporal dynamics of urban growth sustainability in Hohhot City. The study provides an easy-to-adapt illustration on using GEE for image-based ecological and urban modeling.     

The Earth Simulator: roles and impacts

The Earth Simulator Research Project started in March 2002 with the primary objective of producing reliable prediction data for global climate change. Within a couple of months after the start of operation, the Earth Simulator achieved an amazing performance of 35.86 Teraflops (about 90% of the peak performance of 40.96 Teraflops) in the Linpack benchmark test and, more surprisingly, 26.58 Teraflops for a typical application program of global atmospheric circulation model (called AFES) with a horizontal resolution of 10 km. These facts ensure us that the real contribution of the Earth Simulator be far greater than originally expected. Undoubtedly, the Earth Simulator would work to make a paradigm shift in science, industry, and human thinking, as well as finding the best human’s wisdom to keep a sustainable symbiotic relationship with nature.     

An interoperable spatiotemporal weather radar data dissemination system

This paper investigates an interoperable framework to disseminate Earth Science data to different application domains. The proposed framework can manage different Earth science data products and raster snapshots over time through the use of relevant metadata information. The framework generates images to be accessed by GIS software for various Earth science and web‐based applications. The access is enabled through the compliance with OpenGeospatial Consortium's Web Map Service (WMS) for interoperability such that any WMS viewer can access the service. The framework can provide GIS users the capability to incorporate geospatial information from other WMS servers. Using the United States NEXt generation weather RADar (NEXRAD) data, we demonstrate how the proposed framework can facilitate the dissemination of Earth Science data to a broad community in a near real‐time fashion. The proposed framework can be used to manage and disseminate various types of spatiotemporal Earth science data.