Visualization of and Access to CloudSat Vertical Data through Google Earth

Online tools, pioneered by the Google Earth (GE), are facilitating the way in which scientists and general public interact with geospatial data in real three dimensions. However, even in Google Earth, there is no method for depicting vertical geospatial data derived from remote sensing satellites as an orbit curtain seen from above. Here, an effective solution is proposed to automatically render the vertical atmospheric data on Google Earth. The data are first processed through the Giovanni system, then, processed to be 15-second vertical data images. A generalized COLLADA model is devised based on the 15-second vertical data profile. Using the designed COLLADA models and satellite orbit coordinates, a satellite orbit model is designed and implemented in KML format to render the vertical atmospheric data in spatial and temporal ranges vividly. The whole orbit model consists of repeated model slices. The model slices, each representing 15 seconds of vertical data, are placed on the CloudSat orbit based on the size, scale, and angle with the longitude line that are precisely and separately calculated on the fly for each slice according to the CloudSat orbit coordinates. The resulting vertical scientific data can be viewed transparently or opaquely on Google Earth. Not only is the research bridged the science and data with scientists and the general public in the most popular way, but simultaneous visualization and efficient exploration of the relationships among quantitative geospatial data, e.g. comparing the vertical data profiles with MODIS and AIRS precipitation data, becomes possible

3D Online Visualization and Synergy of NASA A-Train Data Using Google Earth

This poster presentation reviews the use of Google Earth to assist in three dimensional online visualization of NASA Earth science and geospatial data. The NASA A-Train satellite constellation is a succession of seven sun-synchronous orbit satellites: (1) OCO-2 (Orbiting Carbon Observatory) (will launch in Feb. 2013), (2) GCOM-W1 (Global Change Observation Mission), (3) Aqua, (4) CloudSat, (5) CALIPSO (Cloud-Aerosol Lidar & Infrared Pathfinder Satellite Observations), (6) Glory, (7) Aura. The A-Train makes possible synergy of information from multiple resources, so more information about earth condition is obtained from the combined observations than would be possible from the sum of the observations taken independentl

Information Technology Infusion Case Study: Integrating Google Earth(Trademark) into the A-Train Data Depot

This poster paper represents the NASA funded project that was to employ the latest three dimensional visualization technology to explore and provide direct data access to heterogeneous A-Train datasets. Google Earth (tm) provides foundation for organizing, visualizing, publishing and synergizing Earth science data

A-Train Data Depot: Integrating and Exploring Data Along the A-Train Tracks

The immense potential for new science findings as a result of inter-instrument data analysis has led to the development of a new data portal at GSFC: the A-train Data Depot. The power and utility of this new service to the general public is amplified immensely when the archived data are used in conjunction with online data analysis services like Giovanni. This presentation details some of the challenges of data usage from multiple distinct missions and how the tool sets we have developed can help to overcome these challenges, considerably cut down on analysis overhead and promote science exploration in an otherwise very challenging arena

Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy

Google Earth (GE) has recently become the focus of increasing interest and popularity among available online virtual globes used in scientific research projects, due to the free and easily accessed satellite imagery provided with global coverage. Nevertheless, the uses of this service raises several research questions on the quality and uncertainty of spatial data (e.g. positional accuracy, precision, consistency), with implications for potential uses like data collection and validation. This paper aims to analyze the horizontal accuracy of very high resolution (VHR) GE images in the city of Rome (Italy) for the years 2007, 2011, and 2013. The evaluation was conducted by using both Global Positioning System ground truth data and cadastral photogrammetric vertex as independent check points. The validation process includes the comparison of histograms, graph plots, tests of normality, azimuthal direction errors, and the calculation of standard statistical parameters. The results show that GE VHR imageries of Rome have an overall positional accuracy close to 1 m, sufficient for deriving ground truth samples, measurements, and large-scale planimetric maps

Application and improvement of soil spatial distribution mapping using advanced modelling techniques

The main purpose of this contribution is to develop  realistic prediction digital soil maps in order to increase their visuality, and to evaluate and compare the performance of different modeling techniques: a) Kriging, b) Artificial Neural Network – Multilayer Perceptron (ANN-MLP) and c) Multiple Polynomial Regressions (MPR). The following  criteria were used to determine selection of the testing site for the modeling: (1) intensive metal ore mining and metallurgical processing; (2) geomorphological natural features; (3) regular geological setting, and (4) the remaining minefields. The success of Digital Soil Mapping and the plausibility of prediction maps increases with the availability of spatial data, the availability of computing power for processing data, the development of data-mining tools, geographical information systems (GIS) and numerous applications beyond geostatistics. Advanced prediction modeling techniques, ANN-MLP and MPR include geospatial parameters sourced from Digital Elevation Models (DEM), land use and remote sensing, applied in combination with costly and time-consuming soil measurements, developed and finally incorporated into the models of spatial distribution in the form of 2D or 3D maps. Innovative approaches to modeling assist us in the reconstruction of different processes that impact the entire study area, simultaneously. This holistic approach represents a novelty in contamination mapping and develops prediction models to help in the reconstruction of main distribution pathways, to assess the real size of the affected area as well as improving the data interpretation.</p

Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations