Improving merge methods for grid-based digital elevation models

Digital Elevation Models (DEMs) are used to represent the terrain in applications such as, for example, overland flow modelling or viewshed analysis. DEMs generated from digitising contour lines or obtained by LiDAR or satellite data are now widely available. However, in some cases, the area of study is covered by more than one of the available elevation data sets. In these cases the relevant DEMs may need to be merged. The merged DEM must retain the most accurate elevation information available while generating consistent slopes and aspects. In this paper we present a thorough analysis of three conventional grid-based DEM merging methods that are available in commercial GIS software. These methods are evaluated for their applicability in merging DEMs and, based on evaluation results, a method for improving the merging of grid-based DEMs is proposed. DEMs generated by the proposed method, called Id:Blend, showed significant improvements when compared to DEMs produced by the three conventional methods in terms of elevation, slope and aspect accuracy, ensuring also smooth elevation transitions between the original DEMs. The results produced by the improved method are highly relevant different applications in terrain analysis, e.g., visibility, or spotting irregularities in landforms and for modelling terrain phenomena, such as overland flow

Moving dunes on the Google Earth

Several methods exist for surveying the dunes and estimate their migration rate. Among methods suitable for the macroscopic scale, the use of the satellite images available on Google Earth is a convenient resource, in particular because of its time series. Some examples of the use of this feature of Google Earth are here proposed.Comment: Keywords: Dunes, Dune Migration, Satellite Imagery, Google Earth, Image Processin

Google Earth

Steven Whitmeyer teams with Google Earth to create computer simulations for student

The Visualization of Historical Structures and Data in a 3D Virtual City

Google Earth is a powerful tool that allows users to navigate through 3D representations of many cities and places all over the world. Google Earth has a huge collection of 3D models and it only continues to grow as users all over the world continue to contribute new models. As new buildings are built new models are also created. But what happens when a new building replaces another? The same thing that happens in reality also happens in Google Earth. Old models are replaced with new models. While Google Earth shows the most current data, many users would also benefit from being able to view historical data. Google Earth has acknowledged this with the ability to view historical images with the manipulation of a time slider. However, this feature does not apply to 3D models of buildings, which remain in the environment even when viewing a time before their existence. I would like to build upon this concept by proposing a system that stores 3D models of historical buildings that have been demolished and replaced by new developments. People may want to view the old cities that they grew up in which have undergone huge developments over the years. Old neighborhoods may be completely transformed with new road and buildings. In addition to being able to view historical buildings, users may want to view statistics of a given area. Users can view such data in their raw format but using 3D visualizations of statistical data allows for a greater understanding and appreciation of historical changes. I propose to enhance the visualization of the 3D world by allowing users to graphically view statistical data such as population, ethnic groups, education, crime, and income. With this feature users will not only be able to see physical changes in the environment, but also statistical changes over time

Google Earth Visualizations: Preview and Delivery of Hydrographic and Other Marine Datasets

Existing hydrographic data analysis and visualization tools are very powerful, but lack easy access to web data management tools. Virtual globe software provides a gateway to a host of important data products in formats usable by specialized tools such as CARIS, Fledermaus, and Arc/Info. With virtual globe interfaces, users see complimentary and consistent geographic representations of available data in an easy-tonavigate format. We present a preview of visualizations that build upon virtual globe software. These examples are viewed in Google Earth, but could also be implemented in a number of alternative programs (e.g. NASA World Wind, Dapple, OSSIM Planet). We have assembled Google Earth visualizations from three datasets to illustrate each of the four primary types of data (handle point, line, area, and time data). The USCG Marine Information for Safety and Law Enforcement (MISLE) database of ship incidents illustrates point data. A short sample of the USCG National Automatic Identification System logs (N-AIS) demonstrates rendering of line data. Area data is exemplified in the United Nations Convention f the Law of the Sea (UNCLOS) multibeam bathymetry. Point, line and area data are combined to present a preview of S57 chart information. Finally, the MISLE database uses time to show maritime incidents that occurred in US waterways. The visualizations for our initial work were created with hand coding and small scripts. However, tools such as Fledermaus and RockWare have added Google Earth export functionality that makes authoring Google Earth resources easy to construct. For large dataset that require additional processing and analyses, Google Earth visualizations can offer users a range of download formats and suggest what software to use. We believe that this virtual globe-based-approach can make geospatial data sets more widely accessible via the world-wide-web

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

Transferring Google Earth observations to GIS-software : example from gully erosion study

High-resolution images available on Google Earth are increasingly being consulted in geographic studies. However, most studies limit themselves to visualizations or on-screen measurements. Google Earth allows users to create points, lines, and polygons on-screen, which can be saved as Keyhole Markup Language (KML) files. Here, the use of R statistics freeware is proposed to easily convert these files to the shapefile format [or .shp file format'], which can be loaded into Geographic Information System (GIS) software (ESRI ArcGIS 9 in our example). The geospatial data integration in GIS strongly increases the analysis possibilities