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Google Earth/KML Files Real-Time EarthquakesDisplay real-time earthquakes, seismicity animations, and several real-time earthquake options including color by age/depth. ShakeMapsMaps of ground motion and shaking intensity for significant earthquakes. Google Earth KML files are in the Downloads area for each individual earthquake under the GIS Files heading. Tectonic Plate BoundariesThe outermost shell of the Earth consists of a mosaic of rigid “plates” that have been moving relative to one another for hundreds of millions of years. GEOSTATIONARY SATELLITE SERVER France - Jeu de données ASTER => conversion en courbe de niveau Bonjour, Je trouve que cela fait un peu brute de coffrage comme cela et j'ai essayé en appliquant un algo de lissage (celui de McMaster), c'est déjà plus sympa visuellement. (cf. Le trait gris est celui qui a reçu le lissage. Concernant ma fusion, effectivement, c'est la fusion d'une même altitude. Pour produire cela à l'échelle mondiale, je pense qu'en une nuit ou 24 heures, c'est réglé (avec un bon ordinateur). @bientôt, Loïc & Flo www.partir-en-vtt.com On jeudi 30 juin 2011, [hidden email] wrote: > Bonjour, > > J'ai regardé les données ASTER, c'est facilement exploitable. Qu'entends tu par fusion et sans fusion ? > S'il faut produire cela à l'échelle Française, je pourrai lancer le > traitement et vous faire un backup dans une base postgis par exemple. J'ai déjà ça pour une bonne partie de l'europe, mais je souhaiterais passer world wide pour mon rendu et je me heurte a des problèmes soit de méthodologie, soit de performance machine. -- sly qui suis-je :

Fire Data in Google Earth KML Access:The links below provide access to several geospatial datasets relevant to fire management in Keyhole Markup Language (KML/KMZ) format for use in Google Earth and other virtual globe applications. Geospatial data are organized by specified geographic region and include location and characterization of satellite fire detections, current large incident locations and NWS fire weather forecasts. All KMLs update automatically to ensure availability of the latest information (Current link). KML Descriptions: Fire Detections - MODIS (1km), VIIRS (375m and 750m), Landsat 8 (30m), AVHRR (1km) and GOES (4km) fire detections by time/date of occurrence within the last 6, 12 and 24 hours, and the 6 days previous to the last 24-hour period. Fire Radiative Power - Measured fire radiative power (fire intensity) for MODIS fire detections within the last 6, 12 and 24 hours, and the 6 days previous to the last 24-hour period.

Spectral Hourglass Wizard (Using ENVI) | Exelis VIS Docs Center Use the Spectral Hourglass Wizard to guide you step-by-step through the ENVI hourglass processing flow to find and map image spectral endmembers from hyperspectral or multispectral data. The Wizard displays detailed instructions and useful information for each function. From the Toolbox, select Spectral > Spectral Unmixing > Spectral Hourglass Wizard to start the workflow. The hourglass processing flow uses the spectrally over-determined nature of hyperspectral data to find the most spectrally pure, or spectrally unique, pixels (called endmembers) within the dataset and to map their locations and sub-pixel abundances. The name of the corresponding ENVI function appears at the top of the panel during each step. After starting the Spectral Hourglass Wizard, refer to the following sections for help:

CGIAR-CSI SRTM 90m DEM Digital Elevation Database Fire Detection GIS Data MODIS Fire Detection GIS Data: MODIS fire detection data for the current year are compiled Terra and Aqua MODIS fire and thermal anomalies data generated from MODIS near real-time direct readout data acquired by the USDA Forest Service Remote Sensing Applications Center, University of Wisconsin Space Science and Engineering Center, University of Alaska-Fairbanks Geographic Information Network of Alaska, the NASA Goddard Space Flight Center Direct Readout Laboratory, and NASA Goddard Space Flight Center MODIS Rapid Response System. These data are provided as the centroids of the 1km fire detections and are a composite dataset compiled from the listed sources. MODIS fire detection data for years 2000 to 2009 are Terra and Aqua MODIS fire and thermal anomalies data from the official NASA MCD14ML product, Collection 5, Version 1. These data are provided as the centroids of the 1km fire detections.

AVHRR NDVI3g 30+ Years of LAI3g and FPAR3g Data Sets We are providing you free access to a 30+ year long global data sets of vegetation leaf area index (LAI3g) and fraction vegetation absorbed photosynthetically active radiation (FPAR3g). These data sets were derived from the third generation GIMMS NDVI3g data set (hence the suffix "3g"). The data sets are at 1/12 degree resolution, 15-day composites (2 per month) and span the period July 1981 to December 2011. Please note the following: (1) If you wish to submit an article to the special dedicated to NDVI3g/LAI3g/FPAR3g of the open source journal "Remote Sensing" you are welcome. (2) The article describing the LAI3g/FPAR3g by Zhu et al. can downloaded from the Special Issue (3) To obtain the data sets, please contact Ranga B.

Shuttle Radar Topography Mission U.S. Releases Enhanced Shuttle Land Elevation Data On September 23, 2014, the White House announced that the highest-resolution topographic data generated from NASA's Shuttle Radar Topography Mission (SRTM) in 2000 was to be released globally by late 2015. The announcement was made at the United Nations Heads of State Climate Summit in New York. See the full JPL Release 2014-321. Previously, SRTM data for regions outside the United States were sampled for public release at 3 arc-seconds, which is 1/1200th of a degree of latitude and longitude, or about 90 meters (295 feet). See an index map of the newly available full-resolution data. The new data are available for download from the USGS EROS Data Center - see Public Data Distribution for details. See the Africa image above and its caption at the PIA04965. These additional fly around videos further illustrate SRTM elevation data: India and the Himalaya Mountains, with Landsat satellite images draped over SRTM elevation data.

MODIS/VIIRS Burn Scar Data MODIS/VIIRS Burn Scar Data: MODIS and VIIRS burn scar data are provided at a spatial resolution of 500 meters and attributed with the approximate day of burning on a per pixel basis (VIIRS burn scar data are oversampled from the native 750 meter resolution). These products are similar to the official MODIS 500 meter burned area product (MCD45A1), but are based on a different algorithmic approach. The algorithm employed for this product is an automated hybrid algorithm developed by Dr. The MODIS burn scar algorithm leverages as inputs daily composite datasets derived from daily Terra and Aqua MODIS observations. The VIIRS burn scar algorithm leverages as inputs daily composite datasets derived from daily Suomi NPP observations. Due to the data and analysis requirements associated with the MODIS and VIIRS science processing algorithms to yield reliable burn scar products, the burn scar data are not available in near real-time.

Bathymetry Data Viewer ●Contact Us Navigating the map Click and drag or use arrow keys to pan Mouse scroll forward or use + key to zoom in Mouse scroll backward or use - key to zoom out Identifying features You have several options to identify features within visible layers: Single-click on the map Or, choose another tool from the "Identify" menu: Click on to draw a rectangle Click on to draw a polygon Click on to enter coordinates for a bounding box A popup will appear with a list of the selected features. Mouse-over the list of files within an instrument folder to highlight features on the map (blue line). Click the magnifying glass icon to zoom to that feature. Searching for data Filter Surveys: opens a dialog where you can specify a desired range of survey years, survey ID, and ship name (wildcards accepted: *).

Natural Hazards Viewer Tsunami Events Tsunami Observations Significant Earthquakes Significant Volcanic Eruptions DART® Deployments Plate Boundaries +- Layers ●Search ●Reset Filter Show as: ●Search ●Reset Filter ●Search ●Reset Filter +- More Information +- Help Basemap▼ Options▼ Position: not available Zoom to Conservation Science Data and Tools WWF constantly looks for new opportunities to improve the effectiveness and efficiency of our conservation work. Sharing that newfound knowledge with scientists across the globe is critical to protecting critical species and places. We share our data with others for valid scientific, conservation and educational purposes. We request that it is properly cited when used and that any modification of the original data by users should be noted. Conservation Science Tools Moabi Moabi is a powerful online tool for tracking information spatially. InVEST InVEST is a family of modeling tools that map, measure and value the goods and services we obtain from nature. WildFinder The WildFinder application enables users to visualize global distribution of animal species based on the WWF terrestrial ecoregion maps. Conservation Science Data Sets Marine Ecoregions of the World Marine Ecoregions of the World (MEOW) is a biogeographic classification of the world's coasts and continental shelves. HydroSHEDS

Timelapse: Landsat Satellite Images of Climate Change TIME and Space | By Jeffrey Kluger Editors note:On Nov. 29, 2016, Google released a major update expanding the data from 2012 to 2016. Read about the update here. Spacecraft and telescopes are not built by people interested in what’s going on at home. That changed when NASA created the Landsat program, a series of satellites that would perpetually orbit our planet, looking not out but down. Over here is Dubai, growing from sparse desert metropolis to modern, sprawling megalopolis. It took the folks at Google to upgrade these choppy visual sequences from crude flip-book quality to true video footage. These Timelapse pictures tell the pretty and not-so-pretty story of a finite planet and how its residents are treating it — razing even as we build, destroying even as we preserve. Chapter 1: Satellite Story | By Jeffrey Kluger It’s a safe bet that few people who have grown up in the Google era have ever heard of Stewart Udall. But in 1966, Udall and his staff had an idea. 1 of 20 1 of 14

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