{ "count": 76, "next": null, "previous": null, "results": [ { "id": 15104, "url": "http://svs.gsfc.nasa.gov/15104/", "result_type": "Produced Video", "release_date": "2026-10-07T00:00:00-04:00", "title": "Science of Smoke Illustrated", "description": "Music: \"Velvet Pulse,\" \"No Time to Breathe,\" Universal Production MusicComplete transcript available. || Smoke_Illustrated_Thumb.png (1280x720) [1013.0 KB] || Smoke_Illustrated_Thumb_print.jpg (1024x576) [157.5 KB] || Smoke_Illustrated_Thumb_searchweb.png (320x180) [94.9 KB] || Smoke_Illustrated_Thumb_thm.png (80x40) [7.3 KB] || Smoke_Illustrated_ProRes.webm (1920x1080) [26.9 MB] || Smoke_Illustrated_YT.mp4 (1920x1080) [245.0 MB] || Smoke_Illustrated_YouTube.mp4 (1920x1080) [246.4 MB] || Smoke_Illustrated.en_US.srt [7.1 KB] || Smoke_Illustrated.en_US.vtt [6.7 KB] || Smoke_Illustrated_ProRes.mov (1920x1080) [3.0 GB] || ", "hits": 262 }, { "id": 5668, "url": "http://svs.gsfc.nasa.gov/5668/", "result_type": "Visualization", "release_date": "2026-08-06T00:00:00-04:00", "title": "Unprecedented Wildfires in the Pacific Northwest Engulf the Region in Smoke", "description": "Record-breaking wildfires impacted the Pacific Northwest. Watch how NASA’s GEOS model tracks the long-range transport of wildfire smoke as it travels across the continent. Additionally, Sentinel satellite imagery reveals the localized ground-level aftermath, showing burn scars and fire spread near Spokane, WA, as well as throughout Washington and Oregon.", "hits": 337 }, { "id": 5665, "url": "http://svs.gsfc.nasa.gov/5665/", "result_type": "Visualization", "release_date": "2026-07-20T13:30:00-04:00", "title": "Long-range Transport of 2026 Canadian Wildfire Smoke into the United States (July 14-20, 2026)", "description": "Powered by NASA's GEOS model, this animation visualizes the transport of brown carbon from July 14 to 20, 2026, and its impact on U.S. air quality.", "hits": 820 }, { "id": 5664, "url": "http://svs.gsfc.nasa.gov/5664/", "result_type": "Visualization", "release_date": "2026-07-17T18:00:00-04:00", "title": "Long-range Transport of 2026 Canadian Wildfire Smoke into the United States (July 14-17, 2026)", "description": "Powered by NASA's GEOS model, this animation visualizes the transport of brown carbon from July 14 to 17, 2026, and its impact on U.S. air quality.", "hits": 239 }, { "id": 5624, "url": "http://svs.gsfc.nasa.gov/5624/", "result_type": "Visualization", "release_date": "2026-05-29T18:59:59-04:00", "title": "Global Smoke Forecast Visualization (Daily Updated)", "description": "This daily global wildfire smoke visualization uses NASA's GEOS-FP model to show smoke transport patterns from yesterday through four days ahead. The visualization shows wildfire smoke intensity based on brown carbon aerosol optical depth, using a 0-1 scale with colors ranging from light brown/red to dark brown. Wildfire smoke contains dangerous PM2.5 particles and can travel thousands of miles, making tracking essential for understanding air quality impacts.", "hits": 595 }, { "id": 5631, "url": "http://svs.gsfc.nasa.gov/5631/", "result_type": "Visualization", "release_date": "2026-04-27T16:00:00-04:00", "title": "Long-range Transport of 2023 Canadian Wildfire Smoke into the Northeastern United States", "description": "In summer 2023, Canada experienced its most intense wildfire season on record. Smoke from wildfires in Alberta and Quebec was transported thousands of miles, driven by favorable meteorological conditions, resulting in record-breaking poor air quality across the northeastern United States. This animation, powered by NASA's GEOS model, visualizes aerosol transport from May 31 – July 7, 2023 — the period of most dramatic impact on eastern U.S. air quality.", "hits": 357 }, { "id": 31388, "url": "http://svs.gsfc.nasa.gov/31388/", "result_type": "Hyperwall Visual", "release_date": "2026-04-08T12:00:00-04:00", "title": "AVATAR: A Virtual Astronaut Tissue Analog Response", "description": "The AVATAR (A Virtual Astronaut Tissue Analog Response) investigation on Artemis II uses organ-on-a-chip devices, or organ chips, to study the effects of increased radiation and microgravity on human health.", "hits": 191 }, { "id": 31386, "url": "http://svs.gsfc.nasa.gov/31386/", "result_type": "Hyperwall Visual", "release_date": "2026-04-02T12:00:00-04:00", "title": "Space Station Research Informs New FDA-Approved Cancer Therapy", "description": "European Space Agency (ESA) astronaut Thomas Pesquet works with Protein Crystallization Facility hardware, used to study protein crystal growth research on the space station.", "hits": 210 }, { "id": 31382, "url": "http://svs.gsfc.nasa.gov/31382/", "result_type": "Hyperwall Visual", "release_date": "2026-04-01T12:00:00-04:00", "title": "Bone Loss Research Aboard the ISS", "description": "The experiment tests how microgravity affects bone-forming and bone-degrading cells and explore potential ways to prevent bone loss. This research could help protect astronauts on future long-duration missions to the Moon and Mars, while also advancing treatments for millions of people on Earth who suffer from osteoporosis.", "hits": 330 }, { "id": 31378, "url": "http://svs.gsfc.nasa.gov/31378/", "result_type": "Hyperwall Visual", "release_date": "2026-03-30T12:00:00-04:00", "title": "Biomanufacturing in Space of Drug-Delivery Medical Devices", "description": "Biomanufacturing in Space of Drug-Delivery Medical Devices (InSPA-Auxilium Bioprinter) 3D prints an implantable medical device that could be used to deliver drugs to support nerve regeneration.", "hits": 91 }, { "id": 31380, "url": "http://svs.gsfc.nasa.gov/31380/", "result_type": "Hyperwall Visual", "release_date": "2026-03-30T12:00:00-04:00", "title": "APEX-12 (Advanced Plant EXperiment-12) on the ISS", "description": "The findings of the APEX series of experiments offer insight into the effects of spaceflight on plant chromosomes, and how these findings could impact human health.", "hits": 108 }, { "id": 5533, "url": "http://svs.gsfc.nasa.gov/5533/", "result_type": "Visualization", "release_date": "2025-05-05T12:30:00-04:00", "title": "Air Quality Model Runs", "description": "NASA utilizes satellite instruments and models to monitor sources of air pollutants and their movement through the atmosphere. This visualization shows concentrations of air pollutants, such as Particulate Matter (PM2.5, fine particles smaller than 2.5 micrometers), Ozone (O~3~), Carbon Monoxide (CO), and Nitrogen Oxides (NO~x~) as they are tracked from NASA's Goddard Earth Observing System Composition Forecasting (GEOS-CF) system.", "hits": 110 }, { "id": 5518, "url": "http://svs.gsfc.nasa.gov/5518/", "result_type": "Visualization", "release_date": "2025-05-05T11:30:00-04:00", "title": "Science On A Sphere: Air Quality Model Runs", "description": "NASA utilizes satellite instruments and models to monitor sources of air pollutants and their movement through the atmosphere. This visualization shows concentrations of air pollutants, such as Particulate Matter (PM2.5, fine particles smaller than 2.5 micrometers), Ozone (O~3~), Carbon Monoxide (CO), and Nitrogen Oxides (NO~x~) as they are tracked from NASA's Goddard Earth Observing System Composition Forecasting (GEOS-CF) system.", "hits": 81 }, { "id": 31390, "url": "http://svs.gsfc.nasa.gov/31390/", "result_type": "Hyperwall Visual", "release_date": "2025-04-08T12:00:00-04:00", "title": "Studying Biological Impacts of Space Radiation", "description": "Researchers developed human brain models in the lab and exposed them to simulated space radiation to study the impacts of space radiation and other spaceflight hazards on the human body.", "hits": 53 }, { "id": 40529, "url": "http://svs.gsfc.nasa.gov/gallery/air-quality-dashboard/", "result_type": "Gallery", "release_date": "2024-12-04T00:00:00-05:00", "title": "Air Quality Dashboard", "description": "NASA's fleet of Earth observing satellites monitor our planet's oceans, biosphere, and atmosphere. Instruments onboard satellites observe air pollutants around the world. The data collected are used by air quality experts and researchers studying the impact of air pollution on human health.\n\n\n\n\n\n\n\n", "hits": 83 }, { "id": 31326, "url": "http://svs.gsfc.nasa.gov/31326/", "result_type": "Hyperwall Visual", "release_date": "2024-11-18T00:00:00-05:00", "title": "GMAO South American Wild Fires", "description": "GMAO South American Wild Fires || 3840x2160_16x9_30p [0 Item(s)] || GMAO SOuth American Wild Fires ||", "hits": 24 }, { "id": 14513, "url": "http://svs.gsfc.nasa.gov/14513/", "result_type": "Produced Video", "release_date": "2024-02-05T00:00:00-05:00", "title": "Ocean Color Countdown with PACE", "description": "Music: \"World of Wonder,\" \"Fretting,\" \"Cool or Die,\" \"Apple Pie,\" \"Conspiracy,\" \"Mister Dreamy,\" \"Mister Smart,\" \"Cats and Dogs,\" Universal Production Music.Complete transcript available. || oceancolor_thumb.png (1280x720) [1.3 MB] || oceancolor_thumb_print.jpg (1024x576) [197.3 KB] || oceancolor_thumb_searchweb.png (320x180) [107.1 KB] || oceancolor_thumb_thm.png (80x40) [7.4 KB] || ColorCountdown_prores.webm (1920x1080) [46.3 MB] || ColorCountdown.mp4 (1920x1080) [430.8 MB] || colorcountdown.en_US.srt [9.2 KB] || colorcountdown.en_US.vtt [8.8 KB] || ColorCountdown_prores.mov (1920x1080) [5.8 GB] || ocean-color-countdown-with-pace.hwshow || ", "hits": 75 }, { "id": 14439, "url": "http://svs.gsfc.nasa.gov/14439/", "result_type": "Produced Video", "release_date": "2023-10-24T11:00:00-04:00", "title": "Air Pollution Model Runs", "description": "Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.Predictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA’s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size. It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets. || ", "hits": 98 }, { "id": 40509, "url": "http://svs.gsfc.nasa.gov/gallery/earth-now-dashboard/", "result_type": "Gallery", "release_date": "2023-10-16T00:00:00-04:00", "title": "Earth Now Dashboard", "description": "NASA satellites provide data on Earth's land, ecosystems, water, air temperature, and climate - and have done so for more than 50 years. Earth information from space supports decision makers, partners, and people in developing the tools they need to mitigate, adapth, and respond to our changing planet.\n\nThe visualizations featured on this dashboard show the latest imagery available.\n\n\n\n\n\n\n\n", "hits": 369 }, { "id": 5147, "url": "http://svs.gsfc.nasa.gov/5147/", "result_type": "Visualization", "release_date": "2023-09-26T17:00:00-04:00", "title": "GEOS-FP Near-Surface Temperature", "description": "Near-surface temperature is calculated by sampling 3-D atmospheric fields from NASA’s GEOS-FP system 3 meters above Earth’s surface. GEOS-FP combines millions of weather observations with a predictive model to create a global best estimate of weather conditions that are used to begin a forecast.", "hits": 156 }, { "id": 5151, "url": "http://svs.gsfc.nasa.gov/5151/", "result_type": "Visualization", "release_date": "2023-09-26T17:00:00-04:00", "title": "Particulate Matter (PM) 2.5", "description": "Near surface concentration of fine particular matter (PM2.5) estimated from NASA’s aerosol and weather fields produced by NASA’s GEOS-CF model.", "hits": 241 }, { "id": 5152, "url": "http://svs.gsfc.nasa.gov/5152/", "result_type": "Visualization", "release_date": "2023-09-26T17:00:00-04:00", "title": "Near surface Ozone (O3)", "description": "Near surface concentration of ozone (O3) estimated by NASA’s GEOS-CF model.", "hits": 170 }, { "id": 5153, "url": "http://svs.gsfc.nasa.gov/5153/", "result_type": "Visualization", "release_date": "2023-09-26T17:00:00-04:00", "title": "Carbon Monoxide (CO)", "description": "Near surface concentration of carbon monoxide (CO) estimated by NASA’s GEOS-CF model.", "hits": 189 }, { "id": 5154, "url": "http://svs.gsfc.nasa.gov/5154/", "result_type": "Visualization", "release_date": "2023-09-26T17:00:00-04:00", "title": "Nitrogen Oxides (NOx)", "description": "Near surface concentration of Nitrogen Oxides (NOx) estimated from concentrations of nitrogen oxide and nitrogen dioxide produced by NASA’s GEOS-CF model.", "hits": 204 }, { "id": 31234, "url": "http://svs.gsfc.nasa.gov/31234/", "result_type": "Hyperwall Visual", "release_date": "2023-07-22T00:00:00-04:00", "title": "Column Carbon Monoxide (CO) from Canada Wildfires", "description": "Column CO from Canada Wildfires || goes-fp-cobbna-nam-jun2023_00000_print.jpg (1024x576) [154.9 KB] || goes-fp-cobbna-nam-jun2023_00000_searchweb.png (320x180) [85.6 KB] || goes-fp-cobbna-nam-jun2023_00000_thm.png (80x40) [6.3 KB] || goes-fp-cobbna-nam-jun2023_1080p30.mp4 (1920x1080) [11.6 MB] || goes-fp-cobbna-nam-jun2023_1080p30.webm (1920x1080) [2.4 MB] || 3840x2160_16x9_30p (3840x2160) [0 Item(s)] || goes-fp-cobbna-nam-jun2023_2160p30.mp4 (3840x2160) [35.4 MB] || earth_observations_5x3.hwshow [570 bytes] || ", "hits": 132 }, { "id": 14056, "url": "http://svs.gsfc.nasa.gov/14056/", "result_type": "Produced Video", "release_date": "2022-03-24T11:00:00-04:00", "title": "NASA Tracks COVID-19’s Atmospheric Fingerprint", "description": "Universal Production Music: The Mysterious Staircase by Brice Davoli [SACEM], Suspended in Time by Brice Davoli [SACEM]Stock Footage: Pond5Complete transcript available. || 14056_Still.jpg (1920x1080) [939.6 KB] || 14056_Still_searchweb.png (320x180) [61.8 KB] || 14056_Still_thm.png (80x40) [5.4 KB] || 14056_Atmo.mov (1920x1080) [3.2 GB] || 14056_Atmo.mp4 (1920x1080) [233.2 MB] || 14056_Twitter_Atmo.mp4 (1280x720) [60.2 MB] || 14056_Twitter_Atmo.webm (1280x720) [24.8 MB] || 14056_atmo.en_US.srt [4.7 KB] || 14056_atmo.en_US.vtt [4.4 KB] || ", "hits": 69 }, { "id": 4959, "url": "http://svs.gsfc.nasa.gov/4959/", "result_type": "Visualization", "release_date": "2021-12-13T00:00:00-05:00", "title": "Reduction in Tropospheric NOx and Ozone Corresponding to Worldwide COVID-19 Lockdowns", "description": "When the world went into lockdown to slow the spread of COVID-19, air pollution emissions started to rapidly decrease leaving a global atmospheric fingerprint detected by a team of scientists at NASA’s Jet Propulsion Laboratory using satellite measurements. These traces provided an unexpected window into what low-emissions world could look like, thus providing a means for identifying effective environmental policies. While many countries in the last few decades have implemented environmental policies to reduce human health risk from air pollution by controlling emissions, the impacts of those policies have not always been clear. The global lockdowns in response to COVID-19 represent a well-observed “scenario-of-opportunity” that allows us to assess how atmospheric emission and composition responds to reduced human activity. COVID-19 lockdowns effectively showed how reducing NOx emissions affects the global atmosphere. Its identifying signature shows up as in the atmosphere’s altered ability to produce harmful ozone pollution and ozone’s reduced influence on Earth’s heat balance that affects climate. These effects are not uniform across the world and depend on the location and season of the emission reductions.The results of this research indicate that in order to design effective environmental policies which benefit both air quality and climate, decision-makers need to carefully consider the complex relationships between emissions and atmospheric composition. || ", "hits": 108 }, { "id": 13871, "url": "http://svs.gsfc.nasa.gov/13871/", "result_type": "Produced Video", "release_date": "2021-06-09T13:30:00-04:00", "title": "NASA Finds Local Lockdowns Brought Global Ozone Reductions", "description": "This video can be freely shared and downloaded. While the video in its entirety can be shared without permission, some individual imagery is provided by pond5.com and is obtained through permission and may not be excised or remixed in other products. Specific details on stock footage may be found here. For more information on NASA’s media guidelines, visit https://www.nasa.gov/multimedia/guidelines/index.html.Music Credit:Universal Production Music: Waiting For Results - Adam John Salkeld [PRS], Neil Pollard [PRS]Complete transcript available. || 13871_Still_Image.jpg (1920x1080) [626.3 KB] || 13871_Still_Image_searchweb.png (320x180) [77.2 KB] || 13871_Still_Image_thm.png (80x40) [7.0 KB] || 13871_COVIDNOx.mov (1920x1080) [1.9 GB] || 13871_COVIDNOx.mp4 (1920x1080) [199.0 MB] || 13871_COVIDNOx.webm (1920x1080) [15.2 MB] || COVIDNOX.en_US.srt [2.1 KB] || COVIDNOX.en_US.vtt [2.1 KB] || ", "hits": 56 }, { "id": 13842, "url": "http://svs.gsfc.nasa.gov/13842/", "result_type": "Produced Video", "release_date": "2021-04-21T13:00:00-04:00", "title": "NASA Explores Earth’s Connections", "description": "Complete transcript available. || EarthDay2021Connectionsv1080.00750_print.jpg (1024x576) [126.5 KB] || EarthDay2021Connectionsv1080.00750_searchweb.png (320x180) [61.3 KB] || EarthDay2021Connectionsv1080.00750_web.png (320x180) [61.3 KB] || EarthDay2021Connectionsv1080.00750_thm.png (80x40) [5.9 KB] || EarthDay2021Connectionsv1080.mp4 (1920x1080) [426.0 MB] || EarthDay2021Connectionsv1080.webm (1920x1080) [34.3 MB] || EarthDay2021ConnectionsFinal.mp4 (3840x2160) [426.2 MB] || EarthDay2021ConnectionsFinal.en_US.srt [5.0 KB] || EarthDay2021ConnectionsFinal.en_US.vtt [5.0 KB] || ", "hits": 62 }, { "id": 4782, "url": "http://svs.gsfc.nasa.gov/4782/", "result_type": "Visualization", "release_date": "2020-03-04T00:00:00-05:00", "title": "Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011", "description": "This visualization with corresponding data dashboard shows the relationship between vegetation index anomalies and outbreaks of Rift Valley fever (RVF) during 2008 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between ENSO events (El Niño and La Niña), above normal vegetaion over land (green) and RVF outbreak locations (orange pins). || NDVI_RVF_SAfrica_Composite_3840x2160_2657_print.jpg (1024x576) [102.7 KB] || NDVI_RVF_SAfrica_Composite_3840x2160_2657_searchweb.png (320x180) [57.8 KB] || NDVI_RVF_SAfrica_Composite_3840x2160_2657_thm.png (80x40) [5.0 KB] || NDVI_RVF_SAfrica_Composite_1920x1080p30.mp4 (1920x1080) [35.6 MB] || NDVI_RVF_SAfrica_Composite_1920x1080p30.webm (1920x1080) [7.1 MB] || Composite (3840x2160) [0 Item(s)] || Composite (3840x2160) [0 Item(s)] || NDVI_RVF_SAfrica_Composite_3840x2160_p30.mp4 (3840x2160) [72.6 MB] || NDVI_RVF_SAfrica_Composite_3840x2160_2657.tif (3840x2160) [31.6 MB] || ", "hits": 73 }, { "id": 4783, "url": "http://svs.gsfc.nasa.gov/4783/", "result_type": "Visualization", "release_date": "2020-02-27T00:00:00-05:00", "title": "Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011", "description": "This visualization with corresponding data dashboard shows the relationship between precipitation anomalies and outbreaks of Rift Valley fever (RVF) during 2008 and 2011 in the South Africa region. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa to take a closer look at the patterns between ENSO events (El Niño and La Niña), above normal precipitation over land (blue) and RVF outbreak locations (orange pins). || PrecipRVF_SAfrica_Composite_3840x2160_3422_print.jpg (1024x576) [97.8 KB] || PrecipRVF_SAfrica_Composite_3840x2160_3422_searchweb.png (320x180) [57.6 KB] || PrecipRVF_SAfrica_Composite_3840x2160_3422_thm.png (80x40) [5.2 KB] || PrecipRVF_SAfrica_Composite_1920x1080p30.mp4 (1920x1080) [31.5 MB] || Composite (3840x2160) [0 Item(s)] || Composite (3840x2160) [0 Item(s)] || PrecipRVF_SAfrica_Composite_3840x2160_p30.mp4 (3840x2160) [68.2 MB] || PrecipRVF_SAfrica_Composite_3840x2160_3422.tif (3840x2160) [4.0 MB] || PrecipRVF_SAfrica_Composite_3840x2160_p30.webm (3840x2160) [14.1 MB] || ", "hits": 83 }, { "id": 4724, "url": "http://svs.gsfc.nasa.gov/4724/", "result_type": "Visualization", "release_date": "2020-02-21T00:00:00-05:00", "title": "Vegetation index anomalies and Rift Valley fever (RVF) outbreaks in Africa and Middle East during 2000-2018", "description": "Data visualization featuring vegetation index anomalies over Africa and Middle East and locations of Rift Valley Fever (RVF) outbreaks (orange pins) during the period of 2000-2018. Frames are provided in 4K resolution. || Africa_NDVIRVF_2000_2018_3840x2160_2430_print.jpg (1024x576) [78.8 KB] || Africa_NDVIRVF_2000_2018_3840x2160_2430_searchweb.png (320x180) [48.8 KB] || Africa_NDVIRVF_2000_2018_3840x2160_2430_thm.png (80x40) [4.4 KB] || Africa_NDVIRVFComposite_2000_2018_3840x2160_1080p30.mp4 (1920x1080) [88.7 MB] || Africa_NDVIRVFComposite_2000_2018_3840x2160_1080p30.webm (1920x1080) [25.5 MB] || Africa_NDVIRVF_2000_2018_Composite (3840x2160) [0 Item(s)] || Africa_NDVIRVF_2000_2018_3840x2160_2430.tif (3840x2160) [6.0 MB] || Africa_NDVIRVFComposite_2000_2018_3840x2160_p30.mp4 (3840x2160) [283.2 MB] || ", "hits": 78 }, { "id": 4747, "url": "http://svs.gsfc.nasa.gov/4747/", "result_type": "Visualization", "release_date": "2020-02-21T00:00:00-05:00", "title": "Vegetation index anomalies and Rift Valley fever (RVF) outbreaks in South Africa during 2009-2011", "description": "This visualization shows the relationship between vegetation index anomalies (Normalized Difference Vegetation Index - NDVI) data and outbreak locations of Rift Valley fever (RVf) during 2008 and 2011. The sequence starts in 2007 looking at the entire continent of Africa and zooms in the region of South Africa slowly to take a closer look at the above normal vegetation (green) and RVF outbreak locations (orange pins). Frames are provided in 4K resolution. || SAfrica_NDVIRVFwDates_3840x2160_1263_print.jpg (1024x576) [86.2 KB] || SAfrica_NDVIRVFwDates_3840x2160_1263_searchweb.png (320x180) [56.0 KB] || SAfrica_NDVIRVFwDates_3840x2160_1263_thm.png (80x40) [4.5 KB] || SAfrica_NDVIRVFComposite_1080p30.mp4 (1920x1080) [31.6 MB] || SAfrica_NDVIRVFComposite_1080p30.webm (1920x1080) [7.0 MB] || Composite (3840x2160) [0 Item(s)] || SAfrica_NDVIRVFwDates_3840x2160_1263.tif (3840x2160) [7.6 MB] || SAfrica_NDVIRVFComposite_3840x2160_p30.mp4 (3840x2160) [96.4 MB] || ", "hits": 49 }, { "id": 4784, "url": "http://svs.gsfc.nasa.gov/4784/", "result_type": "Visualization", "release_date": "2020-02-21T00:00:00-05:00", "title": "ENSO Teleconnections and Rift Valley fever (RVF) Outbreaks", "description": "During the 2008-2011 period, ENSO events brought changes to weather conditions across the globe that triggered infectious disease outbreaks, such as mosquito-borne Rift Valley fever (RVF) in South Africa. This visualization with corresponding data dashboard shows how Sea Surface Temperature (SST) anomalies in the equatorial Pacific Ocean (left) gave rise to Precipitation (center) and Vegetation (right) Index Anomalies in South Africa. During La Niña events, Southern Africa receives persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations with the RVF virus. However, in rare cases there is a departure from this canonical response, as we can observe in 2009-2010, when a mild El Niño event resulted in above normal vegetaton and a large RVF outbreak in South Africa. || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_2960_print.jpg (1024x576) [107.8 KB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525_searchweb.png (320x180) [63.0 KB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525_thm.png (80x40) [6.5 KB] || ENSO_Teleconnections (1920x1080) [0 Item(s)] || SST_Precip_NDVI_Dashboard_2008_2011_1920x1080_p30.mp4 (1920x1080) [22.7 MB] || ENSO_Teleconnections (3840x2160) [0 Item(s)] || ENSO_Teleconnections (3840x2160) [0 Item(s)] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_p30.mp4 (3840x2160) [56.0 MB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_p30.webm (3840x2160) [10.2 MB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_2960.tif (3840x2160) [3.4 MB] || ENSO_TeleconnectionsRVF_2008_2011_3840x2160_3525.tif (3840x2160) [3.4 MB] || ", "hits": 104 }, { "id": 4785, "url": "http://svs.gsfc.nasa.gov/4785/", "result_type": "Visualization", "release_date": "2020-01-09T00:00:00-05:00", "title": "Sea Surface Temperature Anomalies and Patterns of Global Disease Outbreaks: 2009-2018 (4K version)", "description": "This webpage provides the 4K version of: Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (updated), released on January 6, 2020.Content has been created for 4K display systems that can handle finer resolution and details. It is recommended to use content from this version for HD (1920x1080) and lower resolutions. || ", "hits": 130 }, { "id": 4781, "url": "http://svs.gsfc.nasa.gov/4781/", "result_type": "Visualization", "release_date": "2020-01-06T00:00:00-05:00", "title": "Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (updated)", "description": "This visualization shows the variability in global sea surface temperature anomalies, the associated ENSO index timeline and locations of infectious disease outbreaks over the global land surface. || CompositeWLabel_2009_2018_1920x108060fps_1705_print.jpg (1024x576) [135.9 KB] || CompositeWLabel_2009_2018_1920x108060fps_1705_searchweb.png (320x180) [82.6 KB] || CompositeWLabel_2009_2018_1920x108060fps_1705_thm.png (80x40) [7.1 KB] || Composite_StrongElNino (1920x1080) [0 Item(s)] || Composite_StrongElNino (1920x1080) [0 Item(s)] || CompositeWLabel_2009_2018_1920x1080_p30.mp4 (1920x1080) [22.1 MB] || CompositeWLabel_2009_2018_1920x108060fps_1705.tif (1920x1080) [1.3 MB] || CompositeWLabel_2009_2018_1920x1080_p30.webm (1920x1080) [4.6 MB] || CompositeWLabel_2009_2018_1920x1080_p30.mp4.hwshow [205 bytes] || ", "hits": 177 }, { "id": 4765, "url": "http://svs.gsfc.nasa.gov/4765/", "result_type": "Visualization", "release_date": "2019-12-10T00:00:00-05:00", "title": "Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018", "description": "El Niño is an irregularly recurring climate pattern characterized by warmer than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions. This visualization captures monthly Sea Surface Temperature (SST) anomalies around the world from 2009-2018, along with locations of global disease outbreaks and a corresponding timeline showcasing the Niño 3.4 Index. The Niño 3.4 Index represents average equatorial sea surface temperatures in the Pacific Ocean from about the International Date Line to the coast of South America. Highlighted in the timeline are the above average El Niño years, in which sea surface temperature anomalies peaked during 2015-2016. || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_print.jpg (1024x576) [130.6 KB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_searchweb.png (320x180) [79.7 KB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769_thm.png (80x40) [7.0 KB] || Composite (1920x1080) [0 Item(s)] || Composite (1920x1080) [0 Item(s)] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_p30.mp4 (1920x1080) [23.0 MB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_0769.tif (1920x1080) [1.3 MB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_p30.webm (1920x1080) [4.7 MB] || SSTENSO_Diseases_Comp_2009_2018_1920x1080_p30.mp4.hwshow [211 bytes] || ", "hits": 123 }, { "id": 13284, "url": "http://svs.gsfc.nasa.gov/13284/", "result_type": "Produced Video", "release_date": "2019-08-16T11:00:00-04:00", "title": "Students Work with NASA to Forecast Dust Storms", "description": "Four Maryland high school students were inspired by a documentary to find a way to let people know when a potentially hazardous dust storm is incoming. Using National Weather Service forecasts improved by NASA data, their Dust Watch app alerts people about incoming dust storms. || ", "hits": 32 }, { "id": 13281, "url": "http://svs.gsfc.nasa.gov/13281/", "result_type": "Produced Video", "release_date": "2019-08-13T11:00:00-04:00", "title": "NASA Studies How Arctic Fires Change the World", "description": "Music: Stepping Stone Bridge by Timothy Michael Hammond [PRS], Wayne Roberts [PRS]Watching Ladybirds by Benjamin James Parsons [PRS] This video can be freely shared and downloaded. While the video in its entirety can be shared without permission, some individual imagery provided by pond5.com and Artbeats is obtained through permission and may not be excised or remixed in other products. Specific details on stock footage may be found here. For more information on NASA’s media guidelines, visit https://www.nasa.gov/multimedia/guidelines/index.html. Complete transcript available. || Thumbnail_Arctic_Fires_Print.jpg (1920x1080) [917.1 KB] || Thumbnail_Arctic_Fires_Print_searchweb.png (320x180) [121.3 KB] || Thumbnail_Arctic_Fires_Print_thm.png (80x40) [8.0 KB] || 13281_Arctic_Fires_720.mp4 (1280x720) [265.3 MB] || 13281_Arctic_Fires.mov (1920x1080) [3.5 GB] || 13281_Arctic_Fires.webm (1920x1080) [21.2 MB] || 13281_Arctic_Fires.en_US.srt [4.1 KB] || 13281_Arctic_Fires.en_US.vtt [4.0 KB] || ", "hits": 53 }, { "id": 13275, "url": "http://svs.gsfc.nasa.gov/13275/", "result_type": "Produced Video", "release_date": "2019-08-07T11:30:00-04:00", "title": "How NASA Will Protect Astronauts From Space Radiation", "description": "Today, the Apollo-era flares serve as a reminder of the threat of radiation exposure for technology and astronauts in space. Understanding and predicting solar eruptions is crucial for safe space exploration. Almost 50 years since those 1972 storms, the data, technology and resources available to NASA have improved, enabling advancements towards space weather forecasts and astronaut protection — key to NASA’s Artemis program to return astronauts to the Moon.", "hits": 271 }, { "id": 13152, "url": "http://svs.gsfc.nasa.gov/13152/", "result_type": "Produced Video", "release_date": "2019-02-28T12:30:00-05:00", "title": "2015-2016 El Niño Triggered Disease Outbreaks Across the Globe", "description": "Music: Under Offer by Peter Keith Yelland-BrownComplete transcript available. || ENSO_Dengue_Thumbnail.png (1920x1080) [3.2 MB] || ENSO_Dengue_Thumbnail_print.jpg (1024x576) [143.5 KB] || ENSO_Dengue_Thumbnail_searchweb.png (320x180) [88.1 KB] || ENSO_Dengue_Thumbnail_thm.png (80x40) [6.2 KB] || ENSO_Dengue_FINAL_lowres.mp4 (1280x720) [39.4 MB] || ENSO_Dengue_FINAL_lowres.webm (1280x720) [16.2 MB] || ENSO_Dengue_Captions.en_US.srt [2.6 KB] || ENSO_Dengue_Captions.en_US.vtt [2.6 KB] || ENSO_Dengue_FINAL.mov (1920x1080) [3.9 GB] || ", "hits": 57 }, { "id": 4693, "url": "http://svs.gsfc.nasa.gov/4693/", "result_type": "Visualization", "release_date": "2019-02-28T09:00:00-05:00", "title": "Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016", "description": "The 2015-2016 El Niño event brought changes to weather conditions across the globe that triggered regional disease outbreaks, including mosquito-borne dengue fever in Southeast Asia. This visualization with corresponding timeplot graph reveals the relationship between precipitation anomaly in Southeast Asia and dengue outbreaks. Drier than normal habitats drew mosquitoes into populated, urban areas containing the open water needed for laying eggs. As the air warmed, mosquitoes also grew hungrier and reached sexual maturity more quickly, resulting in an increase in mosquito bites. || SEAsia_PrecipDengueComposite_1920x1080_1211_print.jpg (1024x576) [75.8 KB] || SEAsia_PrecipDengueComposite_1920x1080_1211_searchweb.png (320x180) [52.9 KB] || SEAsia_PrecipDengueComposite_1920x1080_1211_thm.png (80x40) [5.4 KB] || SEAsia_PrecipDengueComposite_1920x1080_p30.webm (1920x1080) [6.4 MB] || SEAsia_PrecipDengue_Composite (1920x1080) [0 Item(s)] || SEAsia_PrecipDengueComposite_1920x1080_p30.mp4 (1920x1080) [14.8 MB] || SEAsia_PrecipDengueComposite_1920x1080_1211.tif (1920x1080) [1.5 MB] || SEAsia_PrecipDengueComposite (3840x2160) [0 Item(s)] || ", "hits": 77 }, { "id": 4695, "url": "http://svs.gsfc.nasa.gov/4695/", "result_type": "Visualization", "release_date": "2019-02-28T09:00:00-05:00", "title": "Niño 3.4 Index and Sea Surface Temperature Anomaly Timeline: 1982-2017", "description": "This visualization captures Sea Surface Temperature (SST) anomalies around the world from 1982 to 2017, along with a corresponding timeplot graph focusing on the Niño 3.4 SST Index region (5N-5S, 120W-170W), which represents average equatorial sea surface temperatures in the Pacific Ocean from about the International Date Line to the coast of South America. Highlighted in the timeline are the El Niño years, in which sea surface temperature anomalies peaked: 1982-1983, 1997-1998, and 2015-2016. || NINO3.4SST_FlatMapComposite_1920x1080_00932_print.jpg (1024x576) [104.9 KB] || NINO3.4SST_FlatMapComposite_1920x1080_00932_searchweb.png (320x180) [72.1 KB] || NINO3.4SST_FlatMapComposite_1920x1080_00932_thm.png (80x40) [6.8 KB] || SST_Nino3.4Index_1982_2017_Composite (1920x1080) [0 Item(s)] || NINO3.4SST_FlatMapComposite_1920x1080_p30.mp4 (1920x1080) [57.2 MB] || NINO3.4SST_FlatMapComposite_1920x1080_00932.tif (1920x1080) [1.4 MB] || NINO3.4SST_FlatMapComposite_1920x1080_p30.webm (1920x1080) [9.3 MB] || SSTNino3.4Index_1982_2017_Composite (3840x2160) [0 Item(s)] || ", "hits": 456 }, { "id": 4696, "url": "http://svs.gsfc.nasa.gov/4696/", "result_type": "Visualization", "release_date": "2019-02-28T09:00:00-05:00", "title": "Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016", "description": "The 2015-2016 El Niño event brought changes to weather conditions across the globe that triggered regional disease outbreaks, including mosquito-borne dengue fever in Southeast Asia. This visualization with corresponding timeplot graph reveals the relationship between land surface temperature anomaly in Southeast Asia and dengue outbreaks. Higher than normal land surface temperatures results in an increase of dengue reported locations. || SEAsia_LSTDiseases_1920x1080_1730_print.jpg (1024x576) [85.1 KB] || SEAsia_LSTDiseases_1920x1080_1730_searchweb.png (320x180) [54.4 KB] || SEAsia_LSTDiseases_1920x1080_1730_thm.png (80x40) [5.3 KB] || SEAsia_LSTDengue_Composite (1920x1080) [0 Item(s)] || SEAsia_LSTDiseases_1920x1080_p30.mp4 (1920x1080) [33.8 MB] || SEAsia_LSTDiseases_1920x1080_1730.tif (1920x1080) [1.7 MB] || SEAsia_LSTDiseases_1920x1080_p30.webm (1920x1080) [6.2 MB] || SEAsia_LSTDengue_Composite (3840x2160) [0 Item(s)] || ", "hits": 78 }, { "id": 4697, "url": "http://svs.gsfc.nasa.gov/4697/", "result_type": "Visualization", "release_date": "2019-02-28T09:00:00-05:00", "title": "ENSO teleconnections in South East Asia for the period of 2015-2016", "description": "The 2015-2016 strong El Niño event brought changes to weather conditions across the globe that triggered regional infectious disease outbreaks, including mosquito-borne dengue fever in South East Asia. This visualization with corresponding multi-plot graph shows how Sea Surface Temperature anomalies in the equatorial Pacific Ocean (left), resulted in anomalous drought conditions (center) and increase in land surface temperatures (right) in South East Asia. During the 2015-2016 El Niño event, the South East Asia region received below than normal precipitation resulting in drier and warner than normal conditions, which increased the populations of mosquito vectors in urban areas, where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher than normal temperature on land shortens the maturation time of larvae to adult mosquitos and induces frequent blood feeding/biting of humans by mosquito vectors resulting in the amplification of dengue disease outbreaks over the South East Asia region. || SST_LST_Precip_2014_2016_Comp_print.jpg (1024x576) [82.9 KB] || SST_LST_Precip_2014_2016_Comp_searchweb.png (320x180) [51.5 KB] || SST_LST_Precip_2014_2016_Comp_thm.png (80x40) [6.0 KB] || SST_Precip_LST_Plot_Composite (1920x1080) [0 Item(s)] || SST_LST_Precip_2014_2016_Comp_1080p30.mp4 (1920x1080) [9.7 MB] || SST_LST_Precip_2014_2016_Comp.tif (1920x1080) [1.1 MB] || SST_LST_Precip_2014_2016_Comp_1080p30.webm (1920x1080) [4.2 MB] || TeleconnectionsSEAsia (3840x2160) [0 Item(s)] || SST_LST_Precip_2014_2016_Comp_1080p30.mp4.hwshow [203 bytes] || ", "hits": 118 }, { "id": 4676, "url": "http://svs.gsfc.nasa.gov/4676/", "result_type": "Visualization", "release_date": "2019-02-12T00:00:00-05:00", "title": "Sulfur Dioxide 2018 Update", "description": "China || so2_china_4K.0000_print.jpg (1024x576) [176.6 KB] || so2_china_4K.0000_thm.png (80x40) [6.0 KB] || so2_china_4K.0000_searchweb.png (320x180) [81.6 KB] || so2_china_4K.0000_web.png (320x180) [81.6 KB] || china (3840x2160) [64.0 KB] || so2_china_4K_2160p30.webm (3840x2160) [4.1 MB] || so2_china_4K_2160p30.mp4 (3840x2160) [113.0 MB] || ", "hits": 133 }, { "id": 12966, "url": "http://svs.gsfc.nasa.gov/12966/", "result_type": "Produced Video", "release_date": "2018-06-06T00:00:00-04:00", "title": "Measuring the Atmosphere on ATom's Final Flight Around the World", "description": "NASA's Atmospheric Tomography (ATom) mission team just finished their final trip around the world, taking atmospheric samples from all over our home planet. Flying in NASA's DC-8 plane, the scientists and engineers collected their samples during four different trips, one for each season, to see how the atmosphere changed across fall, winter, spring and summer. || ", "hits": 43 }, { "id": 40348, "url": "http://svs.gsfc.nasa.gov/gallery/esddatafor-societal-benefits/", "result_type": "Gallery", "release_date": "2018-04-24T00:00:00-04:00", "title": "ESD data for Societal Benefit", "description": "No description available.", "hits": 180 }, { "id": 12603, "url": "http://svs.gsfc.nasa.gov/12603/", "result_type": "Produced Video", "release_date": "2017-09-13T11:00:00-04:00", "title": "Predicting Malaria Outbreaks With NASA Satellites", "description": "In the Amazon Rainforest, few animals are as dangerous to humans as mosquitos that transmit malaria. The tropical disease can bring on severe fever, headaches and chills and is particularly severe for children and the elderly and can cause complications for pregnant women. In rainforest-covered Peru the number of malaria cases has spiked such that, in the past five years, it has had on average the second highest rate in the South American continent. In 2014 and 2015 there were 65,000 reported cases in the country.Containing malaria outbreaks is challenging because it is difficult to figure out where people are contracting the disease. As a result, resources such as insecticide-treated bed nets and indoor sprays are often deployed to areas where few people are getting infected, allowing the outbreak to grow.To tackle this problem, university researchers have turned to data from NASA’s fleet of Earth-observing satellites, which are able to track the types of human and environmental events that typically precede an outbreak. With funding from NASA’s Applied Sciences Program, they are working in partnership with the Peruvian government to develop a system that uses satellite and other data to help forecast outbreaks at the household level months in advance and prevent outbreaks.Additional imagery from: Christopher B. Plunkett FortJames GathanyFábio Medeiros da Costa || ", "hits": 68 }, { "id": 30824, "url": "http://svs.gsfc.nasa.gov/30824/", "result_type": "Hyperwall Visual", "release_date": "2016-10-27T00:00:00-04:00", "title": "Mosquito Spread and Health", "description": "Seasonal Occurrence and Abundance of Zika Virus Vector Mosquito || gupta_slide_5_1080p.00001_print.jpg (1024x576) [162.3 KB] || gupta_slide_5_1080p.00001_searchweb.png (180x320) [71.3 KB] || gupta_slide_5_1080p.00001_thm.png (80x40) [6.6 KB] || gupta_slide_5_1080p.mp4 (1920x1080) [6.4 MB] || gupta_slide_5_720p.mp4 (1280x720) [3.4 MB] || gupta_slide_5_720p.webm (1280x720) [3.8 MB] || gupta_slide_5_2304p.mp4 (4096x2304) [19.1 MB] || gupta_slide_5_360p.mp4 (640x360) [1.1 MB] || 4104x2304_16x9_30p (4104x2304) [32.0 KB] || ", "hits": 53 }, { "id": 12332, "url": "http://svs.gsfc.nasa.gov/12332/", "result_type": "Produced Video", "release_date": "2016-08-11T15:00:00-04:00", "title": "PACE -- Plankton, Aerosol, Cloud, ocean Ecosystem", "description": "The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will deliver the most comprehensive look at global ocean color measurements in NASA's history. Not only will PACE monitor the health of our ocean, its science data will expand atmospheric studies by sensing our skies over an exceptionally broad spectrum of wavelengths. A strategic climate continuity mission in support of NASA's Plan for a Climate-Centric Architecture for Earth Observations and Applications from Space (2010), PACE wil monitor aerosol particles, clouds, and many factors related to the marine carbon cycle including the phytoplankton pigment, chlorophyll. Moreover, PACE applications will help with many of our most pressing environmental issues such as harmful algal bloom and air quality forecasts. || ", "hits": 42 }, { "id": 30791, "url": "http://svs.gsfc.nasa.gov/30791/", "result_type": "Hyperwall Visual", "release_date": "2016-07-20T00:00:00-04:00", "title": "Algae in Lake Okeechobee", "description": "A Landsat image show green streaks of algae in Lake Okeechobee. || okeechobee_algae_20160702_print.jpg (1024x574) [248.0 KB] || okeechobee_algae_20160702.png (4104x2304) [14.9 MB] || okeechobee_algae_20160702_searchweb.png (320x180) [124.0 KB] || okeechobee_algae_20160702_thm.png (80x40) [7.7 KB] || okeechobee_algae_20160702.hwshow [218 bytes] || ", "hits": 43 }, { "id": 4447, "url": "http://svs.gsfc.nasa.gov/4447/", "result_type": "Visualization", "release_date": "2016-05-06T00:00:00-04:00", "title": "KORUS-AQ: Surface Ozone Levels Over the Korean Peninsula in June 2013", "description": "These visuals were created in anticipation of the 2016 Korean United States Air Quality study (KORUS-AQ) field campaign which will combine observations from aircraft, satellties, ships and ground stations with air quality models to assess and monitor air quality acorss urban, rural and coastal areas.Ozone gas and particle pollution are two of the main factors that contribute to poor air quality around the world. While ozone gas located high in the stratosphere protects us from the sun’s harmful UV rays, pollution from cars and other human emissions near ground level can cause chemical reactions that lead to ozone formation near the surface. Breathing in high levels of ozone is also bad for human health, causing lung diseases and health impacts on sensitive populations such as children, the elderly and people with asthma. These visuals are showing the ozone that formed near the surface, or 'surface ozone', over the Korean peninsula in June 2013 according to the GEOS-5 Nature Run chemistry model data. Peak ozone in Korea occurs between April and June.Since Seoul is located on a peninsula, the metropolitan area and the pollution produced here are separated from other sources of emissions. In addition, Seoul’s human-produced emissions are concentrated in its urban areas but are surrounded by more rural agricultural areas. The contrast between urban and rural zones on the peninsula allow scientists to study and differentiate human and naturally-produced emissions and better understand how they interact chemically. Understanding the chemical reactions between urban and agricultural emissions is extremely important for improving models that forecast air quality. || ", "hits": 32 }, { "id": 12173, "url": "http://svs.gsfc.nasa.gov/12173/", "result_type": "Produced Video", "release_date": "2016-03-17T12:18:18-04:00", "title": "A Year In Space", "description": "See amazing photos taken by NASA astronaut Scott Kelly during his record-long stay aboard the International Space Station. || c-1024.jpg (1024x576) [236.3 KB] || c-1280.jpg (1280x720) [326.5 KB] || c-1024_print.jpg (1024x576) [249.1 KB] || c-1024_searchweb.png (320x180) [121.3 KB] || c-1024_web.png (320x180) [121.3 KB] || c-1024_thm.png (80x40) [26.8 KB] || ", "hits": 37 }, { "id": 11928, "url": "http://svs.gsfc.nasa.gov/11928/", "result_type": "Produced Video", "release_date": "2015-09-03T11:00:00-04:00", "title": "Western Wildfires", "description": "NASA satellites capture images from space of raging wildfires in the U.S. || c-1280.jpg (1280x720) [546.7 KB] || c-1024.jpg (1024x576) [377.7 KB] || c-1024_print.jpg (1024x576) [384.2 KB] || c-1024_searchweb.png (320x180) [148.3 KB] || c-1024_web.png (320x180) [148.3 KB] || c-1024_thm.png (80x40) [28.0 KB] || ", "hits": 17 }, { "id": 30593, "url": "http://svs.gsfc.nasa.gov/30593/", "result_type": "Hyperwall Visual", "release_date": "2015-04-13T00:00:00-04:00", "title": "Malaria Modeling and Transmission", "description": "Remote sensing data products which are input into malaria model. || malaria_modeling_w_sat_data_print.jpg (1024x574) [158.6 KB] || malaria_modeling_w_sat_data.png (4104x2304) [2.7 MB] || malaria_modeling_w_sat_data_web.jpg (319x179) [21.9 KB] || malaria_modeling_w_sat_data_searchweb.png (180x320) [72.7 KB] || malaria_modeling_w_sat_data_thm.png (80x40) [6.6 KB] || malaria_modeling_w_sat_data_30593.pptx [3.1 MB] || malaria_modeling_w_sat_data_30593.key [5.8 MB] || malaria_modeling_w_sat_data.hwshow [222 bytes] || ", "hits": 27 }, { "id": 30641, "url": "http://svs.gsfc.nasa.gov/30641/", "result_type": "Hyperwall Visual", "release_date": "2014-12-10T10:00:00-05:00", "title": "Simulated Sulfur Dioxide and Sulfate Aerosols", "description": "Sulfur and Sulfates animation of Sept 1 - Dec 31, 2006 || sulfur_globe_c1440_NR_BETA9-SNAP_20060901_0000z.png (5760x2880) [19.9 MB] || sulfur_globe_c1440_NR_BETA9-SNAP_20060901_0000z_print.jpg (1024x512) [117.1 KB] || sulfur_globe_c1440_NR_BETA9-SNAP_20060901_0000z_searchweb.png (180x320) [93.4 KB] || sulfur_globe_c1440_NR_BETA9-SNAP_20060901_0000z_thm.png (80x40) [7.0 KB] || geos_sulfur_720p.mp4 (1280x720) [95.0 MB] || geos_sulfur_720p.webm (1280x720) [11.6 MB] || sulfur_small_c1440_NR_BETA9-SNAP_20060329_1600z_1080.mp4 (1920x1080) [357.4 MB] || geos_sulfur_2304p.mp4 (4096x2304) [667.5 MB] || ", "hits": 92 }, { "id": 30515, "url": "http://svs.gsfc.nasa.gov/30515/", "result_type": "Hyperwall Visual", "release_date": "2014-06-30T13:00:00-04:00", "title": "Simulated Atmospheric Carbon Concentrations", "description": "Carbon exists in many forms—e.g., carbon dioxide (CO2), carbon monoxide (CO)—and continually cycles through Earth’s atmosphere, ocean, and terrestrial ecosystems. This visualization, created using data from the 7-km GEOS-5 Nature Run model, shows average column concentrations of atmospheric CO2 (colored shades) and CO (white shades underneath) from January 1, 2006 to December 31, 2006.CO2 variations are largely controlled by fossil fuel emissions and seasonal fluxes of carbon between the atmosphere and land biosphere. For example, dark red and pink shades represent regions where CO2 concentrations are enhanced by carbon sources, mainly from human activities. During Northern Hemisphere spring and summer months, plants absorb a substantial amount of CO2 through photosynthesis, thus removing CO2 from the atmosphere. Atmospheric CO, a pollutant harmful to human health, is produced mainly from fossil fuel combustion and biomass burning. Here, high concentrations of CO (white) are mainly from fire activity in Africa, South America, and Australia. Scientists use model output data such as these to help answer important questions about Earth’s climate and to help design future satellite missions.These model simulations use fossil fuel emissions estimates provided by the Emissions Database for Global Atmospheric Research (EDGAR). NASA’s Quick Fire Emissions Dataset (QFED) estimates fire emissions using MODIS fire radiative power observations. Additional, observationally constrained estimates of CO2 flux between the atmosphere and land and ocean carbon reservoirs were produced as part of NASA’s Carbon Monitoring System Flux Pilot Project (http://carbon.nasa.gov/cgi-bin/cms/inv_pgp.pl?pgid=581). Land biosphere fluxes come from the Carnegie-Ames-Stanford Approach Global Fire Emissions Database (CASA-GFED) model which incorporates MODIS vegetation classification and AVHRR Normalized Difference Vegetation Index (NDVI) data. Ocean fluxes are produced by the NASA Ocean Biogeochemical Model (NOBM) which incorporates MODIS chlorophyll observations. || ", "hits": 69 }, { "id": 11490, "url": "http://svs.gsfc.nasa.gov/11490/", "result_type": "Produced Video", "release_date": "2014-02-26T16:00:00-05:00", "title": "Landsat 8 Celebrates First Year in Orbit", "description": "On Feb. 11, 2013, Landsat 8 launched into Earth orbit, riding on an Atlas V rocket. Weighing 6,133 pounds, Landsat 8 is the eigth satellite in the long-running Landsat program, jointly managed by NASA and the U.S. Geological Survey. At 16 feet tall, with a 32 foot long solar array, Landsat 8 orbits Earth at an altitude of 438 miles, moving at a speed of 16,760 miles per hour. It takes 99 minutes to complete one orbit, with about 14.5 orbits each day. There have been 5,319 orbits in the first year of Landsat 8's mission. It takes 16 days to build a complete scan of the globe, and on the 17th day the orbit cycle begins again.Between the two instruments on board, Landsat 8 records data in 11 separate wavelength regions spanning visible, infrared, and thermal radiation. The data is transmitted several times a day to the USGS Earth Resources and Observation Science Center in Sioux Falls, SD, where it is added to the archive of Landsat data stretching back to 1972. In its first year, users have downloaded 1,322,969 scenes of Landsat 8 data from the USGS.Landsat 8 continues the decades-long Landsat record of Earth's land surface at a scale where the impacts of humans and nature can be detected and monitored over time. Every continent, every season, every year, at a resolution that can distinguish an area the size of a baseball field. With help from Landsat we can monitor the cultivation of our food crops, quantify our precious water resources as they ebb and flow, and track deforestation globally. Landsat data constitute a key ingredient in decision making for agriculture, climate research, disaster mitigation, ecosystems, forestry, human health, urban growth, and water management. || ", "hits": 95 }, { "id": 4028, "url": "http://svs.gsfc.nasa.gov/4028/", "result_type": "Visualization", "release_date": "2013-03-07T16:00:00-05:00", "title": "National Climate Assessment: 21st Century Precipitation Scenarios", "description": "The National Climate Assessment (NCA) is a central component of the U.S. Global Change Research Program (USGCRP). Every four years, the NCA is required to produce a report for Congress that integrates, evaluates, and interprets the findings of the USGCRP; analyzes the effects of global change on the natural environment, agriculture, energy production and use, land and water resources, transportation, human health and welfare, human social systems, and biological diversity; and analyzes current trends in global change, both human-induced and natural, and projects major trends for the subsequent 25 to 100 years. A draft of the Third National Climate Assessment report is available on the Federal Advisory Committee website. The final report is slated to be released in 2014. These visualizations show model projections of the precipitation anomaly from 2000 to 2100 as a percentage difference between the 30-year precipitation averages and the 1970-1999 average. The dates displayed represent the center of the 30-year average; so, the 30-year spans are +/- 15 years from the displayed dates. The percentages are computed as follows: 100% x ( (30-year span) - (1970-1999 span) ) / (1970-1999 span). Separate animations are shown for annual averages and for seasonal averages in the United States. Spring precipitation is displayed to demonstrate the strong drying signal in the southwest. Summer precipitation is displayed because of agricultural community interest. The other seasons are included for completeness.The data are from fifteen coupled Atmosphere-Ocean General Circulation Models (AOGCMs) from the World Climate Research Programme (WCRP) CMIP3 multi-model dataset (PCMDI 2012). These models are CCSM3, CGCM3.1 (T47), CNRM-CM3, CSIRO-Mk3.0, ECHAM5/MPI-OM, ECHO-G, GFDL-CM2.0, GFDL-CM2.1, INM-CM3.0, IPSL-CM4, MIROC3.2 (medres), MRI-CGCM2.3.2, PCM, UKMO-HadCM3, and UKMO-HadGEM. In those cases where an ensemble of simulations was available from a particular model, only a single ensemble member was used.These climate model runs use assumptions about possible future development patterns and scenarios of greenhouse gas emission rates. Two future scenarios are shown: B1 and A2. In the B1 scenario, global environmental concerns are emphasized. B1 is a lower greenhouse emissions scenario. In the A2 scenarios, future socio-economic development and regional issues are emphasized; and, worldwide cooperation on environmental issues is deemphasized. A2 is a higher greenhouse gas emissions scenario. For each scenario (B1 and A2), five individual precipitation anomaly animations are shown for annual, spring, summer, fall, and winter periods. So, there are a total of ten individual animations: B1 Annual (lower emissions annual outlook) A2 Annual (higher emissions annual outlook) B1 Spring (lower emissions outlook during initial growing season) A2 Spring (higher emissions outlook during initial growing season) B1 Summer (lower emissions outlook during growing season)A2 Summer (higher emissions outlook during growing season)B1 Winter (lower emissions outlook) A2 Winter (higher emissions outlook) B1 Fall (lower emissions outlook)A2 Fall (higher emissions outlook) There is also a mosaic of the six most important of these animations for comparison. || ", "hits": 50 }, { "id": 4029, "url": "http://svs.gsfc.nasa.gov/4029/", "result_type": "Visualization", "release_date": "2013-03-07T16:00:00-05:00", "title": "National Climate Assessment: 21st Century Temperature Scenarios", "description": "The National Climate Assessment (NCA) is a central component of the U.S. Global Change Research Program (USGCRP). Every four years, the NCA is required to produce a report for Congress that integrates, evaluates, and interprets the findings of the USGCRP; analyzes the effects of global change on the natural environment, agriculture, energy production and use, land and water resources, transportation, human health and welfare, human social systems, and biological diversity; and analyzes current trends in global change, both human-induced and natural, and projects major trends for the subsequent 25 to 100 years. A draft of the Third National Climate Assessment report is available on the Federal Advisory Committee website. The final report is slated to be released in 2014. These visualizations show projections of temperature anomalies from 2000 to 2100. For each year, the differences (or anomaly) between the model projected 30-year temperature average and the 1970-1999 average are shown. The dates displayed represent the center of the 30-year average; so, the 30-year spans are +/- 15 years from the displayed dates. Separate animations are shown for annual averages and for seasonal averages in the United States. Summer temperatures are displayed due to stakeholder interest in potential extreme heat events. The other seasons are included for completeness.The data are from fifteen coupled Atmosphere-Ocean General Circulation Models (AOGCMs) from the World Climate Research Programme (WCRP) CMIP3 multi-model dataset (PCMDI 2012). These models are CCSM3, CGCM3.1 (T47), CNRM-CM3, CSIRO-Mk3.0, ECHAM5/MPI-OM, ECHO-G, GFDL-CM2.0, GFDL-CM2.1, INM-CM3.0, IPSL-CM4, MIROC3.2 (medres), MRI-CGCM2.3.2, PCM, UKMO-HadCM3, and UKMO-HadGEM. In those cases where an ensemble of simulations was available from a particular model, only a single ensemble member was used. These climate model runs use assumptions about possible future development patterns and greenhouse gas emission rates. Two future scenarios are shown: B1 and A2.In the B1 scenario, global environmental concerns are emphasized. B1 is a lower greenhouse gas emissions scenario.In the A2 scenarios, future socio-economic development and regional issues are emphasized; and, worldwide cooperation on environmental issues is deemphasized. A2 is a higher greenhouse gas emissions scenario.For each scenario (B1 and A2), five individual temperature anomaly animations are shown for annual, summer, fall, winter, and spring periods. So, there are a total of ten individual animations:B1 Annual (lower emissions annual outlook)A2 Annual (higher emissions annual outlook)B1 Summer (lower emissions outlook of hottest months in US)A2 Summer (higher emissions outlook of hottest months in US)B1 Fall (lower emissions scenario)A2 Fall (higher emissions scenario)B1 Winter (lower emissions scenario)A2 Winter (higher emissions scenario)B1 Spring (lower emissions scenario)A2 Spring (higher emissions scenario)There is also a mosaic of the four most important animations shown for comparison. || ", "hits": 125 }, { "id": 4004, "url": "http://svs.gsfc.nasa.gov/4004/", "result_type": "Visualization", "release_date": "2013-02-20T00:00:00-05:00", "title": "National Climate Assessment Annual Arctic Minimum Sea Ice Extents (1979-2012)", "description": "The National Climate Assessment (NCA) is a central component of the U.S. Global Change Research Program (USGCRP). Every four years, the NCA is required to produce a report for Congress that integrates, evaluates, and interprets the findings of the USGCRP; analyzes the effects of global change on the natural environment, agriculture, energy production and use, land and water resources, transportation, human health and welfare, human social systems, and biological diversity; and analyzes current trends in global change, both human-induced and natural, and projects major trends for the subsequent 25 to 100 years. A draft of the Third National Climate Assessment report is available on the Federal Advisory Committee website. The final report is slated to be released in 2014. This scientific visualization of annual minimum sea ice area over the Arctic from 1979-2012 is one element of the NCA that highlights findings conveyed in the \"Our Changing Climate\", the \"Alaska and the Arctic\" and the \"Impacts of Climate Change on Tribal, Indigenous, and Native Lands and Resources\" chapters of the draft Third NCA report. This record shows a persistent decline in the minimum extent of Arctic sea ice cover. The satellite observations are from passive microwave sensors and processed using the NASA Team algorithm developed by scientists at NASA Goddard Space Flight Center. The sensors that collected the data are the Scanning Multichannel Microwave Radiometer (SMMR) on the NASA Nimbus-7 satellite and a series of Special Sensor Microwave Imagers (SSM/I) and Special Sensor Microwave Imager and Sounders (SSMIS) on U.S. Department of Defense Meteorological Satellite Program (DMSP) satellites. The data from the different sensors are carefully assembled to assure consistency throughout the 34 year record.This visualization is similar to another developed by NASA, but is based on a slightly different algorithm to process the same sensor data. Both show similar downward trends in minimum sea ice area coverage over this time period. || ", "hits": 73 }, { "id": 3905, "url": "http://svs.gsfc.nasa.gov/3905/", "result_type": "Visualization", "release_date": "2012-04-13T09:00:00-04:00", "title": "Mapping Diseases", "description": "The print-resolution still images were created for the February 2012 issue of The Scientist (print and online). In an article in the same issue, NASA scientist Assaf Anyamba explains how he can predict diseases with remote-sensing data.The data used are: 1. NDVI is an index that quantifies the photosynthetic capacity of vegetation. It is derived from visible and near-infrared reflectance measurements made by Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA's polar orbiting satellites (in this case NOAA-17). Taken as time series measurements, NDVI indicates the response of vegetation to seasonal and interannual variations in climate.2. SST data are a blend of direct observations from ships, buoys, satellite imagery also from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) instruments, and SSTs simulated by sea-ice cover. The monthly optimum interpolated fields were derived by a linear interpolation of the weekly fields to daily fields, and then averaging daily values over a month.All anomaly fields (as shown here) are derived by subtracting the monthly values from the respective long-term monthly means. || ", "hits": 67 }, { "id": 40098, "url": "http://svs.gsfc.nasa.gov/gallery/landsat/", "result_type": "Gallery", "release_date": "2012-02-23T00:00:00-05:00", "title": "Landsat", "description": "Since 1972, Landsat satellites have consistently gathered data about our planet for the benefit of the U.S. and the world. The Landsat data archive is the longest continuous remotely sensed global record of Earth’s surface, with all the data free and available to the public. The Landsat satellite missions, jointly managed by NASA and the U.S. Geological Survey, are a central pillar of our national remote sensing capability and established the U.S. as a leader in land imaging.\n\nLandsat 9 is the next satellite in the program, and will add more than 700 scenes a day to this invaluable archive. As Earth’s population approaches 8 billion, Landsat 9 will extend our ability to detect and characterize land surface changes, and will do so at a scale where researchers can differentiate between natural and human-induced change. \r\n \r\nLand cover and land use are changing globally at rates unprecedented in human history. These changes bring profound consequences for weather, ecosystems, resource management, the economy, carbon storage and emissions, human health, and other aspects of society. Landsat datasets are a critical tool in monitoring and managing essential resources in a changing world.\r\n\nBelow are highlights of Landsat videos and graphics. Follow this link to see the entire collection of Landsat multimedia.\n", "hits": 319 }, { "id": 10851, "url": "http://svs.gsfc.nasa.gov/10851/", "result_type": "Produced Video", "release_date": "2011-10-20T16:00:00-04:00", "title": "A Look Back at a Decade of Fires", "description": "For more than a decade, instruments on Terra and Aqua, two of NASA's flagship Earth-observing satellites, have scanned the surface of our planet for fires four times a day. The instruments, both Moderate Resolution Imaging Spectroradiometers (MODIS), have revolutionized what scientists know about fire's role in land cover change, ecosystem processes, and the global carbon cycle by allowing researchers to map the characteristics and global distribution of fires in remarkable detail. The collection of videos below provides perspective on how global fires impact humans and our planet. || ", "hits": 77 }, { "id": 3807, "url": "http://svs.gsfc.nasa.gov/3807/", "result_type": "Visualization", "release_date": "2011-08-31T00:00:00-04:00", "title": "Predicting Disease Outbreaks from Space", "description": "These visualizations were created for the May 18, 2012 Library of Congress Talk Predictiding Disease Outbreaks from Space. In this talk NASA scientist Assaf Anyamba, will present how using remote-sensing data we can see links among weather, diseases and famine.An early warning system more than a decade in development successfully predicted the 2006-2007 outbreak of the deadly Rift Valley Fever (RVF) in East Africa and subsequent outbreaks in Sudan (2007) and South Africa (2008-2011). RVF is a deadly hemorrhagic disease transmitted by mosquitoes that infects livestock and human populations episodically. An international team of research scientists, public-health professionals, agricultural specialists and military personnel had worked for a decade to successfully predict when and where an outbreak of RVF would occur. || ", "hits": 61 }, { "id": 10617, "url": "http://svs.gsfc.nasa.gov/10617/", "result_type": "Produced Video", "release_date": "2010-07-19T00:00:00-04:00", "title": "Three Months of Oil: Satellites View Gulf Oil Spill", "description": "On April 20, 2010, the Deepwater Horizon oil rig exploded in the Gulf of Mexico, triggering the largest oil spill in U.S. history. The MODIS instrument, on board NASA's Terra and Aqua satellites, continues to capture imagery of the region. This short video time series shows a satellite perspective of the spill through July 12, 2010, and updates the earlier NASA video time series released on May 27, 2010. The oil slick appears a dull grayish-beige in the images and changes due in part to to changing weather, ocean currents, and the use of oil dispersing chemicals. The oil slick only appears clearly in MODIS imagery when the sun is a a particular angle in relation to the satellite's position as it orbits over the Gulf. In areas where sunlight reflects off the ocean's surface toward the satellite, oil-slicked water usually looks brighter than cleaner ocean water in the region.Images in the video time series were selected that show the spill most clearly. The full image archive is available on the MODIS Rapid Response web site. || ", "hits": 223 }, { "id": 10612, "url": "http://svs.gsfc.nasa.gov/10612/", "result_type": "Produced Video", "release_date": "2010-05-12T00:00:00-04:00", "title": "The Smog Bloggers", "description": "Has pollen got you sneezing? Wondering what's causing that mysterious afternoon haze? How do you find out what's in the air you are breathing? For the thousands of people who visit the University of Maryland Baltimore County's \"Smog Blog\" each day, the answer is just a web click away. The Smog Bloggers combine laser measurements of current air quality with NASA satellite data to paint a daily picture of air pollution across the US. To date, the blog has received over two million hits, and is itself a big hit with weather forecasters, astronomers, asthma sufferers, and those with just a healthy curiosity about what kinds of pollution they may be breathing in. For complete transcript, click here. || G2010-057_Smog_Bloggers_YouTubeHQ.01477_print.jpg (1024x576) [98.1 KB] || G2010-057_Smog_Bloggers_YouTubeHQ_web.png (320x180) [262.6 KB] || G2010-057_Smog_Bloggers_YouTubeHQ_thm.png (80x40) [17.4 KB] || G2010-057_Smog_Bloggers_appletv.webmhd.webm (960x540) [53.5 MB] || G2010-057_Smog_Bloggers_appletv.m4v (960x720) [146.6 MB] || G2010-057_Smog_Bloggers_Youtube.mov (1280x720) [58.9 MB] || G2010-057_Smog_Bloggers_YouTubeHQ.mov (1280x720) [113.1 MB] || G2010-057_Smog_Bloggers_iPod_large.m4v (640x360) [45.3 MB] || G2010-057_Smog_Bloggers_iPod_small.m4v (320x180) [17.5 MB] || G2010-057_Smog_Bloggers_NASA_PORTAL.wmv (346x260) [46.4 MB] || G2010-057_Smog_Bloggers_SVS.mpg (512x288) [36.2 MB] || ", "hits": 30 }, { "id": 10513, "url": "http://svs.gsfc.nasa.gov/10513/", "result_type": "Produced Video", "release_date": "2009-12-11T00:00:00-05:00", "title": "A Landsat Flyby", "description": "The Landsat program is the longest continuous global record of the Earth's surface, and continues to deliver both visually stunning and scientifically valuable images of our planet. This short video highlights Landsat's many benefits to society. || ", "hits": 50 }, { "id": 10394, "url": "http://svs.gsfc.nasa.gov/10394/", "result_type": "Produced Video", "release_date": "2009-02-19T00:00:00-05:00", "title": "Scanning Electron Microscope Still Image of Pollen Particles", "description": "Aerosols are complex particles that come from a variety of sources. They occur in nature, but can also be generated by human activity. Pollen grains are an example of short-lived aerosols that are difficult to detect but are found near the Earth's surface. Pollen effect human health, but researchers do not consider these aerosols to be part of the climatologically important population of tropospheric aerosols. This Scanning Electron Microscopic image reveals pollen grains from a variety of common plants: sunflower (Helianthus annuus), morning glory (Ipomoea purpurea ), prairie hollyhock (Sidalcea malviflora), oriental lily (Lilium auratum ), evening primrose (Oenothera fruticosa), and castor bean (Ricinus communis). || ", "hits": 159 }, { "id": 10264, "url": "http://svs.gsfc.nasa.gov/10264/", "result_type": "Produced Video", "release_date": "2008-10-14T00:00:00-04:00", "title": "Earth Science Week 2008", "description": "Keep your eyes glued to the Goddard Web site through the week of October 12 for daily videos that answer several questions about our home planet. The videos are all part of Earth Science Week: 2008, themed 'No Child Left Inside.' || ", "hits": 31 }, { "id": 3291, "url": "http://svs.gsfc.nasa.gov/3291/", "result_type": "Visualization", "release_date": "2006-02-15T00:00:00-05:00", "title": "National Map Showing Habitat Suitability for Tamarisk Invasion", "description": "The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being addressed by NASA's Science Mission Directorate through a national application partnership with the US Geological Survey. NASA and USGS are working together to develop a National Invasive Species Forecasting System (ISFS) for the management and control of invasive species on Department of Interior and adjacent lands. The system provides a framework for using USGS's early detection and monitoring protocols and predictive models to process MODIS, ETM+, ASTER and commercial remote sensing data, to create on-demand, regional-scale assessments of invasive species likely habitats.Recent work on the Invasive Species Forecasting System (ISFS) project has shown the importance of remotely-sensed time-series data in geostatistical models for mapping the distribution of Tamarisk and other invasive plant species. This video shows the habitat suitability for a Tamarisk invasion in the continental United States. Red indicates areas that are highly suitable and yellow indicates areas which are less suitable. Texas, New Mexico, and Nevada are the most highly suitable states. Utah and Arizona have the next greatest risk. California, Arizona, Montana, Colorado, Oregon, Ohio, Wyoming, and Florida also have a significant risk. || ", "hits": 18 }, { "id": 3330, "url": "http://svs.gsfc.nasa.gov/3330/", "result_type": "Visualization", "release_date": "2006-02-15T00:00:00-05:00", "title": "Creating the Tamarisk Habitat Suitability Map (for General Use)", "description": "The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being addressed by NASA's Science Mission Directorate through a national application partnership with the US Geological Survey. NASA and USGS are working together to develop a National Invasive Species Forecasting System (ISFS) for the management and control of invasive species on Department of Interior and adjacent lands. The system provides a framework for using USGS's early detection and monitoring protocols and predictive models to process MODIS, ETM+, ASTER and commercial remote sensing data. It can also be used to create on-demand, regional-scale assessments of invasive species patterns and vulnerable habitats.The first step in this process is to collect relevant satellite data which can then be used to derive a Tamarisk Habitat Suitability Map. By combining satellite observed annual vegetation cycles with land cover classification data the likely habitat for Tamarisk can be derived. || ", "hits": 25 }, { "id": 3331, "url": "http://svs.gsfc.nasa.gov/3331/", "result_type": "Visualization", "release_date": "2006-02-15T00:00:00-05:00", "title": "Creating the Tamarisk Habitat Suitability Map (for Science Presentations)", "description": "The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being addressed by NASA's Science Mission Directorate through a national application partnership with the US Geological Survey. NASA and USGS are working together to develop a National Invasive Species Forecasting System (ISFS) for the management and control of invasive species on Department of Interior and adjacent lands. The system provides a framework for using USGS's early detection and monitoring protocols and predictive models to process MODIS, ETM+, ASTER, and commercial remote sensing data, and create on-demand, regional-scale assessments of invasive species patterns and vulnerable habitats.The first step in this process is to collect relevant satellite data which can then be used to derive a Tamarisk Habitat Suitability Map. By combining daily Normalized Differential Vegetation Index (NDVI), daily Enhanced Vegetation Index (EVI), and MODIS Land Cover Classification data the likely Tamarisk habitat suitability map can be derived. || ", "hits": 25 }, { "id": 3332, "url": "http://svs.gsfc.nasa.gov/3332/", "result_type": "Visualization", "release_date": "2006-02-15T00:00:00-05:00", "title": "Deriving the Tamarisk Suitability Map: The Complete Story", "description": "The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being addressed by NASA's Science Mission Directorate through a national application partnership with the US Geological Survey. NASA and USGS are working together to develop a National Invasive Species Forecasting System (ISFS) for the management and control of invasive species on Department of Interior and adjacent lands. The system provides a framework for using USGS's early detection and monitoring protocols and predictive models to process MODIS, ETM+, ASTER and commercial remote sensing data. It can also be used to create on-demand, regional-scale assessments of invasive species patterns and vulnerable habitats. Tamarisk (Salt Ceder) is an invasive plant that typically grows near water and crowds out native species. Tamarisk reflective properties differ from those of its neighboring vegetation throughout the annual life cycle. These different reflective properties can be seen by the naked eye (as in the accompanying seasonal photographs), and can also be seen by satellite sensors. Current Tamarisk infestations and suitable habitats for future growth can be derived from various data sets, including EVI, NDVI, and land cover classifications. || ", "hits": 17 }, { "id": 3075, "url": "http://svs.gsfc.nasa.gov/3075/", "result_type": "Visualization", "release_date": "2004-12-09T12:00:00-05:00", "title": "Biomass Burning over South America", "description": "Biomass burning is the burning of living and dead vegetation. It includes the human-initiated burning of vegetation for land clearing and land-use change as well as natural, lightning-induced fires. Scientists estimate that humans are responsible for about 90% of biomass burning with only a small percentage of natural fires contributing to the total amount of vegetation burned. Burning vegetation releases large amounts of particulates (solid carbon combustion particles) and gases, including greenhouse gases that help warm the Earth. Studies suggest that biomass burning has increased on a global scale over the last 100 years, and computer calculations indicate that a hotter Earth resulting from global warming will lead to more frequent and larger fires. Biomass burning particulates impact climate and can also affect human health when they are inhaled, causing respiratory problems. Here are three images of South America on October 7, 2004. The first image shows clouds and fires on that day. The second image is clouds and nitrous dioxide (NO2) concentrations in the stratosphere. The last image overlays the fires on the NO2 data. || ", "hits": 21 } ] }