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    "results": [
        {
            "id": 4826,
            "url": "https://svs.gsfc.nasa.gov/4826/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Brazil and Novo Progresso Land Use Data Over Time",
            "description": "This animation begins by showing the similar sizes between the country of Brazil and the United States. It then cycles through over three decades of classification data for the entire Northern half of Brazil. We then zoom down to the town of Novo Progresso and compare its relative size to the San Francisco Bay region. Next we cycle through over three decades of transformation in the region showing how the north/south corridor of this region changed over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || novo_progressov_finalcomp.2009_print.jpg (1024x576) [287.1 KB] || novo_progressov_finalcomp.2009_searchweb.png (180x320) [105.7 KB] || novo_progressov_finalcomp.2009_thm.png (80x40) [7.3 KB] || novo_progressov_finalcomp_1080p30.mp4 (1920x1080) [48.9 MB] || example_composite (1920x1080) [0 Item(s)] || novo_progressov_finalcomp_1080p30.webm (1920x1080) [7.9 MB] || novo_progressov_finalcomp_1080p30.mp4.hwshow [199 bytes] || ",
            "hits": 64
        },
        {
            "id": 4827,
            "url": "https://svs.gsfc.nasa.gov/4827/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Novo Progresso Surrounding Region Land Use Data Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. It then zooms down to the region surrounding the town of Novo Progresso and compare its relative size to the San Francisco Bay region. Next we cycle through over three decades of transformation in the region showing how the north/south corridor of this area opened up over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || novo_wide_finalcomp.2009_print.jpg (1024x576) [387.4 KB] || novo_wide_finalcomp.1116_print.jpg (1024x576) [221.0 KB] || novo_wide_finalcomp_1080p30_2.mp4 (1920x1080) [30.2 MB] || novo_wide_finalcomp_1080p30_2.webm (1920x1080) [3.7 MB] || Example_Composite (1920x1080) [0 Item(s)] || novo_wide_finalcomp_1080p30_2.mp4.hwshow [195 bytes] || ",
            "hits": 51
        },
        {
            "id": 4828,
            "url": "https://svs.gsfc.nasa.gov/4828/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Colider Land Use Data Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. It then zooms down to the region surrounding the town of Colider and compares its relative size to Northern California. Next we cycle through over three decades of land use transformation showing cropland a pasture expansion over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || colider_finalcomp.2009_print.jpg (1024x576) [548.1 KB] || colider_finalcomp.2009_searchweb.png (320x180) [144.4 KB] || colider_finalcomp.2009_thm.png (80x40) [8.4 KB] || colider_finalcomp_1080p30.mp4 (1920x1080) [40.2 MB] || colider_finalcomp_1080p30.webm (1920x1080) [4.0 MB] || Example_Composite (1920x1080) [0 Item(s)] || colider_finalcomp_1080p30.mp4.hwshow [191 bytes] || ",
            "hits": 31
        },
        {
            "id": 4829,
            "url": "https://svs.gsfc.nasa.gov/4829/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Ji-Paraná Land Use Data Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. It then zooms down to the region surrounding the town of Ji Parana and compares its relative size to the San Francisco Bay area. Next we cycle through over three decades of land use transformation showing cropland a pasture expansion over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || ji_parana_finalcomp.2009_print.jpg (1024x576) [412.8 KB] || ji_parana_finalcomp.2009_searchweb.png (320x180) [133.8 KB] || ji_parana_finalcomp.2009_thm.png (80x40) [8.2 KB] || ji_parana_finalcomp_1080p30.mp4 (1920x1080) [34.0 MB] || Example_Composite (1920x1080) [0 Item(s)] || ji_parana_finalcomp_1080p30.webm (1920x1080) [3.8 MB] || ji_parana_finalcomp_1080p30.mp4.hwshow [193 bytes] || ",
            "hits": 35
        },
        {
            "id": 4830,
            "url": "https://svs.gsfc.nasa.gov/4830/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Rio Branco Land Use Data Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. It then zooms down to the region surrounding the town of Rio Branco and compares its relative size to the San Francisco Bay area. Next we cycle through over three decades of land use transformation showing pasture expansion over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || rio_branco_finalcomp.2009_print.jpg (1024x576) [331.8 KB] || rio_branco_finalcomp.2009_searchweb.png (320x180) [108.8 KB] || rio_branco_finalcomp.2009_thm.png (80x40) [7.4 KB] || rio_branco_finalcomp_1080p30.mp4 (1920x1080) [24.0 MB] || rio_branco_finalcomp_1080p30.webm (1920x1080) [3.4 MB] || Example_Composite (1920x1080) [0 Item(s)] || rio_branco_finalcomp_1080p30.mp4.hwshow [194 bytes] || ",
            "hits": 40
        },
        {
            "id": 4831,
            "url": "https://svs.gsfc.nasa.gov/4831/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Uatumã Biological Reserve Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. It then zooms down to the Uatumã Biological Reserve and compares its relative size to the San Francisco Bay area. Next we cycle through over three decades of land use transformation to show the lake formation over time as well as the increased pasture and croplands to the west of the lake. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || dam_finalcomp.2009_print.jpg (1024x576) [216.7 KB] || dam_finalcomp.2009_searchweb.png (320x180) [80.9 KB] || dam_finalcomp.2009_thm.png (80x40) [5.9 KB] || dam_finalcomp_1080p30.mp4 (1920x1080) [22.1 MB] || Example_Composite (1920x1080) [0 Item(s)] || dam_finalcomp_1080p30.webm (1920x1080) [3.3 MB] || dam_finalcomp_1080p30.mp4.hwshow [187 bytes] || ",
            "hits": 40
        },
        {
            "id": 4832,
            "url": "https://svs.gsfc.nasa.gov/4832/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Itaituba and Uruara Land Use Data Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. It then zooms down to the region between Itaituba and Uruara and compares its relative size to the San Francisco Bay area. Next we cycle through over three decades of land use transformation showing pasture expansion over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || ruropolis_finalcomp.2009_print.jpg (1024x576) [345.6 KB] || ruropolis_finalcomp.2009_searchweb.png (320x180) [116.9 KB] || ruropolis_finalcomp.2009_thm.png (80x40) [7.6 KB] || ruropolis_finalcomp_1080p30.mp4 (1920x1080) [29.5 MB] || Sample_Composite (1920x1080) [0 Item(s)] || ruropolis_finalcomp_1080p30.webm (1920x1080) [3.5 MB] || ruropolis_finalcomp_1080p30.mp4.hwshow [193 bytes] || ",
            "hits": 27
        },
        {
            "id": 4833,
            "url": "https://svs.gsfc.nasa.gov/4833/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T12:00:00-04:00",
            "title": "Northern Brazil Land Use Data Over Time",
            "description": "This data visualization begins with a wide view of Northern Brazil. While zooming in a little closer an image of the United States fades in to get the relative size of the region. Next we cycle through over three decades of transformation in the region showing land use change over time. Lastly, we fade in 2019 fire data to indicate how the data will continue to change into the upcoming year. || brazil_wide_finalcomp.2009_print.jpg (1024x576) [451.8 KB] || brazil_wide_finalcomp.2009_searchweb.png (320x180) [128.6 KB] || brazil_wide_finalcomp.2009_thm.png (80x40) [8.1 KB] || brazil_wide_finalcomp_1080p30.mp4 (1920x1080) [31.3 MB] || Sample_Composite (1920x1080) [0 Item(s)] || brazil_wide_finalcomp_1080p30.webm (1920x1080) [3.8 MB] || brazil_wide_finalcomp_1080p30.mp4.hwshow [195 bytes] || ",
            "hits": 92
        },
        {
            "id": 4900,
            "url": "https://svs.gsfc.nasa.gov/4900/",
            "result_type": "Visualization",
            "release_date": "2021-04-19T00:00:00-04:00",
            "title": "Novo Progresso Deforestation Soccer Field Comparison",
            "description": "Animation begins with a stylized bright green soccer field. Soccer fields then fall into place over a recently deforested field showing the estimated size of the newly cleared field. The camera then pulls back to reveal all the recently deforested areas (shown in bright green) around Novo Progresso from 2017 to 2018. || soccer_comp.0700_print.jpg (1024x576) [161.5 KB] || soccer_comp.0700_searchweb.png (320x180) [85.8 KB] || soccer_comp.0700_thm.png (80x40) [14.1 KB] || soccer_2017_2018_1080p30.mp4 (1920x1080) [28.6 MB] || 2017_to_2018 (1920x1080) [0 Item(s)] || soccer_2017_2018_1080p30.webm (1920x1080) [5.7 MB] || soccer_2017_2018_1080p30.mp4.hwshow [190 bytes] || ",
            "hits": 55
        },
        {
            "id": 31029,
            "url": "https://svs.gsfc.nasa.gov/31029/",
            "result_type": "Hyperwall Visual",
            "release_date": "2019-03-31T00:00:00-04:00",
            "title": "Shanghai Growth from the International Space Station",
            "description": "An animation comparing Shanghai night lights in 2003 and 2018 || shanghai_2003-2018_wipe_print.jpg (1024x576) [149.6 KB] || shanghai_2003-2018_wipe.png (3840x2160) [9.5 MB] || shanghai_2003-2018_wipe_searchweb.png (320x180) [97.3 KB] || shanghai_2003-2018_wipe_thm.png (80x40) [6.4 KB] || shanghai_2003-2018_wipe_1080p30.mp4 (1920x1080) [6.1 MB] || shanghai_2003-2018_wipe_720p30.mp4 (1280x720) [3.5 MB] || shanghai_2003-2018_wipe_720p30.webm (1280x720) [1.1 MB] || shanghai_2003-2018_wipe_2160p30.mp4 (3840x2160) [13.8 MB] || ",
            "hits": 85
        },
        {
            "id": 11506,
            "url": "https://svs.gsfc.nasa.gov/11506/",
            "result_type": "Produced Video",
            "release_date": "2014-03-20T00:00:00-04:00",
            "title": "Tracking Urban Change With Landsat",
            "description": "For helping communities across the United States stay up-to-date on their flood risk, the NASA/USGS Landsat satellites can take a bow. The Federal Emergency Management Agency uses Landsat images, which can illustrate urban changes, as a key indicator of sites where the agency should further investigate the flooding potential. With its archive of images capturing sprawling cities and new developments, Landsat can help FEMA track how building and construction is impacting an area’s landscapeEarth-observing Landsat satellites have been capturing images of the planet’s surface since 1972. Landsat 8 is the newest satellite in the program, a joint effort between NASA and the U.S. Geological Survey. It launched Feb. 11, 2013, and collects more than 400 images per day. New and archived Landsat data are available free to the public over the internet – and researchers have put the data to a multitude of uses. One is called the National Urban Change Indicator, or NUCI, created by MacDonald, Dettwiler, and Associates, LTD. It’s the results from a process that mines Landsat images over a 27-year period to identify areas of “permanent change,” where soil has been paved over for parking lots or other concrete structures.NUCI results act as a red flag for FEMA, helping the agency focus its mapping efforts and budget. But if maps identify a high risk of floods for a certain community, residents can take action, including elevating houses, building flood barricades, and more. || ",
            "hits": 54
        },
        {
            "id": 4134,
            "url": "https://svs.gsfc.nasa.gov/4134/",
            "result_type": "Visualization",
            "release_date": "2014-01-16T00:00:00-05:00",
            "title": "Groundwater Depletion in India Revealed by GRACE -Extended",
            "description": "Scientists using data from NASA's Gravity Recovery and Climate Experiment (GRACE) have found that the groundwater beneath Northern India has been receding by as much as one foot per year over the past decade. After examining many environmental and climate factors, the team of hydrologists led by Matt Rodell of NASA's Goddard Space Flight Center, Greenbelt, Md. concluded that the loss is almost entirely due to human consumption.Groundwater comes from the natural percolation of precipitation and other surface waters down through Earth's soil and rock, accumulating in aquifers - cavities and layers of porous rock, gravel, sand, or clay. In some subterranean reservoirs, the water may be thousands to millions of years old; in others, water levels decline and rise again naturally each year. Groundwater levels do not respond to changes in weather as rapidly as lakes, streams, and rivers do. So when groundwater is pumped for irrigation or other uses, recharge to the original levels can take months or years. The animation shown here depicts the change in groundwater levels with respect to the 2003-2009 mean, as measured each month from January 2003 to June 2013. || ",
            "hits": 108
        },
        {
            "id": 30182,
            "url": "https://svs.gsfc.nasa.gov/30182/",
            "result_type": "Hyperwall Visual",
            "release_date": "2013-10-17T12:00:00-04:00",
            "title": "Tehran Urbanization",
            "description": "Tehran, Iran’s capital, ranks high among the world’s fast-growing cities. In the early 1940s, Tehran’s population was about 700,000. By 1966, it had risen to 3 million, and by 1986—during the Iran-Iraq war—migrants brought the population to 6 million. Today, the metropolitan area has more than 10 million residents. This explosive growth has environmental and public health consequences, including air and water pollution and the loss of arable land.The Thematic Mapper sensor on NASA’s Landsat 5 satellite acquired these false-color images of Tehran on August 2, 1985, and July 19, 2009. In both images, vegetation appears bright green, urban areas range in color from gray to black, and barren areas appear brown. Whereas non-urbanized areas fringe the earlier image, urbanization fills almost the entire frame of the later image. Major roadways crisscrossing the city in 1985 remain visible in 2009, but many additional roadways have been added, particularly in the north. || ",
            "hits": 127
        },
        {
            "id": 30082,
            "url": "https://svs.gsfc.nasa.gov/30082/",
            "result_type": "Hyperwall Visual",
            "release_date": "2013-10-04T14:00:00-04:00",
            "title": "Rotating Earth at Night",
            "description": "This new space-based view of Earth’s city lights is a composite assembled from data acquired by the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The data was acquired over nine days in April 2012 and thirteen days in October 2012. It took the satellite 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth’s land surface and islands. This new data was then mapped over existing MODIS Blue Marble imagery to provide a realistic view of the planet. The view was made possible by the “day-night band” of Suomi NPP’s Visible Infrared Imaging Radiometer Suite. VIIRS detects light in a range of wavelengths from green to near-infrared and uses “smart” light sensors to observe dim signals such as city lights, auroras, wildfires, and reflected moonlight. This low-light sensor can distinguish night lights tens to hundreds of times better than previous satellites. || ",
            "hits": 460
        },
        {
            "id": 4062,
            "url": "https://svs.gsfc.nasa.gov/4062/",
            "result_type": "Visualization",
            "release_date": "2013-06-30T00:00:00-04:00",
            "title": "Georgia Urban Sprawl",
            "description": "One of the many ways to keep FEMA maps up to date is by tracking urban change using satellite imagery. Take this suburb of Atlanta, Georgia as an example. By mining Landsat images spanning a 27 year period, it's possible to identify areas where the land surface has permanently changed and affect the areas ability to absorb water.The river to the Northwest is the Chattahoochee River. The \"Y\"-shaped roads are Interstate 85 (upper branch) and Route 316 (lower branch). As the years go by, one can see the Mall of Georgia being built in the upper middle part of the screen, immediately north of Interstate 85. Surrounding neighborhoods sprout up throughout this whole area as we move through time. This animation was created for use in a NASA video on water run-off changes related to urban sprawl titled \"FEMA Risk Map\". || ",
            "hits": 47
        },
        {
            "id": 30028,
            "url": "https://svs.gsfc.nasa.gov/30028/",
            "result_type": "Hyperwall Visual",
            "release_date": "2013-04-05T00:00:00-04:00",
            "title": "Earth at Night 2012",
            "description": "This new space-based view of Earth's city lights is a composite assembled from data acquired by the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The data was acquired over nine days in April 2012 and thirteen days in October 2012. It took the satellite 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth's land surface and islands. This new data was then mapped over existing MODIS Blue Marble imagery to provide a realistic view of the planet.The view was made possible by the \"day-night band\" of Suomi NPP's Visible Infrared Imaging Radiometer Suite. VIIRS detects light in a range of wavelengths from green to near-infrared and uses \"smart\" light sensors to observe dim signals such as city lights, auroras, wildfires, and reflected moonlight. This low-light sensor can distinguish night lights tens to hundreds of times better than previous satellites. || ",
            "hits": 424
        },
        {
            "id": 4032,
            "url": "https://svs.gsfc.nasa.gov/4032/",
            "result_type": "Visualization",
            "release_date": "2013-01-14T00:00:00-05:00",
            "title": "Urban Sprawl in Beijing, China (Hyperwall version)",
            "description": "Beijing is one of the oldest, and now, one of the most crowded cities in the world. Established as a city in 1045 BC, King Wu was the first to declare it as a capital in 1057 BC. Having served as the capital of the Liao, Jin, Yuan, Ming and Qing Dynasties, Beijing is now the capital of the People's Republic of China. In these Landsat images, the explosive growth of this ancient city is clearly visible. In 1972, only about 7.89 million people lived there — but by 2010 the population swelled to more than 12 million. This increase in the city's size corresponds to the opening of China to the Western world in the 1970s. Up until 1979, the government restricted housing in the city, limiting it to the confines of the \"Outer City.\" Previously a walled fortress, its outline is still visible today due to the build up of canals and roads along the path of the original wall. Inside this rectangular boundary is the ancient heart of the capital, the moat-lined Forbidden City. Called forbidden because anyone entering needed royal permission, this is where the Imperial Palace still stands, once home to 500 years of Chinese emperors. It was Kublai Khan who established the Forbidden City in 1260 A.D. He called it Khanbaliq but Italian explorer Marco Polo called it Cambuluc. It still stands as Beijing's city center. In 1421 the Chinese took the city back and gave it its current name of Beijing. Today, Beijing is only limited by the rugged Taihang Mountains that run to the west and northwest of the city, pushing the population to spread to the south and east across the relatively flat coastal plain. || ",
            "hits": 44
        },
        {
            "id": 3791,
            "url": "https://svs.gsfc.nasa.gov/3791/",
            "result_type": "Visualization",
            "release_date": "2012-07-23T00:00:00-04:00",
            "title": "Urban Sprawl in Beijing, China",
            "description": "Beijing is one of the oldest, and now, one of the most crowded cities in the world. Established as a city in 1045 BC, King Wu was the first to declare it as a capital in 1057 BC. Having served as the capital of the Liao, Jin, Yuan, Ming and Qing Dynasties, Beijing is now the capital of the People's Republic of China. In these Landsat images, the explosive growth of this ancient city is clearly visible. In 1972, only about 7.89 million people lived there — but by 2010 the population swelled to more than 12 million. This increase in the city's size corresponds to the opening of China to the Western world in the 1970s. Up until 1979, the government restricted housing in the city, limiting it to the confines of the \"Outer City.\" Previously a walled fortress, its outline is still visible today due to the build up of canals and roads along the path of the original wall. Inside this rectangular boundary is the ancient heart of the capital, the moat-lined Forbidden City. Called forbidden because anyone entering needed royal permission, this is where the Imperial Palace still stands, once home to 500 years of Chinese emperors. It was Kublai Khan who established the Forbidden City in 1260 A.D. He called it Khanbaliq but Italian explorer Marco Polo called it Cambuluc. It still stands as Beijing's city center. In 1421 the Chinese took the city back and gave it its current name of Beijing. Today, Beijing is only limited by the rugged Taihang Mountains that run to the west and northwest of the city, pushing the population to spread to the south and east across the relatively flat coastal plain. || ",
            "hits": 100
        },
        {
            "id": 11041,
            "url": "https://svs.gsfc.nasa.gov/11041/",
            "result_type": "Produced Video",
            "release_date": "2012-07-23T00:00:00-04:00",
            "title": "Phoenix, AZ",
            "description": "Arizona's capital of Phoenix and its neighboring towns in Maricopa County have undergone a major population boom in the last 40 years, and its effects are seen in everything from the expansion of town and cities and to an increased demand for fresh water. || ",
            "hits": 88
        },
        {
            "id": 10721,
            "url": "https://svs.gsfc.nasa.gov/10721/",
            "result_type": "Produced Video",
            "release_date": "2012-03-05T17:00:00-05:00",
            "title": "Las Vegas, 1972-2021",
            "description": "Timelapse animation of Lake Mead and the city of Las Vegas, Nevada, from 1972-2021, as captured by Landsat sensors. The images are false-color, showing healthy vegetation in red. || Las_Vegas-wide-2021_print.jpg (1024x576) [226.8 KB] || Las_Vegas-wide-2021_searchweb.png (320x180) [119.1 KB] || Las_Vegas-wide-2021_thm.png (80x40) [7.7 KB] || Las_Vegas_1972-2021-tw.mp4 (1920x1080) [64.7 MB] || Las_Vegas-wide-2021.tif (1920x1080) [7.9 MB] || Las_Vegas_1972-2021-tw.webm (1920x1080) [8.0 MB] || Las_Vegas_1972-2021-yt.mp4 (1920x1080) [129.5 MB] || Las_Vegas_1972-2021.mov (1920x1080) [2.3 GB] || ",
            "hits": 438
        },
        {
            "id": 10513,
            "url": "https://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": 27
        },
        {
            "id": 3637,
            "url": "https://svs.gsfc.nasa.gov/3637/",
            "result_type": "Visualization",
            "release_date": "2009-10-05T12:00:00-04:00",
            "title": "Deforestation of Rondonia, Brazil from 1975 to 2009",
            "description": "In the 1970s, Brazil's Program of National Integration built roads across the Amazon and settled land along these roads with colonists. These roads were catalysts of land use change in the Amazon.Brazil is also home to more than a quarter of Earth's tropical forests. Considering that the band of lush green that circles the globe through many equatorial nations is fundamental to the overall health of the whole planet's environment, careful monitoring of forest health in the tropics is essential. Tropical forests act as major carbon 'sinks', places where ambient carbon dioxide in the atmosphere can be absorbed by growing things and sequestered for years. Definitive evidence shows that excess carbon dioxide can contribute to the greenhouse effect and speed global warming. Similarly, tropical forests also act as a primary producer of oxygen. In the respiration process that absorbs gaseous carbon dioxide, trees and other plants give off oxygen.Data taken in 1975 and 2009 from the Landsat series of spacecraft shows enormous tracts of forest disappearing in Rondonia, Brazil. || ",
            "hits": 58
        },
        {
            "id": 3646,
            "url": "https://svs.gsfc.nasa.gov/3646/",
            "result_type": "Visualization",
            "release_date": "2009-10-05T12:00:00-04:00",
            "title": "2009 Crop Intensity, 2009 Producers, and 2050 Projected Population",
            "description": "The U.S. Department of Agriculture (USDA) and the National Aeronautics and Space Administration (NASA) signed a Memorandum of Understanding (MOU) to strengthen collaboration. In support of this collaboration, NASA and the USDA Foreign Agricultural Service (FAS) jointly funded a new project to assimilate NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data and products into an existing decision support system (DSS) operated by the International Production Assessment Division (IPAD) of FAS. To meet its objectives, FAS/IPAD uses satellite data and data products to monitor agriculture worldwide and to locate and keep track of natural disasters such as short and long term droughts, floods and persistent snow cover which impair agricultural productivity. FAS is the largest user of satellite imagery in the non-military sector of the U.S. government. For the last 20 years FAS has used a combination of Landsat and NOAA-AVHRR satellite data to monitor crop condition and report on episodic events.To successfully monitor worldwide agricultural regions and provide accurate agricultural production assessments, it is important to understand the spatial distribution of croplands. To do this a global croplands mask to identify all sites used for crop production. Croplands are highly variable both temporally and spatially. Croplands vary from year to year due to events such as drought and fallow periods, and they vastly differ across the globe in accordance with characteristics such as cropping intensity and field size. A flexible crop likelihood mask is used to help depict these varying characteristics of global crop cover. Regions featuring intensive agro-industrial farming practices such as the Maize Triangle in South Africa will have higher confidence values in the crop mask as compared to less intensively farmed regions in parts of Sub-Saharan Africa where cropland identification is partly confounded with natural background vegetation phenologies. Thus, a customized threshold can be employed to examine areas of varying cropping intensification. || ",
            "hits": 15
        },
        {
            "id": 3623,
            "url": "https://svs.gsfc.nasa.gov/3623/",
            "result_type": "Visualization",
            "release_date": "2009-08-12T00:00:00-04:00",
            "title": "Groundwater Depletion in India Revealed by GRACE",
            "description": "Scientists using data from NASA's Gravity Recovery and Climate Experiment (GRACE) have found that the groundwater beneath Northern India has been receding by as much as one foot per year over the past decade. After examining many environmental and climate factors, the team of hydrologists led by Matt Rodell of NASA's Goddard Space Flight Center, Greenbelt, Md. concluded that the loss is almost entirely due to human consumption.Groundwater comes from the natural percolation of precipitation and other surface waters down through Earth's soil and rock, accumulating in aquifers - cavities and layers of porous rock, gravel, sand, or clay. In some subterranean reservoirs, the water may be thousands to millions of years old; in others, water levels decline and rise again naturally each year. Groundwater levels do not respond to changes in weather as rapidly as lakes, streams, and rivers do. So when groundwater is pumped for irrigation or other uses, recharge to the original levels can take months or years. More than 109 cubic km (26 cubic miles) of groundwater disappeared from the region's aquifers between 2002 and 2008 — double the capacity of India's largest surface water reservoir, the Upper Wainganga, and triple that of Lake Mead, the largest manmade reservoir in the U.S. The animation shown here depicts the change in groundwater levels as measured each November between 2002 to 2008. || ",
            "hits": 235
        },
        {
            "id": 10402,
            "url": "https://svs.gsfc.nasa.gov/10402/",
            "result_type": "Produced Video",
            "release_date": "2009-03-11T00:00:00-04:00",
            "title": "Rain, Drought, Urbanization Contributing Factors for Storms",
            "description": "On March 14, 2008, a tornado swept through downtown Atlanta, its 130 mile-per-hour winds ripping holes in the roof of the Georgia Dome, blowing out office windows and trashing parts of Centennial Olympic Park. It was an event so rare in an urban landscape that researchers immediately began to examine NASA satellite data and historical archives to see what weather and climatological ingredients may have combined to brew such a storm. Read more at http://www.nasa.gov/topics/earth/features/atlanta_tornado.html. || ",
            "hits": 15
        },
        {
            "id": 3492,
            "url": "https://svs.gsfc.nasa.gov/3492/",
            "result_type": "Visualization",
            "release_date": "2009-03-09T12:00:00-04:00",
            "title": "Atlantic Transport of Anthropogenic Aerosol Optical Depth (AOD)  in 2003",
            "description": "In a new NASA study, researchers taking advantage of improvements in satellite sensor capabilities offer the first measurement-based estimate of the amount of pollution. The new measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on NASA's Terra satellite substantiate the results of previous model-based studies, and are the most extensive to date. Hongbin Yu, an associate research scientist of the University of Maryland Baltimore County working at NASA's Goddard Space Flight Center in Greenbelt, Md., grew up in China and taught there as a university professor, , where he witnessed first-hand and studied how pollution from nearby power plants affected the local environment. Yu points out, however, that the matter of pollution transport is a global one. \"Our study focused on East Asian pollution transport, but pollution also flows from Europe, North America, the broader Asian region and elsewhere, across bodies of water and land, to neighboring areas and beyond,\" he said. \"So we should not simply blame East Asia for this amount of pollution flowing into North America.\" In fact, a recent model study conducted by Mian Chin, co-author of this study and an atmospheric scientist at NASA Goddard suggests that European pollution also makes significant contribution to the pollution inflow to North America. \"Satellite instruments give us the ability to capture finer measurements, on a nearly daily basis across a broader geographic region and across a longer time frame so that the overall result is a better estimate than any other measurement method we've had in the past,\" said study co-author Lorraine Remer, a physical scientist and member of the MODIS science team at NASA Goddard. The MODIS instrument can distinguish between broad categories of particles in the air, and observes Earth's entire surface every one to two days, enabling it to monitor movement of the East Asian pollution aerosols as they rise into the lower troposphere, the area of the atmosphere where we live and breathe, and make their way across the Pacific and up into the middle and upper regions of the troposphere. Remer added that the research team also found that pollution movements fluctuate during the year, with the East Asian airstream carrying its largest \"load\" in spring and smallest in summer. The most extensive East Asian export of pollution across the Pacific took place in 2003, triggered by record-breaking wildfires across vast forests of East Asia and Russia. Notably, the pollution aerosols also travel across the ocean quickly, journeying into the atmosphere above North American in as little as one week. \"We cannot determine at what level of elevation in the atmosphere the pollution ends up once it crosses over to North America, so we do not have a way in this study to assess what actual impact it has on air quality here,\" said Remer. \"Nevertheless, we realize there is indeed impact. For example, particles like these have been linked to regional weather and climate effects. Since pollution transport is such a broad global issue, it is important moving forward to extend this kind of study to other regions, to see how much pollution is migrating from its source regions to others, when, and how fast,\" said Remer. || ",
            "hits": 14
        },
        {
            "id": 3493,
            "url": "https://svs.gsfc.nasa.gov/3493/",
            "result_type": "Visualization",
            "release_date": "2008-04-21T08:00:00-04:00",
            "title": "Chesapeake Bay Cities",
            "description": "This animation takes us on a tour around the Chesapeake Bay region visiting major city centers in the surrounding states: Maryland, Virginia, Delaware and the District of Columbia. The imagery utilized for this animation is a false-color Chesapeake Bay Landsat-7 Mosaic (#3473) composed of eight scenes acquired between 1999-2002, which were put together and color corrected to resemble natural looking colors.The mosaic was created by EarthSat under contract with NASA as part of the GeoCover 2000 product. All images used in GeoCover were acquired by Landsat 7 during the period of 1999-2002. The pixel size of the full resolution image represents 14.25 m on the ground. The Chesapeake Bay mosaic uses portions of eight Landsat-7 scenes. Below you will find a listing of the eight Landsat 7 images that were put together to create the composite image. Landsat scenes are organized by a Path and Row number according to the Worldwide Reference System. (To learn more about Landsat's Worldwide Reference System, please visit: http://landsat.gsfc.nasa.gov/about/wrs.html)Scenes used in the Chesapeake Bay mosaic: Landsat 7 WRS Path 15-Row 32 acquired on Oct. 05, 2001 Landsat 7 WRS Path 14-Row 32 acquired on Sept. 23, 1999 Landsat 7 WRS Path 15-Row 33 acquired on October 05, 2001 Landsat 7 WRS Path 14-Row 33 acquired on July 10, 2001 Landsat 7 WRS Path 15-Row 34 acquired on Sept. 30, 1999 Landsat 7 WRS Path 14-Row 34 acquired on July 10, 2001 Landsat 7 WRS Path 15-Row 35 acquired on Sept. 30, 1999 Landsat 7 WRS Path 14-Row 35 acquired on Sept. 23, 1999 || ",
            "hits": 16
        },
        {
            "id": 3509,
            "url": "https://svs.gsfc.nasa.gov/3509/",
            "result_type": "Visualization",
            "release_date": "2008-04-16T00:00:00-04:00",
            "title": "Las Vegas Growth from Landsat",
            "description": "This sequence of images from the earliest Landsat satellite to the present captures the dramatic growth of Las Vegas, Nevada. From 1973 to 2006, the population of Las Vegas grew from 358,000 to over 2 million. || ",
            "hits": 44
        },
        {
            "id": 3491,
            "url": "https://svs.gsfc.nasa.gov/3491/",
            "result_type": "Visualization",
            "release_date": "2008-03-13T12:00:00-04:00",
            "title": "Pacific Anthropogenic Aerosol Optical Depth (AOD)  in 2003",
            "description": "According to measurements taken with a satellite instrument, vast quantities of industrial aerosols and smoke from biomass burning in East Asia and Russia are traveling from one side of the globe to another. Explosive economic growth in Asia has profound implications for the atmosphere worldwide. Data collected by a NASA satellite shows a dense blanket of polluted air over the Northwestern Pacific. This brown cloud is a toxic mix of ash, acids, and airborne particles from car and factory emissions, as well as from low-tech polluters like coal-burning stoves and from forest fires. This image generated by data from NASA's instrument called MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Terra satellite demonstrates how large and pervasive this transport phenomenon is across vast areas. China's exports fill shelves around the world, but according to a new NASA research paper, China also heavily exports pollution. This week, space agency scientists reveal how Chinese industrialization and Russian forest fires in combination with pollution transported eastward from Europe send roughly 18 teragrams - almost 40 billion pounds-of pollution aerosols into the atmosphere over the Northwestern Pacific every year. The MODIS instrument on NASA's Terra satellite has been tracking the particulate pollution for more than seven years, gathering data as most of it drifted east across the Pacific Ocean. About 4.5 teragrams of particulate pollution each year could reach the western boundary of North America, which is about 15% of local emissions of particulate pollutants from the U.S. and Canada. In the last two decades, China has more than doubled its pollution production. This boom may be contributing to substantial changes in climate and weather in places far from the origin of the particulates. Never in human history-anywhere-has there been industrial growth like that in modern China. But with fast growth comes unintended consequences, and from space evidence of those consequences is starting to emerge. The research relies on measurements of something called \"aerosol optical thickness\". It's a quantitative measurement about how well a slice of atmosphere transmits light. The greater the value of optical thickness for a given location, the less light of a particular wavelength can pass through it. Measurements of aerosol optical thickness describe quantities of tiny particles in a given volume. By measuring how much light can penetrate a region of atmosphere across a variety of wavelengths, scientists can make certain inferences about the quantity and type of particles blocking that light. This visualization shows the seasonal variations of transport of pollution aerosols across the North Pacific. The East Asian airstream carries its largest pollution loading in spring and smallest in summer and fall. With heavy concentrations of aerosols represented by shades of brown, scientists can track the origins and distribution of the particles as they travel in the atmosphere. The sequence also shows a trail of substantial aerosol concentrations from a variety of sources. These sources include heavy industrial activity in East Asia associated with high population density represented in this sequence by gradations of black covering the land surface, and intense Russian forest fires in high latitudes. || ",
            "hits": 25
        },
        {
            "id": 10184,
            "url": "https://svs.gsfc.nasa.gov/10184/",
            "result_type": "Produced Video",
            "release_date": "2008-01-30T00:00:00-05:00",
            "title": "Urban Growth in Las Vegas",
            "description": "In May 1973, less than a year after the first of NASA's Landsat satellites was launched, Las Vegas, Nevada had a population of only 358,000.  By 2006 the population had ballooned to over 2 million.  Still one of America's fastest growing urban areas, this series of Landsat scenes from four different years shows just how dramamtic the growth of Las Vegas has been. || ",
            "hits": 45
        },
        {
            "id": 3152,
            "url": "https://svs.gsfc.nasa.gov/3152/",
            "result_type": "Visualization",
            "release_date": "2005-05-27T12:00:00-04:00",
            "title": "Urban Signatures: Temperature (WMS)",
            "description": "Big cities influence the environment around them. For example, urban areas are typically warmer than their surroundings. Cities are strikingly visible in computer models that simulate the Earth's land surface. This visualization shows average surface temperature predicted by the Land Information System (LIS) for a day in June 2001. Only part of the global computation is shown, focusing on the highly urbanized northeast corridor in the United States, including the cities of Boston, New York, Philadelphia, Baltimore, and Washington. || ",
            "hits": 15
        },
        {
            "id": 3154,
            "url": "https://svs.gsfc.nasa.gov/3154/",
            "result_type": "Visualization",
            "release_date": "2005-05-27T12:00:00-04:00",
            "title": "Urban Signatures: Evaporation (WMS)",
            "description": "Big cities influence the environment around them. For example, urban areas are typically warmer than their surroundings. Cities are strikingly visible in computer models that simulate the Earth's land surface. This visualization shows evaporation rates predicted by the Land Information System (LIS) for a day in June 2001. Evaporation is lower in the cities because water tends to run off pavement and into drains, rather than being absorbed by soil and plants from which it later evaporates. Only part of the global computation is shown, focusing on the highly urbanized northeast corridor in the United States, including the cities of Boston, New York, Philadelphia, Baltimore, and Washington. || ",
            "hits": 56
        },
        {
            "id": 3155,
            "url": "https://svs.gsfc.nasa.gov/3155/",
            "result_type": "Visualization",
            "release_date": "2005-05-27T12:00:00-04:00",
            "title": "Urban Signatures: Thermal Radiation (WMS)",
            "description": "Big cities influence the environment around them. For example, urban areas are typically warmer than their surroundings. Cities are strikingly visible in computer models that simulate the Earth's land surface. This visualization shows outgoing thermal radiation predicted by the Land Information System (LIS) for a day in June 2001. Cities are warmer, so they emit more longwave (infrared) radiation. Only part of the global computation is shown, focusing on the highly urbanized northeast corridor in the United States, including the cities of Boston, New York, Philadelphia, Baltimore, and Washington. || ",
            "hits": 25
        },
        {
            "id": 3156,
            "url": "https://svs.gsfc.nasa.gov/3156/",
            "result_type": "Visualization",
            "release_date": "2005-05-27T12:00:00-04:00",
            "title": "Urban Signatures: Latent Heat Flux (WMS)",
            "description": "Big cities influence the environment around them. For example, urban areas are typically warmer than their surroundings. Cities are strikingly visible in computer models that simulate the Earth's land surface. This visualization shows latent heat flux predicted by the Land Information System (LIS) for a day in June 2001. (Latent heat flux refers to the transfer of energy from the Earth's surface to the air above by evaporation of water on the surface; for a more detailed explanation see http://www.uwsp.edu/geo/faculty/ritter/geog101/textbook/energy/energy_balance.html). Latent heat flux is lower in the cities because there is less evaporation there. Only part of the global computation is shown, focusing on the highly urbanized northeast corridor in the United States, including the cities of Boston, New York, Philadelphia, Baltimore, and Washington. || ",
            "hits": 55
        },
        {
            "id": 3157,
            "url": "https://svs.gsfc.nasa.gov/3157/",
            "result_type": "Visualization",
            "release_date": "2005-05-27T12:00:00-04:00",
            "title": "Urban Signatures: Sensible Heat Flux (WMS)",
            "description": "Big cities influence the environment around them. For example, urban areas are typically warmer than their surroundings. Cities are strikingly visible in computer models that simulate the Earth's land surface. This visualization shows sensible heat flux predicted by the Land Information System (LIS) for a day in June 2001. (Sensible heat flux refers to transfer of heat from the earth's surface to the air above; for further explanation see http://www.uwsp.edu/geo/faculty/ritter/geog101/textbook/energy/energy_balance.html). Sensible heat flux is higher in the cities—that is, they transfer more heat to the atmosphere—because the surface there is warmer than in the surroundings. Only part of the global computation is shown, focusing on the highly urbanized northeast corridor in the United States, including the cities of Boston, New York, Philadelphia, Baltimore, and Washington. || ",
            "hits": 98
        },
        {
            "id": 2979,
            "url": "https://svs.gsfc.nasa.gov/2979/",
            "result_type": "Visualization",
            "release_date": "2004-09-03T12:00:00-04:00",
            "title": "Mississippi Dead Zone",
            "description": "Recent reports indicate that the large region of low oxygen water often referred to as the 'Dead Zone' has spread across nearly 5,800 square miles of the Gulf of Mexico again in what appears to be an annual event. NASA satellites monitor the health of the oceans and spots the conditions that lead to a dead zone. These images show how ocean color changes from winter to summer in the Gulf of Mexico. Summertime satellite observations of ocean color from MODIS Aqua show highly turbid waters which may include large blooms of phytoplankton extending from the mouth of the Mississippi River all the way to the Texas coast. When these blooms die and sink to the bottom, bacterial decomposition strips oxygen from the surrounding water, creating an environment very difficult for marine life to survive in. Reds and oranges represent high concentrations of phytoplankton and river sediment. The National Oceanic and Atmospheric Administration (NOAA) ships measured low oxygen water in the same location as the highly turbid water in the satellite images. Most studies indicate that fertilizers and runoff from human sources is one of the major stresses impacting coastal ecosystems. In the third image using NOAA data, reds and oranges represent low oxygen concentrations. || ",
            "hits": 90
        },
        {
            "id": 2916,
            "url": "https://svs.gsfc.nasa.gov/2916/",
            "result_type": "Visualization",
            "release_date": "2004-02-16T12:00:00-05:00",
            "title": "Earth At Night (WMS)",
            "description": "This image of Earth's city lights was created with data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). Originally designed to view clouds by moonlight, the OLS is also used to map the locations of permanent lights on the Earth's surface.The brightest areas of the Earth are the most urbanized, but not necessarily the most populated. (Compare western Europe with China and India.) Cities tend to grow along coastlines and transportation networks. Even without the underlying map, the outlines of many continents would still be visible. The United States interstate highway system appears as a lattice connecting the brighter dots of city centers. In Russia, the Trans-Siberian railroad is a thin line stretching from Moscow through the center of Asia to Vladivostok. The Nile River, from the Aswan Dam to the Mediterranean Sea, is another bright thread through an otherwise dark region.Even more than 100 years after the invention of the electric light, some regions remain thinly populated and unlit. Antarctica is entirely dark. The interior jungles of Africa and South America are mostly dark, but lights are beginning to appear there. Deserts in Africa, Arabia, Australia, Mongolia, and the United States are poorly lit as well (except along the coast), along with the boreal forests of Canada and Russia, and the great mountains of the Himalaya. || ",
            "hits": 146
        },
        {
            "id": 2911,
            "url": "https://svs.gsfc.nasa.gov/2911/",
            "result_type": "Visualization",
            "release_date": "2004-02-13T12:00:00-05:00",
            "title": "Urbanization around the Pearl River Estuary in China from 1973 through 2001 (WMS)",
            "description": "The region around the Pearl River Estuary in southern China experienced rapid urban growth in the 1980s and 1990s. This growth was spurred by the establishment of special government economic zones, particularly in Shenzhen, just to the east of the estuary. Urban areas increased by more than 300% between 1988 and 1996. This growth can be directly assessed by remote sensing measurements from space, particularly by comparing images from the Landsat sensors for the last thirty years. This animation shows nine such images in sequence, from the years 1973, 1975, 1977, 1979, 1988, 1992, 1995, 2000, and 2001. || ",
            "hits": 33
        },
        {
            "id": 2761,
            "url": "https://svs.gsfc.nasa.gov/2761/",
            "result_type": "Visualization",
            "release_date": "2003-06-23T12:00:00-04:00",
            "title": "Landsat-7 20 Year Urbanization of Deep Bay near Shenzhen, China",
            "description": "The long operational history of the Landsat satellite allows a detailed study of urban growth around the world, as illustrated by this animation of urbanization around Shenzen, China. || ",
            "hits": 33
        },
        {
            "id": 2762,
            "url": "https://svs.gsfc.nasa.gov/2762/",
            "result_type": "Visualization",
            "release_date": "2003-06-23T12:00:00-04:00",
            "title": "Landsat 7 20 Year Urbanization West of Shenzhen, China",
            "description": "The long operational history of the Landsat satellite allows a detailed study of urban growth around the world, as illustrated by this animation of urbanization around Shenzen, China. || ",
            "hits": 31
        },
        {
            "id": 2763,
            "url": "https://svs.gsfc.nasa.gov/2763/",
            "result_type": "Visualization",
            "release_date": "2003-06-23T12:00:00-04:00",
            "title": "Landsat-7 20-Year Urbanization of Shenzhen, China",
            "description": "The long operational history of the Landsat satellite allows a detailed study of urban growth around the world, as illustrated by this animation of urbanization around Shenzen, China. || ",
            "hits": 22
        },
        {
            "id": 2634,
            "url": "https://svs.gsfc.nasa.gov/2634/",
            "result_type": "Visualization",
            "release_date": "2002-08-20T12:30:00-04:00",
            "title": "Impervious Data of the Washington, DC and Baltimore, Maryland Area",
            "description": "Here we see an image of the Washington, D.C.-Baltimore area taken with the Landsat satellite on March 27, 1998. For over 26 years, Landsat images have been used to help urban planners understand where growth is taking place and help geographers evaluate how different urban planning programs effect population growth and land use. || ",
            "hits": 17
        },
        {
            "id": 2636,
            "url": "https://svs.gsfc.nasa.gov/2636/",
            "result_type": "Visualization",
            "release_date": "2002-08-20T12:00:00-04:00",
            "title": "Impervious Data of the Washington, DC Area",
            "description": "Here we see an image of the Washington, D.C. area taken with the Landsat satellite. The dates of the images are from 1986, 1990, 1996, and 2000. For over 26 years, Landsat images have been used to help urban planners understand where growth is taking place and help geographers evaluate how different urban planning programs effect population growth and land use. || ",
            "hits": 18
        },
        {
            "id": 2637,
            "url": "https://svs.gsfc.nasa.gov/2637/",
            "result_type": "Visualization",
            "release_date": "2002-08-20T12:00:00-04:00",
            "title": "Impervious Data of the Baltimore Area",
            "description": "Here we see an image of the Baltimore Maryland area taken with the Landsat satellite. Dates ranging from 1986, 1990, 1996, 2000. For over 26 years, Landsat images have been used to help urban planners understand where growth is taking place and help geographers evaluate how different urban planning programs effect population growth and land use. || ",
            "hits": 24
        }
    ]
}