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The number colorized is MODIS MCD14ML T21 Brightness temperature minus the T31 Brightness temperature.", "width": 320, "height": 90, "pixels": 28800 } } ], "extra_data": {} }, { "id": 351051, "url": "https://svs.gsfc.nasa.gov/3872/#media_group_351051", "widget": "Single image", "title": "", "caption": "", "description": "Colortable showing the MODIS MYD13C1 product of composite 16 day Normalized Difference Vegetation Index (NDVI).", "items": [ { "id": 327369, "type": "media", "extra_data": null, "title": null, "caption": null, "instance": { "id": 482069, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003800/a003872/NDVI_MYD13C1.png", "filename": "NDVI_MYD13C1.png", "media_type": "Image", "alt_text": "Colortable showing the MODIS MYD13C1 product of composite 16 day Normalized Difference Vegetation Index (NDVI).", "width": 320, "height": 90, "pixels": 28800 } } ], "extra_data": {} } ], "studio": "svs", "funding_sources": [ "EAP" ], "credits": [ { "role": "Animator", "people": [ { "name": "Lori Perkins", "employer": "NASA/GSFC" }, { "name": "Greg Shirah", "employer": "NASA/GSFC" } ] }, { "role": "Producer", "people": [ { "name": "Kayvon Sharghi", "employer": "USRA" } ] }, { "role": "Scientist", "people": [ { "name": "Chris Justice", "employer": "University of Maryland" }, { "name": "Louis Giglio", "employer": "SSAI" }, { "name": "Luigi Boschetti", "employer": "University of Maryland College Park" } ] }, { "role": "Writer", "people": [ { "name": "Adam P Voiland", "employer": "Wyle Information Systems" } ] } ], "missions": [ "Terra" ], "series": [ "Agriculture Animations - Fires", "Fires", "Multisensor Fire Observations", "NDVI" ], "tapes": [], "papers": [], "datasets": [ { "name": "Fire Location", "common_name": "", "platform": "Terra and Aqua", "sensor": "MODIS", "type": "Other", "organizations": [], "description": "", "credit": "Fire location data courtesy of MODIS Rapid Response Project (NASA/GSFC and University of Maryland - http://rapidfire.sci.gsfc.nasa.gov)", "url": "", "date_range": null }, { "name": "Normalized Difference Vegetation Index (NDVI)", "common_name": "NDVI", "platform": "Terra and Aqua", "sensor": "MODIS", "type": "Other", "organizations": [ "NASA" ], "description": "", "credit": "", "url": "", "date_range": null } ], "nasa_science_categories": [ "Earth" ], "keywords": [ "Atmospheric science", "Biosphere", "Carbon Cycle and Ecosystems", "Earth Science", "Ecological Dynamics", "Ecosystems", "Fire Occurrence", "Fires", "For Educators", "Forestry", "HDTV", "Human Dimensions", "Hyperwall", "Natural Disaster", "Natural hazards" ], "recommended_pages": [], "related": [ { "id": 10863, "url": "https://svs.gsfc.nasa.gov/10863/", "page_type": "Produced Video", "title": "Forecasting South American Fires", "description": "Human settlement patterns are the primary factor that drives the distribution of fires in the Amazon. Satellite imagery shows, for example, that fishbone-shaped patterns of burned and deforested land extend outward from roads in predictable ways. Likewise, fires are rare in thinly populated areas. In recent years, however, new research has made clear that subtle environmental factors — including minor variations in ocean temperatures — amplify human impacts and underpin much of the variability in the number of fires the region experiences from one year to the next. A study conducted by UC Irvine scientists and published last week in Science even showed that scientists can predict the severity of the South American fire season months in advance by analyzing ocean temperatures in the North Atlantic and Central Pacific. To make the discovery, the researchers compared about a decade of fire observations collected by NASA's Terra and Aqua satellites with records of sea surface temperatures maintained by NOAA. The video below offers a visual representation of the same decade of fire data the scientists used to conduct their study. Look closely to see if you can spot the especially intense fires seasons of 2005, 2007 and 2010. || ", "release_date": "2011-11-15T00:00:00-05:00", "update_date": "2023-05-03T13:53:28.016204-04:00", "main_image": { "id": 481339, "url": "https://svs.gsfc.nasa.gov/vis/a010000/a010800/a010863/Cover.jpg", "filename": "Cover.jpg", "media_type": "Image", "alt_text": "Researchers can use ocean temperatures to predict Amazon fire season severity.", "width": 1024, "height": 576, "pixels": 589824 } } ], "sources": [], "products": [], "newer_versions": [], "older_versions": [], "alternate_versions": [] }