Landsat 9 at Work

Data visualization featuring the glacier rich region of the Himalayas, along with many of Earth’s highest peaks. The visualization sequence starts with a wide view of the Tibetan plateau and moves along a hiking path highlighting Mt. Everest, Mt. Lhotse, Mt Nuptse, the Everest Base Camp, the Khumbhu glacier, all the way to Imja Lake. Moving to a top-down view of Imja Lake, a time series of Landsat data unveils its dramatic growth for the period 1989-2019.This video is also available on our YouTube channel. Glaciers are retreating on a near-global scale due to rising temperatures and climate change. The melt and retreat of glaciers contributes to sea level rise and in the formation of glacial lakes typically right at the foot of the glaciers. In the largest-ever study of glacial lakes, NASA-funded researchers Dan Shugar et al. working under a grant from NASA’s High Mountain Asia Program found that glacial lake volume has increased by about 50% worldwide since 1990. The findings, published in the journal Nature Climate Change with the title Rapid worldwide growth of glacial lakes since 1990 affect how researchers evaluate the amount of glacial meltwater reaching the oceans and contributing to sea level rise as well as evaluate hazard risks for mountain communities downstream. Glacial lakes, which are often dammed by ice or glacial sediment called a moraine, are not stable like the lakes most people are used to swimming or boating in. Rather, they can be quite unstable and can burst their banks or dams, causing massive floods downstream. These kinds of floods from glacial lakes, also known as glacial lake outburst floods or GLOFs, have been responsible for thousands of deaths over the last century, as well as the destruction of villages, infrastructure and livestock.The data visualization featured on this page showcases the glacier rich and wondrous landscape of High Mountain Asia and provides a glimpse into how glacial lakes have increased during the last thirty years, by demonstrating the growth of Imja Lake for the period 1989-2019. It is important to mention that while Imja Lake is just one of the 14,394 glacial lakes analyzed by the science team in the study for the period of 2015-2018, it serves as a vivid example due to its dramatic growth.The visualization sequence starts with a wide view of Asia and the Tibetan plateau and slowly zooms into the Himalayan region, which includes many of Earth’s highest peaks and is paired with the highest concentration of snow and glaciers outside of the polar regions. Soon after a block of the Eastern Himalayan region rises featuring realistically scaled terrain data from the High Mountain Asia 8-meter Digital Elevation Model (DEM). The 8-meter DEM is draped over with Landsat 8 data from the same region. The sequence takes us on a hiking path from Mt. Everest (8,848 m / 29,029 ft), Mt. Lhotse (8,516 m / 27,940 ft) and Mt. Nuptse (7,861 m / 25,791 ft), to the Everest Base Camp, the Khumbu Glacier all the way to Imja Lake. Moving to a top-down view, a time series of geo-registered Landsat data unveils the growth of Imja Lake from 1989 to 2019. Outlines of the Imja Lake extents highlight the growth during the 30 years occurring from meltwater from the adjacent glaciers.Until now climate models that translated glacier melt into sea level change assumed that water from glacier melt is instantaneously transported to the oceans, which presented an incomplete picture. Therefore, understanding how much of glacial meltwater is stored in lakes or groundwater underscores the importance of studying and monitoring glacial lakes worldwide. Data Sources:High Mountain Asia 8-meter Digital Elevation Model (DEM) derived from Optical Imagery, Version 1. The dataset is available from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). The DEM is realistically scaled (Vertical exaggeration 1x) in this visualization. The DEM is generated from very-high-resolution imagery from DigitalGlobe satellites (GEOEYE-1, QUICKBIRD-2, WORLDVIEW-1, WORLDVIEW-2, WORLDVIEW-3) during the period of 28 January 2002 to 24 November 2016.Citation: Shean, D. 2017. High Mountain Asia 8-meter DEM Mosaics Derived from Optical Imagery, Version 1. [Subset Used: HMA_DEM8m_MOS_20170716_tile-677 | subregion with extents 27.7394 E ]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/KXOVQ9L172S2. [Date Accessed: 06/17/2020]. Landsat 5, Landsat 7 and Landsat 8 data comprise the time series of Imja Lake for the period 1989-2019. Landsat 5 Thematic Mapper (TM) Level-1 Data Products (doi: https://doi.org/10.5066/F7N015TQ) were used for the period 1989-1999. The Landsat 5 Product Identifiers are:LT05_L1TP_140041_19891109_20170201_01_T1LT05_L1TP_140041_19900112_20170201_01_T1LT05_L1TP_140041_19910131_20170128_01_T1LT05_L1TP_140041_19921117_20170121_01_T1LT05_L1TP_140041_19931120_20170116_01_T1LT05_L1TP_140041_19941022_20170111_01_T1LT05_L1TP_140041_19951009_20170106_01_T1LT05_L1TP_140041_19961112_20170102_01_T1LT05_L1TP_140041_19970216_20170101_01_T1LT05_L1TP_140041_19981102_20161220_01_T1LT05_L1TP_140041_19990427_20161219_01_T1Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1 Data Products (doi: https://doi.org/10.5066/F7WH2P8G) were used for the period 2000-2012. The Landsat 7 Product Identifiers are:LE07_L1TP_140041_20001030_20170209_01_T1LE07_L1TP_140041_20011017_20170202_01_T1LE07_L1TP_140041_20021020_20170127_01_T1LE07_L1TP_140041_20030124_20170126_01_T1LE07_L1TP_140041_20041110_20170117_01_T1LE07_L1TP_140041_20051113_20170112_01_T1LE07_L1TP_140041_20060116_20170111_01_T1LE07_L1TP_140041_20070103_20170105_01_T1LE07_L1TP_140041_20081020_20161224_01_T1LE07_L1TP_140041_20091023_20161217_01_T1LE07_L1TP_140041_20101026_20161212_01_T1LE07_L1TP_140041_20111013_20161206_01_T1LE07_L1TP_140041_20121015_20161127_01_T1Landsat 8 Operational Land Imagery (OLI) and Thermal Infrared Sensor (TIRS) Level-1 Data Products (doi: https://doi.org/10.5066/F71835S6) were used for the period 2013-2019. The Landsat 8 Product Identifiers are:LC08_L1TP_140041_20131010_20170429_01_T1LC08_L1TP_140041_20140927_20170419_01_T1LC08_L1TP_140041_20150930_20170403_01_T1LC08_L1TP_140041_20161018_20170319_01_T1LC08_L1TP_140041_20171021_20171106_01_T1LC08_L1TP_140041_20181024_20181031_01_T1LC08_L1TP_140041_20191112_20191115_01_T1**Draped over the High Mountain Asia 8-meter Digital Elevation Model (DEM) during the visualization.For the purposes of this data visualization the above Landsat data were processed and color-stretched. Bands 3-2-1 were used for Landsat 5 and 7 data. Bands 4-3-2 were used for Landsat 8 data. In addition, Landsat 7 and 8 data used pan-chromatic sharpening (Band 8). Landsat 5, Landsat 7 and Landsat 8 data courtesy of the U.S Geological Survey and NASA Landsat. Blue Marble: Next Generation was produced by Reto Stöckli, NASA Earth Observatory (NASA Goddard Space Flight Center). Citation: Reto Stöckli, Eric Vermote, Nazmi Saleous, Robert Simmon and David Herring. The Blue Marble Next Generation – A true color earth dataset including seasonal dynamics from MODIS, October 17, 2005.Global 30 Arc-Second Eleveation (GTOPO 30) from USGS. doi: https://doi.org/10.5066/F7DF6PQSShuttle Radar Topography Mission (SRTM) 1 Arc-Second Global. doi: https://doi.org/10.5066/F7PR7TFTNepal city labels and locations were created using Natural Earth 1:10m Cultural Vectors (Populated places database) and OpenStreetMap data.The rest of this webpage offers additional versions and visual material associated with the development of this data-driven visualization. Data visualization content in 9600x3240 resolution. This set of frames can be shown on 3x3 and 5x3 hyperwalls. A lower resolution preview movie is provided and it includes lines to illustrate the extents of the hyperwall screens. Zoom in to the region without the city names - for video editors who may want to fade the names out. Animated gif image of Imja Lake in 1989 and in 2019 using Landsat data.

Satellite data offers a broad, global view of land surface changes, but cloud cover interferes with collecting data. Landsat satellites provide observations every 16 days, and having two satellites reduces that to every 8 days. The European Space Agency Sentinel-2 satellites collect data in similar wavelengths and at a similar spatial resolution, enabling the data to be combined for even more observations. When harmonized into one data set, the result is global observations every two or three days at 30-meter resolution. Any application looking at very dynamic phenomena, where changes occur on the timescales of a few days or weeks, will benefit from the harmonized Landsat/Sentinel dataset. For example, crop condition and area, burned area, or surface water extent. Also, this will benefit any application where short-term environmental conditions (like drought) have a rapid impact on ecosystems. This visualization depicts the orbits and data swaths of the Landsat 8, Landsat 9, Sentinel 2a, and Sentinel 2b satellites. The satellites appear one at a time with their respective data swaths. As time progresses throughout the visualization, the satellites ‘paint’ the globe with imagery to show how the four spacecraft work together to build a complete picture of the Earth. This visualization depicts the orbits and data swaths of the Landsat 8 and Landsat 9 satellites. The satellites appear (beginning first with Landsat 8) with their respective data swaths. As time progresses throughout the visualization, the satellites ‘paint’ the globe with imagery to show how the two spacecraft work together to build a complete picture of the Earth. This visualization depicts the orbits and data swaths of the Landsat 8, Landsat 9, Sentinel 2a, and Sentinel 2b satellites. The satellites appear (beginning first with Landsat 8) with their respective data swaths. As time progresses throughout the visualization, the satellites ‘paint’ the globe with imagery to show how the four spacecraft work together to build a complete picture of the Earth.

The NASA/USGS Landsat 8 mission has allowed new views of the Earth’s glaciers. By tracking displacement of local surface features through the seasons on outlet glaciers from the large ice sheets, researchers from the University of Alaska, the University of Bristol, and the University of Colorado have been able to show that each glacier around Greenland has a unique pattern of flow variation through the seasons. Seasonal variations, seen in this animation on the lower 25 kilometers of Heimdal Glacier in southeast Greenland, are caused by a combination of processes. For Heimdal, the largest forcing for flow variation is likely the input of increasing amounts of surface melt water through the Spring and Summer, but there is also an interplay between calving of ice from the end of the glacier, flow acceleration as shown in the animation, and thinning of the ice due to the extra stretching from the faster flow. By measuring these changes in flow on seasonal timescales, scientists can develop a better understanding of what controls the flow of these glaciers where they meet the ocean. This understanding will improve our ability to anticipate flow responses of these systems in a warming climate. This visualization shows the seasonal ice velocity on the Heimdal Glacier in Greenland between October 2013 and October 2016. The color of the flow vectors represent the speed of the flow, with purple representing the slow moving ice and red showing the fast ice. The color scale is displayed in the lower left corner.This video is also available on our YouTube channel. The above visualization without the date or color bar. The date and colorbar with transparency.

Water managers in 15 states across the U.S. currently use Metric technology to track agricultural water use. Metric measures evapotranspiration (ET)—the amount of water evaporating from the soil and transpiring from a plant’s leaves. The thermal band data on Landsat satellites allows water specialists to measure ET. This process cools the plant down so irrigated farm fields appear cooler (bluer) in Landsat satellite images. The latest evolution of METRIC technology—an application called EEFLUX, will allow anyone in the world to produce field-scale maps of water consumption, including on mobile devices. Metric was developed in the early 2000s and to date EEFLUX has been introduced to the California Department of Water Resources, the California Water Control Board, and the World Bank. Animation begins with a wide view of the entire United States and then zooms down to an area in Nebraska where water usage studies have been done using Landsat-8 satellite data. The camera slowly pans across the area first showing true color Landsat-8 data, then transitioning to temperature data (in shades of orange and violet), then to ETRF (shades of green), ending with an extrusion of water use data (shades of blue) where the camera pulls back to show the entire area of interest. Print resolution still of the Nebraska water use study area. Landsat-8 temperature colorbar. Evapotranspiration Ratio (ETrF) colorbar. Water usage colorbar.

The Landsat Data Continuity Mission (LDCM), also to be named Landsat 8 after its scheduled launch in February 2013, will be the eighth in the series of Landsat satellites. Since 1972, Landsat satellites have been observing and measuring Earth s continental and coastal landscapes at 15 to 30 meter resolution, where human impacts and natural changes can be monitored and characterized over time.This animation portrays how the LDCM satellite will orbit the Earth 13 times per day at an altitude of 705 km collecting landcover data. With a cross-track width of 185 km, the satellite will completely cover the globe in a 16 day period compiling a total of 233 orbits. A day number and the elapsed time are shown to clearly depict the passage of time which starts slowly in the beginning and increases to day-by-day steps at the end of the animation. The terrain is exaggerated by 6 times during the first day portrayed, but is increased to 12 times when the camera pulls out to a global view. An artificial orbit trail is shown following the spacecraft to indicate its position when the satellite itself is too small to be visible. The composite animation of the Landsat Data Continuity Mission (LDCM) satellite orbiting the Earth, with the satellite, Earth, stars and day/time overlay.This video is also available on our YouTube channel. The composite animation with the satellite, Earth and stars. The Earth and satellite layer with transparency. The background star field The day/time overlay with transparency A still image of the LDCM satellite and the ground swath. Frame set designed for a 5x3 hyperwall.