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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

A Web Architecture to Geographically Interrogate CHIRPS Rainfall and eMODIS NDVI for Land Use Change

Monitoring of rainfall and vegetation over the continent of Africa is important for assessing the status of crop health and agriculture, along with long‐term changes in land use change. These issues can be addressed through examination of long‐term precipitation (rainfall) data sets and remote sensing of land surface vegetation and land use types. Two products have been used previously to address these goals: the Climate Hazard Group Infrared Precipitation with Stations (CHIRPS) rainfall data, and multi‐day composites of Normalized Difference Vegetation Index (NDVI) from the USGS eMODIS product. Combined, these are very large data sets that require unique tools and architecture to facilitate a variety of data analysis methods or data exploration by the end user community. To address these needs, a web‐enabled system has been developed to allow end‐users to interrogate CHIRPS rainfall and eMODIS NDVI data over the continent of Africa. The architecture allows end‐users to use custom defined geometries, or the use of predefined political boundaries in their interrogation of the data. The massive amount of data interrogated by the system allows the end‐users with only a web browser to extract vital information in order to investigate land use change and its causes. The system can be used to generate daily, monthly and yearly averages over a geographical area and range of dates of interest to the user. It also provides analysis of trends in precipitation or vegetation change for times of interest. The data provided back to the end‐user is displayed in graphical form and can be exported for use in other, external tools. The development of this tool has significantly decreased the investment and requirements for end‐users to use these two important datasets, while also allowing the flexibility to the end‐user to limit the search to the area of interest.

Burks, Jason E.↗
NASA NTRS1982

Merging LANDSAT Derived Land Covers into Quad-referenced Geographic Information Systems

An approach for merging multiscene LANDSAT data bases into existing geographic information systems having 5-second or smaller cells is described. The approach uses the output from the State of Maryland's UNIVAC 1180-based LANDSAT classification program ASTEP (Algorithm Simulation Test and Evaluation) developed by NASA. The structure of the technique was designed to address the problems that emerged as part of the LANDSAT classification of the 64,000 square mile Chesapeake water shed involving twelve scenes. The removal of overlap among adjacent scenes, the crossreferencing of ground control points, and the isolation of the appropriate pixels from the LANDSAT data base for subsequent positioning into a file containing ancillary data referenced to a specific USGS 7 1/2 minute quadrangle sheet are described. Examples illustrate the clustering of classified LANDSAT pixels to define the dominant land use for each of 8,100 cells within a series of quadrangle sheets distributed over the State of Maryland. The approach uses a hard copy terminal tied to an ASTEP algorithm through telephone lines. A coordinate digitizing board for inputing the position of ground control points is also valuable, although manual measurements are possible. The approach is quite efficient and should be especially attractive for use on regional scale studies.

White, J. M.↗
NASA NTRS1986

The effect of Thematic Mapper spectral properties on land cover mapping for hydrologic modeling

The accuracy of unsupervised land-cover classification from all seven Landsat TM bands and from six combinations of three or four bands is evaluated using images of the Clinton River Basin, a suburban watershed near Detroit. Data from aerial TMS photography, USGS topographic maps, and ground surveys are employed to determine the classification accuracy. The mapping accuracy of all seven bands is found to be significantly better (6 percent overall, 12 percent for residential areas, and 13 percent for commercial districts) than that with bands 2, 3, and 4; but almost the same accuracy is obtained by including at least one band from each major spectral region (visible, NIR, and mid-IR).

Gervin, J. C.↗
NASA NTRS1994

Mesoscale roughness of Venus

The global distribution of multi-kilometer (approx. 9 km) length scale 'roughness' (hereafter mesoscale roughness or MR) on Venus can be estimated from the Magellan global altimetry dataset (GxDR) and then compared with MR data derived for Earth from 5' ETOP5 data and for Mars (from USGS Mars DTM dataset). The mesoscale roughness parameter (MR) represents the RMS variance in meters of the actual planetary surface topography relative to the best fitting tangent plane defined on the basis of a 3x3 pixel sliding window. The best-fit plane was computed using a least-squares solution which minimizes delta H, the sum of the squares of the differences between the 9 local elevation values (H(sub i)), and the elevation of best-fit plane at the same grid location. Using the best-fit plane and delta H, we have computed the RMS 'roughness' var(delta R), where this parameter is always minimized on the basis of its calculation using least squares. We have called this 'ruggedness' parameter the Mesoscale Roughness (MR) because it is directly related to the high-frequency variance of topography after mesoscale slopes and tilts (i.e., for Venus, the baseline over which MR is computed (dx) is approx. 8.8 km and dx for Earth is approx. 9.3 km) are removed. As such, MR represents the degree to which a planetary surface is more rugged than approximately 10 km scale facets or tilts. It should not be confused with the radar 'RMS Roughness' parameter computed at 0.1 to 10 m length scales on the basis of the Magellan radar altimeter echo. We will use our MR parameter to investigate the global ruggedness properties of Venus as they relate to geological provinces and in comparison with the spatial pattern of MR for Earth and Mars.

Garvin, J. B.↗
NASA NTRS2009

Lunar Regolith Characterization for Simulant Design and Evaluation

NASA's Marshall Space Flight Center (MSFC), in conjunction with the United States Geological Survey (USGS), is implementing a new data acquisition strategy to support the development and evaluation of lunar regolith simulants. The objective is to characterize the variance in particle composition, size, shape, and bulk density of the lunar regolith. Apollo drive and drill cores are the preferred samples as they allow for investigation of variation with depth, and many proposed operations on the moon will involve excavation of lunar regolith to depths of at least tens of centimeters. Multiple Apollo cores will be sampled multiple times along their vertical axes and analyzed. This will permit statistical statements about variation both within a core, between closely spaced cores, and between distant areas.

Schrader, Christian M.↗
NASA NTRSDate not supplied

Can Tracked Great Frigatebirds (Fregata minor) be Used to Measure the Dynamics of the Planetary Boundary Layer Height?

Observing the dynamics of the planetary boundary layer (PBL) will require creative combinations of space-based and in-situ measurements. Animals fitted with biologgers have been used to gather data and parameterize physical models in various settings and may be a useful addition to the suite of space-based measurements that NASA is considering. For example, great frigatebirds (Fregata minor) generally fly below 700m but have been shown to routinely climb up to 2,000 meters during the day, occasionally reaching heights over 4,000 meters. This suggests that they may be tracking boundary layer height dynamics as they soar and glide on air currents with little energetic cost. We show here that the high-altitude climbing heights of great frigatebirds carrying GPS/accelerometer biologgers (e-obs GmbH Bird Solar 15g) at Palmrya Atoll, Pacific Ocean, are consistent with the long-term climatology (2006-2019) of the local planetary boundary layer height, and that these soaring heights can be reliably extracted from the tag data. Additional study is needed to determine how well movement data from tracked birds can constrain PBL height, but this work demonstrates the potential for animal-borne sensors to complement space-based measurements. Biologging data were contributed by the USGS and the Nature Conservancy's Palmyra Bluewater Research (PBR) study and analyzed as part of NASA’s Internet of Animals project.

Planetary Boundary Layer Height↗
NASA NTRS1999

Landsat-7 Mission and Early Results

The Landsat-7 mission has the goal of acquiring annual data sets of reflective band digital imagery of the landmass of the Earth at a spatial resolution of 30 meters for a period of five years using the Enhanced Thematic Mapper Plus (ETM+) imager on the Landsat-7 satellite. The satellite was launched on April 15, 1999. The mission builds on the 27-year continuous archive of thematic images of the Earth from previous Landsat satellites. This paper will describe the ETM+ instrument, the spacecraft, and the ground processing system in place to accomplish the mission. Results from the first few months in orbit will be given, with emphasis on performance parameters that affect image quality, quantity, and availability. There will also be a discussion of the Landsat Data Policy and the user interface designed to make contents of the archive readily available, expedite ordering, and distribute the data quickly. Landsat-7, established by a Presidential Directive and a Public Law, is a joint program of the National Aeronautics and Space Administration (NASA) Earth Science Enterprise and the United States Geological Survey (USGS) Earth Resources Observing System (EROS) Data Center.

Dolan, S. Kenneth↗
NASA NTRS2004

The Lifecycle of NASA's Earth Science Enterprise Data Resources

A major endeavor of NASA's Earth Science Enterprise (ESE) is to acquire, process, archive and distribute data from Earth observing satellites in support of a broad set of science research and applications in the U. S. and abroad. NASA policy directives specifically call for the agency to collect, announce, disseminate and archive all scientific and technical data resulting from NASA and NASA-funded research. During the active life of the satellite missions, while the data products are being created, validated and refined, a number of NASA organizations have the responsibility for data and information system functions. Following the completion of the missions, the responsibility for the long-term stewardship of the ocean and atmospheric, and land process data products transitions to the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS), respectively. Ensuring that long-term satellite data be preserved to support global climate change studies and other research topics and applications presents some major challenges to NASA and its partners. Over the last several years, with the launch and operation of the EOS satellites and the acquisition and production of an unprecedented volume of Earth science data, the importance of addressing these challenges has been elevated. The lifecycle of NASA's Earth science data has been the subject of several agency and interagency studies and reports and has implications and effects on agency charters, policies and budgets and on their data system's requirements, implementation plans and schedules. While much remains to be done, considerable progress has been made in understanding and addressing the data lifecycle issues.

McDonald, Kenneth R.↗
NASA NTRS2023

The 50-Year Landsat Collection 2 Archive

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

Christopher J Crawford↗
NASA NTRS2019

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) K(d)(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and K(d)(490) in the range of 0.05–0.12 per m. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

ICESat-2↗
NASA NTRS1985

Three-dimensional adjustment of trilateration data

The three-dimensional locations of the monuments in the USGS Hollister trilateration network were adjusted to fit line length observations observed in 1977, using a Bayesian approach, and incorporating prior elevation estimates as data in the adjustment procedure. No significant discrepancies in the measured line lengths were found, but significant elevation adjustments (up to 1.85 m) were needed to fit the length data.

Sung, L.-Y.↗
NASA NTRS2020

Landsat 9: Empowering Open Science and Applications Through Continuity

The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.

Jeffrey G. Masek↗
NASA NTRS1975

Development of remote sensing techniques for assessment of hydrologic conditions in coal mining regions of Appalachia

In December of 1974 the John F. Kennedy Space Center, NASA, and the Water Resources Division, United States Geological Survey (USGS), acquired photographic, thermal, and multispectral data over the Cumberland region of eastern Tennessee. This data was effectively used to delineate ground water sources, and surface water runoff into river systems in the Cumberlands. The data, coupled with an overview of the area from the Earth Resources Technology Satellite (ERTS), could be useful in determining hydrologic conditions in coal mining regions of the Appalachians.

Pope, C. D.↗
NASA NTRS2007

Land Remote Sensing Overview

A general overview of the USGS land remote sensing program is presented. The contents include: 1) Brief overview of USGS land remote sensing program; 2) Highlights of JACIE work at USGS; 3) Update on NASA/USGS Landsat Data Continuity Mission; and 4) Notes on alternative data sources.

Byrnes, Ray↗
NASA NTRS2009

Mars Structural and Stratigraphic Mapping along the Coprates Rise

This geologic mapping project supports a topical study of structures in east Thaumasia associated with the Coprates rise. The study examines cuesta-like features on the east flank of the Coprates rise first identified by Saunders et al. [1]. Mapping combines detailed local stratigraphy, structural geology and topography. Hogbacks and cuestas indicate erosion of tilted rock units. The extent of the erosion will be determined in the course of the mapping. The region of interest lies along the eastern margin of Thaumasia bounded by latitudes -15 and -35 and longitudes 50 to 70 W (Figure 1). Three MTM geologic quadrangles are being compiled for publication by the USGS (-20057, -25057, -30057). All existing data sources are used including THEMIS, MOC, CTX, HiRISE, MOLA and gravity, as well as higher level data available through the PDS data nodes at ASU, UA and Washington University. The extremely valuable ASU JMARS tools are used for analysis of many of the data sets. ArcGIS software has been obtained and is being learned for the map compilation.

Saunders, R Stephen↗
NASA NTRS2019

Analysis of the 2019 Midwest US Flooding Using NASA Data

In the wake of Winter Storm Ulmer, an intense "bomb cyclone" that set preliminary low-pressure records, historic flooding occurred in 41 locations across the Midwest United States, causing many streams and rivers - including the Platte, Missouri, and Elkhorn Rivers - to rise to all-time record flood levels in Nebraska. Warm and dry conditions, along with 4-8 inches of snowfall, and over 1" of rain caused over 3-5" of storm water runoff from rapid melting.A rare confluence of circumstances produced the flooding, including extreme cold earlier in the winter that preserved a significant amount of snowfall, a thick layer of ice on waterways, and the large downpour of rain from Winter Storm Ulmer. Unusually warm air caused the snow and ice to rapidly melt, producing record amounts of runoff within a short period of time. This poster presents several data sets from NASA and the USGS (our partner agency) that can be used in analyzing floods and flood areas, including soil moisture, snowfall, floodwater areas, vicinity of urban areas and impervious surfaces, and proximity of strategic or critical infrastructure.

DAAC↗
NASA NTRSDate not supplied

Using Landsat 8 Satellite Imagery to Analyze Biogeochemical Constituents in the Waters of the San Francisco Bay Area and Beyond

The ocean's coastal zones play a key role in our planet's health and mitigate the adverse effects of climate change. Building on the success of NASA satellite imagery in mapping land cover changes around the globe in response to climate change, deforestation, and natural disasters, I have worked to determine how aquatic reflectance images from the Landsat 8 sensor could be correlated with biogeochemical constituents within the waters of the San Francisco Bay Area and California coastline. By analyzing Landsat 8 satellite imagery from the years 2013-2020 in collaboration with data from the United States Geological Survey (USGS), San Francisco Estuary Institute (SFEI), and universities involved with the Harmful Algal Bloom Monitoring and Alert Program (HABMAP), I investigated which areas of the San Francisco Bay and California coastline demonstrated significant correlations with values extracted from the satellite imagery. Using a Chlorophyll Index (CI) ratio calculated from two Landsat 8 satellite reflectance bands, it was found that there is a positive correlation between the CI and calculated oxygen, suspended particulate matter, and silicate from several USGS sampling points. In contrast, there is a significant negative correlation between the CI and salinity, nitrite, and phosphate at those same locations. Moreover, large suspected harmful algal blooms (HABs) along the California coastline seen in the Landsat 8 imagery were corroborated with HAB data from various institutions. Understanding the effectiveness of Landsat 8 aquatic reflectance images will allow scientists to predict HABs and other nutrient cycling that may be harmful to aquatic ecosystems around the world.

Landsat↗
NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗