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NASA NTRS2018

Serving NASA GES DISC Multi-Spatiotemporal Earth Science Data to the GIS community

NASA Earth Science (ES) data is essential to a wide range of GIS research and applications. However, for many GIS users, searching, accessing, using and analyzing NASA ES data can be of a great challenge- ranging from the sheer data volumes, types of science parameters, and to the complexity of data encoding formats. As one of the twelve NASA Science Mission Directorate (SMD) Data Centers, Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes petabytes of ES parameters covering atmosphere, land, and ocean fields. Most data are multidimensional and multi-spatiotemporal in nature and are encoded in different science data formats (e.g, HDF, HDF-EOS, netCDF, GRIB, binary), which usually contain multiple variables and different metadata information. By far, GES DISC has been developing a number of services and online tools to help GIS users to easily explore our data products. In this presentation, we will describe our ArcGIS-based data accessing and visualization services and portals, which allow users directly exploring the multi-spatiotemporal ES data in ArcGIS clients without having to pre-download/import the data. The ArcGIS services are also compliant with the Open Geospatial Consortium (OGC) Web Coverage Service (WCS) and Web Map Service (WMS) protocols and can be accessed by any other WCS/WMS clients to get customized GES DISC EO data on-the-fly from such services.

Wei, Jennifer↗

NASA NTRSDate not supplied

Visualization and Analysis of Near Real-Time Global Cloud Composites (GCC): Integration in a Geospatial Web Mapping Application

The NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) team provides tools for retrieving cloud information from operational and research meteorological imager data. LEO and GEO satellite imagers are used for fusing hourly mosaics which create the Global Cloud Composite (GCC). The GCC provides global cloud products with a low latency which can help satisfy the growing needs of the research, modeling and business communities. The Esri ArcGIS system is used to transform the GCC data into Analysis Ready Data (ARD) which can be utilized for a variety of visualization and analysis activities in support of weather diagnoses/forecasting, Earth Sciences remote sensing applications, and disaster management. Additionally, the data is enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services to be consumed via API within a web mapping application. We will present a preview of the new interactive SatCORPS GCC web mapping application and its capabilities in allowing users to access and use GIS data within it.

Web Mapping Application↗

NASA NTRS2015

Improving the Accessibility and Use of NASA Earth Science Data

Many of the NASA Langley Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) multidimensional tropospheric and atmospheric chemistry data products are stored in HDF4, HDF5 or NetCDF format, which traditionally have been difficult to analyze and visualize with geospatial tools. With the rising demand from the diverse end‐user communities for geospatial tools to handle multidimensional products, several applications, such as ArcGIS, have refined their software. Many geospatial applications now have new functionalities that enable the end user to: Store, serve, and perform analysis on each individual variable, its time dimension, and vertical dimension. Use NetCDF, GRIB, and HDF raster data formats across applications directly. Publish output within REST image services or WMS for time and space enabled web application development. During this webinar, participants will learn how to leverage geospatial applications such as ArcGIS, OPeNDAP and ncWMS in the production of Earth science information, and in increasing data accessibility and usability.

Tisdale, Matthew↗

NASA NTRSDate not supplied

SatCORPS Global Cloud Composite (GCC): the Design and Delivery of A High Quality, High Resolution, Global Cloud Product Available in Near-Real Time

The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) supports the development of an analysis ready and cloud-optimized data transformation pipeline and geospatial service enablement of a global cloud composite (GCC) product derived from global geostationary satellite imagery. This geospatial service will be available at high temporal and spatial resolution via the SatCORPS web mapping application for visualization and analysis as well as direct ingestion to common geospatial software and custom programming. The resulting global cloud composite products from the processing pipeline can then be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. Near real time global observations are created through the composition of five geostationary satellites that provides modelling and forecasting communities with the capability to provide high quality and timely information to start the projection process. The Global Cloud Composite product combines information from geostationary satellites, GOES-16, GOES-17, Himawari-8, Meteosat-11 and Meteosat-9 to create a single global composite netcdf file and images using the different products within the netcdf file. The SatCORPS team, though our Global Cloud Composite (GCC) product and web-based visualization tools including Geographical Information System (GIS) services provide near real time global cloud product information to both automated processes and traditional web users that is timely and high quality derived from geostationary satellites. The Global Cloud Composite product takes advantage of the scalable processing resources provided by the AWS batch service to provide new composites every thirty minutes. Because information from each of the low earth orbiting satellites is available on schedules tuned to the specific satellite, the processing algorithm temporally composites the final dataset as each satellite’s information becomes available. The SatCORPS team has leveraged our experience using Amazon Web Services (AWS) to build a low latency high availability tool that allows end users both human and automated to acquire high quality and high-resolution Geostationary Earth Orbiting (GEO) information at zero cost to the end user. This presentation will describe how we architected and implemented the service as well as lessons learned based on our experiences both developing and operating the system. The lessons learned include how we integrated multiple services including Amazon Batch, Amazon S3 and Amazon Lambda service to create a low cost but high-performance processing system that is capable of identifying and processing the most appropriate satellite overpass information into global cloud composites. We will also describe our web-based tools including our Geographic Information System that can be used for visualization and analysis. The products from the processing can be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. The SatCORPS Global Composite Cloud product provides sophisticated global composited cloud research products with very low latency that we see that as filling a rapidly growing need in the research and modelling community with no up-front nor ongoing costs associated with downloading or using the information.

AWS AMCE SMCE GCC SATCORPS GLOBAL CLOUD COMPOSITE ↗

NASA NTRS2023

Southern Idaho Ecological Conservation: Investigating the Impact of Targeted Grazing to Improve Wetland Habitat in the Sterling Wildlife Management Area

Wetland ecosystems are vital for biodiversity conservation and ecosystem services. The Sterling Wildlife Management Area in Bingham County, Idaho, has management concerns about decadent and accumulated vegetation growth encroaching on wetland habitat, which presents challenges for wildlife, decreases biodiversity, and limits public access. Targeted grazing has been proposed as a sustainable alternative to chemical herbicides or burning. Land managers introduced targeted cattle grazing in January 2021 to reduce biomass. NASA DEVELOP partnered with the Idaho Department of Fish and Game to determine the impact of grazing using NASA Earth observations from Landsat 8 Operational Land Imager (OLI) in Google Earth Engine (GEE). Images were processed with TerrSet’s Land Change Modeler and ArcGIS Pro’s Change Detection Wizard to understand land changes following grazing. A Normalized Difference Vegetation Index (NDVI) analysis was performed to assess impacts on vegetation productivity and compare variance in biomass before and after grazing. A Normalized Difference Water Index (NDWI) was used to compare changes in the wetland and its vegetation content to evaluate the suitability of the area for migratory birds post-grazing. Results showed a decrease in the vegetation index and an increase in the water index postgrazing. The DEVELOP team’s analysis suggests that grazing helps break down thick, senesced vegetation and increase soil moisture. Providing a workflow model will aid partners in continuing to monitor this management area and other management areas across the state.

change detection↗

NASA NTRS2002

The NASA John C. Stennis Environmental Geographic Information System

In addition to the Environmental Geographic Information System (EGIS) presentation, we will present two live demonstrations of a portion of the work being performed in support of environmental operations onsite and NASA-wide. These live demonstrations will showcase the NASA EGIS database through working versions of two software packages available from Environmental Systems Research Institute, Inc. (ESRI, Inc.): ArcIMS 3.0 and either ArcView 3.2a or ArcGIS 8.0.2. Using a standard web browser, the ArcIMS demo will allow users to access a project file containing several data layers found in the EGIS database. ArcIMS is configured so that a single computer can be used as the data server and as the user interface, which allows for maximum Internet security because the computer being used will not actually be connected to the World Wide Web. Further, being independent of the Internet, the demo will run at an increased speed. This demo will include several data layers that are specific to Stennis Space Center. The EGIS database demo is a representative portion of the entire EGIS project sent to NASA Headquarters last year. This demo contains data files that are readily available at various government agency Web sites for download. Although these files contain roads, rails, and other infrastructure details, they are generalized and at a small enough scale that they provide only a general idea of each NASA center's surroundings rather than specific details of the area.

Cohan, Tyrus↗

NASA NTRS2005

Geographic Information Systems and Martian Data: Compatibility and Analysis

Planning future landed Mars missions depends on accurate, informed data. This research has created and used spatially referenced instrument data from NASA missions such as the Thermal Emission Imaging System (THEMIS) on the Mars Odyssey Orbiter and the Mars Orbital Camera (MOC) on the Mars Global Surveyor (MGS) Orbiter. Creating spatially referenced data enables its use in Geographic Information Systems (GIS) such as ArcGIS. It has then been possible to integrate this spatially referenced data with global base maps and build and populate location based databases that are easy to access.

Jones, Jennifer L.↗

NASA NTRS2005

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Kocurek, Michael J.↗

NASA NTRS2007

SSC Geopositional Assessment of the Advanced Wide Field Sensor

The objective is to provide independent verification of IRS geopositional accuracy claims and of the internal geopositional characterization provided by Lutes (2005). Six sub-scenes (quads) were assessed; Three from each AWiFS camera. Check points were manually matched to digital orthophoto quarter quadrangle (DOQQ) reference (assumed accuracy approx. 5 m, RMSE) Check points were selected to meet or exceed Federal Geographic Data Committee's guidelines. Used ESRI ArcGIS for data collection and SSC-written MATLAB scripts for data analysis.

Ross, Kenton↗

NASA NTRS2008

Global Geologic Map of Europa

Europa, with its indications of a sub-ice ocean, is of keen interest to astrobiology and planetary geology. Knowledge of the global distribution and timing of Europan geologic units is a key step for the synthesis of data from the Galileo mission, and for the planning of future missions to the satellite. The first geologic map of Europa was produced at a hemisphere scale with low resolution Voyager data. Following the acquisition of higher resolution data by the Galileo mission, researchers have identified surface units and determined sequences of events in relatively small areas of Europa through geologic mapping using images at various resolutions acquired by Galileo's Solid State Imaging camera. These works provided a local to subregional perspective and employed different criteria for the determination and naming of units. Unified guidelines for the identification, mapping and naming of Europan geologic units were put forth by and employed in regional-to-hemispheric scale mapping which is now being expanded into a global geologic map. A global photomosaic of Galileo and Voyager data was used as a basemap for mapping in ArcGIS, following suggested methodology of all-stratigraphy for planetary mapping. The following units have been defined in global mapping and are listed in stratigraphic order from oldest to youngest: ridged plains material, Argadnel Regio unit, dark plains material, lineaments, disrupted plains material, lenticulated plains material and Chaos material.

Doggett, T.↗

NASA NTRS2008

Geologic Mapping of Athabasca Valles

Two factors drive us to map the Athabasca Valles area in unusual detail: (1) the extremely well-preserved and exposed surface morphologies and (2) the extensive high resolution imaging. In particular, the near-complete CTX coverage of Athabasca Valles proper and the extensive coverage of its surroundings have been invaluable. The mapping has been done exclusively in ArcGIS, using individual CTX, THEMIS VIS, and MOC frames overlying the THEMIS IR daytime basemap. MOLA shot points and gridded DTMs are also included. It was found that CTX images processed through ISIS are almost always within 300 m of the MOLA derived locations, and usually within tens of meters, with no adjustments to camera pointing. THEMIS VIS images appear to be systematically shifted to the southwest of their correct positions and MOC images are often kilometers off. The good SNR and minimal artifacts make the CTX images vastly more useful than the THEMIS VIS or MOC images. The bulk of the mapping was done at 1:50,000 scale on CTX images. In more complex areas, mapping at 1:24,000 proved necessary. The CTX images were usually simultaneously viewed on a second monitor using the ISIS3 qview program to display the full dynamic range of the CTX data. Where CTX data was not available, mapping was often done at 1:100,000 and most contacts are mapped as approximate.

Keszthelyi, L. P.↗

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 NTRS2010

Volcanism on Io: Results from Global Geologic Mapping

We have completed a new 1:15,000,000 global geologic map of Jupiter's volcanic moon, Io, based on a set of 1 km/pixel combined Galileo- Voyager mosaics produced by the U.S. Geological Survey. The map was produced over the last three years using ArcGIS(TM) software, and has undergone peer-review. Here we report some of the key results from our global mapping efforts, and how these results relate to questions regarding the volcano-tectonic evolution of Io.

Williams, David A.↗

NASA NTRS2011

Monitoring Areal Snow Cover Using NASA Satellite Imagery

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Harshburger, Brian J.↗

NASA NTRS2011

Use of Remote Sensing/Geographical Information Systems (RS/GIS) to Identify the Distributional Limits of Soil-Transmitted Helminths (STHs) and Their Association to Prevalence of Intestinal Infection in School-Age Children in Four Rural Communities in Boaco, Nicaragua

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

Moreno, Max J.↗

NASA NTRS2012

Geologic Mapping of the Olympus Mons Volcano, Mars

We are in the third year of a three-year Mars Data Analysis Program project to map the morphology of the Olympus Mons volcano, Mars, using ArcGIS by ESRI. The final product of this project is to be a 1:1,000,000-scale geologic map. The scientific questions upon which this mapping project is based include understanding the volcanic development and modification by structural, aeolian, and possibly glacial processes. The project s scientific objectives are based upon preliminary mapping by Bleacher et al. [1] along a approx.80-km-wide north-south swath of the volcano corresponding to High Resolution Stereo Camera (HRSC) image h0037. The preliminary project, which covered approx.20% of the volcano s surface, resulted in several significant findings, including: 1) channel-fed lava flow surfaces are areally more abundant than tube-fed surfaces by a ratio of 5:1, 2) channel-fed flows consistently embay tube-fed flows, 3) lava fans appear to be linked to tube-fed flows, 4) no volcanic vents were identified within the map region, and 5) a Hummocky unit surrounds the summit and is likely a combination of non-channelized flows, dust, ash, and/or frozen volatiles. These results led to the suggestion that the volcano had experienced a transition from long-lived tube-forming eruptions to more sporadic and shorter-lived, channel-forming eruptions, as seen at Hawaiian volcanoes between the tholeiitic shield building phase (Kilauea to Mauna Loa) and alkalic capping phase (Hualalai and Mauna Kea).

Bleacher, J. E.↗

NASA NTRS2012

Alabama Disasters: Leveraging NASA EOS to Explore the Environmental and Economic Impact of the April 27 Tornado Outbreak

The disastrous tornado outbreak in Alabama on April 27, 2011 greatly impacted the economy of the state. On record, the tornado outbreak was the second deadliest tornado outbreak in U.S. When considering the agricultural and value-added activities such as food and timber processing, farm inputs, manufacturing, transportation, and retail sales, the dollar value of Alabama agribusiness annually exceeds $40 billion (NASS, 2011). This research aims to examine how the timber and agriculture damage affected the state economy of Alabama and will be used to aid in long-term economic recovery. ASTER imagery was used along with ground-truthed NASS (National Agriculture Statistics Service) crop location records to verify the economic impact tornadoes had on the agricultural economy of the state. This swath damage can be calculated by correlating tornado path with NASS statistics on crop yield, precisely showing the fields affected and dollars lost to this disaster. Not only can this be executed manually using ENVI and ArcGIS, but also through the use of Python, a programming language that has the ability to automate the process, creating a product for initial damage assessment.

Herdy, Claire↗

NASA NTRS2012

Alabama Disasters: Leveraging NASA EOS to Explore the Environmental and Economic Impact of the April 27 Tornado Outbreak

Herdy, Claire↗