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At least 217 records · Page 12

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NASA NTRS1982

Remote Sensing/gis Integration for Site Planning and Resource Management

The development of an interactive/batch gridded information system (array of cells georeferenced to USGS quad sheets) and interfacing application programs (e.g., hydrologic models) is discussed. This system allows non-programer users to request any data set(s) stored in the data base by inputing any random polygon's (watershed, political zone) boundary points. The data base information contained within this polygon can be used to produce maps, statistics, and define model parameters for the area. Present/proposed conditions for the area may be compared by inputing future usage (land cover, soils, slope, etc.). This system, known as the Hydrologic Analysis Program (HAP), is especially effective in the real time analysis of proposed land cover changes on runoff hydrographs and graphics/statistics resource inventories of random study area/watersheds.

Fellows, J. D.↗

NASA NTRS1983

Evaluation of LANDSAT-4 TM and MSS ground geometry performance without ground control

Techniques and software developed to characterize the Washington, D.C. scene were improved and are being systematically applied to an Imperial Valley, CA scene. Digital elevation files are being acquired. One hundred seventy-two tiepoints were located in the Imperial Valley scene. They were digitized from USGS maps to determine their lat-long coordinates. A least squares fit is currently being performed between line-sample image data and the lat-long positions of the tiepoints. Thematic mapper scanner sweeps were determined for the Imperial Valley P-data. VICAR jobs are currently under way to analyze sample-direction offsets between sweeps in the data, as well as band to band registration offsets. Tiepoint location is about to begin in the Harrisburg, PA scene.

Bryant, N. A.↗

NASA NTRS2003

Mars Data Visualization and E/PO with Marsoweb

Marsoweb is a collaborative web environment that has been developed for the Mars research community to better visualize and analyze Mars orbiter data. Its goal is to enable online data discovery by providing an intuitive, interactive interface to data from the Mars Global Surveyor and other orbiters. Recently Marsoweb has served a prominent role as a resource center for the site selection process for the Mars Explorer Rover 2003 missions. In addition to hosting a repository of landing site memoranda and workshop talks, it includes a Java-based interface to a variety of datamaps and images. This interface enables the display and numerical querying of data, and allows data profiles to be rendered from user-drawn cross-sections. High-resolution Mars Orbiter Camera (MOC) images (currently, over 100,000) can be graphically perused; browser-based image processing tools can be used on MOC images of potential landing sites. An automated VRML atlas allows users to construct 'flyovers' of their own regions-of-interest in 3D. These capabilities enable Marsoweb to be used for general global data studies, in addition to those specific to landing site selection. As of September 2002, over 70,000 distinct users from NASA, USGS, academia, and the general public have accessed Marsoweb.

Gulick, V. C.↗

NASA NTRS2012

Role of MODIS Vegetation Phenology Products in the ForWarn System for Monitoring of Forest Disturbances in the Conterminous United States

This presentation discusses MODIS vegetation phenology products used in the ForWarn Early Warning System (EWS) tool for near real time regional forest disturbance detection and surveillance at regional to national scales. The ForWarn EWS is being developed by the USDA Forest Service NASA, ORNL, and USGS to aid federal and state forest health management activities. ForWarn employs multiple historical land surface phenology products that are derived from MODIS MOD13 Normalized Difference Vegetation Index (NDVI) data. The latter is temporally processed into phenology products with the Time Series Product Tool (TSPT) and the Phenological Parameter Estimation Tool (PPET) software produced at NASA Stennis Space Center. TSPT is used to effectively noise reduce, fuse, and void interpolate MODIS NDVI data. PPET employs TSPT-processed NDVI time series data as an input, outputting multiple vegetation phenology products at a 232 meter resolution for 2000 to 2011, including NDVI magnitude and day of year products for seven key points along the growing season (peak of growing season and the minima, 20%, and 80% of the peak NDVI for both the left and right side of growing season), cumulative NDVI integral products for the most active part of the growing season and sequentially across the growing season at 8 day intervals, and maximum value NDVI products composited at 24 day intervals in which each product date has 8 days of overlap between the previous and following product dates. MODIS NDVI phenology products are also used to compute nationwide NRT forest change products refreshed every 8 days. These include percent change in forest NDVI products that compare the current NDVI from USGS eMODIS products to historical MODIS MOD13 NDVI. For each date, three forest change products are produced using three different maximum value NDVI baselines (from the previous year, three previous years, and all previous years). All change products are output with a rainbow color table in which forests with the most severe NDVI decreases are assigned hot colors (yellow to red) and forests with prominent NDVI increases are assigned cold colors (blue tones). All mentioned products have been integrated as data layers into ForWarn s geospatial data viewer known as the U.S. Forest Change Assessment Viewer (FCAV). The latter is used to view and assess the context of the mentioned forest change products with respect to ancillary data layers, such as land cover, elevation, hydrologic features, climatic data, storm data, aerial disturbance surveys, fire data, and land ownership. The FCAV also includes a temporal NDVI profiler for viewing phenological change in multi-year NDVI associated with known or suspected regionally apparent forest disturbances (e.g., from fire and insects). ForWarn forest change products have been used to detect, track, and assess several biotic and abiotic regional forest disturbance events across the country, including ephemeral and longer lasting damage from storms, drought, and insects. Such change products are most effective for viewing severe disturbances affecting multiple MODIS pixels. MODIS vegetation phenology products contribute vital current information on forest conditions to the ForWarn system and this role is expected to grow as these products are refined and derivative products are added.

Spruce, Joseph P.↗

NASA NTRS1974

The utility of ERTS-1 data for applications in land use classification

A comprehensive study has been undertaken to determine the extent to which conventional image interpretation and computer-aided (spectral pattern recognition) analysis techniques using ERTS-1 data could be used to detect, identify (classify), locate, and measure current land use over large geographic areas. It can be concluded that most of the level 1 and 2 categories in the USGS Circular no. 671 can be detected in the Houston-Gulf Coast area using a combination of both techniques for analysis. These capabilities could be exercised over larger geographic areas, however, certain factors such as different vegetative cover, topography, etc. may have to be considered in other geographic regions. The best results in identification (classification), location, and measurement of level 1 and 2 type categories appear to be obtainable through automatic data processing of multispectral scanner computer compatible tapes.

Dornbach, J. E.↗

NASA NTRSDate not supplied

Analyzing Federal Agency Earth Observation Needs: NASA’s 2022 Satellite Needs Working Group Assessment

Every two years, the National Aeronautics and Space Administration (NASA) leads an assessment of Federal civilian agency Earth observation needs submitted through the Satellite Needs Working Group (SNWG) survey. Nearly 30 agencies participated in the 2022 SNWG survey, submitting 115 high-priority satellite data needs that span Earth Science and represent a wide variety of potential applications for Earth observation data. Analysis of multiple SNWG survey cycles reveals trends in agency needs toward more frequent, higher resolution data that can inform agency decision-making. NASA and partners at the National Oceanic and Atmospheric Administration (NOAA) and U.S. Geological Survey (USGS) evaluated the agency surveys during an eight-month assessment period. Assessment teams comprised of subject matter experts, technology specialists, and agency managers conducted an in-depth interview with each submitting agency to fully understand the need and discuss relevant current and upcoming satellite missions. The teams then proposed over 100 potential solutions, or new activities that NASA, NOAA, and/or USGS could undertake to help meet agency needs. A few cross-cutting potential solutions that are projected to be most valuable to SNWG agencies are under consideration for implementation by NASA in the coming years.

Katrina Virts↗

NASA NTRS1984

An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms

The Bayesian maximum likelihood parametric classifier has been tested against the data-based formulation designated 'linear discrimination analysis', using the 'GLIKE' decision and "CLASSIFY' classification algorithms in the Landsat Mapping System. Identical supervised training sets, USGS land use/land cover classes, and various combinations of Landsat image and ancilliary geodata variables, were used to compare the algorithms' thematic mapping accuracy on a single-date summer subscene, with a cellularized USGS land use map of the same time frame furnishing the ground truth reference. CLASSIFY, which accepts a priori class probabilities, is found to be more accurate than GLIKE, which assumes equal class occurrences, for all three mapping variable sets and both levels of detail. These results may be generalized to direct accuracy, time, cost, and flexibility advantages of linear discriminant analysis over Bayesian methods.

Tom, C. H.↗

NASA NTRS2003

The Joint Agency Commercial Imagery Evaluation Team and Product Characterization Approach

The Joint Agency Commercial Imagery Evaluation (JACIE) team is a collaborative interagency group focused on the characterization of commercial remote sensing data products. The team members - the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA), and the U.S. Geological Survey (USGS) - each have a vested interest in the purchase and use of commercial imagery to support government research and operational applications. For both research and applications, commercial products must be well characterized for precision, accuracy, and repeatability. Since commercial systems are built and operated with no government insight or oversight, the JACIE team provides an independent product characterization of delivered image and image-derived end products. End product characterization differs from the systems calibration approach that is typically used with government systems, where detailed system design information is available. The product characterization approach addresses three primary areas of product performance: geopositional accuracy, image quality, and radiometric accuracy. The JACIE team utilizes well-characterized test sites to support characterization activities. To characterize geopositional accuracy, the team utilizes sites containing several "photo-identifiable" targets and compares their precisely known locations with those defined by the commercial image product. In the area of image quality, spatial response is characterized using edge targets and pulse targets to measure edge response and to estimate image modulation transfer function. Additionally, imagery is also characterized using the National Imagery Interpretability Rating Scale, a means of quantifying the ability to identify certain targets (e.g., rail-cars, airplanes) within an image product. Radiometric accuracy is characterized using reflectance-based vicarious calibration methods at several uniform sites. Each JACIE agency performs an aspect of product characterization based on its area of expertise, thus minimizing duplication of effort. The JACIE team collaborated to perform comprehensive characterization of products from Space Imaging Inc.'s IKONOS satellite and from DigitalGlobe's QuickBird satellite and is currently characterizing products from OrbImage s OrbView-3. JACIE assessments have resulted in several improvements to commercial image product quality and have enhanced working relationships between government and industry. Assessment results are presented at an annual JACIE High Spatial Resolution Commercial Imagery Workshop.

Zanoni, Vicki↗

NASA NTRS2009

Lunar Regolith Characterization for Simulant Design and Evaluation using Figure of Merit Algorithms

NASA's Marshall Space Flight Center (MSFC), in conjunction with the United States Geological Survey (USGS) and aided by personnel from the Astromaterials Research and Exploration Science group at Johnson Space Center (ARES-JSC), is implementing a new data acquisition strategy to support the development and evaluation of lunar regolith simulants. The first analyses of lunar regolith samples by the simulant group were carried out in early 2008 on samples from Apollo 16 core 64001/64002. The results of these analyses are combined with data compiled from the literature to generate a reference composition and particle size distribution (PSD)) for lunar highlands regolith. In this paper we present the specifics of particle type composition and PSD for this reference composition. Furthermore. we use Figure-of-Merit (FoM) routines to measure the characteristics of a number of lunar regolith simulants against this reference composition. The lunar highlands regolith reference composition and the FoM results are presented to guide simulant producers and simulant users in their research and development processes.

Schrader, Christian M.↗

NASA NTRS1975

CARETS: A prototype regional environmental information system. Volume 12: User evaluation of experimental land use maps and related products from the central Atlantic test site

The user interaction and evaluation phase of the USGS/NASA Central Atlantic Regional Ecological Test Site was designed to obtain the input of local, regional, State, and Federal agency users of land-resource information into the development, of a regional information system; to provide users with assistance and data resulting from CARETS research; and to have user. organizations evaluate to what extent the CARETS products meet their needs. The evaluation of CARETS land use and related products revealed that most user agencies interviewed, at all governmental levels, require more detailed data than that provided by the CARETS project. Few agencies found utility in the generalized ERTS Level I land--use maps. Level II data, though reported valuable by several users, was generally considered of secondary utility by most users. The products considered most useful by users at all levels were the high--altitude color-infrared photographs and the USGS orthophotoquads. Recommendations resulting from the evaluation reflect the need to establish a flexible and reliable system for providing more detailed raw and processed land-resource information as well as the need to improve the methods of making information available to users.

Land-use planning↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.

Funk, Chris↗

NASA NTRS2015

Improved Rainfall Estimates and Predictions for 21st Century Drought Early Warning

Funk, Chris↗

NASA NTRS1984

An interactive technique to generate digital elevation data using a vidicon camera

A technique is proposed for the semi-automatic digitization of topographic maps using a vidicon camera interfaced with a mini-computer system. The required input is a black and white map that shows only contour lines and corresponding elevation values such as the advance prints of 7.5 minute USGS topographic sheets. A set of image processing algorithms is implemented on an image of the contour map acquired by a vidicon camera. The system developed performs extraction of contour lines, tagging elevation values and subsequent interpolation of elevations to produce, in 5 sec. x 5 sec. intervals of latitude and longitude, digital elevation matrices. The interpolation of elevations for non-contour points is achieved using a steepest descent algorithm. A communications capability allows the final data to be transferred over telephone lines to an off-site unit such as the floppy disk or a micro-computer.

Sircar, J. K.↗

NASA NTRS2022

Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on-ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor-based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1–1.1 and 0.3–0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14–35 mm mo^(−1) and 10–82 g m^(-2)mo^(−1) are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation-informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states.

Earth System Models↗

NASA NTRS2004

Network Performance Measurements for NASA's Earth Observation System

NASA's Earth Observation System (EOS) Project studies all aspects of planet Earth from space, including climate change, and ocean, ice, land, and vegetation characteristics. It consists of about 20 satellite missions over a period of about a decade. Extensive collaboration is used, both with other US. agencies (e.g., National Oceanic and Atmospheric Administration (NOA), United States Geological Survey (USGS), Department of Defense (DoD), and international agencies (e.g., European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA)), to improve cost effectiveness and obtain otherwise unavailable data. Scientific researchers are located at research institutions worldwide, primarily government research facilities and research universities. The EOS project makes extensive use of networks to support data acquisition, data production, and data distribution. Many of these functions impose requirements on the networks, including throughput and availability. In order to verify that these requirements are being met, and be pro-active in recognizing problems, NASA conducts on-going performance measurements. The purpose of this paper is to examine techniques used by NASA to measure the performance of the networks used by EOSDIS (EOS Data and Information System) and to indicate how this performance information is used.

Loiacono, Joe↗

NASA NTRS2009

Large Area Scene Selection Interface (LASSI). Methodology of Selecting Landsat Imagery for the Global Land Survey 2005

The Global Land Survey (GLS) 2005 is a cloud-free, orthorectified collection of Landsat imagery acquired during the 2004-2007 epoch intended to support global land-cover and ecological monitoring. Due to the numerous complexities in selecting imagery for the GLS2005, NASA and the U.S. Geological Survey (USGS) sponsored the development of an automated scene selection tool, the Large Area Scene Selection Interface (LASSI), to aid in the selection of imagery for this data set. This innovative approach to scene selection applied a user-defined weighting system to various scene parameters: image cloud cover, image vegetation greenness, choice of sensor, and the ability of the Landsat 7 Scan Line Corrector (SLC)-off pair to completely fill image gaps, among others. The parameters considered in scene selection were weighted according to their relative importance to the data set, along with the algorithm's sensitivity to that weight. This paper describes the methodology and analysis that established the parameter weighting strategy, as well as the post-screening processes used in selecting the optimal data set for GLS2005.

Franks, Shannon↗

NASA NTRS2015

Land Processes Distributed Active Archive Center (LP DAAC) 25th Anniversary Recognition "A Model for Government Partnerships". LP DAAC "History and a Look Forward"

This presentation discusses 25 years of interactions between NASA and the USGS to manage a Land Processes Distributed Active Archive Center (LPDAAC) for the purpose of providing users access to NASA's rich collection of Earth Science data. The presentation addresses challenges, efforts and metrics on the performance.

Land Remote Sensing↗

NASA NTRS2020

An Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant damage to infrastructure and economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures. Situational awareness in near real-time is essential to ensure community resilience and enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions at regional to global scale. Given the global coverage of two operational flood models - GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the first component of this project first focuses on integrating the outputs from these two models to classify flood severity and send alerts based on potential for impacts similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes). The second component of the project focuses on using flood outputs derived from earth observation data to validate, update and add additional exposure and impact products to flood alerts. The flood impacts and severity information will be disseminated as alerts and maps through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. In this presentation, the following objectives will be covered: (i) the effective integration of the flood models, (ii) accuracy of the flood model outputs (flood extent and depth) for specific flood events both in the United States and globally in comparison with each other and earth observation data.

Glasscoe, Margaret↗