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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Coronado National Memorial Disasters: Investigating Geohazards & Slope Failure Susceptibility Utilizing NASA Earth Observations

The Coronado National Memorial (CORO), located in Hereford, Arizona, is situated along the United States' southern border, featuring recently established but still incomplete border barrier roads. This landscape is inherently prone to geohazards, and debris flow due to the steep mountainous topography, complex terrain, monsoonal rains, and freeze/thaw action - and the new infrastructure has exhibited these processes in the form of rockfall, embankment failure, and debris flow. NASA DEVELOP partnered with CORO to conduct a feasibility assessment of Earth observations for identification of geohazards and slope failure susceptibility. Leveraging Earth observations (United States Geological Survey 3D Elevation Program Digital Elevation Model and a locally obtained Light Detection And Ranging-derived Digital Elevation Model) from 2019 to 2023, and geospatial datasets starting from 2008, the study was able to provide tools to determine the focus for damage mitigation, identify areas most susceptible to slope failures, and prioritize at risk assets through three products: change detection maps, slope failure susceptibility maps, and a slope failure prioritization model. With an emphasis on monitoring high-risk areas and prioritizing mitigation efforts, the project addresses a critical gap in remediation strategies and aims to enhance preservation and safety of the region. Results of this study found that (i) the most identifiable areas of change were the road cuts and debris directly adjacent to the roads created for border construction, (ii) areas of highest slope failure susceptibility are located in mountainous areas with erosive geology, and (iii) roads resulting from border construction have approximately twice the risk of slope failure as roads created by the national park service.

Geohazard↗
NASA NTRS1974

Coastal and estuarine studies with ERTS-1 and Skylab

Coastal vegetation, land use, current circulation, water turbidity, and ocean waste dispersion were studied by interpreting ERTS-1 and Skylab imagery with the help of ground truth collected during overpasses. Based on high-contrast targets such as piers and roads, the ERTS-1 multispectral scanner was found to have a resolution of 70-100 m, Skylab's S190A cameras about 20-40 m, and its S190B camera about 10-20 m. Important coastal land-use details can be more readily mapped using Skylab's imagery. On the other hand, the regular 18-day cycle of ERTS-1 allows observation of important man-made and natural changes, and facilitates collection of ground truth.

Klemas, V.↗
NASA NTRS1979

Map-guided interpretation of remotely-sensed imagery

A map-guided approach to interpretation of remotely sensed imagery is described, with emphasis on applications involving continuous monitoring of predetermined ground sites. Geometric correspondence between a sensed image and a symbolic reference map is established in an initial stage of processing by adjusting parameters of a sensor model so that image features predicted from the map optimally match corresponding features extracted from the sensed image. Information in the map is then used to constrain where to look in an image and what to look for. With such constraints, previously intractable remote sensing tasks can become feasible, even easy, to automate. Four illustrative examples are given, involving the monitoring of reservoirs, roads, railroad yards, and harbors.

Tenenbaum, J. M.↗
NASA NTRS2021

Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

We propose a framework for resilient autonomous navigation in perceptuallychallenging unknown environments with mobility-stressing elements such asuneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffsand holes, and narrow passages. Environments are GPS-denied and perceptuallydegradedwith variable lighting from dark to lit and obscurants (dust, fog, smoke).Lack of prior maps and degraded communication eliminates the possibility of prioror off-board computation or operator intervention. This necessitates real-time onboardcomputation using noisy sensor data. To address these challenges, we proposea resilient architecture that exploits redundancy and heterogeneity in sensing modalities.Further resilience is achieved by triggering recovery behaviors upon failure.We propose a fast settling algorithm to generate robust multi-fidelity traversabilityestimates in real-time. The proposed approach was deployed on multiple physicalsystems including skid-steer and tracked robots, high-speed RC car and legged robotsand as a part of Team CoSTAR’s effort to theDARPASubterranean Challenge, wherethe team won 2nd and 1st place in the Tunnel and Urban Circuit, respectively.

Agha-mohammadi, Ali-akbar↗
NASA NTRS2016

Reevaluating Suitability Estimates Based on Dynamics of Cropland Expansion in the Brazilian Amazon

Agricultural suitability maps are a key input for land use zoning and projections of cropland expansion. Suitability assessments typically consider edaphic conditions, climate, crop characteristics, and sometimes incorporate accessibility to transportation and market infrastructure. However, correct weighting among these disparate factors is challenging, given rapid development of new crop varieties, irrigation, and road networks, as well as changing global demand for agricultural commodities. Here, we compared three independent assessments of cropland suitability to spatial and temporal dynamics of agricultural expansion in the Brazilian state of Mato Grosso during 2001 2012. We found that areas of recent cropland expansion identified using satellite data were generally designated as low to moderate suitability for rainfed crop production. Our analysis highlighted the abrupt nature of suitability boundaries, rather than smooth gradients of agricultural potential, with little additional cropland expansion beyond the extent of the flattest areas (0-2% slope). Satellite-based estimates of the interannual variability in the use of existing crop areas also provided an alternate means to assess suitability. On average, cropland areas in the Cerrado biome had higher utilization (84%) than croplands in the Amazon region of northern Mato Grosso (74%). Areas of more recent expansion had lower utilization than croplands established before 2002, providing empirical evidence for lower suitability or alternative management strategies (e.g., pasture soya rotations) for lands undergoing more recent land use transitions. This unplanted reserve constitutes a large area of potentially available cropland (PAC)without further expansion, within the management limits imposed for pest management and fallow cycles. Using two key constraints on future cropland expansion, slope and restrictions on further deforestation of Amazon or Cerrado vegetation, we found little available flat land for further legal expansion of crop production in Mato Grosso. Dynamics of cropland expansion from more than a decade of satellite observations indicated narrow ranges of suitability criteria, restricting PAC under current policy conditions, and emphasizing the advantages of field-scale information to assess suitability and utilization.

Morton, Douglas C.↗
NASA NTRS1991

Improved disparity map analysis through the fusion of monocular image segmentations

The focus is to examine how estimates of three dimensional scene structure, as encoded in a scene disparity map, can be improved by the analysis of the original monocular imagery. The utilization of surface illumination information is provided by the segmentation of the monocular image into fine surface patches of nearly homogeneous intensity to remove mismatches generated during stereo matching. These patches are used to guide a statistical analysis of the disparity map based on the assumption that such patches correspond closely with physical surfaces in the scene. Such a technique is quite independent of whether the initial disparity map was generated by automated area-based or feature-based stereo matching. Stereo analysis results are presented on a complex urban scene containing various man-made and natural features. This scene contains a variety of problems including low building height with respect to the stereo baseline, buildings and roads in complex terrain, and highly textured buildings and terrain. The improvements are demonstrated due to monocular fusion with a set of different region-based image segmentations. The generality of this approach to stereo analysis and its utility in the development of general three dimensional scene interpretation systems are also discussed.

Perlant, Frederic P.↗
NASA NTRS1981

A data base approach for prediction of deforestation-induced mass wasting events

A major topic of concern in timber management is determining the impact of clear-cutting on slope stability. Deforestation treatments on steep mountain slopes have often resulted in a high frequency of major mass wasting events. The Geographic Information System (GIS) is a potentially useful tool for predicting the location of mass wasting sites. With a raster-based GIS, digitally encoded maps of slide hazard parameters can be overlayed and modeled to produce new maps depicting high probability slide areas. The present investigation has the objective to examine the raster-based information system as a tool for predicting the location of the clear-cut mountain slopes which are most likely to experience shallow soil debris avalanches. A literature overview is conducted, taking into account vegetation, roads, precipitation, soil type, slope-angle and aspect, and models predicting mass soil movements. Attention is given to a data base approach and aspects of slide prediction.

Logan, T. L.↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Random Forest Classification

Mapping the extent of floodwaters following extreme rainfall aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study seeks to determine how accurately maps of inundation can be generated from L-band SAR imagery through Random Forest classification. Once the extent of water and inundated vegetation is classified, cleanup operations are performed using fuzzy logic to reduce false detections. Estimates of water extent are then combined with datasets describing the distribution of population, buildings, and roads throughout the domain to evaluate societal impacts. Results from the Hurricane Florence case study will be discussed along with the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander Melancon↗
NASA NTRS1988

Hydrologic models for land-atmosphere retrospective studies of the use of LANDSAT and AVHRR data

The use of a Geographic Information System (GIS) and LANDSAT analysis in conjunction with the Simulator for Water Resources on a Rural Basin (SWRRB) hydrologic model to examine the water balance on the Little Washita River basin is discussed. LANDSAT analysis was used to divide the basin into eight non-contiguous land covers or subareas: rangeland, grazed range, winter wheat, alfalfa/pasture, bare soil, water, woodland, and impervious land (roads, quarry). The use of a geographic information system allowed for the calculation of SWRRB model parameters in each subarea. Four data sets were constructed in order to compare SWRRB estimates of hydrologic processes using two methods of maximum LAI and two methods of watershed subdivision. Maximum LAI was determined from a continental scale map, which provided a value of 4.5 for the entire basin, and from its association with the type of land-cover (eight values). The two methods of watershed subdivision were determined according to drainage subbasin (four) and the eight land-covers. These data sets were used with the SWRRB model to obtain daily hydrologic estimates for 1985. The results of the one year analysis lead to the conclusion that the greater homogeneity of a land-cover subdivision provides better water yield estimates than those based on a drainage properties subdivision.

Duchon, Claude E.↗
NASA NTRS2007

Learning and Prediction of Slip from Visual Information

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

rover↗
NASA NTRS1980

Small passenger car transmission test-Chevrolet 200 transmission

The small passenger car transmission was tested to supply electric vehicle manufacturers with technical information regarding the performance of commerically available transmissions which would enable them to design a more energy efficient vehicle. With this information the manufacturers could estimate vehicle driving range as well as speed and torque requirements for specific road load performance characteristics. A 1979 Chevrolet Model 200 automatic transmission was tested per a passenger car automatic transmission test code (SAE J651b) which required drive performance, coast performance, and no load test conditions. The transmission attained maximum efficiencies in the mid-eighty percent range for both drive performance tests and coast performance tests. Torque, speed and efficiency curves map the complete performance characteristics for Chevrolet Model 200 transmission.

Bujold, M. P.↗
NASA NTRSDate not supplied

Using Satellite Soil Moisture and Rainfall in the Landslide Hazard Assessment for Situational Awareness System

The Landslide Hazard Assessment for Situational Awareness system(LHASA)gives a global view of landslide hazard in nearly real time. Currently, it is being upgraded from version 1 to version 2, which entails improvements along several dimensions. These include the incorporation of new predictors, machine learning, and new event-based landslide inventories. As a result, LHASA version 2 substantially improves on the prior performanceand introduces a probabilistic element to the global landslide nowcast. Data from the soil moisture active-passive (SMAP) satellite has been assimilated into a globally consistent data product with a latency less than 3 days, known as SMAP Level 4. In LHASA, thesedata representthe antecedent conditions prior to landslide-triggering rainfall. In some cases, soil moisture may have accumulated over aperiod of many months. The model behind SMAP Level 4 also estimates the amount of snow on the ground, which is an important factor in some landslide events. LHASA also incorporates this information as an antecedent condition that modulates the response torainfall. Slope, lithology, and active faults were also used as predictor variables. These factors can have a strong influence on where landslides initiate.LHASA relies on precipitation estimates from the Global Precipitation Measurement mission to identify the locations where landslides are most probable. The low latency and consistent global coverage of these data make them ideal for real-time applications at continental to global scales. LHASA relies primarily on rainfall from the last 24 hours to spothazardous sites, which is rescaled by the local 99thpercentile rainfall.However, the multi-day latency of SMAP requires the use of a 2-day antecedent rainfall variable to represent the accumulation of rain between the antecedent soil moisture and current rainfall. LHASA merges these predictors with XGBoost, a commonly used machine-learning tool, relying on historical landslide inventories to develop the relationship between landslide occurrence and various risk factors. The resulting model relies heavily on current daily rainfall, but other factors also play an important role. LHASA outputsthe probability oflandslide occurrence ona grid of roughly one kilometer over all continents from 60 North to 60 South latitude. Evaluation over the period 2019-2020 showsthat LHASA version 2 doubles the accuracy of the global landslide nowcast without increasing the global false alarm rate. LHASA also identifies the areas where the human exposure to landslide hazard is most intense. Landslide hazard is divided into 4 levels: minimal, low, moderate, and high. Next, the number of persons and the length of major roads (primary and secondary roads)within each of these areas is calculated for every second-level administrative district (county). These results can be viewedthrough a web portal hosted at the Goddard Space Flight Center. In addition, users can download daily hazard and exposure data.LHASAversion 2uses machine learning and satellite data to identify areas of probable landslide hazard within hours of heavy rainfall. Itsglobal maps are significantly more accurate, and it now includes rapid estimates of exposed populations and infrastructure. In addition, a forecast mode will be implemented soon.

Thomas Stanley↗
NASA NTRS1991

Compiling and editing agricultural strata boundaries with remotely sensed imagery and map attribute data using graphics workstations

The USDA presently uses labor-intensive photographic interpretation procedures to delineate large geographical areas into manageable size sampling units for the estimation of domestic crop and livestock production. Computer software to automate the boundary delineation procedure, called the computer-assisted stratification and sampling (CASS) system, was developed using a Hewlett Packard color-graphics workstation. The CASS procedures display Thematic Mapper (TM) satellite digital imagery on a graphics display workstation as the backdrop for the onscreen delineation of sampling units. USGS Digital Line Graph (DLG) data for roads and waterways are displayed over the TM imagery to aid in identifying potential sample unit boundaries. Initial analysis conducted with three Missouri counties indicated that CASS was six times faster than the manual techniques in delineating sampling units.

Cheng, Thomas D.↗
NASA NTRS1970

On the use of space photography for identifying transportation routes: A summary of problems

It has been widely suggested that space photography may be used for updating maps of transportation networks. Proponents of the argument have suggested that color space photographs of the resolution obtained with Hasselblad 80 mm lenses (about 300 feet) contain enough useful information to update the extensions of major U. S. highways. The present study systematically documents for the Dallas-Fort Worth area the potential of such space photography in detecting, and to a lesser degree identifying, the existing road networks. Color separation plates and an enlargement of the color photograph were produced and all visible roads traced onto transparencies for study. Major roads and roads under construction were the most visible while lower class roads and roads in urban areas had the poorest return. Road width and classification were found to be the major determinant in visibility, varying from 100 per cent visible for divided highways to 15 per cent visible of bladed earth roads. In summary, space photographs of this resolution proved to be difficult to use for accurate road delineation. Only super highways in rural areas with the greatest road-width were completely identifiable, the width being about 1/3 that of the resolution cell.

Simonett, D. S.↗
NASA NTRS1978

Archeological Surveys

NASA remote sensing technology is being employed in archeological studies of the Anasazi Indians, who lived in New Mexico one thousand years ago. Under contract with the National Park Service, NASA's Technology Applications Center at the University of New Mexico is interpreting multispectral scanner data and demonstrating how aerospace scanning techniques can uncover features of prehistoric ruins not visible in conventional aerial photographs. The Center's initial study focused on Chaco Canyon, a pre-Columbia Anasazi site in northeastern New Mexico. Chaco Canyon is a national monument and it has been well explored on the ground and by aerial photography. But the National Park Service was interested in the potential of multispectral scanning for producing evidence of prehistoric roads, field patterns and dwelling areas not discernible in aerial photographs. The multispectral scanner produces imaging data in the invisible as well as the visible portions of the spectrum. This data is converted to pictures which bring out features not visible to the naked eye or to cameras. The Technology Applications Center joined forces with Bendix Aerospace Systems Division, Ann Arbor, Michigan, which provided a scanner-equipped airplane for mapping the Chaco Canyon area. The NASA group processed the scanner images and employed computerized image enhancement techniques to bring out additional detail.

Source record↗
NASA NTRS1999

Satellite Hyperspectral Imaging Simulation

Simulation of generic pushbroom satellite hyperspectral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS(NEMO), Warfighter-1(OrbView-4) and Hyperion(EO-1). The simulations start with a generation of synthetic scenes from material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response using MODTRAN 4 radiance and transmissions calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and etectorr/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratios and expected performance are estimated for typical satellite system specifications and are discussed for all the scenes.

Zanoni, Vicki↗
NASA NTRS1999

Satellite Hyperspectral Imaging Simulation

Simulations of generic pushbroom satellite hyperspetral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS (NEMO), Warfighter-1 (OrbView-4), and Hyperion (EO-1). The simulaitons start with a generation of synthetic scenes form material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response uwing MODTRAN 4 radiance and transmission calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and detector/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation, and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratos and expected performance are estimated for typica satellite system specifications and are discussed for all the scenes.

Zanoni, Vicki↗
NASA NTRSDate not supplied

Multi-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS) Datasets Released by NASA GES DISC and Their Applications for Air Quality

Nitrogen dioxide (NO2), a pervasive air pollutant, comes from vehicles, power plants, industrial emissions, and off-road sources such as construction or lawn and gardening equipment. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) curates many remote sensing datasets with NO2 retrievals, which have been utilized for air quality research and applications. The remotely-sensed datasets include those generated by the Ozone Monitoring Instrument (OMI) on the Aura satellite, the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P), and the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi National Polar-orbiting Partnership (S- NPP). In collaboration with the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Multi-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS) project, the GES DISC recently released MINDS datasets. The NASA MEaSUREs MINDS project aims to develop long-term NO2 global data records by adapting a consistent retrieval algorithm to multiple instrument measurements. Long-term data records will be achieved by applying consistent retrieval approaches to multiple satellite instruments, including OMI (2004 - ); the Global Ozone Monitoring Experiment (GOME, 1995-2011) onboard the second European Remote Sensing satellite (ERS-2); the Scanning Imaging Spectrometer for Atmospheric Cartography (SCIAMACHY, 2002-2012) onboard the ENVIronmental SATellite (ENVISAT); GOME-2 on the Meteorological Operational satellites (MetOp-A and MetOp-B, 2006 - ); and TROPOMI onboard the Copernicus S5P (2017 - ). The long-term record (1995 to present) of MINDS datasets makes them very useful for air quality trend studies. Some MINDS datasets with high spatial resolution of only a few kilometers can be used for air quality research and applications at regional scales. In this presentation, we will introduce all of the MINDS products and services, and demonstrate use cases of MINDS data for studying air quality. We will also present a few other NO2 datasets acquired from NASA’s Health and Air Quality Applied Sciences Team (HAQAST), to be archived and distributed by the GES DISC, and highlight some of their applications for air quality and health.

Feng Ding↗