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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Comparison of multispectral remote-sensing techniques for monitoring subsurface drain conditions

The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.

Goettelman, R. C.↗
NASA NTRS1985

Remote sensing and disaster monitoring - A review of applications in Indonesia

The role played by remote sensors on-board the Landsat and NOAA-7 satellites in monitoring and management of three catastrophic events that took place in 1982 and 1983 in Indonesia (two major volcanic eruptions and a large-scale forest fire) is assessed. The modes of operation of the satellite platforms, the types of data derived, and data availability to the users are described, and the sources of failures and delays (such as the presence of clouds, technical and computer errors, red-tape, etc.) are examined. The major drawbacks of the Landsat-supplied information for monitoring short-lived and fast-changing events were the insufficient frequency of data acquisition, delayed delivery of data, and extended times needed for data distribution and processing. Indonesia's NOAA-7 and GMS sensors, although of low spatial resolution, offered higher frequency of data collection, and profited from the existence of a local receiving station. The present value of the space-based imagery is seen mainly in the baseline assessment of the disaster-prone areas, which can assist in preparing these areas for the impending catastrophic events.

Malingreau, J. P.↗
NASA NTRS2005

Keeping a Beat on the Heart

Feel the relief of a patient suffering from heart arrhythmia, who is able to return home while having her heart monitored by health professionals 24 hours a day, without the fear that she will miss an important indicator and suffer a fatal heart attack - using technology originally developed to conduct experiments on the Space Shuttle. Approximately 400,000 Americans die every year from sudden heart attacks . Medical research revealed that patterns of electrical activity in the heart can act as predictors of these lethal cardiac events known as arrhythmias. Fortunately, certain arrhythmias such as ventricular fibrillation (loss of regular heartbeat and subsequent loss of function) and ventricular tachycardia (rapid heartbeats), can be detected and appropriately treated. Today, patients at moderate risk of arrhythmias can benefit from technology that would permit long- term continuous monitoring of electrical cardiac rhythms outside the hospital environment in the comfort of their own homes. Medical telemetry systems, also known as telemedicine, are evolving rapidly as wireless communication technology advances, evidenced by the commercial products and research prototypes for remote health monitoring that have appeared in recent years. Wireless systems allow patients to move freely in their home and work environment while being monitored remotely by health care professionals.

Liszka, Kathy J.↗
NASA NTRS1977

Monitoring water quality by remote sensing

The author has identified the following significant results. A limited study was conducted to determine the applicability of remote sensing for evaluating water quality conditions in the San Francisco Bay and delta. Considerable supporting data were available for the study area from other than overflight sources, but short-term temporal and spatial variability precluded their use. The study results were not sufficient to shed much light on the subject, but it did appear that, with the present state of the art in image analysis and the large amount of ground truth needed, remote sensing has only limited application in monitoring water quality.

Brown, R. L.↗
NASA NTRS2014

Feasibility of Using Remotely Sensed Data to Aid in Long-Term Monitoring of Biodiversity

Remote sensing is defined as making observations of an event or phenomena without physically sampling it. Typically this is done with instruments and sensors mounted on anything from poles extended over a cornfield,to airplanes,to satellites orbiting the Earth The sensors have characteristics that allow them to detect and record information regarding the emission and reflectance of electromagnetic energy from a surface or object. That information can then be represented visually on a screen or paper map or used in data analysis to inform decision-making.

Biodiversity↗
NASA NTRSDate not supplied

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗
NASA NTRS1977

The utility of short wavelength (1mm) remote sensing techniques for the monitoring and assessment of hydrologic parameters

Remote sensing programs that respond to the requirements of the water resources management and hydrologic communities are reviewed. The major areas where the needs of water resources management are being met involve the mapping and monitoring of snowcovered areas, hydrologic landuse, and surface water area. In the case of snowcovered area mapping the transfer of technology process is now being accomplished in the Western United States in a cooperative effort involving 6 federal agencies and 3 state agencies along with NASA. A new collaborative effort of the U.S. Army Corps of Engineers and NASA involves the mapping of landuse by Landsat and its use in hydrologic engineering watershed models employed in flood control/waterworks planning, design, and management. Improved systems planned for implementation in the 1978-1981 time frame can be expected to result in increased utilization of visible and near infrared remote sensing observations.

Rango, A.↗
NASA NTRS1996

The Use of a Geographic Information System and Remote Sensing Technology for Monitoring Land Use and Soil Carbon Change in the Subtropical Dry Forest Life Zone of Puerto Rico

Aerial photography, one of the first form of remote sensing technology, has long been an invaluable means to monitor activities and conditions at the Earth's surface. Geographic Information Systems or GIS is the use of computers in showing and manipulating spatial data. This report will present the use of geographic information systems and remote sensing technology for monitoring land use and soil carbon change in the subtropical dry forest life zone of Puerto Rico. This research included the south of Puerto Rico that belongs to the subtropical dry forest life zone. The Guanica Commonwealth Forest Biosphere Reserve and the Jobos Bay National Estuarine Research Reserve are studied in detail, because of their location in the subtropical dry forest life zone. Aerial photography, digital multispectral imagery, soil samples, soil survey maps, field inspections, and differential global positioning system (DGPS) observations were used.

Velez-Rodriguez, Linda L.↗
DOE OSTI2025

Remote Sensing Approach for Monitoring Tree Health Adjacent to Transmission Corridors

This study presents an initial proof-of-concept for a satellite-based remote sensing approach to identify and monitor potential areas of poor tree health across the entire BPA service territory on an annual basis. We tested three variants of “delta peak NDVI” ( ΔPN ) change detection metrics that express interannual variation in primary productivity relative to a baseline by comparing ΔPN values for known insect/disease disturbances and nearby reference locations. All three metrics showed promise for detecting poor tree health in the year during disturbance, but the metric based on the difference from the long-term (2016-2024) median ( Δ Med PN ) was preferred due to its responsiveness to change in the years during and after disturbance, resilience to interannual variation, and ease of interpretation as being above or below normal. Comparison of Δ Med PN grouped by relative severity of disturbance indicated it was not sensitive enough to detect “low” severity disturbances, as mapped by USGS’s LANDFIRE program, but could distinguish “moderate” and “high” severity disturbances from reference locations. These findings informed selection of a threshold for Δ Med PN , which was combined with areas exhibiting negative NDVI to map potential areas of concern. Visual inspection of before/after high-resolution imagery and NDVI time series showed that many areas of concern aligned with visible signs of defoliation and die-off as well as other types of disturbance (e.g., landslides, logging, road grading, flooding). Some areas of concern are thought to be false detections caused by persistent shadow, and some could not be explained with visual inspection due to spatiotemporal limitations of before/after imagery. In summary, our approach shows promise for large-scale monitoring of tree health adjacent to BPA transmission lines, but additional work is recommended to improve model sophistication and remove noise.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2023

Front Range Wildland Fires: Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods

Over the last several decades, wildfire frequency and severity in forested areas along Colorado’s Front Range have increased due to a buildup of fuels. This has led to an increase in forest treatments, as well as an increased need to evaluate the success of these treatments. Remote sensing products offer an efficient and cost-effective way to monitor forest treatments; however, not all remote sensing products and analysis techniques have been explored by Coloradan land managers. Specifically, project partners at the Colorado State Forest Service (CSFS) and the Colorado Forest Restoration Institute (CFRI) were interested in using an effective and streamlined method of mapping canopy cover to better monitor forest treatment success. To support their needs, the NASA DEVELOP Front Range Wildland Fires team explored National Agricultural Imagery Program (NAIP) imagery at different spatial resolutions and numbers of training points with NASA’s Shuttle Radar Topography Mission (SRTM) Data Elevation Model (DEM) as a predictor in addition to NAIP imagery spectral predictors. From this analysis, we created classified canopy cover rasters, and compared accuracy metrics across model iterations. We also determined that the best performing model, with an overall accuracy of 0.900 uses 2021 NAIP imagery at 2-meter resolution, 800 training points, 200 testing points, does not use topographic predictors, and reclassifies shadow pixels via a pre-selected NDVI threshold.

Remote Sensing↗
NASA NTRS2023

Bhutan Agriculture III: Monitoring Cropland Changes in Bhutan using Remote Sensing to Bolster Food Security and Support Crop Monitoring

The Bhutan Agriculture III team aimed to improve agricultural efficiency in Bhutan. Bhutan is a nation heavily reliant on agriculture, but it faces challenges such as geophysical limitations and lack of scientific agricultural practice. The team partnered with a primary end user, Bhutan’s Department of Agriculture (DoA), and with collaborators; the Bhutan Foundation, National Plant Protection Centre (NPPC), Agricultural Research Department Centre (ARDC), National Statistics Bureau (NSB), and the Ugyen Wangchuck Institute for Conservation and Environment Research (UWICER). Advised by NASA SERVIR, the team developed crop masks and monitored rice distribution from 2015 to 2022 utilizing Earth observations such as Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), Sentinel-2 MultiSpectral Instrument (MSI) and Shuttle Radar Topography Mission (SRTM). The team gathered 5,000 points from the five dzongkhags that yield the most rice in Bhutan (Paro, Punakha, Samtse, Sarpang and Wangue Phodrang) using Collect Earth Online (CEO). With the data collected, the team split the data into training and validation data on Google Earth Engine (GEE) for a random forest (RF) classifier for rice and non-rice classification. After running the data on the Random Forest (RF) model, the team got an accuracy score of 81.48%, a kappa score of 55.75% and an F1 score of 86.11%. This data supports better agricultural decision-making for the governing body of Bhutan, helps enhance farming efficiency and foster sustainable practices, assists in overcoming data inaccuracy and bolsters food security in the country.

Sonam Seldon Tshering↗
DOE OSTI2025

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2020

InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal

Mapping and monitoring landslides in remote areas with steep and mountainous terrain is logistically challenging, expensive, and time consuming. Yet, in order to mitigate hazards and prevent loss of life in these areas, and to better understand landslide processes, high-resolution measurements of landslide activity are necessary. Satellite-based synthetic aperture radar interferometry (InSAR) provides millimeter-scale measurements of ground surface deformation that can be used to identify and monitor landslides in remote areas where ground-based monitoring techniques are not feasible. Here we present a novel InSAR deformation detection approach, which uses double difference time-series with local and regional spatial filters and pixel clustering methods to identify and monitor slow-moving landslides without making a priori assumptions of the location of landslides. We apply our analysis to freely available Copernicus Sentinel-1 satellite data acquired between 2014 and 2017 centered on the Trishuli River drainage basin in Nepal. We found a minimum of 6 slow-moving landslides that all occur within the Ranimatta lithologic formation (phyllites, metasandstones, meta basics). These landslides have areas ranging from 0.39 to 1.66 sq.km and long-term dry-season displacement rates ranging from 2.1 to 8.8 cm/yr. Due to periods of low coherence during the monsoon season (June – September) each year, and following the 25 April 2015 Mw7.8 Gorkha earthquake, our time series analysis is limited to the 2014-2015 and 2016-2017 dry seasons (September - May). We found that each of the landslides displayed slightly higher rates during the 2014 period, likely as a result of higher cumulative rainfall that fell during the 2014 monsoon season. Although we do not have high quality InSAR data to show the landslide evolution directly following the Gorkha earthquake, the similar rates of movement before (2014-2015) and after (2016-2017) Gorkha suggest the earthquake had negligible long-term impact on these landslides. Our findings highlight the potential for region-wide mapping of slow-moving l and slides using freely available remote sensing data in remote areas such as Nepal and future work will benefit from expanding our methodology to other regions around the world.

Synthetic Aperture Radar↗
NASA NTRS1993

Subpixel Resolution In Depth Perceived Via 3-D Television

Report describes experiment in which two black vertical bars on featureless white background placed near intersection of optical axes of two charge-coupled-device video cameras positioned to give stereoscopic views. Trained human observers found to perceive depths at subpixel resolutions in stereoscopic television images. This finding significant for remote stereoscopic monitoring, expecially during precise maneuvers of remotely controlled manipulators. Also significant for research in processing of visual information by human brain.

Diner, Daniel B.↗
NASA NTRS1980

Investigation of the application of remote sensing technology to environmental monitoring

Activities and results are reported of a project to investigate the application of remote sensing technology developed for the LACIE, AgRISTARS, Forestry and other NASA remote sensing projects for the environmental monitoring of strip mining, industrial pollution, and acid rain. Following a remote sensing workshop for EPA personnel, the EOD clustering algorithm CLASSY was selected for evaluation by EPA as a possible candidate technology. LANDSAT data acquired for a North Dakota test sight was clustered in order to compare CLASSY with other algorithms.

Rader, M. L.↗
NASA NTRS1987

Monitoring the environment by remote sensing

Structural features of ecosystems, such as leaf area index, phytomass and canopy chemical contents, are beginning to be estimated from remotely sensed data. This development, in combination with ecological modeling, is permitting the estimation of functional features of ecosystems including primary productivity and nutrient cycling. Such techniques are also being applied to the problem of monitoring the effects of air or water pollutants on biota. Sensors that obtain data at a coarse spatial scale (1 km2 or more) are also permitting the observation of biospheric patterns at a large regional or global scale for the first time. When coupled with atmospheric measurements, field data and simulation models, such data may serve to address ecological processes, including pollution effects, at large regional or global scales.

Westman, Walter E.↗
NASA NTRS1988

Volcano monitoring by short wavelength infrared satellite remote sensing

The use of short wavelength IR Landsat TM data for volcano monitoring is examined. By determining the pixel-integrated from the TM data, it is possible to estimate the temperature and size of hot areas which occupy less than one complete pixel. Examples of volcano monitoring with remote sensing data are discussed. It is suggested that the entire volcanic temperature range (100-1200 C) could be accomplished by decreasing the band 6 gain by just one order of magnitude so that it was sensitive to radiance from 1 to 100 mW/sq cm/sr/micron.

Rothery, D. A.↗