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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Digital image classification approach for estimating forest clearing and regrowth rates and trends

A technique is presented to monitor vegetation changes for a selected study area in Costa Rica. A normalized difference vegetation index was computed for three dates of Landsat satellite data and a modified parallelipiped classifier was employed to generate a multitemporal greenness image representing all three dates. A second-generation image was created by partitioning the intensity levels at each date into high, medium, and low and thereby reducing the number of classes to 21. A sampling technique was applied to describe forest and other land cover change occurring between time periods based on interpretation of aerial photography that closely matched the dates of satellite acquisition. Comparison of the Landsat-derived classes with the photo-interpreted sample areas can provide a basis for evaluating the satellite monitoring technique and the accuracy of estimating forest clearing and regrowth rates and trends.

Sader, Steven A.↗
NASA NTRS2021

Southwest Water Resources: Monitoring Surface Water Extents of Remote Stock Ponds in the Southwestern United States Using Earth Observing Systems for Enhanced Water Resources Management

Due to increasingly frequent and severe drought conditions in the southwestern US, land managers and livestock producers need to monitor stock ponds with increasing regularity. The ability to assess stock pond water levels with Earth observing satellite systems would enhance monitoring efforts of partners at the US Forest Service, Arizona Department of Game and Fish, and the Diablo Trust. This study employed Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to monitor surface water extent for hundreds of critical stock ponds in Arizona. Using methods adapted from previously developed image processing workflows, this project conducted a time-series analysis to capture seasonal and interannual variations in surface water area between 2013 to 2021. In addition, end users can monitor the surface water extent of stock ponds through the developed Google Earth Engine software tool called Surface Water Identification and Forecasting Tool (SWIFT). SWIFT incorporates the Automated Water Extraction Index, Modified Normalized Difference Water Index, and Tasseled Cap-Wetness Index for optical imagery and the incidence angle, VV and VH polarization bands for Sentinel-1 imagery to detect small water bodies in the study area with an overall accuracy range of 88-93%. These tools will empower our partners to monitor the extents of water in their stock ponds remotely, enabling them to develop data-informed and sustainable management solutions for decades to come.

Rainey Aberle↗
NASA NTRS1973

Principal sources and dispersal patterns of suspended particulate matter in nearshore surface waters of the northeast Pacific Ocean

The author has identified the following significant results. Release-recovery paths of drift cards released in conjunction with ERTS-1 overflight show that nearshore surface currents along the central and northern California coast flowed southward at an average rate in excess of 10 cm/sec (8.5 km/day) during August and September 1973 (California Current). By the middle of October 1973, the nearshore surface currents had reversed and the dominant flow velocity was northward at an average rate in excess of 20 cm/sec (17 km/ day) (Davidson Current). The August-September data suggested the presence of counterclockwise gyres in Monterey Bay and the Gulf of the Farallones, but by the middle of October, the gyres were no longer evident. Imagery of April 1973 showed well developed plumes of suspended sediment in Monterey Bay from the Salinas River and in the Gulf of the Farallones from San Francisco Bay. ERTS-1 imagery provides an effective means of monitoring timber harvest in the redwood forest along the northern California coast. ERTS-1 imagery also clearly portrays contrasting topographic belts characterized by fluvial erosion and by mass movement. The most visually apparent and most persistent river mouth suspended sediment plumes are associated with those rivers that drain belts of topography that appear to have been eroded primarily by mass movement.

Carlson, P. R.↗
NASA NTRS1974

The first earth resources technology satellite nearly two years of operation

A brief status report on the performance of the ERTS-1, and an overview of the applications derived from the images are presented. The ERTS-1 spacecraft, sensor and picture processing systems have continued to perform almost flawlessly since August 1972. Registered, multispectral images of all major land masses of the earth, both polar and some oceanic regions are continuously made, covering daily an area of about 5 million square kilometers. The systematic repetition of these observations, which were made over most parts of the world at least once every season, and the high accuracy of thematic mapping that can be obtained from the images, have resulted in many applications that have immense potential benefits for developing countries. Among these applications are the detection and accurate mensuration of surface water; the identification and mensuration of forests, rangeland, crops and soils; the monitoring and mapping of water quality, wildlife habitats and of the effects of land use practices on food and water resources; the assessment of flooding and earthquake hazards; and the facilitation of mineral exploration.

Nordberg, W.↗
NASA NTRS1979

Applications of a high-altitude powered platform /HAPP/

The high-altitude powered platform (HAPP) is a conceptual unmanned vehicle which could be either an airship or airplane. It would keep station at an altitude of 70,000 ft above a fixed point on the ground. A microwave power transmission system would beam energy from the ground up to the HAPP to power an electric motor-driven propeller and the payload. A study of the HAPP has shown that it could potentially be a cost-competitive platform for such remote sensing applications as forest fire detection, Great Lakes ice monitoring and Coast Guard law enforcement. It also has significant potential as a communications relay platform for (among other things) direct broadcast to home TVs over a large region.

Kuhner, M. B.↗
NASA NTRS1986

Error analysis of leaf area estimates made from allometric regression models

Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).

Feiveson, A. H.↗
NASA NTRS1997

Human Migration and Agricultural Expansion: An Impending Threat to the Maya Biosphere Reserve

Evidence is presented of the current threats to the Maya Biosphere Reserve in northern Guatemala as derived through time-series Landsat Thematic Mapper observations and analysis. Estimates of deforestation rates and trends are examined for different management units within the reserve and buffer zones. The satellite imagery was used to quantify and monitor rates, patterns, and trends of forest clearing during a time period corresponding to new road construction and significant human migration into the newly accessible forest region. Satellite imagery is appropriate technology in a vast and remote tropical region where aerial photography and extensive field-based methods are not cost-effective and current, timely data is essential for establishing conservation priorities.

Sader, Steven↗
NASA NTRSDate not supplied

AIST Technology Perspectives for ESO Mission Processing and Open-sourced Science

NASA is formulating the Earth System Observatory (ESO), a set of Earth-focused missions to provide key information to guide efforts related to monitoring climate change, mitigating disasters, fighting forest fires, and improving real-time agricultural processes. Within that framework, this presentation will introduce the Advanced Collaborative Frameworks (ACF) concepts that have been developed as part of the Advanced Information Systems Technology (AIST) Program, demonstrating the capability for facilitating open-sourced science and with a strong transition/infusion potential.

Jacqueline Le Moigne↗
DOE OSTI2015

The extreme ultraviolet deficit: Jet connection in the quasar 1442+101

In previous studies, it has been shown that the long-term time-averaged jet power, Q-bar , is correlated with spectral index in the extreme ultraviolet (EUV), α{sub EUV} (defined by F{sub ν}∼ν{sup −α{sub E}{sub U}{sub V}} computed between 700 and 1100 Å ). Larger Q-bar tends to decrease the EUV emission. This is a curious relationship because it connects a long-term average over ∼10{sup 6} years with an instantaneous measurement of the EUV. The EUV appears to emit adjacent to the central supermassive black hole and the most straightforward explanation of this correlation is that the EUV-emitting region interacts in real time with the jet-launching mechanism. Alternatively stated, the Q-bar –α{sub EUV} correlation is a manifestation of a contemporaneous (real time) jet power, Q(t), correlation with α{sub EUV}. In order to explore this possibility, this paper considers the time variability of the strong radio jet of quasar 1442+101, which is not aberrated by strong Doppler enhancement. This high-redshift (z = 3.55) quasar is uniquely suited for this endeavor as the EUV is redshifted into the optical observing window allowing for convenient monitoring. More importantly, it is bright enough to be seen through the Lyman forest and its radio flux is strong enough that it has been monitored frequently. Quasi-simultaneous monitoring (five epochs spanning ∼40 years) shows that increases in Q(t) correspond to decreases in the EUV as expected.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Abstract Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux‐based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long‐term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long‐term monitoring programs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Evaluating the Type and State of Alaska Taiga Forests with Imaging Radar for Use in Ecosystem Models

Changes in the seasonal CO2 flux of the boreal forests may result from increased atmospheric CO2 concentrations and associated global warming patterns. To monitor this potential change, a combination of information derived from remote sensing data, including forest type and growing season length, and ecophysiological models which predict the CO2 flux and its seasonal amplitude based on meteorological data, are required. In this paper we address the use of synthetic aperture radar (SAR) to map fores type and monitor canopy and soil freeze/thaw, which define the growing season for conifers, and leaf on/off, which defines the growing season for deciduous species. Aircraft SAR (AIRSAR) data collected in March 1988 during a freeze/thaw event are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off transitions and soil freeze/thaw. Finally, a CO2 flux algorithm is presented which utilizes SAR data and an ecophysiological model to estimate CO2 flux. CO2 flux maps are generated, from which areal estimates of CO2 flux are derived.

Alaska↗
DOE OSTI2016

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. In addition, the dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (N mass ), mass-based carbon concentration (C mass ), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R 2 = 0.6–0.8 for temporal variability; R 2 = 0.3–0.7 for cross-site variability; R 2 = 0.4–0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of N mass , C mass and LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. In conclusion, our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87 Sr 0.13 CrO 3 , Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH 4 , two types of natural gas simulants, and CH 4 +NH 3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

03 NATURAL GAS↗
NASA NTRS1996

Differential Radar Interferometry

Progress and future plans regarding the following objectives are presented: (1) Test differential radar interferometry as a new monitoring technique for remote sensing of a forest site, a farm site, and a desert site; and (2) Generate topographic maps of test sites from radar data.

Goldstein, R. M.↗
NASA NTRS2008

NASA's Contributions to the Gulf of Mexico Alliance

This viewgraph document reviews the contribution that NASA has made and the plans for future missions that will assist the mission of the Gulf of Mexico Alliance (GOMA). Specific reference to the work of the Stennis Space Center is reviewed. Some of the projects are: Coastal Online Assessment and Synthesis Tool (COAST), Regional Sediment Management, Coral Reef Early Warning System, Harmful Algal Bloom, Hypoxia, Land-Use and Land-Cover (LULC) Change from 1974-2008 around Mobile Bay, AL, Satellite Estimation of Suspended Particulate Loads in and around Mobile Bay, AL, Estimating Relative Nutrient Contributions of Agriculture and Forests Using MODIS Time Series, Coastal Marsh Monitoring for Persistent Saltwater Intrusion, Standardized Remote Sensing PRoduct for Water Clarity estimation within Gulf of Mexico Coastal Waters.

Glorioso, Mark↗
NASA NTRS2010

Exploiting the Free Landsat Archive for Operational Monitoring of Ecosystem Condition and Change Across the Chesapeake Bay Watershed

For the first time, all imagery acquired by the Landsat series of satellites is being made available by the USGS to users at no cost. This represents a key opportunity to use Landsat in a truly operational monitoring framework: large regions of the U.S. such as the Chesapeake Bay Watershed can now be analyzed using "wall-to-wall" imagery at timescales from approximately 1 month to several years. With the future launch of the Landsat Data Continuity Mission (LDCM) and Decadal Survey missions such as the hyperspectral HyspIRI, it is imperative to develop robust processing systems to perform annual ecosystem assessments over large regions such as the Chesapeake Bay. We have been working at NASA's Goddard Space Flight Center (GSFC) to develop an integrative framework for inserting 30m, annual, Landsat based data and derived products into the existing decision support system for the Bay, with a particular focus on ecosystem condition and changes over the entire watershed. The basic goal is to use a 'stack' of Landsat imagery with 40% or less cloud cover to produce multi-date (2005-2009 period), cloud/shadow/gap-free composited surface reflectance products that will support the creation of watershed scale land cover/ use products and the monitoring of ecosystem change across the Bay. Our scientific focus extends beyond the conventional definition of land cover (i.e. a classification of vegetation type) as we propose to monitor both changes in surface type (e.g. forest to urban), vegetation structure (e.g. forest disturbance due to logging or insect damage), as well as winter crop cover. These processes represent a continuum from large, interannual changes in land cover type, to subtler, intra-annual changes associated with short-term disturbance. The free Landsat data are being processed to surface reflectance and composited using the existing Landsat Ecosystem Disturbance Adaptive Processing System here at NASA/ GSFC, and land cover products (type, tree cover, impervious cover, winter cover) are being produced using well-established decision tree and regression tree algorithms. The goal of this session is to present the data products that we have been developing to the Bay science community and to discuss potential avenues for improvements and usage of the products for decision support.

BrowndeColstoun, Eric↗