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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

A Comparison of Local Variance, Fractal Dimension, and Moran's I as Aids to Multispectral Image Classification

The accuracy of traditional multispectral maximum-likelihood image classification is limited by the skewed statistical distributions of reflectances from the complex heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze Landsat 7 imagery of Atlanta, Georgia. Although segmentation of panchromatic images is possible using indicators of spatial complexity, different land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification. The addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques.

Emerson, Charles W.↗
NASA NTRS2020

Critical land change information enhances the understanding of carbon balance in the United States

Large‐scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine‐scale processes including land use and land cover change into a large‐scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1‐km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

carbon sequestration↗
NASA NTRS1981

Texture measurements from Seasat - SAR images for urban land use interpretation

Different grey tones in Seasat Synthetic Aperture Radar (SAR) images for the same type of urban land cover considerably impair the possibilities of establishing automatic classification procedures for these types of data. Since the orientation of the main features like street patterns and buildings with respect to the azimuth angle of the radar antenna is the crucial factor for the observed differences in grey tone, prior information on these elements and special processing of the data would be required to eliminate this effect. Another approach suggested in this paper is to make use of the textural information in the image rather than of its grey tone. For different study sites within the Los Angeles urbanized area texture measures could be derived which result in characteristic values for specific types of land cover and are largely independent of the azimuth angle effect. At the same time the results for the study area indicate an improvement of the overall separability for the different land cover types included in the analysis.

Fasler, F.↗
NASA NTRS1999

Remote Sensing of Urban Thermal Landscape Characteristics and Their Affects on Local and Regional Meteorology and Air Quality: An Overview of NASA EOS-IDS Project Atlanta

As an entity, the city is a manifestation of human "management" of the land. The act of city-building, however, drastically alters the biophysical environment, which ultimately, impacts local and regional land-atmosphere energy exchange processes. Because of the complexity of both the urban landscape and the attendant energy fluxes that result from urbanization, remote sensing offers the only real way to synoptically quantify these processes. One of the more important land-atmosphere fluxes that occurs over cities relates to the way that thermal energy is partitioned across the heterogeneous urban landscape. The individual land cover and surface material types that comprise the city, such as pavements and buildings, each have their own thermal energy regimes. As the collective urban landscape, the individual thermal energy responses from specific surfaces come together to form the urban heat island phenomena, which prevails as a dome of elevated air temperatures over cities. Although the urban heat island has been known to exist for well over 150 years, it is not understood how differences in thermal energy responses for land covers across the city interact to produce this phenomenon, or how the variability in thermal energy responses from different surface types drive its development. Additionally, it can be hypothesized that as cities grow in size through time, so do their urban heat islands. The interrelationships between urban sprawl and the respective growth of the urban heat island, however, have not been investigated. Moreover, little is known of the consequential effects of urban growth, land cover change, and the urban heat island as they impact local and regional meteorology and air quality.

Quattrochi, Dale A.↗
NASA NTRS2021

Combining Satellite Data and Spatial Analysis to Assess the UHI Amplitude and Structure within Urban Areas: The Case of Moroccan Cities

Landsat-8 surface temperature and the European Space Agency land cover are used to assess the impact of land cover on the Urban Heat Island (UHI) and Urban Heat Sink (UHS). We analyzed five Moroccan cities selected for their different local climate, size, and typology during summer at three different spatial scales. The results show multiple causes defining the different forms and amplitudes of the UHI, namely: the ambient climate, the proximity to the sea, the presence of landscaped areas, and the color of building roofs and walls. Contrary to what was expected, the vegetation was not systematically an island of coolness, either because of its typology or its irrigation status. In the coastal cities of Tangier and Casablanca, UHIs around 20°C are observed on the seaside, whereas a UHS of up to 11°C is observed between the city center and the southern periphery of Casablanca. A moderate amplitude UHI of 7°C is formed in the mountainous city of Ifrane. For cities built in desert-like environments, well-defined UHSs between 9°C and 12°C are observed in Smara and Marrakech, respectively. At a finer scale, towns recorded lower temperatures than their immediate surroundings, which are attributed to evaporation from irrigated plants.

Laila El Ghazouani↗
DOE OSTI2020

Infrastructure-Relevant Storms of the Last Century

Historical extreme storm events are widely used to make Probable Maximum Precipitation (PMP) estimates, which form the cornerstone of large water management infrastructure safety. Past studies suggest that extreme precipitation processes can be sensitive to land surface feedback and the planetary warming trend, which makes the future safety of large infrastructures questionable given the projected changes in land cover and temperature in the coming decades. In this study, a numerical modeling framework was employed to reconstruct 10 extreme storms over CONUS that occurred during the past 100 years, which are used by the engineering profession for PMP estimation for large infrastructures such as dams. Results show that the correlation in daily rainfall for such reconstruction can range between 0.4 and 0.7, while the correlation for maximum 3-day accumulation (a standard period used in infrastructure design) is always above 0.5 for post-1948 storms. This suggests that current numerical modeling and reanalysis data allow us to reconstruct big storms after 1948 with acceptable accuracy. For storms prior to 1948, however, reconstruction of storms shows inconsistency with observations. Our study indicates that numerical modeling and data may not have advanced to a sufficient level to understand how such old storms (pre-1948) may behave in future warming and land cover conditions. However, the infrastructure community can certainly rely on the use of model reconstructed extreme storms of the 1948-present period to reassess safety of our large water infrastructures under assumed changes in temperature and land cover.

Chen, Xiaodong↗
NASA NTRS2006

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Quattrochi, Dale A.↗
NASA NTRS1975

LANDSAT data for state planning

The results of an effort to generate and apply automated classification of LANDSAT digital data to state of Georgia problems are presented. This phase centers on an analysis of the usefulness of LANDSAT digital data to provide land-use data for transportation planning. Hall County, Georgia was chosen as a test site because it is part of a seventeen county area for which the Georgia Department of Transportation is currently designing a Transportation Planning Land-Use Simulation Model. The land-cover information derived from this study was compared to several other existing sources of land-use data for Hall County and input into this simulation. The results indicate that there is difficulty comparing LANDSAT derived land-cover information with previous land-use information since the LANDSAT data are acquired on an acre by acre grid basis while all previous land-use surveys for Hall County used land-use data on a parcel basis.

Faust, N. L.↗
NASA NTRS1990

Attempting to detect and record brushland in the northeastern United States using MSS data - Schoharie County, N.Y., as a case study

Before county and local governments will utilize satellite data extensively for landcover inventories, digital image processing techniques must be developed to identify transitional land-use conditions and create large-scale, readable land-cover maps. This study examines a satellite-based land-cover inventory done for Schoharie County, New York and reviews the problems encountered in identifying and mapping brushland - a significant transitional land use - within the county and region. Brushland throughout the Northeast represents a barometer for measuring the shift away from agricultural land use to second-home property. Detecting and locating brushland on a regular basis can greatly assist county and local government officials in planning and managing a county's future.

Baumann, Paul R.↗
NASA NTRS2023

New York Ecological Conservation: Evaluating Agricultural Conservation Easement Impact Using Earth Observations to Examine Avoided Soil Carbon Loss to Development

Farmland provides ecosystems and communities with services ranging from habitat conservation to food security. As total U.S. farmland continues to decline, agricultural lands near urban areas are especially vulnerable. Our project partners—Finger Lakes Land Trust, Genesee Land Trust, and Saratoga Preserving Land and Nature (PLAN)—can use study results to better profile farmland vulnerability, issuing conservation easements to protect maximum acreage in Saratoga County and the Finger Lakes Region of New York. Multiple existing studies effectively use remote sensing imagery to analyze historical land cover and forecast future change. This study examined soil carbon stocks and land cover change to estimate avoided soil carbon loss. We also predicted farmland vulnerability. We completed these analyses using European Space Agency (ESA) and NASA Earth observations that include Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-2 Multispectral Instrument (MSI), and Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) for nighttime lights data that aided in the land change model analysis. We determined that the conversion of agriculture to development from 1990 to 2022 occurred at rates of 0.91% (Finger Lakes) and 7% (Saratoga). Urban development is predicted to increase surrounding urban centers through 2030 and 2050. We also estimate that between 26.5 and 348,101 kilotons (Finger Lakes) and 3.7 and 58,006 kilotons (Saratoga) of soil carbon losses have been avoided through agricultural easements. Findings from this study will support our partners in determining agricultural conservation easement benefits and prioritizing the acquisition of future easement sites.

Agriculture↗
DOE OSTI2025

Future Spatially Explicit Patterns of Land Transitions in the United States With Multiple Stressors

Climate change, income and population growth, and changing diets are major drivers of the global food system with implications for land use change. Land use in the U.S. will be affected directly by local and regional forces and indirectly through international trade. In order to investigate the effects of several potential forces on land use changes in the U.S., we advanced capabilities in representing the interactions between natural and human systems by linking a multisectoral and multiregional socio-economic model of the world economy to a model that downscales land use to a 0.5°grid scale. This enables us to translate regional projections of future land use into higher-resolution representations of time-evolving land cover (effectively spatially explicit land use transitions). We applied the framework over the U.S., with a particular interest in the Mississippi River Basin and its four sub-basins, to consider how a range of global drivers affect land use and cover in the target regions. Our results show that under scenarios of high pressure on the world food system a comparative advantage in livestock production amplifies the recent trend toward less cropland and more pastures in the U.S. Under low pressures on the world food system agricultural land is used less intensively. However, there can be key differences among the various land-use transitions at the sub- basin scale. Overall, these results highlighted the need for high resolution details to explicitly understand the implications of land use change on environmental impacts such as carbon storage, soil erosion, chemical use, hydrology, and water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
NASA NTRS1978

Applications of remote sensing to hydrologic planning

The transfer of LANDSAT remote sensing technology from the research sector to user operational applications requires demonstration of the utility and accuracy of LANDSAT data in solving real problems. This report describes such a demonstration project in the area of water resources, specifically the estimation of non-point source pollutant loads. Non-point source pollutants were estimated from land cover data from LANDSAT images. Classification accuracies for three small watersheds were above 95%. Land cover was converted to pollutant loads for a fourth watershed through the use of coefficients relating significant pollutants to land use and storm runoff volume. These data were input into a simulator model which simulated runoff from average rainfall. The result was the estimation of monthly expected pollutant loads for the 17 subbasins comprising the Magothy watershed.

Loats, H., Jr.↗
DOE OSTI2024

National Pollinator-Solar Energy Research Network

Pollinators, which are important to global food systems and economically linked to local agricultural activities, are declining globally due to land-cover change and land management practices. Solar energy development is a driver of a land-use change that has the potential to either reduce or promote pollinator habitat. To date, pollinators are the most widely addressed wildlife group in solar energy development (e.g., through state pollinator habitat scorecards for solar facilities). Yet, many questions remain about pollinator-solar energy interactions, requiring research that tests observations and defines pollinator-friendly solar.

14 SOLAR ENERGY↗
NASA NTRS1975

The use of Skylab data to study the early detection of insect infestations and density and distribution of host plants

The detection of insect infestations and the density and distribution of host plants were studied using Skylab data, aerial photography and ground truth simultaneously. Additional ground truth and aerial photography were acquired between Skylab passes. Three test areas were selected: area 1, of high density citrus, was located northwest of Mission, Texas; area 2, 20 miles north of Weslaco, Texas, irrigated pastures and brush-covered land; area 3 covered the entire Lower Rio Grande Valley and adjacent areas of Mexico. A color composite picture of S-190A data showed patterns of vegetation on both sides of the Rio Grande River clearly delineating the possible avenues of entry of pest insects from Mexico into the United States or from the United States into Mexico. Vegetation that could be identified with conventional color and color IR film included: citrus, brush, sugarcane, alfalfa, irrigated and unimproved pastures.

Hart, W. G.↗
NASA NTRS1992

Interactive boundary delineation of agricultural lands using graphics workstations

A review is presented of the computer-assisted stratification and sampling (CASS) system developed to delineate the boundaries of sample units for survey procedures. CASS stratifies the sampling units by land-cover and land-use type, employing image-processing software and hardware. This procedure generates coverage areas and the boundaries of stratified sampling units that are utilized for subsequent sampling procedures from which agricultural statistics are developed.

Cheng, Thomas D.↗
NASA NTRS1998

NASA's Earth Science Enterprise: Future Science Missions, Objectives and Challenges

NASA has been actively involved in studying the planet Earth and its changing environment for well over thirty years. Within the last decade, NASA's Earth Science Enterprise has become a major observational and scientific element of the U.S. Global Change Research Program. NASA's Earth Science Enterprise management has developed a comprehensive observation-based research program addressing all the critical science questions that will take us into the next century. Furthermore, the entire program is being mapped to answer five Science Themes (1) land-cover and land-use change research (2) seasonal-to-interannual climate variability and prediction (3) natural hazards research and applications (4) long-term climate-natural variability and change research and (5) atmospheric ozone research. Now the emergence of newer technologies on the horizon and at the same time continuously declining budget environment has lead to an effort to refocus the Earth Science Enterprise activities. The intent is not to compromise the overall scientific goals, but rather strengthen them by enabling challenging detection, computational and space flight technologies those have not been practically feasible to date. NASA is planning faster, cost effective and relatively smaller missions to continue the science observations from space for the next decade. At the same time, there is a growing interest in the world in the remote sensing area which will allow NASA to take advantage of this by building strong coalitions with a number of international partners. The focus of this presentation is to provide a comprehensive look at the NASA's Earth Science Enterprise in terms of its brief history, scientific objectives, organization, activities and future direction.

Habib, Shahid↗
NASA NTRS2002

Smooth Sailing for Weather Forecasting

Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.

Source record↗