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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Using Satellite Remote Sensing Data in a Spatially Explicit Price Model

Famine early warning organizations use data from multiple disciplines to assess food insecurity of communities and regions in less-developed parts of the World. In this paper we integrate several indicators that are available to enhance the information for preparation for and responses to food security emergencies. The assessment uses a price model based on the relationship between the suitability of the growing season and market prices for coarse grain. The model is then used to create spatially continuous maps of millet prices. The model is applied to the dry central and northern areas of West Africa, using satellite-derived vegetation indices for the entire region. By coupling the model with vegetation data estimated for one to four months into the future, maps are created of a leading indicator of potential price movements. It is anticipated that these maps can be used to enable early warning of famine and for planning appropriate responses.

Brown, Molly E.↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
NASA NTRS2020

Gila Water Resources III - Modeling the Impacts of Post-fire Restoration Methods on Vegetation Recovery in the Gila National Forest

In recent years, wildfires in New Mexico’s Gila National Forest have become increasingly common and more severe. Wildfires can have powerful impacts on hydrology and soil stability, including erosion, flooding, and debris-flows that threaten lives and infrastructure downstream. Vegetation restoration treatments like seeding and mulching can mitigate these effects and facilitate ecosystem recovery. Understanding the effectiveness of various restoration methods is vital to planning a cost-effective and successful post-fire recovery strategy. The immediate response to a fire on US Forest Service land is coordinated by a Burned Area Emergency Response (BAER) team, a group responsible for mitigating immediate post-fire risks to human life, property, and critical natural and cultural resources. This study created a proof-of-concept methodology for a decision-support tool designed to help BAER teams identify the restoration treatments most likely to succeed in a given burned area. Leveraging random forest regression, Google Earth Engine, and Landsat 7 and 8 Earth observations, this study modeled vegetation recovery after the 2013 Silver Fire for seeded areas, seeded/mulched areas, and untreated areas. Treatment type and initial burn severity were the largest drivers of vegetation recovery across the landscape. Seeded/mulched areas showed higher recovery levels than untreated areas three months post-fire, but by four years post-fire, treated and untreated areas displayed similar recovery levels. To produce a robust predictive tool for the Gila National Forest, the model should be trained on many more fires and incorporate post-fire weather conditions into the process. Such a model will help partners ensure efficient resource use and plan effective post-fire restoration strategies.

DEVELOP Project Summary↗
NASA NTRS1974

The action of ultrasounds on Bezostaia 1 winter wheat grown in sand pots treated with Knop solution

The results of 3 years of research are presented concerning the increase in length and weight of the aerial biomass as compared with the underground biomass, as well as the productivity indexes of Bezostaia 1 winter wheat. The experiment involved two varieties, each of them with nine repetitions. Sterile sifted sand treated several times with a Knop solution during the vegetative period was used as a seed bed. The seeds were kept in water for 2 hours before treatment. Biometric and statistical measurements showed significant responses in the treated varieties.

Albu, N.↗
NASA NTRS2013

Interagency Collaborators Develop and Implement ForWarn, a National, Near Real Time Forest Monitoring Tool

ForWarn is a satellite-based forest monitoring tool that is being used to detect and monitor disturbances to forest conditions and forest health. It has been developed through the synergistic efforts, capabilities and contributions of four federal agencies, including the US Forest Service Eastern Forest and Western Wildland Environmental Threat Assessment Centers, NASA Stennis Space Center (SSC), Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) and US Geological Survey Earth (USGS) Earth Research Observation System (EROS), as well as university partners, including the University of North Carolina Asheville's National Environmental Modeling and Analysis Center (NEMAC). This multi-organizational partnership is key in producing a unique, path finding near real-time forest monitoring system that is now used by many federal, state and local government end-users. Such a system could not have been produced so effectively by any of these groups on their own. The forests of the United States provide many societal values and benefits, ranging from ecological, economic, cultural, to recreational. Therefore, providing a reliable and dependable forest and other wildland monitoring system is important to ensure the continued health, productivity, sustainability and prudent use of our Nation's forests and forest resources. ForWarn does this by producing current health indicator maps of our nation's forests based on satellite data from NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Such a capability can provide noteworthy value, cost savings and significant impact at state and local government levels because at those levels of government, once disturbances are evident and cause negative impacts, a response must be carried out. The observations that a monitoring system like ForWarn provide, can also contribute to a much broader-scale understanding of vegetation disturbances.

Underwood, Lauren↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRS2014

Impact of Dust Radiative Forcing upon Climate

Dust aerosols perturb the atmospheric radiative flux at both solar and thermal wavelengths, altering the energy and water cycles. The climate adjusts by redistributing energy and moisture, so that local temperature perturbations, for example, depend upon the forcing over the entire extent of the perturbed circulation. Within regions frequently mixed by deep convection, including the deep tropics, dust particles perturb the surface air temperature primarily through radiative forcing at the top of the atmosphere (TOA). Many models predict that dust reduces global precipitation. This reduction is typically attributed to the decrease of surface evaporation in response to dimming of the surface. A counterexample is presented, where greater shortwave absorption by dust increases evaporation and precipitation despite greater dimming of the surface. This is attributed to the dependence of surface evaporation upon TOA forcing through its influence upon surface temperature and humidity. Perturbations by dust to the surface wind speed and vegetation (through precipitation anomalies) feed back upon the dust aerosol concentration. The current uncertainty of radiative forcing attributed to dust and the resulting range of climate perturbations calculated by models remain a useful test of our understanding of the mechanisms relating dust radiative forcing to the climate response.

Feedback upon dust mobilization↗
NASA NTRS2009

Surface Mining and Reclamation Effects on Flood Response of Watersheds in the Central Appalachian Plateau Region

Surface mining of coal and subsequent reclamation represent the dominant land use change in the central Appalachian Plateau (CAP) region of the United States. Hydrologic impacts of surface mining have been studied at the plot scale, but effects at broader scales have not been explored adequately. Broad-scale classification of reclaimed sites is difficult because standing vegetation makes them nearly indistinguishable from alternate land uses. We used a land cover data set that accurately maps surface mines for a 187-km2 watershed within the CAP. These land cover data, as well as plot-level data from within the watershed, are used with HSPF (Hydrologic Simulation Program-Fortran) to estimate changes in flood response as a function of increased mining. Results show that the rate at which flood magnitude increases due to increased mining is linear, with greater rates observed for less frequent return intervals. These findings indicate that mine reclamation leaves the landscape in a condition more similar to urban areas rather than does simple deforestation, and call into question the effectiveness of reclamation in terms of returning mined areas to the hydrological state that existed before mining.

Ferrari, J. R.↗
NASA NTRS1981

Remote sensing applied to agriculture: Basic principles, methodology, and applications

The general principles of remote sensing techniques as applied to agriculture and the methods of data analysis are described. the theoretical spectral responses of crops; reflectance, transmittance, and absorbtance of plants; interactions of plants and soils with reflectance energy; leaf morphology; and factors which affect the reflectance of vegetation cover are dicussed. The methodologies of visual and computer-aided analyses of LANDSAT data are presented. Finally, a case study wherein infrared film was used to detect crop anomalies and other data applications are described.

Dejesusparada, N.↗
NASA NTRS1992

Radiative transfer theory for polarimetric remote sensing of pine forest

The radiative transfer theory is applied to interpret polarimetric radar backscatter from pine forest with clustered vegetation structures. To take into account the clustered structures with the radiative transfer theory, the scattering function of each cluster is calculated by incorporating the phase interference of scattered fields from each component. Subsequently, the resulting phase matrix is used in the radiative transfer equations to evaluate the polarimetric backscattering coefficients from random medium layers embedded with vegetation clusters. Upon including the multi-scale structures, namely, trunks, primary and secondary branches, as well as needles, we interpret and simulate the polarimetric radar responses from pine forest for different frequencies and looking angles. The preliminary results are shown to be in good agreement with the measured backscattering coefficients at the Landes maritime pine forest during the MAESTRO-1 experiment.

Hsu, C. C.↗
NASA NTRS1979

Microwave backscatter dependence on surface roughness, soil moisture, and soil texture. II - Vegetation-covered soil

Results are presented for an experimental investigation to determine the relationship between radar backscatter coefficient (sigma) and soil moisture for vegetation-covered soil. These results extend a previous report which showed the experimental relationship between sigma and soil moisture for bare soil. It is shown that the highest correlation between sigma and soil moisture is 0.92 for the combined response of four crop types measured at 4.25 GHz, 10 deg incidence angle, and HH polarization. Radar look direction, relative to the crop row direction, is shown to have an insignificant effect on soil-moisture estimation if the radar frequency is higher than 4 GHz. The dependence on soil type can be minimized by expressing soil moisture in units of percent of field capacity. The possibility of using a single radar for measuring soil moisture for both bare and vegetated fields is demonstrated with a linear estimation algorithm having an experimental correlation coefficinet of 0.8.

Ulaby, F. T.↗
NASA NTRS2018

Modeling the Interactive Effects of Spruce Beetle Infestation and Climate on Subalpine Vegetation

In the subalpine zone of the Rocky Mountains, climate change is predicted to result in an increase in the frequency and severity of spruce beetle outbreaks. Climate change itself may affect vegetation, potentially leading to changes in species composition. The direct and indirect effects of climate and disturbances on forest composition, biomass, and dynamics open the possibility for non-linear ecosystem responses. Modeling studies allow for the study of the interaction of these effects and their impact on the forest system. University of Virginia Forest Model Enhanced (UVAFME), an individual-based gap model that simulates forest dynamics and characteristics, is updated with a spruce beetle subroutine that calculates the probability for beetle infestation and potential mortalityof each tree on a plot. The updated model is then run with multiple scenarios that combine beetle infestation with current or altered climate at sites across the southern Rocky Mountains. Results show that spruce beetle infestations acted to facilitate competition with invading lower-elevation species, resulting in an increase in the biomass of historically lower elevation species and a further decline in Engelmann spruce biomass than occurred with solely bark beetle disturbance or solely climate change. We also found an initial enhancing effect between spruce beetle infestation and climate change; however, by the end of 100 yr of climate change and potential beetle infestation, climate had a dampening effect on spruce beetle infestation, through loss of host trees. These results are an important step in understanding the possible futures for vegetation of the Rocky Mountains as well as for spruce forests across the western United States and Canada.

Foster, Adrianna C.↗
NASA NTRSDate not supplied

Post-Severe Thunderstorm Damage Assessment from March 2-3, 2020, Nashville, TN, Using Synthetic Aperture Radar Observations

Severe weather events (e.g., hurricanes, tornadoes) are responsible for most weather-related infrastructure and building damages. The destruction caused by these events can cross several states, making damage estimates difficult, especially in heavily vegetated or rural areas. Drone or optical imagery is often relied upon in these cases but is limited by solar and atmospheric conditions. Synthetic Aperture Radar (SAR) is an active sensor allowing for day and night collections in all weather conditions. This research highlights the benefits and limitations of using SAR to detect tornado tracks and associated damages while also assessing the strengths and weaknesses of two SAR sensors with varying wavelengths and spatial resolutions from the March 2020 Tornado Outbreak. The outbreak occurred overnight on March 2-3, when several supercell thunderstorms tracked across multiple states producing numerous tornadoes (EF-0 through EF-4) and large hail. Most of the damage occurred in central Tennessee, resulting in 25 fatalities, hundreds of injuries, and over a billion dollars worth of damage. Publicly available C-band (~6 cm) imagery from the European Space Agency’s Sentinel-1 satellite and commercial X-band (~3 cm) imagery from Airbus’s TerraSAR-X satellite were used to generate amplitude and coherence products. The closest post-event collections were used for the amplitude products, and the closest pre- and post-collections were used to create the coherence pairs. If damage tracks were identified in any of the SAR products, the length (miles) and max width (yards) were recorded and compared to the National Weather Service Storm Data official records. SAR successfully detected five rated EF-1 or higher out of ten recorded tornadoes. However, none of the four EF-0 tornadoes were identified. X- and C-band SAR sensors are heavily impacted by dense vegetation, underestimating the extent of damage. Despite these limitations, SAR products can be beneficial when looking at severe weather impacts, especially during the winter and early spring when the coherence products are less influenced by vegetation. The upcoming L-band (~24 cm) NISAR mission will provide more accurate estimates of damage, with the longer wavelength, expanding the applications of SAR to assist in damage assessment caused by severe thunderstorms.

Hannah G Pankratz↗
NASA NTRS1976

Soil, water, and vegetation conditions in south Texas

The author has identified the following significant results. Field spectral measurements and laboratory densitometric measurements showed that tree canopy reflectance differences among the Marrs, Redblush, and Valencia varieties in the visible spectral region were due to their different leaf chlorophyll concentrations. Field measurements of visible light reflectance were directly related to the tonal responses on infrared color photos of the varietal tree canopies. Consequently, densitometric measurements of the foliage on the infrared color transparency with red-filtered light successfully discriminated among the three varieties. Reflectance measurements with a field spectroradiometer on nine dates the growing season of two wheat varieties, Milam and Penjamo, documented their spectra over the 0.45 to 2.50 micron wavelength interval associated with plant cover and physiological development. An image analyzer system was used to optically planimeter the percentage of soil background, vegetation and shadow in the vertical photographs taken within the FOV of the spectroradiometer on each measurement date.

Wiegand, C. L.↗
NASA NTRS1980

Effects of heavy metals on the absorbance and reflectance spectra of plants

The spectral responses of plants to various concentrations of heavy metals in their rooting media are investigated in relation to the application of remote sensing methods to the detection of vegetation under stress. Absorption photometry of chloroplasts, measurements of metal and chlorophyll concentrations and reflectance spectrometry were performed on leaves of pea, sunflower and soybean plants grown under greenhouse conditions with the addition of various concentrations of Cd, Cu, Pb and Zn to their rooting media and on leaves of oak trees growing naturally in an area of a copper-arsenic mineralization. Under laboratory conditions, the most general effect observed was growth inhibition and ultimately death, with pea plants also exhibiting changes of chlorophyll a/ chlorophyll b ratios with Cd and Cu and reflectance increases in the visible and decreases in the infrared. Although results for other species indicate that reflectance effects are dependent on species, correlations between reflectance and metal exposure is confirmed by the field investigations. It is concluded that a remote sensing system would be improved by the inclusion of bands around 1.65 and 2.20 microns to detect soil mineralization from plant spectra.

Horler, D. N. H.↗
NASA NTRS1992

Statistical properties of the Mueller matrix of distributed targets

Attention is given to the statistical properties of the Mueller matrix of distributed targets, with emphasis on the copolarized phase angle phi sub c. The latter is shown to exhibit a pdf that is characterized completely by two parameters alpha and zeta, both of which are related to the elements of the Mueller matrix of the target. The parameter alpha governs the width of the pdf, and zeta governs the mean value of phi sub c. It is shown that the magnitudes S sub vv, S sub hh, and S sub hv, and the parameters alpha and zeta completely specify the Mueller matrix, and therefore contain all the statistical information available from the polarimetric backscatter response of the target. These results are verified with experimental observations that show that alpha and zeta are related to the physical and dielectric properties of soil and vegetation targets.

Ulaby, F. T.↗
NASA NTRS1992

Biosphere/atmosphere CO2 exchange in tundra ecosystems - Community characteristics and relationships with multispectral surface reflectance

CO2 exchange rates were measured at selected tundra sites near Bethel, Alaska using portable, climate-controlled, instrumented enclosures. The empirically modeled exchange rate for a representative area of vegetated tundra was 1.2 +/- 1.2 g/sq m/d, compared to a tower-measured exchange over the same time period of 1.1 +.0- 1.2 g/sq m/d. Net exchange in response to varying light levels was compared to wet meadow and dry upland tundra, and to the net exchange measured by the micrometeoroidal tower technique. The multispectral reflectance properties of the sites were measured and related to exchange rates in order to provide a quantitative foundation for the use of satellite remote sensing to monitor biosphere/atmosphere CO2 exchange in the tundra biome.

Whiting, Gary J.↗
NASA NTRS2012

Improving Post-Hurricane Katrina Forest Management with MODIS Time Series Products

Hurricane damage to forests can be severe, causing millions of dollars of timber damage and loss. To help mitigate loss, state agencies require information on location, intensity, and extent of damaged forests. NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data products offers a potential means for state agencies to monitor hurricane-induced forest damage and recovery across a broad region. In response, a project was conducted to produce and assess 250 meter forest disturbance and recovery maps for areas in southern Mississippi impacted by Hurricane Katrina. The products and capabilities from the project were compiled to aid work of the Mississippi Institute for Forest Inventory (MIFI). A series of NDVI change detection products were computed to assess hurricane induced damage and recovery. Hurricane-induced forest damage maps were derived by computing percent change between MODIS MOD13 16-day composited NDVI pre-hurricane "baseline" products (2003 and 2004) and post-hurricane NDVI products (2005). Recovery products were then computed in which post storm 2006, 2007, 2008 and 2009 NDVI data was each singularly compared to the historical baseline NDVI. All percent NDVI change considered the 16-day composite period of August 29 to September 13 for each year in the study. This provided percent change in the maximum NDVI for the 2 week period just after the hurricane event and for each subsequent anniversary through 2009, resulting in forest disturbance products for 2005 and recovery products for the following 4 years. These disturbance and recovery products were produced for the Mississippi Institute for Forest Inventory's (MIFI) Southeast Inventory District and also for the entire hurricane impact zone. MIFI forest inventory products were used as ground truth information for the project. Each NDVI percent change product was classified into 6 categories of forest disturbance intensity. Stand age and stand type raster data, also provided by MIFI, were used along with the forest disturbance/recovery products to create forest damage stratification products integrating 3 stand type classes, 6 stand age classes, and 6 forest disturbance intensity classes. This stratification product will be used to aid MIFI timber inventory planning and to prepare for damage assessments due to future hurricane events. Validation of MODIS percent NDVI change products was performed by comparing the MODIS percent NDVI change products to those from Landsat data for the same time and MIFI inventory district area.

Lewis, Mark David↗